papers_in_methodological_journals_annotated.csv

origin;tags;title;keywords;journal;authors;abstract
methods;IPM, heterogeneity, estimation, assumptions;integrated population models: model assumptions and inference;"bayesian; heterogeneity; integrated population models; model assumption violation; parameter estimation";METHODS IN ECOLOGY AND EVOLUTION;"RIECKE TV;WILLIAMS PJ;BEHNKE TL;GIBSON D;LEACH AG;SEDINGER BS;STREET PA;SEDINGER JS";integrated population models (ipms) have become increasingly popular for the modelling of populations, as investigators seek to combine survey and demographic data to understand processes governing population dynamics. these models are particularly useful for identifying and exploring knowledge gaps within life histories, because they allow investigators to estimate biologically meaningful parameters, such as immigration or reproduction, that were previously unidentifiable without additional data. as ipms have been developed relatively recently, there is much to learn about model behaviour. behaviour of parameters, such as estimates near boundaries, and the consequences of varying degrees of dependency among datasets, has been explored. however, the reliability of parameter estimates remains underexamined, particularly when models include parameters that are not identifiable from one data source, but are indirectly identifiable from multiple datasets and a presumed model structure, such as the estimation of immigration using capture-recapture, fecundity and count data, combined with a life-history model. to examine the behaviour of model parameter estimates, we simulated stable populations closed to immigration and emigration. we simulated two scenarios that might induce error into survival estimates: marker induced bias in the capture-mark-recapture data and heterogeneity in the mortality process. we subsequently fit capture-mark-recapture, state-space and fecundity models, as well as ipms that estimated additional parameters. simulation results suggested that when model assumptions are violated, estimation of additional, previously unidentifiable, parameters using ipms may be extremely sensitive to these violations of model assumption. for example, when annual marker loss was simulated, estimates of survival rates were low and estimates of immigration rate from an ipm were high. when heterogeneity in the mortality process was induced, there were substantial relative differences between the medians of posterior distributions and truth for juvenile survival and fecundity. our results have important implications for biological inference when using ipms, as well as future model development and implementation. specifically, using multiple datasets to identify additional parameters resulted in the posterior distributions of additional parameters directly reflecting the effects of the violations of model assumptions in integrated modelling frameworks. we suggest that investigators interpret posterior distributions of these parameters as a combination of biological process and systematic error.
methods;Behavior, internesting period;overcoming field monitoring restraints in estimating marine turtle internesting period by modelling individual nesting behaviour using capture-mark-recapture data;"chelonia mydas; lepidochelys olivacea; internesting period; iteroparity; nesting abortion; eastern atlantic";ECOLOGICAL MODELLING;"HANCOCK J;VIEIRA S;LIMA H;SCHMITT V;PEREIRA J;REBELO R;GIRONDOT M";"marine turtles are intra-seasonal iteroparous animals; they nest from one to up to 14 times during the nesting season, laying up to 180 eggs each time. their annual reproductive effort can therefore be estimated from clutch size, nesting frequency, and length of the nesting season. moreover, the estimation of nesting frequency, usually obtained from the internesting period (i.e., the time in days between two nesting events) is essential for assessing the number of females in a population. however, the intemesting period is strongly influenced by variation in individual behaviour of the nesting female, including abortion of nesting attempts. it is also affected by imprecise detection of females during beach monitoring, often related with a lack of fidelity to the nesting beach. using an individual-focused model based on capture-mark-recapture data we were able to statistically characterize the nesting behaviour of the populations of green turtles (chelonia mydas) and olive ridley turtles (lepidochelys olivacea) in sao tome and principe (eastern atlantic). the developed model proposes a novel approach in estimating the internesting period, by including the different factors that lead to the heterogeneity observed in the duration of intemesting periods across a single season, corrected for the probability of a female aborting a nesting process. the calculated lengths of the intemesting periods for the two species are congruent with previous estimates, validating the model. furthermore, the inference of the rank of a nest for an individual female is predicted by the model with high accuracy, even when the recapture rate is low and the time between observations is long. a limitation of the model is its inability to estimate the true clutch frequency at the scale of the population but it was not its purpose."
methods;Mortality cause, recovery, opportunistic, SSM, data combination;jointly estimating survival and mortality: integrating recapture and recovery data from complex multiple predator systems;"capture-recapture-recovery; hierarchical bayesian; population dynamics; state-space models";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"PAYTON Q;HOSTETTER NJ;EVANS AF";identifying where, when, and how many animals live and die over time is principal to understanding factors that influence population dynamics. capture-recapture-recovery (crr) models are widely used to estimate animal survival and, in many cases, quantify specific causes of mortality (e.g., harvest, predation, starvation). however, the restrictive crr framework can inhibit the consideration and inclusion of some types of recovery data. we developed an extension to the crr framework to allow for the incorporation of recoveries from indeterminate temporal or spatial origin. this model jointly estimates cause-specific mortality and survival probabilities across multiple spatial and temporal scales, while accounting for differences in mortality-specific reporting and recovery rates. we fitted the model to data on a group of juvenile steelhead trout (oncorhynchus mykiss) marked with passive integrated transponder tags in the columbia river basin, usa. following tagging and release, fish were detected alive at up to six downstream locations and/or recovered dead on one of nine bird colonies during seaward migration. we estimated that, in aggregate, avian predators consumed 31% of juvenile steelhead during outmigration to the ocean (95% cri: [27, 36]). colony-specific predation rates ranged from<1 to 14% among river reaches, with avian predation accounting for>95% of all steelhead mortality within some reaches. this integrated modelling approach provides a flexible framework to integrate multiple recapture and recovery data sources, providing a more holistic understanding of animal life history, including direct comparisons of cause-specific mortality factors and the cumulative impact of multiple mortality factors across time or space.
methods;Drink-drinking, zero-truncated;the identity of the zero-truncated, one-inflated likelihood and the zero-one-truncated likelihood for general count densities with an application to drink-driving in britain;"capture-recapture; chao estimator; behavioral response; power series distribution; mixture model; zero-truncated model; nonparametric estimator of population size";ANNALS OF APPLIED STATISTICS;"BOHNING D;VAN DER HEIJDEN PGM";for zero-truncated count data, as they typically arise in capture-recapture modelling, we consider modelling under one-inflation. this is motivated by police data on drink-driving in britain which shows high one-inflation. the data, which are used here, are from the years 2011 to 2015 and are based on dr10 endorsements. we show that inference for an arbitrary count density with one-inflation can be equivalently based upon the associated zero-one truncated count density. this simplifies inference considerably including maximum likelihood estimation and likelihood ratio testing. for the drink-driving application, we use the geometric distribution which shows a good fit. we estimate the total drink-driving as about 2,300,000 drink-drivers in the observational period. as 227,578 were observed, this means that only about 10% of the drink-driving population is observed with a bootstrap confidence interval of 9%-12%.
methods;IPM, estimation;efficient sequential monte carlo algorithms for integrated population models;"bayesian inference; capture-recapture; integrated population models; model comparison; sequential monte carlo; state-space models";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"FINKE A;KING R;BESKOS A;DELLAPORTAS P";in statistical ecology, state-space models are commonly used to represent the biological mechanisms by which population counts-often subdivided according to characteristics such as age group, gender or breeding status-evolve over time. as the counts are only noisily or partially observed, they are typically not sufficiently informative about demographic parameters of interest and must be combined with additional ecological observations within an integrated data analysis. fitting integrated models can be challenging, especially if the constituent state-space model is nonlinear/non-gaussian. we first propose an efficient particle markov chain monte carlo algorithm to estimate demographic parameters without a need for linear or gaussian approximations. we then incorporate this algorithm into a sequential monte carlo sampler to perform model comparison. we also exploit the integrated model structure to enhance the efficiency of both algorithms. the methods are demonstrated on two real data sets: little owls and grey herons. for the owls, we find that the data do not support an ecological hypothesis found in the literature. for the herons, our methodology highlights the limitations of existing models which we address through a novel regime-switching model. supplementary materials accompanying this paper appear online.
methods;Social network, SSM, social species;inferring animal social networks with imperfect detection;"bayesian inference; capture-recapture; multistate models; social networks";ECOLOGICAL MODELLING;"GIMENEZ O;MANSILLA L;KLAICH MJ;COSCARELLA MA;PEDRAZA SN;CRESPO EA";social network analysis provides a powerful tool for understanding social organisation of animals. however, in free-ranging populations, it is almost impossible to monitor exhaustively the individuals of a population and to track their associations. ignoring the issue of imperfect and possibly heterogeneous individual detection can lead to substantial bias in standard network measures. here, we develop capture-recapture models to analyse network data while accounting for imperfect and heterogeneous detection. we carry out a simulation study to validate our approach. in addition, we show how the visualisation of networks and the calculation of standard metrics can account for detection probabilities. the method is illustrated with data from a population of commerson's dolphin (cephalorhynchus commersonii) in patagonia argentina. our approach provides a step towards a general statistical framework for the analysis of social networks of wild animal populations.
methods;SCR, closure, assumptions;population closure and the bias-precision trade-off in spatial capture-recapture;"mortality; population dynamics; recruitment; spatial capture-recapture; time-to-event modelling";METHODS IN ECOLOGY AND EVOLUTION;"DUPONT P;MILLERET C;GIMENEZ O;BISCHOF R";spatial capture-recapture (scr) is an increasingly popular method for estimating ecological parameters. scr often relies on data collected over relatively long sampling periods. while longer sampling periods can yield larger sample sizes and thus increase the precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. the sampling period characteristics are therefore likely to play an important role in this bias-precision trade-off. yet few studies have studied this trade-off and none has done so for scr models. in this study, we explored the influence of the length and timing of the sampling period on the bias-precision trade-off of scr population size estimators. using a continuous time-to-event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by scr models. while longer sampling periods benefit the study of slow-living species (increased precision and lower bias), they lead to pronounced overestimation of population size for fast-living species. in addition, we show that both bias and uncertainty increase when the sampling period overlaps the reproductive season of the study species. based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. we argue that both spatial and non-spatial capture-recapture studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. the simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.
methods;clustered point process, camera traps, data combination, point process, camera trapping;cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations;"capture-recapture; neyman-scott process; palm intensity; spatial capture-recapture; thomas process; unmanned aerial vehicles";BIOMETRICS;"STEVENSON BC;BORCHERS DL;FEWSTER RM";"capture-recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. many modern-day wildlife surveys detect animals without physical capturevisual detection by cameras is one such example. however, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? an inability to resolve every such decision to a high degree of certainty prevents the use of standard capture-recapture methods, impeding the estimation of animal density. here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high-definition cameras. identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. by modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. we illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface."
methods;abundance, multistate models;estimation of population size when capture probability depends on individual states;"abundance; closed population; individual heterogeneity; transition probabilities";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"WORTHINGTON H;MCCREA RS;KING R;GRIFFITHS RA";we develop a multi-state model to estimate the size of a closed population from capture-recapture studies. we consider the case where capture-recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. the proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. the model allows for heterogeneity within the capture probabilities associated with each state while also permitting individuals to move between the different discrete states. a closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. the link between existing models for capture heterogeneity is established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. the proposed unconditional approach is also compared to a conditional approach to assess estimation bias. the model derived in this paper is motivated by a real ecological data set on great crested newts, triturus cristatus. supplementary materials accompanying this paper appear online.
methods;NA;the recent past and promising future for data integration methods to estimate species' distributions;"data fusion; integrated distribution model; joint likelihood; spatial point process; species distribution modelling";METHODS IN ECOLOGY AND EVOLUTION;"MILLER DAW;PACIFICI K;SANDERLIN JS;REICH BJ";with the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. this has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. we outline the general principles that have guided data integration methods and review recent developments in the field. we then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. key to recent advances has been using point-process thinking to combine estimators developed for different data types. extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. these along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. recent developments form a strong foundation for implementation of data integration models. wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.
methods;Social species, citizen science data, stopover;caste-specific demography and phenology in bumblebees: modelling beewalk data;"citizen science; mixture models; phenology; population trends; productivity; stopover models";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MATECHOU E;FREEMAN SN;COMONT R";we present novel dynamic mixture models for the monitoring of bumblebee populations on an unprecedented geographical scale, motivated by the uk citizen science scheme beewalk. the models allow us for the first time to estimate bumblebee phenology and within-season productivity, defined as the number of individuals in each caste per colony in the population in that year, from citizen science data. all of these parameters are estimated separately for each caste, giving a means of considerable ecological detail in examining temporal changes in the complex life cycle of a social insect in the wild. due to the dynamic nature of the models, we are able to produce population trends for a number of uk bumblebee species using the available time-series. via an additional simulation exercise, we show the extent to which useful information will increase if the survey continues, and expands in scale, as expected. bumblebees are extraordinarily important components of the ecosystem, providing pollination services of vast economic impact and functioning as indicator species for changes in climate or land use. our results demonstrate the changes in both phenology and productivity between years and provide an invaluable tool for monitoring bumblebee populations, many of which are in decline, in the uk and around the world. supplementary materials accompanying this paper appear online.
methods;Robust design, BUGS, temporary emigration;parameterizing the robust design in the bugs language: lifetime carry-over effects of environmental conditions during growth on a long-lived bird;"bayesian; branta bernicla nigricans; breeding probability; capture-mark-recapture; carry-over effects; fitness; population dynamics; robust design";METHODS IN ECOLOGY AND EVOLUTION;"RIECKE TV;LEACH AG;GIBSON D;SEDINGER JS";since the initial development of the robust design, this capture-recapture model structure has been modified to estimate temporary emigration and expanded to include auxiliary information such as band recovery and live resight data using maximum likelihood approaches. these developments have allowed investigators to separately assess individual and group effects on true survival, site fidelity, and temporary emigration. additionally, recent advances in the bugs language have allowed researchers to develop increasingly complex, user-specified models in bayesian frameworks. the robust design has rarely been implemented in the bugs language, and previous attempts to parameterize the robust design in bugs exhibited strong bias in estimates of temporary emigration rates. given the limitations of current parameterizations of the robust design in bayesian frameworks, and our research objectives, we have developed a parameterization of the robust design in the bugs language that produces unbiased estimates of all model parameters. we use this novel model structure to examine lifetime carry-over effects of environmental conditions during early life on annual breeding probabilities of pacific black brent branta bernicla nigricans breeding on the yukon-kuskokwim river delta in western alaska. we found that individuals that were more structurally developed as goslings bred at increased rates as adults (beta = 0.14, f = 0.94), with no effect on adult survival (beta = 0.01, f = 0.62). additionally, we provide evidence for long-term declines in apparent survival of breeding adult females at the population level (beta = -0.01, f = 0.90). this novel model structure can be easily expanded (gibson et al., in press) and has important implications for population modelling at broad scales, where we apply it to a declining population of pacific black brent. given long-term declines in gosling growth on the yukon-kuskokwim delta, we predict future declines in population trajectories as a result of lifetime carry-over effects of environmental conditions during growth on adult fecundity and long-term declines in adult survival.
methods;Individual growth, heterogeneity;bayesian modeling of individual growth variability using back-calculation: application to pink cusk-eel (genypterus blacodes) off chile;"individual variability; von bertalanffy model; student-t distribution; pink cusk-eel; chile";ECOLOGICAL MODELLING;"CONTRERAS REYES JE;QUINTERO FOL;WIFF R";the von bertalanffy growth function (vbgf) with random effects has been widely used to estimate growth parameters incorporating individual variability of length-at-age. trajectories of individual growth can be inferred using either mark-recapture or back-calculation of length-at-age from growth marks in hard body parts such as otoliths. modem statistical methods evaluate individual variation usually from mark-recapture data, and the parameters describing this function are estimated using empirical bayes methods assuming gaussian error. in this paper, we combine recent studies in non-gaussian distributions and a bayesian approach to model growth variability using back-calculated data in harvested fish populations. we presumed that errors in the vbgf can be assumed as a student-t distribution, given the abundance of individuals with extreme length values. the proposed method was applied and compared to the standard methods using back-calculated length-at-age data for pink cusk-eel (genypterus blacodes) off chile. considering several information criteria, and comparing males and females, we have found that males grow significantly faster than females, and that length-at-age for both sexes exhibits extreme length observations. comparisons indicated that a student-t model with mixed effects describes best back-calculated data regarding pink cusk-eel. this framework merged the strengths of different approaches to estimate growth parameters in harvested fish populations, considering modeling of individual variability of length-at-age, bayesian inference, and distribution of errors from the student-t model.
methods;Recruitment, point process, spatial;estimating recruitment from capture-recapture data by modelling spatio-temporal variation in birth and age-specific survival rates;"birth-death process; camera trapping; individual-based models; spatial capture-recapture; spatial demography; spatio-temporal point process; white-tailed deer";METHODS IN ECOLOGY AND EVOLUTION;"CHANDLER RB;ENGEBRETSEN K;CHERRY MJ;GARRISON EP;MILLER KV";1. understanding the factors influencing recruitment in animal populations is an important objective of many research and conservation programmes. however, evaluating hypotheses is challenging because recruitment is the outcome of birth and survival processes that are difficult to directly observe. capture-recapture is the most general framework for estimating recruitment in the presence of observation error, but existing methods ignore the underlying birth and survival processes, as well as age effects and spatial variation in vital rates. 2. we present an individual-based, spatio-temporal model that can be fit to capture-recapture data to draw inferences on the birth and survival processes governing recruitment dynamics. the number, dates, and spatial distribution of births are modelled as outcomes of a point process, and survival is modelled using a failure time approach. survival parameters can be modelled as functions of individuals traits and time-varying, spatial covariates. continuous-and discrete-time formulations are possible. we demonstrate the model using 7 months of camera data collected on white-tailed deer odocoileus virginianus fawns in big cypress national preserve. spot patterns were used to individually identify 28 fawns, detected 1,454 times between december 1, 2015 and july 1, 2016. 3. a total of 37 (95% ci: 30-49) fawns were born, of which 16 (95% ci: 10-23) survived 180 days to the recruitment age. mean parturition date was february 14 (95% ci: february 6-february 22), much earlier than in more temperate parts of the species' range, but coinciding with the dry season in southern florida. we found little evidence that mortality rates decreased with age, but the estimate of the age effect was imprecise. in contrast, we found strong evidence that encounter rates were age-specific and increased rapidly over the first month of life as fawns became more mobile. 4. our case study demonstrates the potential of this new model for advancing knowledge of spatial population dynamics by providing insights into the birth and juvenile survival processes that influence recruitment. because the model can be applied to data from noninvasive survey methods such as camera trapping, it is possible to apply it at broad spatial scales to understand how environmental variables and predator communities influence recruitment.
methods;Species richness;joining the incompatible: exploiting purposive lists for the sample-based estimation of species richness;"difference estimator; probabilistic sampling; purposive survey; supporting list; simulation";ANNALS OF APPLIED STATISTICS;"CHIARUCCI A;DI BIASE RM;FATTORINI L;MARCHESELLI M;PISANI C";the lists of species obtained by purposive sampling by field ecologists can be used to improve the sample-based estimation of species richness. a new estimator is here proposed as a modification of the difference estimator in which the species inclusion probabilities are estimated by means of the species frequencies from incidence data. if the species list used to support the estimation is complete the estimator guesses the true richness without error. in the case of incomplete lists, the estimator provides values invariably greater than the number of species detected by the combination of sample-based and purposive surveys. an asymptotically conservative estimator of the mean squared error is also provided. a simulation study based on two artificial communities is carried out in order to check the obvious increase in accuracy and precision with respect to the widely applied estimators based on the sole sample information. finally, the proposed estimator is adopted to estimate species richness in the maremma regional park, italy.
methods;Bioacoustic, SCR;modelling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations;"acoustic monitoring; bioacoustics; distance sampling; least-cost path models; sound attenuation; sound pressure audiogram; spatial capture-recapture; telemetry";METHODS IN ECOLOGY AND EVOLUTION;ROYLE JA;1. acoustic sampling methods are becoming increasingly important in biological monitoring. sound attenuation is one of the most important dynamics affecting the utility of acoustic data as it directly affects the probability of detection of individuals by acoustic sensor arrays and especially the localization of acoustic signals necessary in telemetry studies. therefore, models of sound attenuation are necessary to make efficient use of acoustic data in ecological monitoring and assessment applications. models of attenuation in widespread use are based on euclidean distance between source and sensor, which is justified under spherical attenuation of sound waves in homogeneous environments. 2. in this paper, i develop a model of sound attenuation based on a non-euclidean cost-weighted distance metric which contains attenuation coefficients that characterize the attenuation of sound due to environmental heterogeneity in the vicinity of an acoustic sensor array. 3. i show that parameters of the proposed attenuation model can be estimated by maximum likelihood using experimental data from an array of fixed sources, thus allowing investigators who use bioacoustic methods to devise explicit models of sound attenuation in situ and apply them to localization of sources and density estimation. in addition, drawing on analogy with spatial capture-recapture models, i argue that parameters of the non-euclidean model of attenuation can be estimated when source locations are unknown. thus, the models can be applied to real field studies which require estimation of attenuation parameters or localization of signals. 4. models of heterogeneous sound attenuation allow more accurate descriptions of acoustic monitoring data, and therefore should produce more accurate estimates of ecological parameters of interest, including source locations, density, and movement trajectories. moreover, the ability to test specific hypotheses about the effects of habitat and landscape structure on sound attenuation can improve the design of acoustic monitoring arrays and lead to more efficient deployment of acoustic sensing technology.
methods;Recruitment;a length-based mark-recapture model for estimating abundance and recruitment: removing bias due to size-selective capture gear;"mark-recapture; growth; abundance; size selectivity; length-based model; bias";ECOLOGICAL MODELLING;"VAN POORTEN BT;TAYLOR N;O BRIEN D;WALTERS CJ";we describe an unbiased length-based, age-structured mark-recapture (lamr) model for estimating length based abundance and recruitment of fish populations. many mark-recapture studies employ capture gear that is size-selective, leading to a larger and faster growing marked sub-population with a different capture probability than the unmarked sub-population, resulting in a basic violation of assumptions for many mark-recapture models. persistent differences in marked and unmarked individuals are estimated in our model using growth type group accounting. simulation-evaluation results indicate that the model produces largely unbiased estimates of recruitment and abundance across a range of sampling scenarios and population life-history types, and is robust to growth parameter misspecification. however, in older, slow growing populations, the model is prone to 'smearing' of recruitment estimates across early year-classes. the lamr model is applied to data from multiple wild populations of rainbow trout to estimate recruitment and abundance. overall, results indicate that the lamr model addresses shortcomings associated with using size-selective gear in mark-recapture studies to produce reliable estimates of recruitment and size-based abundance.
methods;SCR, genetic tagging;using partial aggregation in spatial capture recapture;"partially aggregated binary model; spatial capture recapture; wolverines";METHODS IN ECOLOGY AND EVOLUTION;"MILLERET C;DUPONT P;BROSETH H;KINDBERG J;ROYLE JA;BISCHOF R";1. spatial capture-recapture (scr) models are commonly used for analysing data collected using noninvasive genetic sampling (ngs). opportunistic ngs often leads to detections that do not occur at discrete detector locations. therefore, spatial aggregation of individual detections into fixed detectors (e.g., centre of grid cells) is an option to increase computing speed of scr analyses. however, it may reduce precision and accuracy of parameter estimations. 2. using simulations, we explored the impact that spatial aggregation of detections has on a trade-off between computing time and parameter precision and bias, under a range of biological conditions. we used three different observation models: the commonly used poisson and bernoulli models, as well as a novel way to partially aggregate detections (partially aggregated binary model [pab]) to reduce the loss of information after aggregating binary detections. the pab model divides detectors into k subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of k. finally, we demonstrate the consequences of aggregation and the use of the pab model using ngs data from the monitoring of wolverine (gulo gulo) in norway. 3. spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. scr models estimated abundance with a low bias (< 10%) even at high degree of aggregation, but only for the poisson and pab models. overall, the cost of aggregation is mitigated when using the poisson and pab models. at the same level of aggregation, the pab observation model out-performs the bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count-based model. 4. we recommend that detector spacing after aggregation does not exceed 1.5 times the scale-parameter of the detection function in order to limit bias. we recommend the use of the pab observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the bernoulli model.
methods;GOF, R package;r2ucare: an r package to perform goodness-of-fit tests for capture-recapture models;"arnason-schwarz; capture-mark-recapture; cormack-jolly-seber; model validation; r2ucare";METHODS IN ECOLOGY AND EVOLUTION;"GIMENEZ O;LEBRETON JD;CHOQUET R;PRADEL R";1. assessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference. 2. we introduce r2ucare, an r package to perform goodness-of-fit tests for open single- and multi-state capture-recapture models. r2ucare also has various functions to manipulate capture-recapture data. 3. we remind the basics and provide guidelines to navigate towards testing the fit of capture-recapture models. we demonstrate the functionality of r2ucare through its application to real data.
methods;SCR, mark-refighting;spatial capture-mark-resight estimation of animal population density;"capture-mark-resight model; density estimation; maximum likelihood; overdispersion; spatial mark-resight; spatially explicit capture-recapture";BIOMETRICS;"EFFORD MG;HUNTER CM";"sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by markov chain monte carlo sampling. as implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. we develop a suite of likelihood-based spatial mark-resight models that either include the marking phase (capture-mark-resight models) or require a known distribution of marked animals (narrow-sense mark-resight). the new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation-based adjustment for overdispersion in the sightings of unmarked animals. simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. the methods are applied to a study of ship rats rattus rattus using live traps and video cameras in a new zealand forest, and to previously published data."
methods;Continuous, point process;continuous-time capture-recapture in closed populations;"capture-recapture; likelihood factorization; markov chain monte carlo; nonhomogenous poisson process";BIOMETRICS;"SCHOFIELD MR;BARKER RJ;GELLING N";the standard approach to fitting capture-recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. we show how continuous-time models can be fitted as easily as discrete-time alternatives. the likelihood is factored so that efficient markov chain monte carlo algorithms can be implemented for bayesian estimation, available online in the r package ctime. we consider goodness-of-fit tests for behavior and heterogeneity effects as well as implementing models that allow for such effects.
methods;Record linkage, human rights;bayesian propagation of record linkage uncertainty into population size estimation of human rights violations;"capture-recapture; counting casualties; data linkage; decomposable graphical model; duplicate detection; entity resolution; multiple-systems estimation; multiple record linkage";ANNALS OF APPLIED STATISTICS;SADINLE M;multiple-systems or capture-recapture estimation are common techniques for population size estimation, particularly in the quantitative study of human rights violations. these methods rely on multiple samples from the population, along with the information of which individuals appear in which samples. the goal of record linkage techniques is to identify unique individuals across samples based on the information collected on them. linkage decisions are subject to uncertainty when such information contains errors and missingness, and when different individuals have very similar characteristics. uncertainty in the linkage should be propagated into the stage of population size estimation. we propose an approach called linkage-averaging to propagate linkage uncertainty, as quantified by some bayesian record linkage methodologies, into a subsequent stage of population size estimation. linkage-averaging is a two-stage approach in which the results from the record linkage stage are fed into the population size estimation stage. we show that under some conditions the results of this approach correspond to those of a proper bayesian joint model for both record linkage and population size estimation. the two-stage nature of linkage-averaging allows us to combine different record linkage models with different capture-recapture models, which facilitates model exploration. we present a case study from the salvadoran civil war, where we are interested in estimating the total number of civilian killings using lists of witnesses' reports collected by different organizations. these lists contain duplicates, typographical and spelling errors, missingness, and other inaccuracies that lead to uncertainty in the linkage. we show how linkage-averaging can be used for transferring the uncertainty in the linkage of these lists into different models for population size estimation.
methods;NA;assessing the dynamics of natural populations by fitting individual-based models with approximate bayesian computation;"approximate bayesian computation; individual-based models; metapopulation dynamics; multiple data sources; population dynamics";METHODS IN ECOLOGY AND EVOLUTION;"SIREN J;LENS L;COUSSEAU L;OVASKAINEN O";1. individual-based models (ibms) allow realistic and flexible modelling of ecological systems, but their parameterization with empirical data is statistically and computationally challenging. approximate bayesian computation (abc) has been proposed as an efficient approach for inference with ibms, but its applicability to data on natural populations has not been yet fully explored. 2. we construct an ibm for the metapopulation dynamics of a species inhabiting a fragmented patch network, and develop an abc method for parameterization of the model. we consider several scenarios of data availability from count data to combination of mark-recapture and genetic data. we analyse both simulated and real data on white-starred robin (pogonocichla stellata), a passerine bird living in montane forest environment in kenya, and assess how the amount and type of data affect the estimates of model parameters and indicators of population state. 3. the indicators of the population state could be reliably estimated using the abc method, but full parameterization was not achieved due to strong posterior correlations between model parameters. while the combination of the data types did not provide more accurate estimates for most of the indicators of population state or model parameters than the most informative data type (ringing data or genetic data) alone, the combined data allowed robust simultaneous estimation of all unknown quantities. 4. our results show that abc methods provide a powerful and flexible technique forparameterizing complex ibms with multiple data sources, and assessing the dynamics of the population in a robust manner.
methods;Point process, continuous;markov-modulated poisson processes as a new framework for analysing capture-recapture data;"capture-recapture; likelihood; markov-modulated poisson process; non-constant time interval; uncertainty";METHODS IN ECOLOGY AND EVOLUTION;CHOQUET R;1. opportunistic capture-recapture data consists of observations over non-constant time intervals and so fails to satisfy the basic assumptions of traditional capture-recapture models. analysing opportunistic capture-recapture data is often done by discretizing time intervals or summarizing data, but without taking into account the continuous time process of the state and/or the capture. 2. to deal with non-constant time-intervals, continuous time closed capture-recapture models have been proposed by yip, huggins, and lin (1996), hwang and chao (2002), schofield, barker, and gelling (2017) for estimating population size. more recently, a continuous time cormack-jolly-seber model has been proposed by fouchet, santin-janin, sauvage, yoccoz, and pontier (2016) to reduce bias in survival rates, and a two-state process has been proposed by choquet, garnier, awuve, and besnard (2017) to estimate reproduction rates and survival rates of young within a season. 3. the aim of the current study is to demonstrate how an approach based on a markov-modulated poisson process (mmpp) (freed & shepp, 1982) allows, in a similar way to a multistate model, to model opportunistic data, using several states. to this end, several multistate models were rewritten as mmpp models, showing, the potential for this approach to address the ecological questions as multistate models, but using an extended data framework. in particular, it is a useful framework for dealing with data that has unordered levels of uncertainty. 4. the methods were illustrated using simulations and analysis of data on the alpine ibex (capra ibex).
methods;SCR, camera trapping, multiple marks, partial identification;spatial capture-recapture with partial identity: an application to camera traps;"spatial capture-recapture; partial identity; camera trapping; multiple marks";ANNALS OF APPLIED STATISTICS;"AUGUSTINE BC;ROYLE JA;KELLY MJ;SATTER CB;ALONSO RS;BOYDSTON EE;CROOKS KR";camera trapping surveys frequently capture individuals whose identity is only known from a single flank. the most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and, while not previously recognized, introducing bias. here, we present the spatial partial identity model (spim), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. we show that the spim outperforms other analytical alternatives. we then apply the spim to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. the spim improves inference in both cases and, in the ocelot example, individual sex is determined from photographs used to further resolve partial identities-one of which is resolved to near certainty. the spim opens the door for the investigation of trapping designs that deviate from the standard two camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.
methods;N-mixture, assumptions, heterogeneity;on the reliability of n-mixture models for count data;"ancillary statistic; capture recapture; log linear model; n-mixture models; partial likelihood";BIOMETRICS;"BARKER RJ;SCHOFIELD MR;LINK WA;SAUER JR";n-mixture models describe count data replicated in time and across sites in terms of abundance n and detectability p. they are popular because they allow inference about n while controlling for factors that influence p without the need for marking animals. using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of n and p problematic using just count data. one cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. this makes uncontrolled variation in p problematic. by counter example, we show that even if p is constant after adjusting for covariate effects (the constant p assumption) scientifically plausible alternative models in which n (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an n-mixture model. this is particularly the case for sparse data as is commonly seen in applications. we conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. relative abundance models for counts can be readily fitted using poisson regression in standard software such as r and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. if users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.
methods;Data combination, distance sampling;simultaneous modelling of movement, measurement error, and observer dependence in mark-recapture distance sampling: an application to arctic bird surveys;"aerial survey; double-observer; mark-recapture distance sampling; measurement error; movement; point independence";ANNALS OF APPLIED STATISTICS;"CONN PB;ALISAUSKAS RT";mark-recapture distance sampling is a promising method for surveying bird populations from aircraft in open landscapes. however, commonly available distance sampling estimators require that distances to target animals are made without error and that animals are stationary while sampling is being conducted. motivated by a recent bird survey where these requirements were routinely violated, we describe a marginal likelihood framework for estimating abundance from double-observer data that can accommodate movement and measurement error when observations are made consecutively (as with front and rear observers), when animals are uniformly distributed during detection by the first observer, and when detections consist of both moving and stationary animals. assuming that all animals are subject to measurement error and that some animals can move between detections, we integrate over unknown animal locations to construct a marginal likelihood for detection, movement, and measurement error parameters. estimates of animal abundance are then obtained using a modified horvitz-thompson-like estimator. in addition, unmodelled heterogeneity in detection probability can be accommodated through observer dependence parameters. using simulation, we show that our approach yields low bias compared to approaches that ignore movement and/or measurement error, including in cases where there is considerable detection heterogeneity. applying our approach to data from a double-observer waterfowl helicopter survey in northern canada, we are able to estimate bird density accounting for movement and measurement error and corrected for observer heterogeneity. our approach appears promising for generating unbiased estimates of bird abundance necessary for reliable conservation and management.
methods;Sparse, assumptions, protocol optimization;estimating the size of an open population using sparse capture-recapture data;"extrapolation; frequentist inference; mark-capture-recapture; population size estimation; sparse recaptures";BIOMETRICS;"HUGGINS R;STOKLOSA J;ROACH C;YIP P";sparse capture-recapture data from open populations are difficult to analyze using currently available frequentist statistical methods. however, in closed capture-recapture experiments, the chao sparse estimator (chao, 1989, biometrics45, 427-438) may be used to estimate population sizes when there are few recaptures. here, we extend the chao (1989) closed population size estimator to the open population setting by using linear regression and extrapolation techniques. we conduct a small simulation study and apply the models to several sparse capture-recapture data sets.
methods;Protocol optimization;a simplified mark-release-recapture protocol to improve the cost effectiveness of repeated population size quantification;"bog fritillary butterfly; boloria aquilonaris; boloria eunomia; capture-mark-recapture; catch effort; cranberry fritillary butterfly; long-term monitoring; mark software; sampling efforts";METHODS IN ECOLOGY AND EVOLUTION;"TURLURE C;PE ER G;BAGUETTE M;SCHTICKZELLE N";obtaining an accurate quantification of population size is often of prime importance in ecology and conservation biology (e.g. population viability analysis, a basic step for assessing species and population status in a given area and guiding effective conservation). when obtaining a reliable quantification of absolute (vs. relative) population size is required, mark-release-recapture (mrr) is a method of choice for many organisms. this is a highly reliable but costly procedure in terms of time and potential impact on species and sites. consequently, less costly alternatives are highly desirable for conservation and population ecologists. we present here a simplified mrr protocol to mitigate this cost of repeated mrr sampling with little compromise on the quality of the population size estimation. using one of the largest existing butterfly mrr databases, collected on two fritillary species over a period of 20years and >20 populations in belgium, we assessed the possibility to reduce the effort of collecting mrr data while keeping accurate quantification of total population size. by downsampling from the full datasets and calculating a range of demographic census metrics, we specifically investigated whether marking individuals is necessary, and whether the number of sampling sessions can be reduced. we found that (1) counting individuals is not enough: some individual marking, even in a simplistic way to differentiate newly recorded from previously seen individuals, is essential for estimating population size. (2) a simple linear conversion function (number of missed individuals for each marked one) can be used to compute population size from the number of individuals marked over a small number of mrr sampling sessions. (3) parameterizing this function is system specific, because it depends on detectability of individuals, but only requires an initial effort of traditional high-effort mrr in a few populations encompassing the expected range of population size, combined with previous knowledge on the species about potential factors affecting detectability. our simplified mrr protocol should allow scientists to obtain absolute population size estimates almost as good as with traditional high-effort mrr, but at a cost lowered in both the marking procedure and the intensity of field visits.
methods;Assumptions, temporary emigration;performance of multistate mark-recapture models for temporary emigration in the presence of survival costs;"bias; power; simulation; survival; temporary emigration";METHODS IN ECOLOGY AND EVOLUTION;"HENLE K;GRUBER B";temporary emigration is widespread in animals and plants and has important implications for ecological processes, evolution and the conservation of species. it is increasingly studied with capture-mark-recapture (cmr) models. temporary emigration provides particular challenges to cmr analyses if it involves movement to an unobservable state. a multistate model in which individuals may move between an observable and an unobservable state (called te model) was developed for such cases. the model assumes equal survival probability in both states. this assumption may be violated, especially if temporary emigration involves trade-offs between survival and reproduction. a comprehensive assessment of the effects of unequal survival probability on power to detect temporary emigration and on bias and precision of estimates is still needed to understand the applicability and limits of the model. we assessed power to detect temporary emigration for four goodness-of-fit tests and evaluated bias and precision of estimates for the te model and for its combination with a robust design. our simulation study, based on 16,650 parameter combinations, shows that temporary emigration is more challenging than currently usually acknowledged. the tests 2.ct and 2.c are largely independent of the difference in survival probability between the states. in contrast, test 3.sr is sensitive to the difference in survival probability but also to emigration probability. tests 2.c and 2.ct have high power if a large part of the population temporarily emigrates and a large fraction of the individuals return on the next capture occasion. under this condition, bias is low and precision adequate even if the assumption of equal survival probability is violated. bias and precision are also satisfactory if the assumption is met but unsatisfactory or unreliable for the remaining parameter space. we conclude that the uncertainties whether an appropriate model was selected and whether the estimates from the selected model may be biased should be clearly communicated and that every endeavour should be made to make the unobservable state observable.
methods;Measurement errors chao;a generalized chao estimator with measurement error and external information;"chao estimator; external information; simex";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"DOTTO F;FARCOMENI A";we present a generalized chao (gc) estimator based on a subject-occasion-specific design matrix. we then extend the gc estimator to (i) external information, in the form of non-linear constraints on subpopulation sizes and (ii) measurement error. for the first, we propose a reparameterization of the estimating equations. as a result, the constrained mle can be found with no additional computational efforts. for the second we generalize simex procedure to multiple measurement methods. in simulation we show that (even incorrect) external information can substantially decrease the mse. we illustrate with an application to a whale shark (rhincodon typus) population, where mostly jouvenile males are observed. we use external information on gender ratio of whale sharks to correct for low catchability of females, and our multivariate simex procedure to correct for measurement error in assessment of shark length. the resulting population size estimates are about 60% larger than the unconstrained-uncorrected counterparts.
methods;GOF, heterogeneity;a test of positive association for detecting heterogeneity in capture for capture-recapture data;"cormack-jolly-seber model; goodman-kruskal's gamma; goodness-of-fit";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"JEYAM A;MCCREA RS;BREGNBALLE T;FREDERIKSEN M;PRADEL R";the cormack-jolly-seber (cjs) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. in order to detect and identify this phenomenon in a cjs framework, we propose a test of positive association between previous and future encounters using goodman-kruskal's gamma. this test is based solely on the raw capture histories and makes no assumption on model structure. the development of the test is motivated by a dataset of sandwich terns (thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. we use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, leslie's test of equal catchability and carothers' extension of the leslie test. the test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. we recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. supplementary materials accompanying this paper appear online.
methods;Data combination, spatial, distance sampling, uncertainty;accounting for uncertainty in duplicate identification and group size judgements in mark-recapture distance sampling;"abundance; coefficient of variation; common dolphins; double-observer aerial surveys; group size; mark-recapture distance sampling";METHODS IN ECOLOGY AND EVOLUTION;"HAMILTON ONP;KINCAID SE;CONSTANTINE R;KOZMIAN LEDWARD L;WALKER CG;FEWSTER RM";1. mark-recapture distance sampling (mrds) surveys with two independent observers are widely used to estimate wildlife population abundance. the analysis relies on accurate identification of duplicate sightings common to both observers, and correct judgements of group size, both of which are hard to achieve for species that exhibit complex grouping patterns. 2. in this paper, we examine the impact of these sources of uncertainty on bias and precision of abundance estimates, using a case study of 22 aerial surveys of common dolphins (delphinus delphis) in the hauraki gulf, new zealand. we develop a novel probabilistic method to identify duplicate observations, and account for various sources of uncertainty using a simulation-intensive approach. 3. for our case study, identifying duplicates using reasonable but arbitrary thresholds of time and angle discrepancies created a range of abundance estimates differing by up to 20%, whereas our novel threshold-free probabilistic analysis returned an estimate roughly central to this range. uncertainty in group size made a smaller impact of up to 5% on abundance estimates. all analysis choices returned similar values for the coefficient of variation, from 20 to 23%. 4. generating robust estimates of abundance, and accounting for all associated sources of uncertainty, is critical for informing conservation management. our novel approach provides a way to eliminate arbitrary decisions associated with mrds, and account for a wider range of uncertainties. our method allows for the reliable application of mrds to a wider range of terrestrial and marine species, and will be a useful tool for producing robust abundance estimates for species that exhibit complex grouping patterns.
methods;Point process, distance sampling, spatial;point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales;"distance sampling; spatio-temporal modeling; stochastic partial differential equations; inla; spatial point process";ANNALS OF APPLIED STATISTICS;"YUAN Y;BACHL FE;LINDGREN F;BORCHERS DL;ILLIAN JB;BUCKLAND ST;RUE H;GERRODETTE T";distance sampling is a widely used method for estimating wildlife population abundance. the fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. estimates are usually obtained at survey stratum level. for an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. temporal variation in the spatial structure is also important. we formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-gaussian cox process. the method adopts a flexible stochastic partial differential equation (spde) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested laplace approximation (inla) for bayesian inference. it allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. we estimate blue whale density in the eastern tropical pacific ocean from thirteen shipboard surveys conducted over 22 years. we find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consistent with no trend in density across years. our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.
methods;HMM, batch marking;hidden markov models for extended batch data;"batch marking; integrated population modeling; mark-recapture; open n-mixture models; viterbi algorithm; weather-loach";BIOMETRICS;"COWEN LLE;BESBEAS P;MORGAN BJT;SCHWARZ CJ";"batch marking provides an important and efficient way to estimate the survival probabilities and population sizes of wild animals. it is particularly useful when dealing with animals that are difficult to mark individually. for the first time, we provide the likelihood for extended batch-marking experiments. it is often the case that samples contain individuals that remain unmarked, due to time and other constraints, and this information has not previously been analyzed. we provide ways of modeling such information, including an open n-mixture approach. we demonstrate that models for both marked and unmarked individuals are hidden markov models; this provides a unified approach, and is the key to developing methods for fast likelihood computation and maximization. likelihoods for marked and unmarked individuals can easily be combined using integrated population modeling. this allows the simultaneous estimation of population size and immigration, in addition to survival, as well as efficient estimation of standard errors and methods of model selection and evaluation, using standard likelihood techniques. alternative methods for estimating population size are presented and compared. an illustration is provided by a weather-loach data set, previously analyzed by means of a complex procedure of constructing a pseudo likelihood, the formation of estimating equations, the use of sandwich estimates of variance, and piecemeal estimation of population size. simulation provides general validation of the hidden markov model methods developed and demonstrates their excellent performance and efficiency. this is especially notable due to the large numbers of hidden states that may be typically required"
methods;NA;the 2012 census of agriculture: a capture-recapture analysis;NA;JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"YOUNG LJ;LAMAS AC;ABREU DA";the census of agriculture is conducted every 5 years, in years ending in 2 and 7. the census list frame is incomplete, resulting in undercoverage. not all operations on the list frame respond, and, based on the response, some misclassification occurs. in 2012, a capture-recapture analysis was conducted to adjust for undercoverage, nonresponse, and misclassification. this was the first time capture-recapture methods were used to produce official statistics for an establishment survey. the number of records on the census mailing list that were classified as farms was 1,382,099, and the published estimate of the number of farms was 2,109,303, a 34.5% adjustment. the adjustment was greatest for farms with low production levels and for specialty farms, both of which are difficult to identify and add to the list. the methods used are described. challenges that arose in the implementation process are discussed. areas for enhancement being targeted for the 2017 census of agriculture are highlighted. supplementary materials accompanying this paper appear online.
methods;Robust design, metapopulation, temporary emigration, multistate;applying the multistate capture-recapture robust design to characterize metapopulation structure;"distribution; heterogeneity; local transitions; mark-recapture; spatial scales; subpopulations; wildlife conservation";METHODS IN ECOLOGY AND EVOLUTION;"CHABANNE DBH;POLLOCK KH;FINN H;BEJDER L";"1. population structure must be considered when developing mark-recapture (mr) study designs as the sampling of individuals from multiple populations (or subpopulations) may increase heterogeneity in individual capture probability. conversely, the use of an appropriate mr study design which accommodates heterogeneity associated with capture occasion varying covariates due to animals moving between 'states' (i.e. geographic sites) can provide insight into how animals are distributed in a particular environment and the status and connectivity of subpopulations. 2. the multistate closed robust design (mscrd) was chosen to investigate: (i) the demographic parameters of indo-pacific bottlenose dolphin (tursiops aduncus) subpopulations in coastal and estuarine waters of perth, western australia; and (ii) how they are related to each other in a metapopulation. using 4 years of year-round photo-identification surveys across three geographic sites, we accounted for heterogeneity of capture probability based on how individuals distributed themselves across geographic sites and characterized the status of subpopulations based on their abundance, survival and interconnection. 3. mscrd models highlighted high heterogeneity in capture probabilities and demographic parameters between sites. high capture probabilities, high survival and constant abundances described a subpopulation with high fidelity in an estuary. in contrast, low captures, permanent and temporary emigration and fluctuating abundances suggested transient use and low fidelity in an open coastline site. 4. estimates of transition probabilities also varied between sites, with estuarine dolphins visiting sheltered coastal embayments more regularly than coastal dolphins visited the estuary, highlighting some dynamics within the metapopulation. 5. synthesis and applications. to date, bottlenose dolphin studies using mark-recapture approach have focussed on investigating single subpopulations. here, in a heterogeneous coastal-estuarine environment, we demonstrated that spatially structured bottlenose dolphin subpopulations contained distinct suites of individuals and differed in size, demographics and connectivity. such insights into the dynamics of a metapopulation can assist in local-scale species conservation. the mscrd approach is applicable to species/populations consisting of recognizable individuals and is particularly useful for characterizing wildlife subpopulations that vary in their vulnerability to human activities, climate change or invasive species."
methods;Continuous, opportunistic;transient state estimation using continuous-time processes applied to opportunistic capture-recapture data;"breeding; continuous modeling; markov model; opportunistic data; survival";ECOLOGICAL MODELLING;"CHOQUET R;GARNIER A;AWUVE E;BESNARD A";typically, analyze of capture-recapture data rely on standard protocols of individual detection. encounters and reencounters of marked individuals are made during short sessions separated by constant time intervals. very often, it can be reasonably assumed that the time between two sessions is the same for all individuals. however, in some studies, data is collected opportunistically within a season. this is often due to difficulties concerning fieldwork and/or a lack of people involved in data collection. it can also be intentionally chosen, for example, if collecting data within a season provides additional information on the status of the individual (e.g. whether it is a breeder or a non-breeder). when using capture-recapture data, opportunistic data has typically been analyzed by using discretize time intervals, to avoid taking into account the continuous-time processes of state and capture. in this study, we modeled the transition between a transient state (non-breeder) to one absorbing state (breeder) in a continuous process over time. a poisson point process was used to model the capture data, allowing us to directly model the opportunistic data set. the main advantage of this new approach is that it allows a full use of opportunistic data - i.e. all available information can be used. as an illustrative working example, we applied the approach to investigate alpine ibex (capra ibex) reproduction over one year. we jointly estimated the following parameters: the breeding rate in the summer and the survival rate of the young from birth to the following spring. our findings suggest that this modeling approach has the potential to open new perspectives in population ecology. (c) 2017 elsevier b.v. all rights reserved.
methods;NA;using optimal transport theory to estimate transition probabilities in metapopulation dynamics;"lagrangian; optimal transport; population dynamics; transition probabilities; movement ecology; cost-energy function";ECOLOGICAL MODELLING;"NICHOLS JM;SPENDELOW JA;NICHOLS JD";this work considers the estimation of transition probabilities associated with populations moving among multiple spatial locations based on numbers of individuals at each location at two points in time. the problem is generally underdetermined as there exists an extremely large number of ways in which individuals can move from one set of locations to another. a unique solution therefore requires a constraint. the theory of optimal transport provides such a constraint in the form of a cost function, to be minimized in expectation over the space of possible transition matrices. we demonstrate the optimal transport approach on marked bird data and compare to the probabilities obtained via maximum likelihood estimation based on marked individuals. it is shown that by choosing the squared euclidean distance as the cost, the estimated transition probabilities compare favorably to those obtained via maximum likelihood with marked individuals. other implications of this cost are discussed, including the ability to accurately interpolate the population's spatial distribution at unobserved points in time and the more general relationship between the cost and minimum transport energy. (c) 2017 elsevier b.v. all rights reserved.
methods;NA;a general approach to model movement in (highly) fragmented patch networks;"metapopulation; patch transition; spatial configuration; landscape connectivity; memory";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MORALES JM;DI VIRGILIO A;DELGADO MD;OVASKAINEN O";landscape heterogeneity can often be represented as a series of discrete habitat or resource patches surrounded by a matrix of non-habitat. understanding how animals move in such networks of patches is important for many theoretical and applied questions. the probability of going from one patch to another is affected in a non-trivial way by the characteristics and location of other patches in the network. nearby patches can compete as possible destinations, and a particular patch can be shadowed by neighboring patches. we present a way to account for the effects of the spatial configuration of patches in models of space use where individuals alternate between spending time in a patch and moving to other patches in the network. the approach is based on the original derivation of ovaskainen and cornell (j appl probab 40:557-580, 2003) for a diffusion model that considered all possible ways in which an individual leaving a particular patch can eventually reach another patch before dying or leaving the patch network. by replacing the theoretical results of ovaskainen and cornell by other appropriate functions, we provide generality and thus make their approach useful in contexts where diffusion is not a good approximation of movement. furthermore, we provide ways to estimate time spent in the non-habitat matrix when going from patch to patch and implement a method to incorporate the effect of the history of previous visits on future patch use. we present an mcmc way to fit these models to data and illustrate the approach with both simulated data and data from sheep moving among seasonally flooded meadows in northern patagonia.supplementary materials accompanying this paper appear online.
methods;Behavior, HMM, dispersal, migration;analysing movement behaviour and dynamic space-use strategies among habitats using multi-event capture-recapture modelling;"amphibian; capture-recapture; dispersal; habitat selection; movement; multi-event model";METHODS IN ECOLOGY AND EVOLUTION;"CAYUELA H;PRADEL R;JOLY P;BESNARD A";"1. the environment of most species is heterogeneous at different spatial and temporal scales; this heterogeneity can have a direct effect on various components of fitness. as a consequence, individual space-use and movement strategies are central issues in ecology and conservation and receive considerable attention from researchers. 2. in the last 30 years, this issue has led to the development of capture-recapture models that allow movement between sites to be quantified, while handling imperfect detection. for studies involving numerous recapture sites in which the emphasis is on dispersal or migration rather than movement between particular sites, lagrange et al. recently proposed a parsimonious cr multi-event model that contrasts individuals that move and individuals that stay in place, irrespective of the sites involved. 3. in this study, we developed a generalized version of this model to allow survival probability and movement probability to differ for different types of habitat to which the individual sites may be assigned. we investigated the potential of this new parameterization by studying the movements of an amphibian, the yellow-bellied toad (bombina variegata), in a set of breeding and resting/foraging ponds. 4. our capture-recapture multi-event model provides a highly flexible tool allowing users to model movements within and between several habitats. this approach can be potentially used to study movement behaviour and space-use strategies of a wide range of taxa."
methods;Tag loss, multistate, telemetry;accounting for false mortality in telemetry tag applications;"murray cod; trout cod; golden perch; capture-recapture; telemetry; tag failure";ECOLOGICAL MODELLING;"BIRD T;LYON J;WOTHERSPOON S;KING R;MCCARTHY M";deaths of animals in the wild are rarely observed directly, which often limits understanding of survival rates. telemetry transmitters offer field ecologists the opportunity to observe mortality events in cases as the absence of animal movement. when observations of mortality are based on factors such as the absence of animal movement, live individuals can be mistaken for dead, resulting in biased estimates of survival. additionally, tag failure or emigration might also influence estimates of survival in telemetry studies. failing to account for mis-classification, tag failure, and emigration rates can result in overestimates of mortality rates by up two-fold, even when the data are corrected for obviously mistaken entries. we use a multi-state capture-recapture model with a misclassification parameter in estimating both the rate of permanent emigration and/or tag failure and the rate at which individuals are mistakenly identified as dead. we use this method on an annual telemetry survey of three species of native fish in the murray river, australia: murray cod (maccullochella peelii), trout cod (maccullochella macquariensis) and golden perch (macquaria ambigua). evidence for higher mortality rates in the first year post-implantation occurred for murray cod and golden perch, which is likely an effect of tagging and/or the transmitter, or transmitters shedding. using simulations, we confirm that our model approach is robust to a broad range of misclassification and transmitter failure rates. with these simulations we also demonstrate that misclassification models that do not account for emigration will likely be erroneous if live and dead animals have different probabilities of detection. these findings will have a broad interest to ecologists wishing to account for multiple sources of misclassification error in capture-mark-recapture studies, with the caveat that the specifics of the approach are dependent on species, transmitter types and other aspects of experimental design which may or may not be amenable to the misclassification framework. (c) 2017 elsevier b.v. all rights reserved.
methods;Data combination, abundance, CAR;bayesian methods for estimating animal abundance at large spatial scales using data from multiple sources;"capture-recapture survey; car model; hierarchical bayes; model selection; occupancy survey; spatial confounding";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"DEY S;DELAMPADY M;PARAMESHWARAN R;KUMAR NS;SRIVATHSA A;KARANTH KU";estimating animal distributions and abundances over large regions is of primary interest in ecology and conservation. specifically, integrating data from reliable but expensive surveys conducted at smaller scales with cost-effective but less reliable data generated from surveys at wider scales remains a central challenge in statistical ecology. in this study, we use a bayesian smoothing technique based on a conditionally autoregressive (car) prior distribution and bayesian regression to address this problem. we illustrate the utility of our proposed methodology by integrating (i) abundance estimates of tigers in wildlife reserves from intensive photographic capture-recapture methods, and (ii) estimates of tiger habitat occupancy from indirect sign surveys, conducted over a wider region. we also investigate whether the random effects which represent the spatial association due to the car structure have any confounding effect on the fixed effects of the regression coefficients.
methods;GOF, graphical diagnostics;graphical diagnostics for occupancy models with imperfect detection;"dunn-smyth residuals; goodness-of-fit; imperfect detection; probability integral transforms";METHODS IN ECOLOGY AND EVOLUTION;"WARTON DI;STOKLOSA J;GUILLERA ARROITA G;MACKENZIE DI;WELSH AH";occupancy-detection models that account for imperfect detection have become widely used in many areas of ecology. as with any modelling exercise, it is important to assess whether the fitted model encapsulates the main sources of variation in the data, yet there have been few methods developed for occupancy-detection models that would allow practitioners to do so. in this paper, a new type of residual for occupancy-detection models is developed according to the method of dunn & smyth (journal of computational and graphical statistics, 5, 1996, 236-244). residuals are separately constructed to diagnose the occupancy and detection components of the model. because the residuals are quite noisy, we suggest fitting a smoother through plots of residuals against predictors of fitted values, with 95% confidence bands, to diagnose lack-of-fit. the method is illustrated using swiss squirrel data, and evaluated using simulations based on that dataset. plotting residuals against predictors or against fitted values performed reasonably well as methods for diagnosing violations of occupancy-detection model assumptions, particularly plots of residuals against a missing predictor. relatively high false positive rates were sometimes observed, but this seems to be controlled reasonably well by fitting smoothers to these plots and being guided in interpretation by 95% confidence bands around the smoothers.
methods;Migration, nonparametric, continuous, stopover;modelling individual migration patterns using a bayesian nonparametric approach for capture-recapture data;"chinese restaurant process; great crested newts; poisson-gamma process; reed warblers; shot-noise cox process; stopover data";ANNALS OF APPLIED STATISTICS;"MATECHOU E;CARON F";we present a bayesian nonparametric approach for modelling wildlife migration patterns using capture-recapture (cr) data. arrival times of individuals are modelled in continuous time and assumed to be drawn from a poisson process with unknown intensity function, which is modelled via a flexible nonparametric mixture model. the proposed cr framework allows us to estimate the following: (i) the total number of individuals that arrived at the site, (ii) their times of arrival and departure, and hence their stopover duration, and (iii) the density of arrival times, providing a smooth representation of the arrival pattern of the individuals at the site. we apply the model to data on breeding great crested newts (triturus cristatus) and on migrating reed warblers (acrocephalus scirpaceus). for the former, the results demonstrate the staggered arrival of individuals at the breeding ponds and suggest that males tend to arrive earlier than females. for the latter, they demonstrate the arrival of migrating flocks at the stopover site and highlight the considerable difference in stopover duration between caught and not-caught individuals.
methods;Nonparametric, heterogeneity;petersen estimator, chapman adjustment, list effects, and heterogeneity;"bias correction; capture-recapture; nonidentifiability";BIOMETRICS;"MAO CX;HUANG RC;ZHANG SJ";we use a nonparametric mixture model for the purpose of estimating the size of a population from multiple lists in which both the individual effects and list effects are allowed to vary. we propose a lower bound of the population size that admits an analytic expression. the lower bound can be estimated without the necessity of model-fitting. the asymptotical normality of the estimator is established. both the estimator itself and that for the estimable bound of its variance are adjusted. these adjusted versions are shown to be unbiased in the limit. simulation experiments are performed to assess the proposed approach and real applications are studied.
methods;Migration, connectivity, data combination, telemetry, GPS;the integration of mark re-encounter and tracking data to quantify migratory connectivity;"integrated model; multi-state model; mark-recapture data; gps tracking; geolocation; data analysis; bayesian methods";ECOLOGICAL MODELLING;"KORNER NIEVERGELT F;PREVOT C;HAHN S;JENNI L;LIECHTI F";animals which spend subsequent seasons in different areas connect geographical regions. the connection between breeding and non-breeding grounds is defined as migratory connectivity. the quantification of such connectivity is important, because movements between different locations can have strong consequences for the moving animal as well as the encountered habitats or ecosystems. connectivity is usually investigated either on the basis of (few unsystematic) re-encounters of (often large numbers of) marked individuals or by observations of a few individuals tracked by remote sensing techniques, i.e. gps or geolocation. the combination of qualitatively different data sets can reduce the limitations of each type of data and thus improve the accuracy of the estimated connectivity parameters considerably. we formally combine individual tracking data and mark re-encounter data in a probabilistic model framework for quantifying connectivity. in a first example, we quantify migratory connectivity of a long-distance passerine migrant based on ring re-encounter and geolocator data. as a second example, we combine re-encounter data of ear-tagged wild boars with gps tracking data to estimate the spatial distribution of wild boars during the hunting and the non-hunting seasons. these two examples illustrate the use of the model in two different framework: 1) long-distance migration and, 2) seasonal (e.g. hunting induced) non-migratory movements. results from the integrated analyses provided more information than the informal comparison of the results from independent analyses on each data set separately. parameter estimates were more precise in the integrated analyses compared to the separate analyses and stronger conclusions could be drawn. the integration of mark re-encounter and tracking data reduces sampling bias and increases the value of both data sets but the weighting of each data set needs further investigation. (c) 2016 elsevier b.v. all rights reserved.
methods;Nonparametric, heterogeneity;bayesian population size estimation using dirichlet process mixtures;"capture-recapture; casualties in conflicts; dirichlet process mixtures; latent class models; model selection";BIOMETRICS;MANRIQUE VALLIER D;we introduce a new bayesian nonparametric method for estimating the size of a closed population from multiple-recapture data. our method, based on dirichlet process mixtures, can accommodate complex patterns of heterogeneity of capture, and can transparently modulate its complexity without a separate model selection step. additionally, it can handle the massively sparse contingency tables generated by large number of recaptures with moderate sample sizes. we develop an efficient and scalable mcmc algorithm for estimation. we apply our method to simulated data, and to two examples from the literature of estimation of casualties in armed conflicts.
methods;Missing data, multiple imputation, covariates;estimation in closed capture-recapture models when covariates are missing at random;"inverse probability weighting; missing at random; multiple imputation; population size estimation; regression calibration";BIOMETRICS;"LEE SM;HWANG WH;TAPSOBA JD";individual covariates are commonly used in capture-recapture models as they can provide important information for population size estimation. however, in practice, one or more covariates may be missing at random for some individuals, which can lead to unreliable inference if records with missing data are treated as missing completely at random. we show that, in general, such a naive complete-case analysis in closed capture-recapture models with some covariates missing at random underestimates the population size. we develop methods for estimating regression parameters and population size using regression calibration, inverse probability weighting, and multiple imputation without any distributional assumptions about the covariates. we show that the inverse probability weighting and multiple imputation approaches are asymptotically equivalent. we present a simulation study to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. we also illustrate an analysis using data on the bird species yellow-bellied prinia collected in hong kong.
methods;Heterogeneity, mixture, stopover;bayesian analysis of jolly-seber type models;"capture-recapture-resight data sets; integrated modelling; mixture models; reversible jump; semipalmated sandpipers; stopover data";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"MATECHOU E;NICHOLLS GK;MORGAN BJT;COLLAZO JA;LYONS JE";we propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. we demonstrate this approach using a data set of migrating semipalmated sandpipers (calidris pussila) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. we demonstrate the use of reversible jump mcmc methods to derive posterior distributions for the model parameters and the models, simultaneously. the algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. the approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes.
methods;HMM;efficient markov chain monte carlo sampling for hierarchical hidden markov models;"capture-recapture; effective sample size; hidden markov model; hierarchical model; mcmc; nimble; sampling efficiency";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"TUREK D;DE VALPINE P;PACIOREK CJ";traditional markov chain monte carlo (mcmc) sampling of hidden markov models (hmms) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. when potentially many hmms are embedded within a hierarchical model, this can result in prohibitively long mcmc runtimes. we study combinations of existing methods, which are shown to vastly improve computational efficiency for these hierarchical models while maintaining the modeling flexibility provided by embedded hmms. the methods include discrete filtering of the hmm likelihood to remove latent states, reduced data representations, and a novel procedure for dynamic block sampling of posterior dimensions. the first two methods have been used in isolation in existing application-specific software, but are not generally available for incorporation in arbitrary model structures. using the nimble package for r, we develop and test combined computational approaches using three examples from ecological capture-recapture, although our methods are generally applicable to any embedded discrete hmms. these combinations provide several orders of magnitude improvement in mcmc sampling efficiency, defined as the rate of generating effectively independent posterior samples. in addition to being computationally significant for this class of hierarchical models, this result underscores the potential for vast improvements to mcmc sampling efficiency which can result from combinations of known algorithms.
methods;Nonparametric, heterogeneity, cluster sampling, survey design;estimating abundance: a non parametric mark recapture approach for open and closed systems;"abundance; capture-recapture; cluster sampling; heterogeneity; non-parametric; population estimation";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"REHMAN Z;TOMS CN;FINCH C";"we present a novel, non-parametric, frequentist approach for capture-recapture data based on a ratio estimator, which offers several advantages. first, as a non-parametric model, it does not require a known underlying distribution for parameters nor the associated assumptions, eliminating the need for post-hoc corrections or additional modeling to account for heterogeneity and other violated assumptions. second, the model explicitly deals with dependence of trials by considering trials to be dependent; therefore, cluster sampling is handled naturally and additional adjustments are not necessary. third, it accounts for ordering, utilizing the fact that a system with a small population will have a greater frequency of recaptures ""early"" in the survey work compared to an identical system with a larger population. we provide mathematical proof that our estimator attains asymptotic minimum variance under open systems. we apply the model to a data set of bottlenose dolphins (tursiops truncatus) and compare results to those from classic closed models. we show that the model has an impressive rate of convergence and demonstrate that there's an inverse relationship between population size and the proportion of the population that need to be sampled, while achieving the same degree of accuracy for abundance estimates. the model is flexible and can apply to ecological situations as well as other situations that lend themselves to capture recapture sampling."
methods;Camera trap, data management, R package;camtrapr: an r package for efficient camera trap data management;"biodiversity surveys; camera trapping; data management; detection history; monitoring; occupancy models; photo trapping; spatial capture-recapture models; wildlife studies";METHODS IN ECOLOGY AND EVOLUTION;"NIEDBALLA J;SOLLMANN R;COURTIOL A;WILTING A";"1. camera trapping is a widely applied method to study mammalian biodiversity and is still gaining popularity. it can quickly generate large amounts of data which need to be managed in an efficient and transparent way that links data acquisition with analytical tools. 2. we describe the free and open-source r package camtrapr, a new toolbox for flexible and efficient management of data generated in camera trap-based wildlife studies. the package implements a complete workflow for processing camera trapping data. it assists in image organization, species and individual identification, data extraction from images, tabulation and visualization of results and export of data for subsequent analyses. there is no limitation to the number of images stored in this data management system; the system is portable and compatible across operating systems. 3. the functions provide extensive automation to minimize data entry mistakes and, apart from species and individual identification, require minimal manual user input. species and individual identification are performed outside the r environment, either via tags assigned in dedicated image management software or by moving images into species directories. 4. input for occupancy and (spatial) capture-recapture analyses for density and abundance estimation, for example in the r packages unmarked or secr, is computed in a flexible and reproducible manner. in addition, survey summary reports can be generated, spatial distributions of records can be plotted and exported to gis software, and single-and two-species activity patterns can be visualized. 5. camtrapr allows for streamlined and flexible camera trap data management and should be most useful to researchers and practitioners who regularly handle large amounts of camera trapping data."
methods;Survey design, temporary emigration, robust design;integrating biology, field logistics, and simulations to optimize parameter estimation for imperiled species;"multi-state open robust design; parameter estimation; temporary emigration; anaxyrus boreas boreas; survey design";ECOLOGICAL MODELLING;"LANIER WE;BAILEY LL;MUTHS E";conservation of imperiled species often requires knowledge of vital rates and population dynamics. however, these can be difficult to estimate for rare species and small populations. this problem is further exacerbated when individuals are not available for detection during some surveys due to limited access, delaying surveys and creating mismatches between the breeding behavior and survey timing. here we use simulations to explore the impacts of this issue using four hypothetical boreal toad (anaxyrus boreas boreas) populations, representing combinations of logistical access (accessible, inaccessible) and breeding behavior (synchronous, asynchronous). we examine the bias and precision of survival and breeding probability estimates generated by survey designs that differ in effort and timing for these populations. our findings indicate that the logistical access of a site and mismatch between the breeding behavior and survey design can greatly limit the ability to yield accurate and precise estimates of survival and breeding probabilities. simulations similar to what we have performed can help researchers determine an optimal survey design(s) for their system before initiating sampling efforts. (c) 2016 elsevier b.v. all rights reserved.
methods;Stopover;open models for removal data;"common lizard; depletion; great crested newts; rjmcmc; stopover model";ANNALS OF APPLIED STATISTICS;"MATECHOU E;MCCREA RS;MORGAN BJT;NASH DJ;GRIFFITHS RA";individuals of protected species, such as amphibians and reptiles, often need to be removed from sites before development commences. usually, the population is considered to be closed. all individuals are assumed to (i) be present and available for detection at the start of the study period and (ii) remain at the site until the end of the study, unless they are detected. however, the assumption of population closure is not always valid. we present new removal models which allow for population renewal through birth and/or immigration, and population depletion through sampling as well as through death/emigration. when appropriate, productivity may be estimated and a bayesian approach allows the estimation of the probability of total population depletion. we demonstrate the performance of the models using data on common lizards, zootoca vivipara, and great crested newts, triturus cristatus.
methods;N-mixture, assumptions, heterogeneity;intrinsic heterogeneity in detection probability and its effect on n-mixture models;"abundance-estimation; behaviour; density dependence; detection probability; model testing; monitoring; negative binomial; poisson; population survey; zero-inflated poisson";METHODS IN ECOLOGY AND EVOLUTION;"VEECH JA;OTT JR;TROY JR";"estimating the abundance or density of animal populations is often a fundamental task in ecological research and species conservation. n-mixture models are widely used to estimate the detection probability of individual organisms that thusly leads to more accurate estimates of a species' true abundance. however, individuals likely vary in their probabilities of being detected. during a survey, heterogeneity (variation) in individual detection probability might arise due to conditions of the surveying process; this form of extrinsic heterogeneity can be accounted for by the use of appropriate covariates in the models. in contrast, intrinsic heterogeneity in the detection probabilities of individuals arises when intraspecific variation in behaviour results in individual organisms differing in their latent (inherent) probabilities of being detected. this form of heterogeneity is not tractable by the use of covariates and its possible effects on model performance have not been investigated to date. using simulated data, we evaluated the performance of poisson, negative binomial and zero-inflated poisson versions of n-mixture models under the conditions of intrinsic heterogeneity in individual detection probability. most versions of n-mixture models performed well in estimating abundance as indicated by relatively low root-mean-square-error values (rmse<1). error distributions indicated a lack of substantial bias and relatively high precision and accuracy when simulated detection probabilities of individuals were high (>05) and heterogeneity was random. otherwise, with structured heterogeneity (particularly positive density dependence) and low detection probabilities (<05), model performance was reduced (rmse>2). the poorest performing model was the zero-inflated poisson version of n-mixture model applied to data from low survey effort. our results suggest that n-mixture models are robust to intrinsic heterogeneity in individual detection probabilities except when the detection probabilities are low. when model-estimated detection probabilities are low (<05), model users should be aware that estimates of abundance could be erroneous if there was non-random intrinsic heterogeneity in individual detection probabilities during the surveys. remedying this situation might require redesigning the basic survey protocol such that it does not rely on behavioural traits (as cues to detection) that are intrinsically variable among individuals."
methods;zero-truncated;a flexible ratio regression approach for zero-truncated capture-recapture counts;"capture-recapture; mixed binomial distributions; ratio regression estimator; zero-truncated model";BIOMETRICS;"BOHNING D;ROCCHETTI I;ALFO M;HOLLING H";"capture-recapture methods are used to estimate the size of a population of interest which is only partially observed. in such studies, each member of the population carries a count of the number of times it has been identified during the observational period. in real-life applications, only positive counts are recorded, and we get a truncated at zero-observed distribution. we need to use the truncated count distribution to estimate the number of unobserved units. we consider ratios of neighboring count probabilities, estimated by ratios of observed frequencies, regardless of whether we have a zero-truncated or an untruncated distribution. rocchetti et al. (2011) have shown that, for densities in the katz family, these ratios can be modeled by a regression approach, and rocchetti et al. (2014) have specialized the approach to the beta-binomial distribution. once the regression model has been estimated, the unobserved frequency of zero counts can be simply derived. the guiding principle is that it is often easier to find an appropriate regression model than a proper model for the count distribution. however, a full analysis of the connection between the regression model and the associated count distribution has been missing. in this manuscript, we fill the gap and show that the regression model approach leads, under general conditions, to a valid count distribution; we also consider a wider class of regression models, based on fractional polynomials. the proposed approach is illustrated by analyzing various empirical applications, and by means of a simulation study."
methods;Tag-reporting;modelling temporal and spatial variability in tag reporting-rates for newfoundland cod (gadus morhua);"cod; mark-recapture; tag reporting-rate; tagging; tmb";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"KONRAD C;BRATTEY J;CADIGAN NG";"mark-recapture experiments can be used to estimate the exploitation rate of a fishery; however, the estimate is influenced by the tag reporting-rate by the fishers. we present two methods to estimate the reporting rates in high/low reward ($100 and $10 cad respectively) long-term cod tagging experiments. we fit two binomial logistic mixed-effect models, one with temporal auto-correlation in the reporting-rate year-effects and one with independent year-effects. we estimate reporting-rates separately for recreational and commercial fishers, and test for spatial variation using fixed-effects for spatial regions. due to the complexity of the fishery, our models account for factors such as recapture-fishery type, fish-size and time-at-liberty. our results indicate that the recreational fishers reporting-rate was constant at 0.51 across all regions and years. the commercial fishery showed more spatial and temporal variation, with the reporting-rates estimates lying between 0.67 and 0.87 for the independent year-effect model, and between 0.57 and 0.84 for the random walk model. furthermore, we assessed the model performance as well as the coverage probability of nominal 95 % confidence intervals using simulations. we found that the models performed adequately; however, the nominal 95 % confidence intervals tended to be too narrow."
methods;Bioacoustic, assumptions;the influence of environmental parameters on the performance and detection range of acoustic receivers;"aatams - imos; acoustic telemetry; detection probability; detection range; sentinel tags";METHODS IN ECOLOGY AND EVOLUTION;"HUVENEERS C;SIMPFENDORFER CA;KIM S;SEMMENS JM;HOBDAY AJ;PEDERSON H;STIEGLITZ T;VALLEE R;WEBBER D;HEUPEL MR;PEDDEMORS V;HARCOURT RG";acoustic telemetry is being increasingly used to study the ecology of many aquatic organisms. this widespread use has been advanced by national and international tracking programs that coordinate deployment of passive acoustic telemetry networks on a regional and continental scale to detect tagged animals. while it is well-known that environmental conditions can affect the performance of acoustic receivers, these effects are rarely quantified despite the profound implications for tag detection and hence the ecological inferences. here, we deployed eight receivers at different depths within the water column and at different orientations (hydrophone up or down) and 12 tags 200-800m from the receivers for 234days to investigate how the tag detection range of acoustic receivers varied through time and under different meteorologic and oceanographic conditions. the study showed that receiver depth and orientation, and time since deployment had the largest effect on the detection range. thermocline gradient and depth, and wind speed were the environmental factors most affecting detection range, while wind direction, precipitation and atmospheric pressure had negligible or no effect. comparison of results to a proposed general acoustic theory model and previous studies showed that findings from specific habitat types cannot be generalised and applied across other habitats or environments. a good understanding of the acoustic coverage and temporal variations in relation to environmental conditions are crucial to accurate interpretation of results, and ensuing management recommendations. we recommend that each study include stationary reference tags to measure changes in detection probability with time, help refine detection range, and be used to improve confidence in the reporting and interpretation of the data.
methods;SSM;hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession;"animal abundance; carabids; cold-adapted species; detection probability; removal sampling; sex-ratio";ECOLOGICAL MODELLING;"TENAN S;MAFFIOLETTI C;CACCIANIGA M;COMPOSTELLA C;SEPPI R;GOBBI M";chronosequences of glacier retreat are useful for investigating primary successions over time periods that are longer than direct observation would permit. in this context, space-for-time substitution studies have been applied to assess the effects of climate change on invertebrate assemblages. however, population dynamics of insect species following retreating glaciers has been under-investigated until now due to difficulty in applying capture-recapture methods and correctly identifying species in the field. removal sampling methods are commonly used, but imperfect detectability is rarely accounted for in the analytical framework. in this paper we study the effects of environmental drivers of spatial, and indirectly temporal, variation in population size and sex-ratio of cold-adapted insects through a hierarchical framework for abundance. we show the importance of a metapopulation design, where samples are replicated in space and time, to model data from small and scattered populations, typically present in habitats with climate mediated selective pressure like those along glacier forelands. this scattered distribution can influence the observation or sampling process and thus species detectability. our results show that glacier retreat differently affects species-specific changes of population size and sex ratio along the chronosequence, even if the species are taxonomically related. small-sized populations occur on the glacier surface, near the glacier front, and in sites deglaciated for at least 100 yrs. on the contrary, larger populations occupy sites deglaciated for more than 20 yrs, but less than 100 yrs. this pattern is described by the concave relationship of abundance with both species richness of other arthropods (proxy of habitat complexity) and soil organic matter (proxy of soil maturity). sex-ratio showed opposite patterns in relation to time since deglaciation. hierarchical models that estimate abundance of spatially distinct subpopulations represent useful tools for accurately assessing changes in species abundance following climate change while accounting for possible bias associated with imperfect detectability, an issue which is often neglected in space-for-time substitution studies on invertebrates and, more generally, in studies involving pitfall trapping. (c) 2016 elsevier b.v. all rights reserved.
methods;HMM, memory;semi-markov arnason-schwarz models;"capture-recapture-recovery; dwell-time distribution; hidden markov model; multi-state model";BIOMETRICS;"KING R;LANGROCK R";we consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. traditionally, the arnason-schwarz model has been fitted to such data where the state process is modeled as a first-order markov chain, though second-order models have also been proposed and fitted to data. however, low-order markov models may not accurately represent the underlying biology. for example, specifying a (time-independent) first-order markov process involves the assumption that the dwell time in each state (i.e., the duration of a stay in a given state) has a geometric distribution, and hence that the modal dwell time is one. specifying time-dependent or higher-order processes provides additional flexibility, but at the expense of a potentially significant number of additional model parameters. we extend the arnason-schwarz model by specifying a semi-markov model for the state process, where the dwell-time distribution is specified more generally, using, for example, a shifted poisson or negative binomial distribution. a state expansion technique is applied in order to represent the resulting semi-markov arnason-schwarz model in terms of a simpler and computationally tractable hidden markov model. semi-markov arnason-schwarz models come with only a very modest increase in the number of parameters, yet permit a significantly more flexible state process. model selection can be performed using standard procedures, and in particular via the use of information criteria. the semi-markov approach allows for important biological inference to be drawn on the underlying state process, for example, on the times spent in the different states. the feasibility of the approach is demonstrated in a simulation study, before being applied to real data corresponding to house finches where the states correspond to the presence or absence of conjunctivitis.
methods;Continuous, point process, covariates;an r package for analysing survival using continuous-time open capture-recapture models;"estimation bias; frequentist inference; inhomogeneous poisson process; maximum likelihood estimation; survival analysis model";METHODS IN ECOLOGY AND EVOLUTION;"FOUCHET D;SANTIN JANIN H;SAUVAGE F;YOCCOZ NG;PONTIER D";1. capture-recapture software packages have proven to be very powerful tools for analysing factors affecting survival in wild populations. however, all such packages are limited to discrete-time protocols. appropriate survival analysis tools are still lacking for data acquired from continuous-time protocols. 2. we have developed a statistical method and propose an r package for analysing such data based on an extension of classical survival analysis models incorporating an inhomogeneous poisson process for modelling capture histories. first, data were simulated from a continuous-time protocol. these data were used to (i) compare survival estimation biases of discrete-and continuous-time approaches and (ii) investigate the performance and accuracy of our r package for four types of covariates: factors varying between individuals (like sex), in time (like climatic factors), both in time and between individuals (like physical condition) and age (as a categorical factor). secondly, the r package has been applied to a real data set for survival analysis of cats in the kerguelen archipelago (regrouping 682 cats over 20 years) as an illustrative example. 3. results of the simulated data analysis show that the method performs better than its discrete-time counterpart for analysing data acquired from continuous-time protocols. it provides unbiased parameter estimates for all parameters except those that vary both in time and between individuals - which is not surprising, since in our case, these factors were not updated in continuous time (i.e. only upon capture). when applied to the kerguelen cat data set, the results suggest that survival is lower in juveniles than in adults and subadults, varies between study sites and increases with physical condition, and this latter effect being more important in females than in males. sex, season, temporal linear trend in survival and the ndvi vegetation index were also tested but were not found to be significant. however, confidence intervals were too large (due to a low recapture rate) for excluding such effects. further analyses are still needed for rigorous covariate testing in this context. 4. in conclusion, continuous-time approaches - such as that presented in this paper - should be preferred when data acquired from continuous-time protocols is analysed.
methods;Natural selection, assumptions, covariates;the measurement of selection when detection is imperfect: how good are naive methods?;"capture-mark-recapture; directional selection; mark; natural selection; selection gradients; simulation; stabilizing selection";METHODS IN ECOLOGY AND EVOLUTION;"WALLER J;SVENSSON EI";1. the life spans of animals can be measured in natural populations by uniquely marking individuals and then releasing them into the field. selection on survival (a component of fitness) can subsequently be quantified by regressing the life spans of these marked individuals on their trait values. however, marked individuals are not always seen on every subsequent catching occasion, and for this reason, imperfect detection is considered a problem when estimating survival selection in natural populations. 2. capture-mark-recapture methods have been advocated as a powerful means to correct for imperfect detection. here, we use simulated and field data sets to evaluate the effect of assuming perfect detection ('naive methods'), when detection is really imperfect. we compared the performance of the naive methods with methods correcting for imperfect detection (mark-recapture methods, or mr). 3. although the effects of trait-dependent recapture probability are mitigated when recapture probability is high, mark-recapture methods still provide the safest choice when recapture probability might be trait-dependent. in our simulations, mark-recapture methods had a power advantage over naive methods, but all methods lost statistical power at low recapture probabilities. 4. the main advantage of mark-recapture methods over naive methods is the ability to control for hidden trait-dependent recapture probability, as it is often hard to tell a priori if trait dependence is an issue in a particular study. however, when trait-dependent recapture probability is weak, naive methods and mark-recapture methods perform similarly as long as recapture rates do not become too low, and the main problem of survival selection studies is still low statistical power. we provide a r package (easymark) alongside with this paper to facilitate future integration between mr methods and classical selection studies. easymark provides the opportunity to convert the regression coefficients from mr-approaches in to classical standardized selection gradients.
methods;Misidentification;extending the latent multinomial model with complex error processes and dynamic markov bases;"bayesian inference; markov basis; markov chain monte carlo; mark-recapture; misidentification; queen snake (regina septemvittata)";ANNALS OF APPLIED STATISTICS;"BONNER SJ;SCHOFIELD MR;NOREN P;PRICE SJ";the latent multinomial model (lmm) of link et al. [biometrics 66 (2010) 178-185] provides a framework for modelling mark-recapture data with potential identification errors. key is a markov chain monte carlo (mcmc) scheme for sampling configurations of the latent counts of the true capture histories that could have generated the observed data. assuming a linear map between the observed and latent counts, the mcmc algorithm uses vectors from a basis of the kernel to move between configurations of the latent data. schofield and bonner [biometrics 71 (2015) 1070-1080] shows that this is sufficient for some models within the framework but that a larger set called a markov basis is required when errors are more complex. we address two further challenges: (1) that models with complex error mechanisms may not fit within the lmm framework and (2) that markov bases can be difficult to compute for studies of even moderate size. we extend the framework to model the capture/demographic and error processes separately and develop a new mcmc algorithm using dynamic markov bases. our work is motivated by a study of queen snakes (regina septemvittata) and we use simulation to compare estimates of survival rates when snakes are marked with pit tags which have perfect identification versus brands which are prone to error.
methods;SCR, heterogeneity;capture-recapture abundance estimation using a semi-complete data likelihood approach;"bugs; capture-recapture; closed populations; individual heterogeneity; jags; spatially explicit";ANNALS OF APPLIED STATISTICS;"KING R;MCCLINTOCK BT;KIDNEY D;BORCHERS D";"capture-recapture data are often collected when abundance estimation is of interest. in this manuscript we focus on abundance estimation of closed populations. in the presence of unobserved individual heterogeneity, specified on a continuous scale for the capture probabilities, the likelihood is not generally available in closed form, but expressible only as an analytically intractable integral. model-fitting algorithms to estimate abundance most notably include a numerical approximation for the likelihood or use of a bayesian data augmentation technique considering the complete data likelihood. we consider a bayesian hybrid approach, defining a ""semi-complete"" data likelihood, composed of the product of a complete data likelihood component for individuals seen at least once within the study and a marginal data likelihood component for the individuals not seen within the study, approximated using numerical integration. this approach combines the advantages of the two different approaches, with the semi-complete likelihood component specified as a single integral (over the dimension of the individual heterogeneity component). in addition, the models can be fitted within bugs/jags (commonly used for the bayesian complete data likelihood approach) but with significantly improved computational efficiency compared to the commonly used superpopulation data augmentation approaches (between about 10 and 77 times more efficient in the two examples we consider). the semicomplete likelihood approach is flexible and applicable to a range of models, including spatially explicit capture-recapture models. the model-fitting approach is applied to two different data sets: the first relates to snowshoe hares where model m-h is applied and the second to gibbons where a spatially explicit capture-recapture model is applied."
methods;Data combination, distance sampling, dependence;accounting for lack of independence and partial overlap of observation zones in line-transect mark-recapture distance sampling;"abundance estimation; cephalorhynchus hectori hectori; dependent sightings; hector's dolphins; line-transect survey; mark-recapture distance sampling; partial overlap";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MACKENZIE DI;CLEMENT D";line-transect mark-recapture distance sampling methods can be used to estimate abundance when at least two observers sight and record distances to detected groups of individuals within the survey area. however, a lack of independence between the observer's detections will cause biased abundance estimates. studies are also typically designed such that there is complete overlap of the regions searched by the two observers, but that may not always be possible. here we detail an intuitive approach for line-transect distance sampling applications based upon logistic regression to account for a potential lack of independence by using the detections of one observer as a covariate in the detection function of the second observer. partial overlap of the observer survey regions can be addressed by constraining detection probability to equal 0 for the respective observer outside of the overlap zone. we show via simulation that the method provides reliable estimates of abundance and is not affected by random unmodeled heterogeneity in detection probability. the method is illustrated by estimating abundance within the covered region of an aerial line-transect survey for new zealand's endemic hector's dolphin (cephalorhynchus hectori hectori) conducted in the austral summer of 2013, the motivating application for this work. supplementary materials accompanying this paper appear on-line.
methods;NA;a population-size model for protein spot detection in proteomic studies;"binomial mixture; capture-recapture; lower bounds; nonidentifiability";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MAO CX;CHEN SN;YANG YT";"in proteomic studies, a population of proteins are often examined on a gel using a technique called two-dimensional gel eletrophoresis. the technique separates the protein population into individual protein spots on a two-dimensional gel by isoelectric charge and molecular weight. the resulting gel images are then processed by a software system for spot detection and subsequent analysis. the performance of a spot-detection program is evaluated by the total number of spots that are detected. a popular spot-detection program uses the ""master-slave"" approach, where all spots on ""slave images"" are subsets of the spots on the ""master image."" we argue that this approach potentially misses a large proportion of proteins and propose a model that quantifies the lack of performance. we provide nonparametric estimators for the protein population size and the expected number of proteins to be detected if a ""fusion-gel"" approach was used. using the data from a rat liver proteome study, we estimate that more than half of the protein population is missed by the master-slave approach."
methods;heterogeneity;a general class of recapture models based on the conditional capture probabilities;"aitchinson-silvey algorithm; capture history; equality constraints; heterogeneity; population size";BIOMETRICS;FARCOMENI A;we propose an mhotb model for population size estimation in capture-recapture studies. the tb part is based on equality constraints for the conditional capture probabilities, leading to an extremely rich model class. observed and unobserved heterogeneity are dealt with by means of a logistic parameterization. in order to explore the model class, we introduce a penalized version of the likelihood. the conditional likelihood and penalized conditional likelihood are maximized by means of efficient em algorithms. simulations and two real data examples illustrate the approach.
methods;Behavior, memory;flexible behavioral capture-recapture modeling;"behavioral response; markov models; mark-recapture; memory effect; memory-related summary statistics; population size";BIOMETRICS;"FEGATELLI DA;TARDELLA L";we develop alternative strategies for building and fitting parametric capture-recapture models for closed populations which can be used to address a better understanding of behavioral patterns. in the perspective of transition models, we first rely on a conditional probability parameterization. a large subset of standard capture-recapture models can be regarded as a suitable partitioning in equivalence classes of the full set of conditional probability parameters. we exploit a regression approach combined with the use of new suitable summaries of the conditioning binary partial capture histories as a device for enlarging the scope of behavioral models and also exploring the range of all possible partitions. we show how one can easily find unconditional mle of such models within a generalized linear model framework. we illustrate the potential of our approach with the analysis of some known datasets and a simulation study.
methods;Mark-resighting, stopover, migration, SSM, data combination;population size and stopover duration estimation using mark-resight data and bayesian analysis of a superpopulation model;"capture-recapture; data augmentation; jolly-seber; mark-resight; migration; state-space model";BIOMETRICS;"LYONS JE;KENDALL WL;ROYLE JA;CONVERSE SJ;ANDRES BA;BUCHANAN JB";we present a novel formulation of a mark-recapture-resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. we use a bayesian analysis of a state-space formulation of the jolly-seber mark-recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. we also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state-space model. we conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. this provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). application of the methods is demonstrated with data from a study of migratory sandpipers.
methods;NA;a functional model for characterizing long-distance movement behaviour;"argos; bayesian model; canada lynx; functional data analysis; movement modelling; splines; telemetry";METHODS IN ECOLOGY AND EVOLUTION;"BUDERMAN FE;HOOTEN MB;IVAN JS;SHENK TM";advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. these data sets have prompted the development of a number of statistical methodologies for modelling animal movement. telemetry data sets are often collected for purposes other than fine-scale movement analysis. these data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. these data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour. we developed a bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. the bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. the model is flexible, easy to implement and computationally efficient. we apply this model to data from colorado canada lynx (lynx canadensis) and use derived quantities to identify changes in movement behaviour.
methods;Species richness;estimating diversity via frequency ratios;"alpha diversity; biodiversity; capture-recapture; characterization of distributions; microbial ecology; species richness";BIOMETRICS;"WILLIS A;BUNGE J";we wish to estimate the total number of classes in a population based on sample counts, especially in the presence of high latent diversity. drawing on probability theory that characterizes distributions on the integers by ratios of consecutive probabilities, we construct a nonlinear regression model for the ratios of consecutive frequency counts. this allows us to predict the unobserved count and hence estimate the total diversity. we believe that this is the first approach to depart from the classical mixed poisson model in this problem. our method is geometrically intuitive and yields good fits to data with reasonable standard errors. it is especially well-suited to analyzing high diversity datasets derived from next-generation sequencing in microbial ecology. we demonstrate the method's performance in this context and via simulation, and we present a dataset for which our method outperforms all competitors.
methods;Point process, data combination, distance sampling;double-observer line transect surveys with markov-modulated poisson process models for animal availability;"abundance estimation; availability bias; cox point process; mark-recapture; maximum likelihood";BIOMETRICS;"BORCHERS DL;LANGROCK R";we develop maximum likelihood methods for line transect surveys in which animals go undetected at distance zero, either because they are stochastically unavailable while within view or because they are missed when they are available. these incorporate a markov-modulated poisson process model for animal availability, allowing more clustered availability events than is possible with poisson availability models. they include a mark-recapture component arising from the independent-observer survey, leading to more accurate estimation of detection probability given availability. we develop models for situations in which (a) multiple detections of the same individual are possible and (b) some or all of the availability process parameters are estimated from the line transect survey itself, rather than from independent data. we investigate estimator performance by simulation, and compare the multiple-detection estimators with estimators that use only initial detections of individuals, and with a single-observer estimator. simultaneous estimation of detection function parameters and availability model parameters is shown to be feasible from the line transect survey alone with multiple detections and double-observer data but not with single-observer data. recording multiple detections of individuals improves estimator precision substantially when estimating the availability model parameters from survey data, and we recommend that these data be gathered. we apply the methods to estimate detection probability from a double-observer survey of north atlantic minke whales, and find that double-observer data greatly improve estimator precision here too.
methods;Misidentification, photo-id, genetic tagging;connecting the latent multinomial;"capture-recapture; linear constraint; markov basis; markov chain monte carlo; misidentification";BIOMETRICS;"SCHOFIELD MR;BONNER SJ";link et al. (2010, biometrics 66, 178-185) define a general framework for analyzing capture-recapture data with potential misidentifications. in this framework, the observed vector of counts, y, is considered as a linear function of a vector of latent counts, x, such that y = ax, with x assumed to follow a multinomial distribution conditional on the model parameters, theta. bayesian methods are then applied by sampling from the joint posterior distribution of both x and theta. in particular, link et al. (2010) propose a metropolis-hastings algorithm to sample from the full conditional distribution of x, where new proposals are generated by sequentially adding elements from a basis of the null space (kernel) of a. we consider this algorithm and show that using elements from a simple basis for the kernel of a may not produce an irreducible markov chain. instead, we require a markov basis, as defined by diaconis and sturmfels (1998, the annals of statistics 26, 363-397). we illustrate the importance of markov bases with three capture-recapture examples. we prove that a specific lattice basis is a markov basis for a class of models including the original model considered by link et al. (2010) and confirm that the specific basis used in their example with two sampling occasions is a markov basis. the constructive nature of our proof provides an immediate method to obtain a markov basis for any model in this class.
methods;Heterogeneity;mixture regression models for closed population capture-recapture data;"archimedean copulas; closed population; maximum likelihood; random unit effect; residual heterogeneity; unit level covariates";BIOMETRICS;"TOUNKARA F;RIVEST LP";in capture-recapture studies, the use of individual covariates has been recommended to get stable population estimates. however, some residual heterogeneity might still exist and ignoring such heterogeneity could lead to underestimating the population size (n). in this work, we explore two new models with capture probabilities depending on both covariates and unobserved random effects, to estimate the size of a population. inference techniques including horvitz-thompson estimate and confidence intervals for the population size, are derived. the selection of a particular model is carried out using the akaike information criterion (aic). first, we extend the random effect model of darroch et al. (1993, journal of american statistical association 88, 1137-1148) to handle unit level covariates and discuss its limitations. the second approach is a generalization of the traditional zero-truncated binomial model that includes a random effect to account for an unobserved heterogeneity. this approach provides useful tools for inference about n, since key quantities such as moments, likelihood functions and estimates of n and their standard errors have closed form expressions. several models for the unobserved heterogeneity are available and the marginal capture probability is expressed using the logit and the complementary log-log link functions. the sensitivity of the inference to the specification of a model is also investigated through simulations. a numerical example is presented. we compare the performance of the proposed estimator with that obtained under model m-h of huggins (1989 biometrika 76, 130-140).
methods;Heterogeneity, individual growth, age uncertainty;individual heterogeneity in growth and age at sexual maturity: a gamma process analysis of capture-mark-recapture data;"hierarchical bayesian analysis; mixed effects; salamanders; von bertalanffy model";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"LINK WA;HESED KM";knowledge of organisms' growth rates and ages at sexual maturity is important for conservation efforts and a wide variety of studies in ecology and evolutionary biology. however, these life history parameters may be difficult to obtain from natural populations: individuals encountered may be of unknown age, information on age at sexual maturity may be uncertain and interval-censored, and growth data may include both individual heterogeneity and measurement errors. we analyzed mark-recapture data for red-backed salamanders (plethodon cinereus) to compare sex-specific growth rates and ages at sexual maturity. aging of individuals was made possible by the use of a von bertalanffy model of growth, complemented with models for interval-censored and imperfect observations at sexual maturation. individual heterogeneity in growth was modeled through the use of gamma processes. our analysis indicates that female p. cinereus mature earlier and grow more quickly than males, growing to nearly identical asymptotic size distributions as males.
methods;IPM, immigration, assumptions;estimating immigration using a bayesian integrated population model: choice of parametrization and priors;"bayesian model; immigration; integrated population model; prior sensitivity; source-sink dynamics";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"SCHAUB M;FLETCHER D";bayesian integrated population modelling provides a natural tool for estimating immigration into a single study population when we have indices of population size, mark-recapture data and fecundity data. we consider the choice of both the parametrization of immigration and its prior. using a simulation study for a model that is typical of those used for short-live bird species, we assess the effect of specifying immigration in terms of the number of immigrants each year, as opposed to an immigration rate. we also assess the effect of the assumption of independence of the data sets, which is commonly required in such modelling. if immigration is occurring, our results suggest that parametrizing the model in terms of number of immigrants will provide a more precise estimate, compared to a parametrization involving an immigration rate, even if we wish to estimate the rate. if there is little or no immigration, use of a model parametrized in terms of an immigration rate can result in overestimation, whereas a model in which immigration is specified as a number offers the possibility to use priors that have a negative lower bound with the consequence that immigration is correctly estimated. use of such a model appears to be robust to the assumption of independence being wrong, our results for independent and dependent data sets being remarkably similar in terms of the distribution, across all simulations, of the posterior means and standard deviations.
methods;PGR, photo-id;first time series of estimated humpback whale (megaptera novaeangliae) abundance in prince william sound;"humpback whale; jolly-seber; mark; mark-recapture; megaptera novaeangliae; photo identification; popan; rate of increase";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"TEERLINK SF;VON ZIEGESAR O;STRALEY JM;QUINN TJ;MATKIN CO;SAULITIS EL";"in prince william sound (pws), changes in abundance of humpback whales (megaptera novaeangliae), one of pws's primary marine predator species, have until now been largely unknown. using a historical dataset (1978-2009), we constructed the first time series of estimated humpback whale abundance in western pws that is also one of the longest time series used in analyses of humpback whale mark-recapture data. photographs from this dataset were used to ""mark"" and re-sight individual animals using the unique pigmentation pattern on the ventral flukes of each whale in a mark-recapture analysis. specifically, the popan implementation of the jolly-seber mark-recapture model in program mark was used. estimates of probabilities of capture and survival, recruitment parameters, and total abundance over the study were obtained, leading to a time series of abundance estimates. our results show an increase from 39 (se = 26) to 194 (se = 17) whales (500 %) over the time series. the average annual rate of increase (roi) was 4.53 % (95 % ci 3.28-5.79 %) which is only slightly lower than the 5-7 % roi estimated for the north pacific. trends in the number of whales encountered per unit effort were not consistent with abundance estimates from mark-recapture, showing that sightability changes annually."
methods;REM, bioacoustic, camera-trapping;a generalised random encounter model for estimating animal density with remote sensor data;"acoustic detection; camera traps; marine; population monitoring; simulations; terrestrial";METHODS IN ECOLOGY AND EVOLUTION;"LUCAS TCD;MOORCROFT EA;FREEMAN R;ROWCLIFFE JM;JONES KE";wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. a method without these requirements, the random encounter model (rem), has been successfully applied to estimate animal densities from count data generated from camera traps. however, count data from acoustic detectors do not fit the assumptions of the rem due to the directionality of animal signals. we developed a generalised rem (grem), to estimate absolute animal density from count data from both camera traps and acoustic detectors. we derived the grem for different combinations of sensor detection widths and animal signal widths (a measure of directionality). we tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement. we find that the grem produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. however, larger sensor detection and animal signal widths were found to be more precise. while the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. we found no effect of different animal movement models on the accuracy and precision of the grem. we conclude that the grem provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. the grem is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). as sensors such as camera traps and acoustic detectors become more ubiquitous, the grem will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.
methods;Missing data, multiple imputation, covariates;analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation;"individual time-varying continuous covariates; mark-recapture-recovery data; missing values; multiple imputation; two-step algorithm";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"WORTHINGTON H;KING R;BUCKLAND ST";"we consider mark-recapture-recovery data with additional individual time-varying continuous covariate data. for such data it is common to specify the model parameters, and in particular the survival probabilities, as a function of these covariates to incorporate individual heterogeneity. however, an issue arises in relation to missing covariate values, for (at least) the times when an individual is not observed, leading to an analytically intractable likelihood. we propose a two-step multiple imputation approach to obtain estimates of the demographic parameters. firstly, a model is fitted to only the observed covariate values. conditional on the fitted covariate model, multiple ""complete"" datasets are generated (i.e. all missing covariate values are imputed). secondly, for each complete dataset, a closed form complete data likelihood can be maximised to obtain estimates of the model parameters which are subsequently combined to obtain an overall estimate of the parameters. associated standard errors and 95 % confidence intervals are obtained using a non-parametric bootstrap. a simulation study is undertaken to assess the performance of the proposed two-step approach. we apply the method to data collected on a well-studied population of soay sheep and compare the results with a bayesian data augmentation approach. supplementary materials accompanying this paper appear on-line."
methods;NA;estimating the size of populations at high risk for hiv using respondent-driven sampling data;"hard-to-reach population sampling; model-based survey sampling; network sampling; social networks; successive sampling";BIOMETRICS;"HANDCOCK MS;GILE KJ;MAR CM";the study of hard-to-reach populations presents significant challenges. typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. this is especially true of populations at high risk for hiv/aids. respondent-driven sampling (rds) is often used in such settings with the primary goal of estimating the prevalence of infection. in such populations, the number of people at risk for infection and the number of people infected are of fundamental importance. this article presents a case-study of the estimation of the size of the hard-to-reach population based on data collected through rds. we study two populations of female sex workers and men-who-have-sex-with-men in el salvador. the approach is bayesian and we consider different forms of prior information, including using the unaids population size guidelines for this region. we show that the method is able to quantify the amount of information on population size available in rds samples. as separate validation, we compare our results to those estimated by extrapolating from a capture-recapture study of el salvadorian cities. the results of our case-study are largely comparable to those of the capture-recapture study when they differ from the unaids guidelines. our method is widely applicable to data from rds studies and we provide a software package to facilitate this.
methods;SCR, spatial, landscape, covariates;modelling non-euclidean movement and landscape connectivity in highly structured ecological networks;"abundance; animal movement; dendritic ecological network; density; ecological distance; functional connectivity; habitat network; stream distance; structural connectivity";METHODS IN ECOLOGY AND EVOLUTION;"SUTHERLAND C;FULLER AK;ROYLE JA";movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. instead, movement is typically measured using euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. a recently proposed extension of spatial capture-recapture (scr) models resolves this issue using spatial encounter histories of individuals to calculate least-cost paths (ecological distance: ecology, 94, 2013, 287) thereby relaxing the euclidean assumption. we evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space-use patterns and landscape connectivity. we simulated scr data in a riparian habitat network, using the ecological distance model under a range of scenarios where space-use in and around the landscape was increasingly associated with water (i.e. increasingly less euclidean). to assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and euclidean distance based models. we then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. finally, we provide a method for calculating landscape connectivity based on modelled species-landscape interactions generated from capture-recapture data. using ecological distance always produced unbiased estimates of abundance. explicitly modelling the strength of the species-landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. abundance under the euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity. the ecological distance scr model uses spatially indexed capture-recapture data to estimate how activity patterns are influenced by landscape structure. as well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about species-landscape interactions. the incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species-specific landscape connectivity.
methods;Bioacoustic, SCR;a general framework for animal density estimation from acoustic detections across a fixed microphone array;"anura; bootstrap; frog advertisement call; maximum likelihood; pyxicephalidae; spatially explicit capture-recapture; time of arrival";METHODS IN ECOLOGY AND EVOLUTION;"STEVENSON BC;BORCHERS DL;ALTWEGG R;SWIFT RJ;GILLESPIE DM;MEASEY GJ";acoustic monitoring can be an efficient, cheap, non-invasive alternative to physical trapping of individuals. spatially explicit capture-recapture (secr) methods have been proposed to estimate calling animal abundance and density from data collected by a fixed array of microphones. however, these methods make some assumptions that are unlikely to hold in many situations, and the consequences of violating these are yet to be investigated. we generalize existing acoustic secr methodology, enabling these methods to be used in a much wider variety of situations. we incorporate time-of-arrival (toa) data collected by the microphone array, increasing the precision of calling animal density estimates. we use our method to estimate calling male density of the cape peninsula moss frog arthroleptella lightfooti. our method gives rise to an estimator of calling animal density that has negligible bias, and 95% confidence intervals with appropriate coverage. we show that using toa information can substantially improve estimate precision. our analysis of the a.lightfooti data provides the first statistically rigorous estimate of calling male density for an anuran population using a microphone array. this method fills a methodological gap in the monitoring of frog populations and is applicable to acoustic monitoring of other species that call or vocalize.
methods;Misidentification, record linkage, missin data, heterogeneity, genetic tagging;probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification;"data augmentation; individual heterogeneity; latent multinomial; mark recapture; missing data; population size; probit regression; record linkage";ANNALS OF APPLIED STATISTICS;"MCCLINTOCK BT;BAILEY LL;DREHER BP;LINK WA";as noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). however, current methods do not allow for individual variation in parameters, such as detection or survival probability. here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. to facilitate bayesian inference using our approach, we extend standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. we also present a novel metropolis-hastings within gibbs algorithm for fitting these models using markov chain monte carlo. using closed population abundance models for illustration, we re-visit a dna capture-recapture population study of black bears in michigan, usa and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. we also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. our model was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. in the absence of misidentification, our probit formulation also provides a convenient and efficient gibbs sampler for bayesian analysis of traditional closed population capture-recapture data.
methods;Genetic tagging, continuous, uncertainty;closed-population capture-recapture modeling of samples drawn one at a time;"capture-recapture; continuous-time; genotyping error; noninvasive sampling";BIOMETRICS;"BARKER RJ;SCHOFIELD MR;WRIGHT JA;FRANTZ AC;STEVENS C";motivated by field sampling of dna fragments, we describe a general model for capture-recapture modeling of samples drawn one at a time in continuous-time. our model is based on poisson sampling where the sampling time may be unobserved. we show that previously described models correspond to partial likelihoods from our poisson model and their use may be justified through arguments concerning s- and bayes-ancillarity of discarded information. we demonstrate a further link to continuous-time capture-recapture models and explain observations that have been made about this class of models in terms of partial ancillarity. we illustrate application of our models using data from the european badger (meles meles) in which genotyping of dna fragments was subject to error.
methods;Misidentification, genetic tagging, camera trapping;maximum likelihood estimation for model m-t,m-alpha for capture-recapture data with misidentification;"genetic capture-recapture; latent multinomial; mark recapture; misidentification; natural tags; photo-identification";BIOMETRICS;"VALE RTR;FEWSTER RM;CARROLL EL;PATENAUDE NJ";we investigate model mt, for abundance estimation in closed-population capture-recapture studies, where animals are identified from natural marks such as dna profiles or photographs of distinctive individual features. model mt, extends the classical model mt to accommodate errors in identification, by specifying that each sample identification is correct with probability and false with probability 1-. information about misidentification is gained from a surplus of capture histories with only one entry, which arise from false identifications. we derive an exact closed-form expression for the likelihood for model mt, and show that it can be computed efficiently, in contrast to previous studies which have held the likelihood to be computationally intractable. our fast computation enables us to conduct a thorough investigation of the statistical properties of the maximum likelihood estimates. we find that the indirect approach to error estimation places high demands on data richness, and good statistical properties in terms of precision and bias require high capture probabilities or many capture occasions. when these requirements are not met, abundance is estimated with very low precision and negative bias, and at the extreme better properties can be obtained by the naive approach of ignoring misidentification error. we recommend that model mt, be used with caution and other strategies for handling misidentification error be considered. we illustrate our study with genetic and photographic surveys of the new zealand population of southern right whale (eubalaena australis).
methods;Tag loss, HMM;hidden markov model for dependent mark loss and survival estimation;"capture-recapture; cormack-jolly-seber (cjs); hidden markov model; mark loss; tag loss";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"LAAKE JL;JOHNSON DS;DIEFENBACH DR;TERNENT MA";mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. we describe a hidden markov model (hmm) framework that integrates a mark loss model with a cormack-jolly-seber model for survival estimation. mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. we use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. the hmm approach is demonstrated with a dataset of black bears (ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. the data were analyzed with and without the tattoo. dropping the tattoos resulted in estimates of survival that were reduced by 0.005-0.035 due to tag loss dependence that could not be modeled. we also analyzed the data with and without the tattoo using a single tag. by not using. supplementary materials accompanying this paper appear on-line.
methods;Missing data, incomplete identification, mark-resighting, ;mark-resight abundance estimation under incomplete identification of marked individuals;"capture-resight; latent variables; mark-recapture; mark-resighting; missing data; population size";METHODS IN ECOLOGY AND EVOLUTION;"MCCLINTOCK BT;HILL JM;FRITZ L;CHUMBLEY K;LUXA K;DIEFENBACH DR";often less expensive and less invasive than conventional mark-recapture, so-called 'mark-resight' methods are popular in the estimation of population abundance. these methods are most often applied when a subset of the population of interest is marked (naturally or artificially), and non-invasive sighting data can be simultaneously collected for both marked and unmarked individuals. however, it can often be difficult to identify marked individuals with certainty during resighting surveys, and incomplete identification of marked individuals is potentially a major source of bias in mark-resight abundance estimators. previously proposed solutions are ad hoc and will tend to underperform unless marked individual identification rates are relatively high (>90%) or individual sighting heterogeneity is negligible. based on a complete data likelihood, we present an approach that properly accounts for uncertainty in marked individual detection histories when incomplete identifications occur. the models allow for individual heterogeneity in detection, sampling with (e.g. poisson) or without (e.g. bernoulli) replacement, and an unknown number of marked individuals. using a custom markov chain monte carlo algorithm to facilitate bayesian inference, we demonstrate these models using two example data sets and investigate their properties via simulation experiments. we estimate abundance for grassland sparrow populations in pennsylvania, usa when sampling was conducted with replacement and the number of marked individuals was either known or unknown. to increase marked individual identification probabilities, extensive territory mapping was used to assign incomplete identifications to individuals based on location. despite marked individual identification probabilities as low as 67% in the absence of this territorial mapping procedure, we generally found little return (or need) for this time-consuming investment when using our proposed approach. we also estimate rookery abundance from alaskan steller sea lion counts when sampling was conducted without replacement, the number of marked individuals was unknown, and individual heterogeneity was suspected as non-negligible. in terms of estimator performance, our simulation experiments and examples demonstrated advantages of our proposed approach over previous methods, particularly when marked individual identification probabilities are low and individual heterogeneity levels are high. our methodology can also reduce field effort requirements for marked individual identification, thus, allowing potential investment into additional marking events or resighting surveys.
methods;heterogeneity;mc(mc)mc: exploring monte carlo integration within mcmc for mark-recapture models with individual covariates;"data augmentation; mark-recapture; markov chain monte carlo; monte carlo integration; monte carlo within mcmc";METHODS IN ECOLOGY AND EVOLUTION;"BONNER S;SCHOFIELD M";estimating abundance from mark-recapture data is challenging when capture probabilities vary among individuals. initial solutions to this problem were based on fitting conditional likelihoods and estimating abundance as a derived parameter. more recently, bayesian methods using full likelihoods have been implemented via reversible jump markov chain monte carlo sampling (rjmcmc) or data augmentation (da). the latter approach is easily implemented in available software and has been applied to fit models that allow for heterogeneity in both open and closed populations. however, both rjmcmc and da may be inefficient when modelling large populations. we describe an alternative approach using monte carlo (mc) integration to approximate the posterior density within a markov chain monte carlo (mcmc) sampling scheme. we show how this monte carlo within mcmc (mcwm) approach may be used to fit a simple, closed population model including a single individual covariate and present results from a simulation study comparing rjmcmc, da and mcwm. we found that mcwm can provide accurate inference about population size and can be more efficient than both rjmcmc and da. the efficiency of mcwm can also be improved by using advanced mc methods like antithetic sampling. finally, we apply mcwm to estimate the abundance of meadow voles (microtus pennsylvanicus) at the patuxent wildlife research center in 1982 allowing for capture probabilities to vary as a function body mass.
methods;dispersal, spatial, true survival;estimating true instead of apparent survival using spatial cormack-jolly-seber models;"cormack-jolly-seber model; dispersal; lanius collurio; movement; sampling bias; spatial capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;"SCHAUB M;ROYLE JA";survival is often estimated from capture-recapture data using cormack-jolly-seber (cjs) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. consequently, apparent survival is lower than true survival unless study area fidelity equals one. underestimation of true survival from capture-recapture data is a main limitation of the method. we develop a spatial version of the cjs model that allows estimation of true survival. besides the information about whether a specific individual was encountered at a given occasion, it is often recorded where the encounter occurred. thus, information is available about the fraction of dispersal that occurs within the study area, and we use it to model dispersal and estimate true survival. our model is formulated hierarchically and consists of survival, dispersal and observation submodels, assuming that encounters are possible anywhere within a study area. in a simulation study, our new spatial cjs model produced accurate estimates of true survival and dispersal behaviour for various sizes and shapes of the study area, even if emigration is substantial. however, when the information about dispersal is scarce due to low survival, low recapture probabilities and high emigration, the estimators are positively biased. moreover, survival estimates are sensitive to the assumed dispersal kernel. we applied the spatial cjs model to a data set of adult red-backed shrikes (lanius collurio). apparent survival of males (c.05) estimated with the cjs model was larger than in females (c.04), but the application of the spatial cjs model revealed that both sexes had similar survival probabilities (c.06). the mean breeding dispersal distance in females was c.700m, while males dispersed only c.250m between years. spatial cjs models enable study of dispersal and survival independent of study design constraints such as imperfect detection and size of the study area provided that some of the dispersing individuals remain in the study area. we discuss possible extensions of our model: alternative dispersal models and the inclusion of covariates and of a habitat suitability map.
methods;SCR, temporary emigration, robust design, true survival;separating mortality and emigration: modelling space use, dispersal and survival with robust-design spatial capture-recapture data;"arvicoline (arvicolinae) rodents; bayesian analysis; competing risks; dispersal ecology; hierarchical modelling; individual random effects; life-history evolution; openbugs; winbugs; jags; posterior predictive checks; spacing behaviour";METHODS IN ECOLOGY AND EVOLUTION;"ERGON T;GARDNER B";capture-recapture (cr) techniques are commonly used to gain information about population dynamics, demography and life-history traits of populations. however, traditional cr models cannot separate mortality from emigration. recently developed spatial-capture-recapture (scr) models explicitly incorporate spatial information into traditional cr models, thus allowing for individuals' movements to be modelled explicitly. in this paper, we extend scr models using robust-design data to allow for both processes in which individuals can disappear from the population, mortality and dispersal, to be estimated separately. we formulate a general robust-design spatial capture-recapture (rd-scr) model, explore the properties of the model in a simulation study and compare the results to a cormack-jolly-seber model and a non-spatial robust-design model with temporary emigration. in the case study, we fit several versions of the general model to data on field voles (microtus agrestis) and compare the results with those from the non-spatial models fitted to the same data. we also evaluate assumptions of the fitted models with a series of simulation-based posterior predictive goodness-of-fit checks that are applicable to the scr models in general and the rd-scr model in particular. the simulation results show that the model preforms well under a wide range of dispersal distances. our model outperforms the traditional cr models in terms of both accuracy and precision for survival. the case study showed that adult females have an c. 35 times higher mortality rate than adult males. males have larger home ranges and disperse longer distances than females, but both males and females mostly move their activity centres within their previous home range between trapping sessions at 3-week intervals. our rd-scr model has several advantages compared to other approaches to estimate true' survival instead of only apparent' survival. additionally, the model extracts information about space use and dispersal distributions that are relevant for behavioural studies as well as studies of life-history variation, population dynamics and management. the model can be widely applied due to the flexible framework, and other variations of the model could easily be implemented.
methods;heterogeneity, GEE;a generalized estimating equations approach for capture-recapture closed population models;"capture-recapture experiment; gee; heterogeneity; model selection; population size estimation; qic";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"AKANDA MAS;ALPIZAR JARA R";the estimation of population density animal population parameters, such as capture probability, population size, or population density, is an important issue in many ecological applications. capture-recapture data may be considered as repeated observations that are often correlated over time. if these correlations are not taken into account then parameter estimates may be biased, possibly producing misleading results. we propose a generalized estimating equations (gee) approach to account for correlation over time instead of assuming independence as in the traditional closed population capture-recapture studies. we also account for heterogeneity among observed individuals and over-dispersion, modelling capture probabilities as a function of covariates. the gee versions of all closed population capture-recapture models and their corresponding estimating equations are proposed. we evaluate the effect of accounting for correlation structures on capture-recapture model selection based on the quasi-likelihood information criterion (qic). an example is used for an illustrative application and for comparison to currently used methodology. a horvitz-thompson-like estimator is used to obtain estimates of population size based on conditional arguments. a simulation study is conducted to evaluate the performance of the gee approach in capture-recapture studies. the gee approach performs well for estimating population parameters, particularly when capture probabilities are high. the simulation results also reveal that estimated population size varies on the nature of the existing correlation among capture occasions.
methods;NA;advances and applications of occupancymodels;"amphibian disease; batrachochytrium dendrobatidis; detection probability; dynamic multistate occurrence models; false positive; misidentification; multiscale; study design";METHODS IN ECOLOGY AND EVOLUTION;"BAILEY LL;MACKENZIE DI;NICHOLS JD";the past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. we discuss some recent occupancy estimation methods and the biological systems that motivated their development. collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. unlike many mark-recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute detection' of a target species. subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. we demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian-pathogen system) and discuss situations where the use of occupancy models has been criticized. finally, we use these applications to suggest future research and model development.
methods;IPM, SCR;spatially explicit integrated population models;"count data; detection data; integrated population models; louisiana black bear; population dynamics; spatial capture-recapture; spatial scaling";METHODS IN ECOLOGY AND EVOLUTION;"CHANDLER RB;CLARK JD";"studies of demographic processes are typically restricted to small geographic areas and short time periods due to the costs of marking and monitoring individuals. however, environmental changes are occurring at much broader spatial and temporal scales, and thus, inferences about the mechanisms governing population dynamics need to be scaled accordingly. recently developed integrated population models (ipms) represent an approach for doing so, by jointly analysing survey data and capture-recapture data. although promising, several shortcomings of conventional ipms exist, including difficulties accounting for spatial variation in demographic, movement and detection parameters; limited ability to make spatially explicit predictions of abundance or vital rates; and a requirement that the survey data and the capture-recapture data are independent. we demonstrate how each of these limitations can be resolved by adopting a spatial population dynamics model upon which both the survey data and the capture-recapture data are conditioned. we applied the model to 6 years of hair data collected on the threatened louisiana black bear ursus americanus luteolus. for years in which the hair samples were genotyped, the resulting data are information-rich (but expensive) spatial capture-recapture (scr) data. for the remaining years, the data are binary detection data, of the type often analysed using occupancy models. we compared estimates of demographic parameters and annual abundance using various combinations of the scr and detection data, and found that combining the scr data and the detection data resulted in more precise estimates of abundance relative to estimates that did not use the detection data. a simulation study provided additional evidence of increased precision, as well as evidence that the estimators of annual abundance are approximately unbiased. the ability to combine survey data and capture-recapture data using a spatially explicit model opens many possibilities for designing cost effective studies and scaling up inferences about the demographic processes influencing spatial and temporal population dynamics."
methods;Behavior, missing data, genetic tagging, non-identification;accounting for behavioural response to capture when estimating population size from hair snare studies with missing data;"behavioural response; closed population; dna; hair snare; mark-recapture; missing data";METHODS IN ECOLOGY AND EVOLUTION;"AUGUSTINE BC;TREDICK CA;BONNER SJ";1 hair snares have become an established method for obtaining mark-recapture data for population size estimation of ursids and have recently been used to study other species including other carnivores, small mammals and ungulates. however, bias due to a behavioural response to capture in the presence of missing data has only recently been recognized and no statistical methodology exists to accommodate it. in a hair snare mark-recapture experiment, data can be missing if animals encounter a hair snare without leaving a hair sample, poor-quality samples are not genotyped, a fraction of all samples collected are genotyped due to cost considerations (subsampling) and/or not all genotyped hair samples provide an individual identification. these are all common features of hair snare mark-recapture experiments. here, we present methodology that accounts for a behavioural response to capture in the presence of missing data from (i) subsampling and (ii) failure of hair samples to produce an individual identification. four subprocesses are modelled-animal capture, hair deposition, researcher subsampling and dna amplification with key parameters estimated from functions of the number of hair samples left by individuals at traps. we assess the properties of this methodology (bias and interval coverage) via simulation and then apply this methodology to a previously published data set. our methodology removes bias and provides nominal interval coverage of population size for the simulation scenarios considered. in the example data set, we find that removing 75% of the hair samples leads to a 40% lower estimate of population size. our methodology corrects about half of this bias and we identify a second source of bias that has not previously been reported associated with differential trap visitation rates among individuals within trapping occasions. our methodology will allow researchers to reliably estimate the size of a closed population in the presence of a behavioural response to capture and missing data for a subset of missing data scenarios. it also provides a framework for understanding this generally unrecognized problem and for further extension to handle other missing data scenarios.
methods;Distance sampling;using mark-recapture distance sampling methods on line transect surveys;"mark-recapture; distance sampling; line transects; double-observer survey; program distance";METHODS IN ECOLOGY AND EVOLUTION;"BURT ML;BORCHERS DL;JENKINS KJ;MARQUES TA";"1.mark-recapture distance sampling (mrds) methods are widely used for density and abundance estimation when the conventional ds assumption of certain detection at distance zero fails, as they allow detection at distance zero to be estimated and incorporated into the overall probability of detection to better estimate density and abundance. however, incorporating mr data in ds models raises survey and analysis issues not present in conventional ds. conversely, incorporating ds assumptions in mr models raises issues not present in conventional mr. as a result, being familiar with either conventional ds methods or conventional mr methods does not on its own put practitioners in good a position to apply mrds methods appropriately. this study explains the sometimes subtly different varieties of mrds survey methods and the associated concepts underlying mrds models. this is done as far as possible without giving mathematical details - in the hope that this will make the key concepts underlying the methods accessible to a wider audience than if we were to present the concepts via equations. we illustrate use of the two main types of mrds model by using data collected on two different types of survey: a survey of ungulate faecal pellets where two observers searched independently of each other; and a cetacean survey that used a search protocol that could accommodate responsive movement, with only one observer searching independently and the other being aware of all detections.synthesis and applications. mark-recapture ds is a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. by way of making the methods more accessible to practicing ecologists, we describe the key ideas underlying mrds methods, the sometimes subtle differences between them, and we illustrate these by applying different kinds of mrds method to surveys of two different target species using different survey configurations."
methods;IPM, PVA;using integrated population models to improve conservation monitoring: california spotted owls as a case study;"california spotted owl; integrated population model; markov chain monte carlo; population analysis; population decline";ECOLOGICAL MODELLING;"TEMPEL DJ;PEERY MZ;GUTIERREZ RJ";"integrated population models (ipms) constitute a relatively new approach for estimating population trends and demographic parameters that makes use of multiple, independent data sources (e.g., count and mark-recapture data) within a unified statistical framework. in principle, ipms offer several advantages over more conventional modeling approaches that rely on a single source of data, including greater precision in parameter estimates and the ability to estimate demographic parameters for which no explicit data are available. however, to date, the ipm literature has focused primarily on model development and evaluation, and few ""real-world"" applications have demonstrated that ipms can strengthen inferences about population dynamics in a species of conservation concern. here, we combined 23 years of count, occupancy, reproductive, and mark-recapture data into an ipm framework to estimate population trends and demographic rates in a population of california spotted owls (strix occidentalis occidentalis). using this framework, we observed a significant population decline, as evidenced by the geometric mean of the finite annual rate of population change ( <(<(lambda)over bar>(t) )over cap> = 0.969, 95% cri 0.957-0.980) and the resulting realized population change (proportion of the initial population present in 2012; (delta) over cap (2012) = 0.501, 95% cri 0.383-0.641). the estimated decline was considerably greater than the approximately 30% decline estimated using conventional mark-recapture and occupancy approaches (tempel and gutierrez, 2013). the ipm likely yielded a greater decline because it allowed for the inclusion of three years of data from the beginning of the study that were omitted from previous analyses to meet the assumptions of mark-recapture models. the ipm may also have yielded a greater estimate of decline than occupancy models owing to an increase in the number of territories occupied by single owls over the study period. all demographic parameters (adult and juvenile apparent survival, reproductive rate, immigration rate) were positively correlated with (lambda) over cap (t), but immigration was fairly high ((immt) over cap = 0.097, 95% cri 0.055-0.140) and contributed most to temporal variation in (lambda) over cap (t), suggesting that changes in owl abundance were influenced by processes occurring outside of our study area. more broadly, our results indicated that the ipm framework has the potential to strengthen inference in population monitoring and demographic studies, particularly for those involving long-lived species whose abundance may be slowly declining. in our case, the conservation implications from the results of the ipm suggested a decline in the population of owls that was steeper than previously thought. (c) 2014 elsevier b.v. all rights reserved."
methods;Left truncation, neonate survival;dead before detection: addressing the effects of left truncation on survival estimation and ecological inference for neonates;"age-dependent survival; truncated data; mark recapture; population ecology; survival analysis; wildlife; neonate";METHODS IN ECOLOGY AND EVOLUTION;"GILBERT SL;LINDBERG MS;HUNDERTMARK KJ;PERSON DK";"<list list-type=""1"" id=""mee312234-list-0001""> neonate survival is a key life history trait, yet remains challenging to measure in wild populations because neonates can be difficult to capture at birth. estimates of survival from neonates that are opportunistically captured might be inaccurate because some individuals die before sampling, resulting in data that are left truncated. the resulting overestimation of survival rates can further affect ecological inference through biased estimates of covariate effects in survival models, yet is not addressed in most studies of animal survival. here, we quantify the effects of left truncation on survival estimates and subsequent ecological inference. vaginal implant transmitters (vits) enable capture of ungulates at birth, yielding data without left truncation. the effects of left truncation on survival estimation were quantified using age-dependent survival models for vit and opportunistically captured neonatal deer. differences in daily survival rates (dsrs) and cumulative survival probability were calculated for the first 70days of life. in addition, left truncation was simulated by removing fawns that died during the first 1 or 2days of life from the vit-caught sample, isolating the effect of left truncation. cumulative probability of survival during the first 70days of life was overestimated by 7-23% for fawns caught opportunistically compared with those caught by vit, depending on model design. differences in dsrs were large at age 1day, but had converged by age 30days. simulated left truncation resulted in overestimates of survival of up to 31%. model selection and covariate coefficients were strongly affected by left truncation, producing spurious ecological inference, including changes to sign and/or magnitude of inferred effects of all covariates. we recommend (i) every effort be made to capture neonates; (ii) consistent capture methods, using at least in part non-truncating techniques, be implemented across years and study areas; and (iii) exclusion of left-truncated data from survival estimates until dsrs converge with those calculated from non-truncated data. this work emphasizes the importance of accounting for left truncation in survival estimation for any species with strong age-dependent survival in order to prevent biased conclusions produced by sampling method rather than true ecological effects. <doi origin=""wiley"" registered=""yes"">10.1111/(issn)2041-210x</doi"
methods;senescence, competing risks, frailty, HMM, covariates;methods for studying cause-specific senescence in the wild;"ageing; capture-reencounter; competing risk analysis; frailty; harvest; heterogeneity; hidden markov model; predation";METHODS IN ECOLOGY AND EVOLUTION;"KOONS DN;GAMELON M;GAILLARD JM;AUBRY LM;ROCKWELL RF;KLEIN F;CHOQUET R;GIMENEZ O";the founding evolutionary theories of ageing indicate that the force of mortality imposed by environmental factors should influence the strength of natural selection against actuarial senescence and its evolution. to rigorously test this idea, field biologists need methods that yield estimates of age-specific mortality according to cause of death. here, we present existing methods commonly applied in studies of human health that could be used to accomplish these goals in studies of wild species for which fate can be determined with certainty. we further present a new application of hidden markov models for capture-reencounter studies of wild animals that can be used to estimate age-specific trajectories of cause-specific mortality when detection is imperfect. by applying our new hidden markov model with the e-surge and mark softwares to capture-reencounter data sets for long-lived species, we demonstrate that senescence can be severe for natural causes of mortality in the wild, while being largely non-existent for anthropogenic causes. moreover, we show that conflation of mortality causes in commonly used survival analyses can induce an underestimation of the intensity of senescence and overestimation of mortality for pre-senescent adults. these biases have important implications for both age-structured population modelling used to guide conservation and comparative analyses of senescence across species. similar to frailty, individual differences in causes of death can generate individual heterogeneity that needs to be accounted for when estimating age-specific mortality patterns. the proposed hidden markov method and other competing risk estimators can nevertheless be used to formally account for these confounding effects, and we additionally discuss how our new method can be used to gain insight into the mechanisms that drive variation in ageing across the tree of life.
methods;heterogeneity;hierarchical modeling of abundance in closed population capture-recapture models under heterogeneity;"bayesian; capture recapture; complete data likelihood; data augmentation; hierarchical; mcmc; reversible jump; superpopulation; trans-dimensional";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"SCHOFIELD MR;BARKER RJ";hierarchical modeling of abundance in space or time using closed-population mark-recapture under heterogeneity (model) presents two challenges: (i) finding a flexible likelihood in which abundance appears as an explicit parameter and (ii) fitting the hierarchical model for abundance. the first challenge arises because abundance not only indexes the population size, it also determines the dimension of the capture probabilities in heterogeneity models. a common approach is to use data augmentation to include these capture probabilities directly into the likelihood and fit the model using bayesian inference via markov chain monte carlo (mcmc). two such examples of this approach are (i) explicit trans-dimensional mcmc, and (ii) superpopulation data augmentation. the superpopulation approach has the advantage of simple specification that is easily implemented in bugs and related software. however, it reparameterizes the model so that abundance is no longer included, except as a derived quantity. this is a drawback when hierarchical models for abundance, or related parameters, are desired. here, we analytically compare the two approaches and show that they are more closely related than might appear superficially. we exploit this relationship to specify the model in a way that allows us to include abundance as a parameter and that facilitates hierarchical modeling using readily available software such as bugs. we use this approach to model trends in grizzly bear abundance in yellowstone national park from 1986 to 1998.
methods;Random effects;fitting animal survival models with temporal random effects;"band return; generalized linear mixed models; r software; ring recoveries; schall's algorithm";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"CUBAYNES S;LAVERGNE C;GIMENEZ O";estimating temporal variance in animal demographic parameters is of particular importance in population biology. we implement the schall's algorithm for incorporating temporal random effects in survival models using recovery data. our frequentist approach is based on a formulation of band-recovery models with random effects as generalized linear mixed models and a linearization of the link function conditional on the random effects. a simulation study shows that our procedure provides unbiased and precise estimates. the method is then implemented on two case studies using recovery data on fish and birds.
methods;GOF, data combination;diagnostic goodness-of-fit tests for joint recapture and recovery models;"contingency tables; great cormorants; multi-site models; parameter redundancy; permanent emigration; transience; trap-effects";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MCCREA RS;MORGAN BJT;PRADEL R";diagnostic goodness-of-fit tests for capture-recapture models are routinely used prior to model fitting and analysis. however, when data include a mixture of live recaptures and dead recoveries, it is frequently standard practice for the information from recoveries not to be used, so that tests are applied to the recapture data alone. we present new diagnostic tests for joint recapture-recovery data, which make full use of all of the data, and evaluate their power through simulation. the importance of including all available data is clearly shown. we see in addition that current procedures may fail to identify the correct model. the work is generalised to the case of multi-site joint recapture-recovery data and is illustrated on a data set of great cormorants. this article has supplementary material online.
methods;SCR, assumptions;bias from heterogeneous usage of space in spatially explicit capture-recapture analyses;"bias; density estimates; heterogeneity; home range; resource selection function; rsf; secr; spatially explicit capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;EFFORD MG;1. royle et al. (methods in ecology and evolution, 2013, 4, 520) proposed a spatially explicit capture-recapture (secr) model in which an animal's usage of a site, and hence its probability of detection, depends on a function of site-specific covariates normalized using a weighted sum of such values across the animal's home range. 2. from simulations supposedly based on the model, they drew the conclusion that existing methods will produce 'extremely biased' estimates of population size when animals use space selectively. this conclusion is faulty because they simulated data from a different model, omitting the normalization needed to represent selection of resources at the home-range level. 3. new simulations show that the null secr estimator of population size is nearly unbiased for low to moderate levels of selective space use when the generating model includes normalization. including detector-level covariates of detection, as allowed in standard software, nearly eliminates bias due to strongly selective space use, whether or not the generating model includes normalization.
methods;SCR, data combination, telemetry;reply to efford on 'integrating resource selection information with spatial capture-recapture';"animal population sampling; capture-recapture; density estimation; modelling; population ecology";METHODS IN ECOLOGY AND EVOLUTION;"ROYLE JA;CHANDLER RB;SUN CC;FULLER AK";1. we proposed (methods in ecology and evolution, 2013, 4) a model for combining telemetry data with spatial capture-recapture (scr) data that was vigorously criticized by efford (methods in ecology and evolution, 2014, 000, 000). efford's main claim was that our encounter probability model was incorrect, and therefore our r code and simulation results were wrong. 2. in fact, our encounter probability model is correct under the poisson point process model that we used as a basis for our integrated model. on the other hand, the basis for efford's claims clearly rest on the assumption of an alternative model which, while possibly useful, is distinct from that analysed in royle et al. (methods in ecology and evolution, 2013, 4). 3. a key point of royle et al. (methods in ecology and evolution, 2013, 4) was that active resource selection induces heterogeneity in encounter probability which, if unaccounted for, should bias estimates of population size or density. the models of royle et al. (methods in ecology and evolution, 2013, 4) and efford (methods in ecology and evolution, 2014, 000, 000) merely amount to alternative models of resource selection, and hence varying amounts of heterogeneity in encounter probability.
methods;Data combination, telemetry, temporary emigration;estimating population size in the presence of temporary migration using a joint analysis of telemetry and capture-recapture data;"bayesian methods; modelling; population ecology; sampling; statistics";METHODS IN ECOLOGY AND EVOLUTION;"BIRD T;LYON J;NICOL S;MCCARTHY M;BARKER R";1. temporary migration - where individuals can leave and re-enter a sampled population - is a feature of many capture-mark-recapture (cmr) studies of mobile populations which, if unaccounted for, can lead to biased estimates of population capture probabilities and consequently biased estimates of population abundance. 2. we present a method for incorporating radiotelemetry data within a cmr study to eliminate bias due to temporary migration using a bayesian state-space model. 3. our results indicate that using a relatively small number of telemetry tags, it is possible to greatly reduce bias in estimates of capture probabilities using telemetry data to model transition probabilities in and out of the sampling area. in a capture-recapture data set for trout cod in the murray river, australia, accounting for temporary migration led to overall higher estimates of capture probabilities than models assuming permanent or zero migration. also, individual heterogeneity in detectability can be managed through explicit modelling. we show how accounting for temporary migration when estimating capture probabilities can be used to estimate the abundance and size distribution of a population as though it were closed. 4. our model provides a basis for more complex models that might integrate telemetry data into other cmr scenarios, thus allowing for greater precision in estimates of vital rates that might otherwise be biased by temporary migration. our results highlight the importance of accounting for migration in survey design and parameter estimation, and the potential scope for supplementing large-scale cmr data sets with a subset of auxiliary data that provide information on processes that are hidden to primary sampling processes.
methods;SCR, continuous, camera-trapping, point process;continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey;"animal movement; data aggregation; density estimation; statistical methods; sufficiency";METHODS IN ECOLOGY AND EVOLUTION;"BORCHERS D;DISTILLER G;FOSTER R;HARMSEN B;MILAZZO L";1. many capture-recapture surveys of wildlife populations operate in continuous time, but detections are typically aggregated into occasions for analysis, even when exact detection times are available. this discards information and introduces subjectivity, in the form of decisions about occasion definition. 2. we develop a spatiotemporal poisson process model for spatially explicit capture-recapture (secr) surveys that operate continuously and record exact detection times. we show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time, our continuous-time (ct) model is equivalent to an existing model based on detection frequencies. we use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a ct estimator and discrete-occasion estimators with various levels of temporal aggregation. this includes investigation of the effect on the estimators of spatiotemporal correlation induced by animal movement. 3. the ct estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatiotemporal correlation. it is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. 4. our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated ct models and estimators. it allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions and fully utilizes ct data. we identify a need for developing ct methods that incorporate spatiotemporal dependence in detections and see potential for ct models being combined with telemetry-based animal movement models to provide a richer inference framework.
methods;PGR, hierarchical;hierarchical modelling of population growth rate from individual capture-recapture data;"bayesian analysis; balearic shearwater; gibbs variable selection; mark-recapture; population dynamics; rate of population change; scopoli's shearwater; storm petrel; survival; temporal symmetry model";METHODS IN ECOLOGY AND EVOLUTION;"TENAN S;PRADEL R;TAVECCHIA G;IGUAL JM;SANZ AGUILAR A;GENOVART M;ORO D";1. estimating rates of population change is essential to achieving theoretical and applied goals in population ecology, and the pradel (1996, biometrics, 52: 703.) temporal symmetry method permits direct estimation and modelling of the growth rate of open populations, using capture-recapture data frommarked animals. 2. we present a bayesian formulation of the pradel approach that permits a hierarchical modelling of the biological and sampling processes. two parametrizations for the temporal symmetry likelihood are presented and implemented into a general purpose software in bugs language. 3. we first consider a set of simulated scenarios to evaluate performance of a bayesian variable selection approach to test the temporal linear trend on survival and seniority probability, population growth rate and detectability. we then provide an example application on individual detection information of three species of burrowing nesting seabirds, whose populations cannot be directly counted. for each species, we assess the strength of evidence for temporal random variation and the temporal linear trend on survival probability, population growth rate and detectability. 4. the bayesian formulation provides more flexibility, by easily allowing the extension of the original fixed time effects structure to random time effects, an option that is still impractical in a frequentist framework.
methods;dispersal, data combination, assignment, immigration, recruitment;using imputation and mixture model approaches to integrate multi-state capture-recapture models with assignment information;"capture-recapture; dispersal; genetic assignment tests; imputation approach; kangaroo rat; mixture model; multi-state; population assignment procedure; robust-design; semiparametric; superpopulation";BIOMETRICS;"WEN Z;POLLOCK KH;NICHOLS JD;WASER PM;CAO WH";"in this article, we first extend the superpopulation capture-recapture model to multiple states (locations or populations) for two age groups., wen et al., (2011; 2013) developed a new approach combining capture-recapture data with population assignment information to estimate the relative contributions of in situ births and immigrants to the growth of a single study population. here, we first generalize wen et al., (2011; 2013) approach to a system composed of multiple study populations (multi-state) with two age groups, where an imputation approach is employed to account for the uncertainty inherent in the population assignment information. then we develop a different, individual-level mixture model approach to integrate the individual-level population assignment information with the capture-recapture data. our simulation and real data analyses show that the fusion of population assignment information with capture-recapture data allows us to estimate the origination-specific recruitment of new animals to the system and the dispersal process between populations within the system. compared to a standard capture-recapture model, our new models improve the estimation of demographic parameters, including survival probability, origination-specific entry probability, and especially the probability of movement between populations, yielding higher accuracy and precision."
methods;NA;fitting occupancy models with e-surge: hidden markov modelling of presence-absence data;"capture-recapture; detectability; detection-non-detection; e-surge; hidden markov models; presence-absence; species occurrence";METHODS IN ECOLOGY AND EVOLUTION;"GIMENEZ O;BLANC L;BESNARD A;PRADEL R;DOHERTY PF;MARBOUTIN E;CHOQUET R";occupancy - the proportion of area occupied by a species - is a key notion for addressing important questions in ecology, biogeography and conservation biology. occupancy models allow estimating and inferring about species occurrence while accounting for false absences (or imperfect species detection). occupancy models can be formulated as hidden markov models (hmm) in which the state process captures the markovian dynamic of the actual but latent states, while the observation process consists of observations that are made from these underlying states. we show how occupancy models can be implemented in program e-surge, which was initially developed to analyse capture-recapture data in the hmm framework. replacing individuals by sites provides the user with access to several features of e-surge that are not available altogether or just not available in standard occupancy software: i) flexible model specification through a user-friendly syntax without having to write custom code, ii) decomposition of the observation and state processes in several steps to provide flexible parameterisation, iii) up-to-date diagnostics of model identifiability, and iv) advanced numerical algorithms to produce fast and reliable results (including site random effects). to illustrate e-surge features, we provide implementation and analysis details for several occupancy models. we also provide simulated and real-world examples as well as further specifications and information in a companion wiki platform http://occupancyinesurge.wikidot.com/.
methods;NA;hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence;"biodiversity monitoring; false absence; observer effect; site-occupancy models; survey effort";METHODS IN ECOLOGY AND EVOLUTION;"BORNAND CN;KERY M;BUECHE L;FISCHER M";"ecology and conservation require reliable data on the occurrence of animals and plants. a major source of bias is imperfect detection, which, however, can be corrected for by estimation of detectability. in traditional occupancy models, this requires repeat or multi-observer surveys. recently, time-to-detection models have been developed as a cost-effective alternative, which requires no repeat surveys and hence costs could be halved. we compared the efficiency and reliability of time-to-detection and traditional occupancy models under varying survey effort. two observers independently searched for 17 plant species in 44100m(2) swiss grassland quadrats and recorded the time-to-detection for each species, enabling detectability to be estimated with both time-to-detection and traditional occupancy models. in addition, we gauged the relative influence on detectability of species, observer, plant height and two measures of abundance (cover and frequency). estimates of detectability and occupancy under both models were very similar. rare species were more likely to be overlooked; detectability was strongly affected by abundance. as a measure of abundance, frequency outperformed cover in its predictive power. the two observers differed significantly in their detection ability. time-to-detection models were as accurate as traditional occupancy models, but their data easier to obtain; thus they provide a cost-effective alternative to traditional occupancy models for detection-corrected estimation of occurrence."
methods;Renewal process;limited-information modeling of loggerhead turtle population size;"bayesian methods; capture-recapture; ecological applications; environmental applications; gibbs sampling; mark-recapture; renewal process";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"GREGO JM;HITCHCOCK DB";we attempt to estimate the size of a population of female loggerhead turtles. in traditional capture-recapture experiments to estimate the size of an animal population, individual animals are tagged and the information about which individuals are captured repeatedly is crucial. for these loggerhead turtle data, information about individual turtles is not available. rather, we observe only the counts of successful and failed nestings at a location over a series of days (in our case, three). we view the turtles' nesting behavior as an alternating renewal process, model it using parametric distributions, and then derive probability distributions that describe the behavior of the turtles during the three days via a 3-way contingency table. we adopt a bayesian approach, formulating our model in terms of parameters about which strong prior information is available. we use a gibbs sampling algorithm to sample from the posterior distribution of our random quantities, the most crucial of which is the number of turtles remaining offshore during the entire sampling period. we illustrate the method using data sets from loggerhead turtle sites along the south carolina coast. we provide a simulation study which illustrates the quality and robustness of the method and investigates sensitivity to prior parameter specification.
methods;Assignment, robust design, genetic tagging, immigration, recruitment;a robust design capture-recapture model with multiple age classes augmented with population assignment data;"capture-recapture; genetic assignment procedures; kangaroo rat; robust design; single site; superpopulation; two age groups";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"WEN Z;NICHOLS JD;POLLOCK KH;WASER PM";the relative contribution of in situ reproduction versus immigration to the recruitment process is important to ecologists. here we consider a robust design superpopulation capture-recapture model for a population with two age classes augmented with population assignment data. we first use age information to estimate the entry probabilities of new animals originating via in situ reproduction and immigration separately for all except the first period. then we combine age and population assignment information with the capture-recapture model, which enables us to estimate the entry probability of in situ births and the entry probability of immigrants separately for all sampling periods. further, this augmentation of age specific capture-recapture data with population assignment data greatly improves the estimators' precision. we apply our new model to a capture-recapture data set with genetic information for banner-tailed kangaroo rats in southern arizona. we find that many more individuals are born in situ than are immigrants for all time periods. young animals have lower survival probabilities than adults born in situ. adult animals born in situ have higher survival probabilities than adults that were immigrants.
methods;Phylogeny, multispecies;importance of accounting for phylogenetic dependence in multi-species mark-recapture studies;"bayesian hierarchical model; comparative demography; mark-recapture; phylogeny; procellariiformes; survival probability";ECOLOGICAL MODELLING;"ABADI F;BARBRAUD C;BESSON D;BRIED J;CROCHET PA;DELORD K;FORCADA J;GROSBOIS V;PHILLIPS RA;SAGAR P;THOMPSON P;WAUGH S;WEIMERSKIRCH H;WOOD AG;GIMENEZ O";species in comparative demography studies often have a common phylogenetic or evolutionary ancestry and hence, they cannot fully be treated as independent samples in the statistical analysis. although the serious implication of ignoring phylogeny has long been recognized, no attempt has been made so far to account for the lack of statistical independence due to phylogeny in multi-species mark-recapture comparative demography studies. in this paper, we propose a bayesian hierarchical model that explicitly accounts for phylogenetic dependence among species, and to correct for imperfect detection, which is a common phenomenon in free-ranging species. we illustrate the method using individual mark-recapture data collected from 16 seabird species of the order procellariiformes. data on body mass and phylogeny of these species are compiled from literature. we investigate the relationship between adult survival and body mass with and without accounting for phylogeny. if we ignore phylogeny, we obtain a positive survival-body mass relationship. however, this relationship is no longer statistically significant once phylogenetic dependence is taken into account, implying that survival may actually depend on an unmeasured variable that is correlated with body mass due to a shared dependence on phylogeny. the proposed model allows the integration of multi-species mark-recapture data and phylogenetic information, and it is therefore a valuable tool in ecological and evolutionary biology. (c) 2013 elsevier b.v. all rights reserved.
methods;Social network, social species, photo-id;using social structure to improve mortality estimates: an example with sperm whales;"likelihood; management; mark-recapture; physeter macrocephalus; population parameters; social organization; status; stock assessment; survival";METHODS IN ECOLOGY AND EVOLUTION;"WHITEHEAD H;GERO S";estimates of mortality are fundamental to studies of population ecology and assessments of conservation status. mortality is frequently estimated using individual identifications by means of mark-recapture methods. these estimates become biased with heterogeneity in identification and especially if patterns of heterogeneity change with time. if animals are social, then survival may be inferred from the identifications of social partners. we produce a likelihood model for estimating mortality using such social data. we show using simulation that this method can produce less biased and more precise estimates of mortality than standard methods when individuals are almost always identified with associates, and when there are time-varying patterns of heterogeneity in identifiability. the method seems little affected by some change in social affiliations or by growth or decline in population size. ses and confidence intervals of mortality estimates can be estimated using likelihood methods. we apply the method to data from a population of sperm whales (physeter macrocephalus) in the eastern caribbean, obtaining estimates that are more precise and probably less biased than those from other methods. the method should be useful in improving mortality estimates for social species.
methods;SCR, stratified sampling, hierarchical;hierarchical spatial capture-recapture models: modelling population density in stratified populations;"bayesian analysis; data augmentation; density estimation; small-mammal trapping; spatial capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;"ROYLE JA;CONVERSE SJ";capture-recapture studies are often conducted on populations that are stratified by space, time or other factors. in this paper, we develop a bayesian spatial capture-recapture (scr) modelling framework for stratified populations - when sampling occurs within multiple distinct spatial and temporal strata. we describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture-recapture sampling, and also for modelling variation in density among strata. we use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular bugs software packages. we provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids. many capture-recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. we propose a hierarchical model that allows explicit modelling of group or strata effects. because the model is formulated for individual encounter histories and is easily implemented in the bugs language and other free software, it also provides a general framework for modelling individual effects, such as are present in scr models.
methods;continuous;heterogeneity and behavioral response in continuous time capture-recapture, with application to street cannabis use in italy;"behavioral response; capture-recapture; drug abuse; frailty; heterogeneity; horvitz-thompson estimator";ANNALS OF APPLIED STATISTICS;"FARCOMENI A;SCACCIATELLI D";we propose a general and flexible capture-recapture model in continuous time. our model incorporates time-heterogeneity, observed and unobserved individual heterogeneity, and behavioral response to capture. behavioral response can possibly have a delayed onset and a finite-time memory. estimation of the population size is based on the conditional likelihood after use of the em algorithm. we develop an application to the estimation of the number of adult cannabinoid users in italy.
methods;Temporary emigration;breeding return times and abundance in capture-recapture models;"abundance; breeding return times; capture-recapture analysis; lake sturgeon; temporary emigration; unobserved state";BIOMETRICS;"PLEDGER S;BAKER E;SCRIBNER K";for many long-lived animal species, individuals do not breed every year, and are often not accessible during non-breeding periods. individuals exhibit site fidelity if they return to the same breeding colony or spawning ground when they breed. if capture and recapture is only possible at the breeding site, temporary emigration models are used to allow for only a subset of the animals being present in any given year. most temporary emigration models require the use of the robust sampling design, and their focus is usually on probabilities of annual survival and of transition between breeding and non-breeding states. we use lake sturgeon (acipenser fulvescens) data from a closed population where only a simple (one sample per year) sampling scheme is possible, and we also wish to estimate abundance as well as sex-specific survival and breeding return time probabilities. by adding return time parameters to the schwarz-arnason version of the jolly-seber model, we have developed a new likelihood-based model which yields plausible estimates of abundance, survival, transition and return time parameters. an important new finding from investigation of the model is the overestimation of abundance if a jolly-seber model is used when markovian temporary emigration is present.
methods;Individual growth, heterogeneity, covariates;modeling individual specific fish length from capture-recapture data using the von bertalanffy growth curve;"capture-recapture; hierarchical modeling; mcmc; model checking; von bertalanffy";BIOMETRICS;"SCHOFIELD MR;BARKER RJ;TAYLOR P";"we use bayesian methods to explore fitting the von bertalanffy length model to tag-recapture data. we consider two popular parameterizations of the von bertalanffy model. the first models the data relative to age at first capture; the second models in terms of length at first capture. using data from a rainbow trout oncorhynchus mykiss study we explore the relationship between the assumptions and resulting inference using posterior predictive checking, cross validation and a simulation study. we find that untestable hierarchical assumptions placed on the nuisance parameters in each model can influence the resulting inference about parameters of interest. researchers should carefully consider these assumptions when modeling growth from tag-recapture data."
methods;multilist, human rights;a comparison of marginal and conditional models for capture-recapture data with application to human rights violations data;"capture-recapture; human rights; marginal models; multiple systems estimation";BIOMETRICS;"MITCHELL S;OZONOFF A;ZASLAVSKY AM;HEDT GAUTHIER B;LUM K;COULL BA";human rights data presents challenges for capture-recapture methodology. lists of violent acts provided by many different groups create large, sparse tables of data for which saturated models are difficult to fit and for which simple models may be misspecified. we analyze data on killings and disappearances in casanare, colombia during years 1998 to 2007. our estimates differ whether we choose to model marginal reporting probabilities and odds ratios, versus modeling the full reporting pattern in a conditional (log-linear) model. with 2629 observed killings, a marginal model we consider estimates over 9000 killings, while conditional models we consider estimate 6000-7000 killings. the latter agree with previous estimates, also from a conditional model. we see a twofold difference between the high sample coverage estimate of over 10,000 killings and low sample coverage lower bound estimate of 5200 killings. we use a simulation study to compare marginal and conditional models with at most two-way interactions and sample coverage estimators. the simulation results together with model selection criteria lead us to believe the previous estimates of total killings in casanare may have been biased downward, suggesting that the violence was worse than previously thought. model specification is an important consideration when interpreting population estimates from capture recapture analysis and the casanare data is a protypical example of how that manifests.
methods;chao;a generalization of chao's estimator for covariate information;"bias reduction; chao's estimator; closed capture-recapture; covariate modelling";BIOMETRICS;"BOHNING D;VIDAL DIEZ A;LERDSUWANSRI R;VIWATWONGKASEM C;ARNOLD M";this note generalizes chao's estimator of population size for closed capture-recapture studies if covariates are available. chao's estimator was developed under unobserved heterogeneity in which case it represents a lower bound of the population size. if observed heterogeneity is available in form of covariates we show how this information can be used to reduce the bias of chao's estimator. the key element in this development is the understanding and placement of chao's estimator in a truncated poisson likelihood. it is shown that a truncated poisson likelihood (with log-link) with all counts truncated besides ones and twos is equivalent to a binomial likelihood (with logit-link). this enables the development of a generalized chao estimator as the estimated, expected value of the frequency of zero counts under a truncated (all counts truncated except ones and twos) poisson regression model. if the regression model accounts for the heterogeneity entirely, the generalized chao estimator is asymptotically unbiased. a simulation study illustrates the potential in gain of bias reduction. comparisons of the generalized chao estimator with the homogeneous zero-truncated poisson regression approach are supplied as well. the method is applied to a surveillance study on the completeness of farm submissions in great britain.
methods;Mark-resighting, aerial surveys;insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year;"bayesian; discrete uniform; greater yellowstone ecosystem (gye); mark-recapture; population size";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"HIGGS MD;LINK WA;WHITE GC;HAROLDSON MA;BJORNLIE DD";mark-resight designs for estimation of population abundance are common and attractive to researchers. however, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. in the greater yellowstone ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (fcoy), and inference suffers from both limitations. to overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. we model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. we discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide openbugs code for fitting such models. the application provides valuable insights into subtleties of implementing bayesian inference for latent multinomial models. we tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.
methods;photo-id, record linkage, partial identification;accounting for matching uncertainty in two stage capture-recapture experiments using photographic measurements of natural marks;"closed population; monodon monoceros; multivariate normal record linkage; photo-identification; population size estimation";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"TANCREDI A;AUGER METHE M;MARCOUX M;LISEO B";we propose a bayesian hierarchical modeling approach for estimating the size of a closed population from data obtained by identifying individuals through photographs of natural markings. we assume that noisy measurements of a set of distinctive features are available for each individual present in a photographic catalogue. to estimate the population size from two catalogues obtained during two different sampling occasions, we embed the standard two-stage capture-recapture model for closed population into a multivariate normal data matching model that identifies the common individuals across the catalogues. in addition to estimating the population size while accounting for the matching process uncertainty, this hierarchical modelling approach allows to identify the common individuals by using the information provided by the capture-recapture model. this way, our model also represents a novel and reliable tool able to reduce the amount of effort researchers have to expend in matching individuals. we illustrate and motivate the proposed approach via a real data set of photo-identification of narwhals. moreover, we compare our method with a set of possible alternative approaches by using both the empirical data set and a simulation study.
methods;abundance, covariates;population abundance, size structure and sex-ratio in an insular lizard;"population size; data augmentation; capture-mark-recapture; individual covariates; insular lizard; sex-ratio";ECOLOGICAL MODELLING;"TENAN S;VALLESPIR AR;IGUAL JM;MOYA O;ROYLE JA;TAVECCHIA G";"estimating population size and understanding its variation is a fundamental, yet complicated, aim of many ecological studies. we considered the problem of estimating spring and autumn population abundance, size-dependent population structure and sex-ratio of the endemic balearic lizard, podarcis lilfordi from a three occasions capture-recapture study. we used a bayesian formulation of individual covariate models to incorporate individual sex, size and trap-response. we first considered a set of simulated data with a medium-to-low probability of recapture and individual recapture heterogeneity to evaluate potential problems in model fitting and selection. results from simulated data indicated a low performance in parameter estimation and model selection when probability of detection was low (0.15-0.30). we found a negative permanent trap response and a positive effect of size on detection probability in the spring survey but not in the autumn one. the estimated mean densities varied from about 800 to 1000 lizards ha(-1), a high value when comparing with mainland lizard populations. the observed increase in abundance was probably due to a drop in territorial behaviour and the immigration of females into the area sampled. as a consequence, sex-ratio changed from nearly even in june (mean posterior, 95%cri; 0.928, 0.676-1.167) to a female-skewed population in october (0.612, 0.478-0.772). (c) 2013 elsevier b.v. all rights reserved."
methods;SSM, bioacoustic;estimating individual animal movement from observation networks;"acoustic telemetry; detection probability; ornstein-uhlenbeck process; state-space model";METHODS IN ECOLOGY AND EVOLUTION;"PEDERSEN MW;WENG KC";observation network data comprise animal presences detected by observer stations at fixed spatial locations. statistical analysis of these data is complicated by spatial bias in sampling and temporal variability in detection conditions. advanced methods for analysis of these data are required but are currently underdeveloped. we propose a state-space model (ssm) for observation network data to estimate detailed movements of individual animals. the underlying movement model is an ornstein-uhlenbeck (ou) process, which is stationary, and therefore has an inherent mechanism that models home range behaviour. an integral part of the approach is the detection function, which models the probability of logging animal presences. the detection function is also used to provide absence information when animals are undetected. since the ability to detect an animal often depends on time-varying external factors such as environmental conditions, we use covariate information about detection efficiency as control variables. via simulation, we found that movement estimation error scales log-linearly with network sparsity. this result can be used to indicate the number of stations necessary to achieve a desired upper bound on estimation error. furthermore, we found that the ssm outperforms existing techniques in terms of estimating detailed movements and that estimates are robust towards mis-specification of the detection function. we also tested the importance of accounting for time-varying detection conditions and found that the probability of making wrong conclusions decreases substantially when covariate information is exploited. the model is used to estimate movements and home range of a humphead wrasse (cheilinus undulatus) at palmyra atoll in the central pacific ocean. here, detection conditions have a strong diel component, which is controlled for using detection efficiency information from a reference device. the presented approach enhances the toolbox for analysis of observation network data as collected by acoustic telemetry or potentially other aspiring methods such as camera trapping and mobile phone tagging. by explicitly modelling movement and observation processes, the model integrates all sources of uncertainty and provides a sound statistical basis for making well-informed management decisions from imperfect information.
methods;Chao;on population size estimators in the poisson mixture model;"capture-recapture; lower bounds; lower confidence limits";BIOMETRICS;"MAO CX;YANG N;ZHONG JH";estimating population sizes via capture-recapture experiments has enormous applications. the poisson mixture model can be adopted for those applications with a single list in which individuals appear one or more times. we compare several nonparametric estimators, including the chao estimator, the zelterman estimator, two jackknife estimators and the bootstrap estimator. the target parameter of the chao estimator is a lower bound of the population size. those of the other four estimators are not lower bounds, and they may produce lower confidence limits for the population size with poor coverage probabilities. a simulation study is reported and two examples are investigated.
methods;Photo-id, multiple marks;mark-recapture with multiple, non-invasive marks;"latent multinomial model; mark-recapture; multiple marks; non-invasive marks; photo-identification; whale sharks";BIOMETRICS;"BONNER S;HOLMBERG J";non-invasive marks, including pigmentation patterns, acquired scars, and genetic markers, are often used to identify individuals in mark-recapture experiments. if animals in a population can be identified from multiple, non-invasive marks then some individuals may be counted twice in the observed data. analyzing the observed histories without accounting for these errors will provide incorrect inference about the population dynamics. previous approaches to this problem include modeling data from only one mark and combining estimators obtained from each mark separately assuming that they are independent. motivated by the analysis of data from the ecocean online whale shark (rhincodon typus) catalog, we describe a bayesian method to analyze data from multiple, non-invasive marks that is based on the latent-multinomial model of link et al. (2010, biometrics 66, 178-185). further to this, we describe a simplification of the markov chain monte carlo algorithm of link et al. (2010, biometrics 66, 178-185) that leads to more efficient computation. we present results from the analysis of the ecocean whale shark data and from simulation studies comparing our method with the previous approaches.
methods;SSM, HMM, missing data, covariates;maximum likelihood estimation of mark-recapture-recovery models in the presence of continuous covariates;"arnason-schwarz model; hidden markov model; markov chain; missing values; soay sheep; state-space model";ANNALS OF APPLIED STATISTICS;"LANGROCK R;KING R";we consider mark-recapture-recovery (mrr) data of animals where the model parameters are a function of individual time-varying continuous co-variates. for such covariates, the covariate value is unobserved if the corresponding individual is unobserved, in which case the survival probability cannot be evaluated. for continuous-valued covariates, the corresponding likelihood can only be expressed in the form of an integral that is analytically intractable and, to date, no maximum likelihood approach that uses all the information in the data has been developed. assuming a first-order markov process for the covariate values, we accomplish this task by formulating the mrr setting in a state-space framework and considering an approximate likelihood approach which essentially discretizes the range of covariate values, reducing the integral to a summation. the likelihood can then be efficiently calculated and maximized using standard techniques for hidden markov models. we initially assess the approach using simulated data before applying to real data relating to soay sheep, specifying the survival probability as a function of body mass. models that have previously been suggested for the corresponding covariate process are typically of the form of diffusive random walks. we consider an alternative nondiffusive ar(1)-type model which appears to provide a significantly better fit to the soay sheep data.
methods;continuous, telemetry;apparent survival estimation from continuous mark-recapture/resighting data;"barker joint data; cormack-jolly-seber; model structure adequacy; telemetry";METHODS IN ECOLOGY AND EVOLUTION;"BARBOUR AB;PONCIANO JM;LORENZEN K";1. the recent expansion of continuous-resighting telemetry methods (e.g. acoustic receivers, pit tag antennae) has created a class of ecological data not well suited for traditional mark-recapture statistics. estimating survival when continuous recapture data is available ensues a practical problem, because classical capture-recapture models were derived under a discrete sampling scheme that assumes sampling events are instantaneous with respect to the interval between events. 2. to investigate the use of continuous data in survival analysis, we conducted a model structure adequacy simulation that tested the cormack-jolly-seber (cjs) and barker joint data survival estimation models, which mainly differ through the barker's inclusion of secondary period information. we simulated a population in which survival and detection occurred as a near continuous (daily) process and collapsed detection information into monthly sampling bins for survival estimation. 3. while both models performed well when survival was time-independent, the cjs was substantially biased for low survival values and time-dependent conditions. additionally, unlike the cjs, the barker model consistently performed well over multiple sample sizes (number of marked individuals). however, the high number of parameters in the barker model led to convergence difficulties, resulting in a need for an alternative optimization method (simulated annealing). 4. we recommend the use of the barker model when using continuous data for survival analysis, because it outperformed the cjs over a biologically reasonable range of potential parameter values. however, the practical difficulty of implementing the barker model combined with its shortcomings during two simulations leaves room for the specification of novel statistical methods tailored specifically for continuous mark-resighting data.
methods;estimation, R package;marked: an r package for maximum likelihood and markov chain monte carlo analysis of capture-recapture data;"automatic differentiation model builder; capture-recapture; cormack-jolly-seber; jolly-seber; mark-recapture; markov chain monte carlo; population analysis";METHODS IN ECOLOGY AND EVOLUTION;"LAAKE JL;JOHNSON DS;CONN PB";1. we describe an open-source r package, marked, for analysis of mark-recapture data to estimate survival and animal abundance. 2. currently, marked is capable of fitting cormack-jolly-seber (cjs) and jolly-seber models with maximum likelihood estimation (mle) and cjs models with bayesian markov chain monte carlo methods. the cjs models can be fitted with mle using optimization code in r or with automatic differentiation model builder. the latter allows incorporation of random effects. 3. some package features include: (i) individual-specific time intervals between sampling occasions, (ii) generation of optimization starting values from generalized linear model approximations and (iii) prediction of demographic parameters associated with unique combinations of individual and time-specific covariates. 4. we demonstrate marked with a commonly analysed european dipper (cinclus cinclus) data set. 5. the package will be most useful to ecologists with large mark-recapture data sets and many individual covariates.
methods;software, species richness;program simassem: software for simulating species assemblages and estimating species richness;"biodiversity; community ecology; landscape ecology; relative abundance distribution; simulation; spatial aggregation";METHODS IN ECOLOGY AND EVOLUTION;"REESE GC;WILSON KR;FLATHER CH";1. species richness, the number of species in a defined area, is the most frequently used biodiversity measure. despite its intuitive appeal and conceptual simplicity, species richness is often difficult to quantify, even in well-surveyed areas, because of sampling limitations such as survey effort and species detection probability. nonparametric estimators have generally performed better than other options, but no particular estimator has consistently performed best across variation in assemblage and survey parameters. 2. in order to evaluate estimator performances, we developed the program simassem. simassem can: (i) simulate assemblages and surveys with user-specified parameters, (ii) process existing species encounter history files, (iii) generate species richness estimates not available in other programs and (iv) format encounter history data for several other programs. 3. simassem can help elucidate relationships between assemblage and survey parameters and the performance of species richness estimators, thereby increasing our understanding of estimator sensitivity, improving estimator development and defining the bounds for appropriate application.
methods;robust design, HMM, data combination, temporary emigration;combining dead recovery, auxiliary observations and robust design data to estimate demographic parameters from marked individuals;"capture-recapture; closed robust design; florida manatee; multievent; multistate model; program mark; recoveries; resightings; unobservable state";METHODS IN ECOLOGY AND EVOLUTION;"KENDALL WL;BARKER RJ;WHITE GC;LINDBERG MS;LANGTIMM CA;PENALOZA CL";1. when estimating demographic parameters for wild populations, using multiple data sources can increase robustness through greater precision, reducing bias and permitting the estimation of otherwise confounded parameters. 2. we present a method that combines recapture data from marked individuals, collected at a single study site, under a robust design framework, with dead recoveries and auxiliary resightings collected at any time and place. this model permits the joint modelling of survival, permanent and temporary emigration from the study area. 3. we demonstrate that the usefulness of this model is compelling in the case of long-lived species with substantial rates of temporary emigration, to mitigate bias in survival at the end of the time series and to permit conservation decisions based on more current information. we use the case of florida manatees as an example. 4. our model can easily be extended to account for an arbitrary number of phenotypic states and account for state uncertainty. the increase in precision overall in vital rates, and the mitigation of bias in survival estimation in the final years of a time series, permits managers to base resource decisions on more robust and timely information. the model also provides the ability to adapt monitoring to changing conditions or specific management objectives, via dynamic allocation of effort to auxiliary resightings.
methods;Survey design, varying effort;varyingeffort incapture-recapture studies;"asynchronous sampling; binomial distribution; data pooling; dna; grizzly bear; hazard; spatially explicit capture-recapture; secr; ursus arctos";METHODS IN ECOLOGY AND EVOLUTION;"EFFORD MG;BORCHERS DL;MOWAT G";"1. the standard spatial capture-recapture design for sampling animal populations uses a fixed array of detectors, each operated for the same time. however, methods are needed to deal with the unbalanced data that may result from unevenness of effort due to logistical constraints, partial equipment failure or pooling of data for analysis. 2. we describe adjustments for varying effort for three types of data each with a different probability distribution for the number of observations per individual per detector per sampling occasion. a linear adjustment to the expected count is appropriate for poisson-distributed counts (e.g. faeces per searched quadrat). a linear adjustment on the hazard scale is appropriate for binary (bernoulli-distributed) observations at either traps or binary proximity detectors (e.g. automatic cameras). data pooled from varying numbers of binary detectors have a binomial distribution; adjustment is achieved by varying the size parameter of the binomial. 3. we compared a hazard-based adjustment to a more conventional covariate approach in simulations of one temporal and one spatial scenario for varying effort. the hazard-based approach was the more parsimonious and appeared more resistant to bias and confounding. 4. we analysed a dataset comprising dna identifications of female grizzly bears ursus arctos sampled asynchronously with hair snares in british columbia in 2007. adjustment for variation in sampling interval had negligible effect on density estimates, but unmasked an apparent decline in detection probability over the season. duration-dependent decay in sample quality is an alternative explanation for the decline that could be included in future models. 5. allowing for known variation in effort ensures that estimates of detection probability relate to a consistent unit of effort and improves the fit of detection models. failure to account for varying effort may result in confounding between effort and density variation in time or space. adjustment for effort allows rigorous analysis of unbalanced data with little extra cost in terms of precision or processing time. we suggest it should become routine in capture-recapture analyses. the methods have been made available in the r package secr."
methods;Unknown age, heterogeneity, senescence, covariates;estimating age-specific survival when age is unknown: open population capture-recapture models with age structure and heterogeneity;"brushtail possums; finite mixtures; senescence";METHODS IN ECOLOGY AND EVOLUTION;"MATECHOU E;PLEDGER S;EFFORD M;MORGAN BJT;THOMSON DL";1. when studying senescence in wildlife populations, we are often limited by the sparseness of the available information on the ages of the individuals under study. additionally, heterogeneity between individuals can be substantial. ignoring this heterogeneity can lead to biased estimates of the population parameters of interest and can mask senescence. 2. this article demonstrates the use of a recently developed capture-recapture model for extracting age-dependent estimates of survival probabilities for individuals of unknown age and extends the model by allowing for heterogeneity in survival and capture probabilities using finite mixtures. 3.using simulation, we show that the estimates of age-dependent survival probabilities when age is unknown can be biased when heterogeneity in capture probabilities is not modelled, in contrast to the case of time-dependent survival probabilities when the estimates are robust to similar violations of model assumptions. 4. the methods are demonstrated using a long-term data set of female brushtail possums (trichosurus vulpecula kerr) for which age-specific models for survival probabilities indicating senescence are strongly favoured. we found no evidence of heterogeneity in survival but strong evidence of heterogeneity in capture probabilities. 5. these models have a wide range of applications for estimating age dependence in survival when the age is unknown as they can be applied to any capture-recapture data set, as long as it is collected over a period which is longer, and preferably considerably so, than the life span of the species studied.
methods;SCR, RSF, movement;integrating resource selection information with spatial capture-recapture;"animal movement; animal sampling; encounter probability; hierarchical modeling; marginal likelihood; resource selection; space usage; spatial capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;"ROYLE JA;CHANDLER RB;SUN CC;FULLER AK";"understanding space usage and resource selection is a primary focus of many studies of animal populations. usually, such studies are based on location data obtained from telemetry, and resource selection functions (rsfs) are used for inference. another important focus of wildlife research is estimation and modeling population size and density. recently developed spatial capture-recapture (scr) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. scr models include encounter probability functions that are intuitively related to rsfs, but to date, no one has extended scr models to allow for explicit inference about space usage and resource selection. in this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating scr data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. we provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the scr and rsf models. our method leads to increases in precision for estimating parameters of ordinary scr models. importantly, we also find that scr models alone can estimate parameters of rsfs and, as such, scr methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. finally, we find that scr models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture-recapture methods."
methods;Data combination, photo-id, genetic tagging;response to: a new method for estimating animal abundance with two sources of data in capture-recapture studies;"genetic markers; mark-recapture; multiple marks; non-invasive marks; photo-identification";METHODS IN ECOLOGY AND EVOLUTION;BONNER S;"mark-recapture studies that rely on multiple marks to identify individuals pose modeling challenges if the marks for each individual are not always linked. if an individual with unlinked marks is encountered on two occasions and different marks are observed, then it will appear that two different individuals were captured. failing to account for these missed matches will produce incorrect inference. madon etal. (methods in ecology and evolution 2011; 2: 390) proposes a modification of the jolly-seber estimator for such data computed by adjusting the observed counts of individuals first captured, recaptured or not captured but known to be alive on each occasion. the adjustment involves multiplying each of these counts by a constant factor, iid, intended to correct for double counting of individuals and constrained between 0 and 1. results of a simulation study provided in madon etal. (methods in ecology and evolution 2011; 2: 390) show that the proposed estimator is almost unbiased, but its uncertainty is underestimated and the true coverage of confidence intervals is consistently below the nominal value. i compute separate adjustment factors for each of the counts and show (i) that a constant adjustment is not appropriate and (ii) that the theoretical adjustment factor is sometimes >1. i believe that the use of a single adjustment factor between 0 and 1 is what causes the uncertainty to be underestimated and that complete models of the observation process are required to obtain valid results."
methods;SCR, partial identification;spatially explicit models for inference about density in unmarked or partially marked populations;"abundance estimation; camera traps; data augmentation; hierarchical models; n-mixture model; neyman-scott process; poisson cluster process; point counts; spatial capture-recapture; spatial point process; population density";ANNALS OF APPLIED STATISTICS;"CHANDLER RB;ROYLE JA";recently developed spatial capture-recapture (scr) models represent a major advance over traditional capture-recapture (cr) models because they yield explicit estimates of animal density instead of population size within an unknown area. furthermore, unlike nonspatial cr methods, scr models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. although the utility of scr methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. in this paper, we develop models for situations in which individual recognition is not possible, thereby allowing scr concepts to be applied in studies of unmarked or partially marked populations. the data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. a simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5-10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. marking a subset of the population substantially increases posterior precision and is recommended whenever possible. we applied our model to avian point count data collected on an unmarked population of the northern parula (parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% ci: 0.19-1.64) birds/ha. our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density-rather, spatial dependence can be informative about individual distribution and density.
methods;dependence, random effects, heterogeneity;estimating demographic parameters from capturerecapture data with dependence among individuals within clusters;"capturerecapture; dispersal; heterogeneity; within-group variance; mixed models; pairs; siblings";METHODS IN ECOLOGY AND EVOLUTION;"CHOQUET R;SANZ AGUILAR A;DOLIGEZ B;NOGUE E;PRADEL R;GUSTAFSSON L;GIMENEZ O";two-level data, in which level-1 units or individuals are nested within level-2 units or clusters, are very common in natural populations. however, very few multilevel analyses are conducted for data with imperfect detection of individuals. multilevel analyses are important to quantify the variability at each level of the data. in this study, we present two-level analyses for estimating demographic parameters from data with imperfect detection of individuals and with a source of individual variability that is nested within a source of cluster variability. this method allows separating and quantifying the phenotypic plasticity or facultative behavioural responses from the evolutionary responses. we illustrate our approach using data from studies of a long-lived perennially monogamous seabird, the cory's shearwater (calonectris diomedea) and a patchy population of collared flycatchers (ficedula albicollis). we demonstrate the existence of dependence in recapture probability between paired individuals in the cory's shearwater. in addition, we show that family structure has no influence on parentoffspring resemblance in collared flycatchers dispersal. the new method is implemented in program e-surge which is freely available from the internet.
methods;NA;flexible continuous-time modelling for heterogeneous animal movement;"animal movement; continuous time; diffusion process; spatial heterogeneity";ECOLOGICAL MODELLING;"HARRIS KJ;BLACKWELL PG";we describe a flexible class of continuous-time models for animal movement, allowing movement behaviour to depend on location in terms of a discrete set of regions and also on an underlying behavioural state. we demonstrate the ability of these models to represent complex behaviour and spatial heterogeneity, as found in real movement studies, while retaining tractability and the conceptual advantages of a continuous-time formulation. we discuss the relationship between the models defined here and a range of important applications, both when movement behaviour is the main focus and when it is essentially a nuisance process, for example in spatially explicit capture-recapture. (c) 2013 elsevier b.v. all rights reserved.
methods;stopover, data combination;integrated analysis of capture-recapture-resighting data and counts of unmarked birds at stop-over sites;"combined likelihoods; integrated population modeling; mark-recapture-resight model; mark-resight analysis; semipalmated sandpipers; stop-over duration";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MATECHOU E;MORGAN BJT;PLEDGER S;COLLAZO JA;LYONS JE";the models presented in this paper are motivated by a stop-over study of semipalmated sandpipers, calidris pusilla. two sets of data were collected at the stop-over site: a capture-recapture-resighting data set and a vector of counts of unmarked birds. the two data sets are analyzed simultaneously by combining a new model for the capturere-capture-resighting data set with a binomial likelihood for the counts. the aim of the analysis is to estimate the total number of birds that used the site and the average duration of stop-over. the combined analysis is shown to be highly efficient, even when just 1 % of birds are recaptured, and is recommended for similar investigations. this article has supplementary material online.
methods;Photo-id, missed matches;analysis of photo-id data allowing for missed matches and individuals identified from opposite sides;"grey seal; markrecapture; maximum likelihood; photo-id; population studies; survival";METHODS IN ECOLOGY AND EVOLUTION;"HIBY L;PATERSON WD;REDMAN P;WATKINS J;TWISS SD;POMEROY P";in many species, photo-identification could be used as an alternative to artificial marking to provide data on demographic parameters. however, unless the population is very small or fragmented, software may be required to pre-screen and reject most image pairs as potential matches. depending on the species and method used to obtain images, currently available software may falsely reject some matches. we estimate the false rejection rate (frr) of the extractcompare (ec) program when used to pre-screen images of female grey seals. filtering images manually to reduce the frr involves subjective assessment of image quality, reduces the amount of data available and may bias the results in favour of relatively well-marked individuals. the data may contain individuals identified only from the left side or the right side, as well as individuals identified from both sides. missed matches resulting from false rejections by pre-screening software and/or inclusion of individuals identified only from opposite sides cause some individuals to generate multiple encounter histories. we describe an open population model for data of this type which, given a measured risk of missing a match between a randomly selected pair of images of the same individual, provides maximum likelihood (ml) estimates of initial population size, survival/emigration and immigration/recruitment by calculating the expected frequency of any encounter history that could be generated. as a case study for the method, we used ec to pre-screen photographs of female grey seals on a breeding colony and generate encounter histories over five successive seasons. allowing for the measured frr, we calculated ml estimates for comparison with estimates from previous studies. we also used the model with encounter histories simulated using the same frr to give the same mixture of left side, right side and both sides histories and derived ml estimates for comparison with the values used to drive the simulation. with frr set at up to 33%, the method gave estimates of the abundance and survival parameters used in the simulation model that were biased by at most 4 center dot 7% up and 3% down, respectively. the results of the grey seal case study were consistent with previous estimates of apparent survival and trends in abundance.
methods;Stopover, HMM;estimating stop over duration in the presence of trap-effects;"capture-mark-recapture; hidden nonhomogeneous markov chain; hidden hybrid markov/semi-markov chain; immediate trap-effects";ECOLOGICAL MODELLING;"CHOQUET R;GUEDON Y;BESNARD A;GUILLEMAIN M;PRADEL R";detection probability of individuals is increasingly taken into account during field monitoring schemes and in demographic models. conversely, it is often taken for granted that trappability of animals will remain fairly constant and broadly similar between individuals present in a given area. however, animals may change their behaviour after being trapped. in this paper, we introduce a new hidden markovian model to estimate stop over duration in the presence of trap-effects. this model combines nonhomogeneous markovian states with semi-markovian states in the non-observable state process, and simple distributions with first-order markov chains as observation models. this model generalizes previously proposed models and enables the joint modeling of the time of residence and the trap effect. two cases are considered, depending on whether or not emigration is time-dependent since arrival. we illustrate the latter with teal anas crecca wintering in camargue, southern france and we demonstrate the importance of handling trap-effects. (c) 2012 elsevier b.v. all rights reserved.
methods;NA;towards good practice guidance in using camera-traps in ecology: influence of sampling design on validity of ecological inferences;"arctic; subarctic ecosystem; detectability; error rate; occupancy; precision; sampling strategy; scavengers; species occurrence";METHODS IN ECOLOGY AND EVOLUTION;"HAMEL S;KILLENGREEN ST;HENDEN JA;EIDE NE;ROED ERIKSEN L;IMS RA;YOCCOZ NG";the development of camera-traps has provided an opportunity to study ecological relationships and population dynamics of species that are rare, difficult to observe or capture. their use has seen a major increase recently, particularly with the recent progress in methods adapted to species for which individuals cannot be identified. we took advantage of extensive camera-trap data sets from large spatiotemporal-scale studies of a diverse assemblage of avian and mammalian scavengers in subarctic/arctic tundra to determine sampling designs that minimize detection errors (false-negative) and to evaluate the influence of sampling design on estimation of site occupancy. results showed that raw error rates in daily presence varied between 5 and 30% among species when using time-triggered cameras with a 5-min interval. using movement-triggered cameras resulted in larger raw error rates, between 30 and 70%, as well as a lower number of daily presences detected. increasing the time interval from 5 to 20min greatly increased the raw error rate in daily presence, but it had negligible impacts on estimates and precision of occupancy and detection probability. occupancy estimates were mostly influenced by variation in the number of days included during the sampling period. for most species, a threshold of between 20 and 30 problem-free days (i.e. without camera-related technical problems) was required to stabilize occupancy and detection probability, as well as to maximize their precision. based on the results, we discuss guidelines for establishing sampling designs according to the different ecological questions researchers might want to answer. to our knowledge, our study is the first to directly test the influence of sampling design in camera-trap studies, providing guidelines that are likely to be directly applicable to a large range of species and ecosystems.
methods;Plant traits, detection, multispecies, covariates;a general model of detectability using species traits;"false absence; impact assessment; priors; surveillance; survey effort; trait-based model";METHODS IN ECOLOGY AND EVOLUTION;"GARRARD GE;MCCARTHY MA;WILLIAMS NSG;BEKESSY SA;WINTLE BA";imperfect detectability is a critical source of variation that limits ecological progress and frustrates effective conservation management. available modelling methods provide valuable detectability estimates, but these are typically species-specific. we present a novel application of time-to-detection modelling in which detectability of multiple species is a function of plant traits and observer characteristics. the model is demonstrated for plants in a temperate grassland community in south-eastern australia. we demonstrate that detectability can be estimated using observer experience, species population size and likelihood of flowering. the inclusion of flower colour and species distinctiveness improves the capacity of the model to predict detection rates for new species. we demonstrate the application of the general model to plants in a temperate grassland community, but this modelling method may be extended to other communities or taxa for which time-to-detection models are appropriate. detectability is influenced by traits of the species and the observer. general models can be used to derive detectability estimates where repeat survey data, point counts or mark-recapture data are not available. as these data are almost always absent for species of conservation concern, general models such as ours will be useful for informing minimum survey requirements for monitoring and impact assessment, without the delays and costs associated with data collection.
methods;Missing data, covariates;implementing the trinomial mark-recapture-recovery model in program mark;"mark-recapture-recovery; missing data; program mark; rmark; time varying individual covariates; trinomial model";METHODS IN ECOLOGY AND EVOLUTION;BONNER SJ;time-varying individual covariates present a challenge in modelling data from markrecapturerecovery (mrr) experiments of wild animals. many values of the covariate will be unknown because they can be observed only when an individual is captured, and the missing values cannot be ignored. catchpole et al. [journal of the royal statistical society: series b (statistical methodology), 70, 445460, 2008] presents one solution to this problem by constructing a conditional likelihood depending only on the observed covariate information the so-called trinomial model. this paper describes the link between the trinomial model and the markrecapturerecovery model of burnham (marked individuals in the study of bird population, 199213, 1993) and shows how the trinomial model can be implemented in the software package program mark. this provides the user with access to all of the features of program mark including the facilities for model building and model selection without having to write custom code. i provide details on the analysis of a simulated data set and discuss an r package developed to help users format their data and to implement the model through the existing rmark package.
methods;SSM, random effect, individual growth, covariates;combining a bayesian nonparametric method with a hierarchical framework to estimate individual and temporal variation in growth;"atlantic salmon; bayesian state space model; growth model; mark-recapture; random effects; gaussian process";ECOLOGICAL MODELLING;"SIGOURNEY DB;MUNCH SB;LETCHER BH";growth modeling has long played an important role in ecology, conservation and management of many species. however, adopting a statistical framework that includes both temporal and individual variability in the growth dynamics has proven challenging. in this paper, we use a bayesian state space framework (bssf) to estimate parameters of a discrete time model from a mark-recapture data set of age-1 juvenile atlantic salmon. we use a gaussian process (gp) based approach to model variation in seasonal growth potential. in addition, we use auxiliary information on the food environment as prior knowledge of seasonal fluctuations in growth. parameters for the gp prior and measurement error variances were fixed to speed convergence. posterior estimates of model parameters were relatively insensitive to these choices. our model captures the seasonal growth dynamics of juvenile atlantic salmon as evidenced by close agreement between observed and predicted lengths (r(2) = 0.98). in addition, the relatively narrow confidence intervals indicated significant learning in the parameters of interest. finally, our model approach was able to accurately recover missing data points. although this model was applied to a mark-recapture dataset of atlantic salmon, the generality of the approach should make it applicable to a wide variety of size trajectory datasets, and thus, provides a useful tool to estimate individual and temporal variability in growth from datasets with repeated measurements. (c) 2012 elsevier b.v. all rights reserved.
methods;heterogeneity, photo-id;estimating stray dog populations with the regression method versus beck's method: a comparison;"animal welfare; capture-recapture; heterogeneous capture probabilities; photographic survey; population size; stray dogs";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"FEI SY;CHIANG JT;FEI CY;CHOU CH;TUNG MC";statistical procedures for wildlife population estimation have been greatly improved since the last decade. for estimation of stray dog population size, however, the simple methods recommended by the 1990 who/wspa guidelines seem to remain the popular favorites among researchers. although the methods are very easy to use, their usefulness relies heavily on certain assumptions that are generally unrealistic. using simulation studies, we conclude that beck's method, one of the estimators recommended by the guidelines, performs fairly well and can be safely used to get a quick population estimate, as long as the underlying assumptions are not severely violated.
methods;HMM, heterogeneity, random effect;incorporating individual variability into mark-recapture models;"hidden markov model; individual heterogeneity; marine reserve; mark-recapture; multi-state; north atlantic humpback whales";METHODS IN ECOLOGY AND EVOLUTION;"FORD JH;BRAVINGTON MV;ROBBINS J";understanding individual variation is a key challenge in ecology. inherent individual differences in movement and behaviour pose fundamental problems in the analysis of markrecapture data as unmodelled individual differences can bias estimates of population size and survival rates. multi-state markrecapture models have been the focus of much recent research but have yet to explicitly incorporate individual variability. we use a multi-state markrecapture model with individual-level random effects, built in admb-re, a software tool that automatically provides an accurate analytical approximation of the likelihood which is otherwise intractable. we tested the model using simulation studies and applied the model to data from north atlantic humpback whales in the stellwagen bank national marine sanctuary where heterogeneity is apparent in both sighting probability and site preference. simulation studies demonstrated accurate estimation of true parameter values with random effects models but bias sometimes resulted from fitting simpler models. in application to data from the north atlantic humpback whales, we were able to estimate both annual variation in the local population and three measures of individual-level variation. results indicate considerable heterogeneity within this population in both sighting probability and site preference. ignoring random effects led to bias in estimates of proportion of time within a marine reserve.
methods;R package, SCR;program spacecap: software for estimating animal density using spatially explicit capture-recapture models;"abundance estimation; camera traps; carnivore conservation; carnivore monitoring; density estimation; faecal dna sampling; hair snares; mark-recapture; marked animals; spatial models";METHODS IN ECOLOGY AND EVOLUTION;"GOPALASWAMY AM;ROYLE JA;HINES JE;SINGH P;JATHANNA D;KUMAR NS;KARANTH KU";the advent of spatially explicit capturerecapture models is changing the way ecologists analyse capturerecapture data. however, the advantages offered by these new models are not fully exploited because they can be difficult to implement. to address this need, we developed a user-friendly software package, created within the r programming environment, called spacecap. this package implements bayesian spatially explicit hierarchical models to analyse spatial capturerecapture data. given that a large number of field biologists prefer software with graphical user interfaces for analysing their data, spacecap is particularly useful as a tool to increase the adoption of bayesian spatially explicit capturerecapture methods in practice.
methods;Photo-id, software;a computer-assisted system for photographic mark-recapture analysis;"giraffa camelopardalis; giraffe; noninvasive methods; photographic mark-recapture; scale invariant feature transform; survival; tanzania; tarangire";METHODS IN ECOLOGY AND EVOLUTION;"BOLGER DT;MORRISON TA;VANCE B;LEE D;FARID H";1. photographic markrecapture is a cost-effective, non-invasive way to study populations. however, to efficiently apply photographic markrecapture to large populations, computer software is needed for image manipulation and pattern matching. 2. we created an open-source application for the storage, pattern extraction and pattern matching of digital images for the purposes of markrecapture analysis. the resulting software package is a stand-alone, multiplatform application implemented in java. our program employs the scale invariant feature transform (sift) operator that extracts distinctive features invariant to image scale and rotation. 3. we applied this system to a population of masai giraffe (giraffa camelopardalis tippelskirchi) in the tarangire ecosystem in northern tanzania. over 1200 images were acquired in the field during three primary sampling periods between september 2008 and december 2009. the pattern information in these images was extracted and matched resulting in capture histories for over 600 unique individuals. 4. estimated error rates of the matching system were low based on a subset of test images that were independently matched by eye. 5. encounter histories were subsequently analysed with open population models to estimate apparent survival rates and population size. 6. this new open-access tool allowed photographic markrecapture to be applied successfully to this relatively large population.
methods;robust design, classification uncertainty;beyond the robust design: accounting for changing, uncertain states and sparse, biased detection in a multistate mark-recapture model;"multistate mark recapture; state classification uncertainty; detection probability; post-stratification; steller sea lion; bayesian";ECOLOGICAL MODELLING;"TAYLOR RL;BOOR GKH";we develop a multistate mark-recapture likelihood function to estimate reproductive rate when state classification uncertainty is present (alone female may or may not have offspring), females may transition between states (give birth) during many of the within-season sampling periods, and female detection is uneven because it is a function of an unknown admixture of state-dependency and observer effort, which are linked via location. we show how to address these multiple problems, even with sparse data, by deriving explicit functions for state transitions and state detection, and by combining a modicum of auxiliary information indicative of a female's reproductive state with a mild assumption about the relationship between detection rate and reproductive rate. we incorporate this likelihood function into a bayesian framework to estimate reproductive rate for the threatened eastern stock of steller sea lions (eumetopias jubatus) in southeast alaska, a population for which reproductive rate has never been estimated. our methods relax previous assumptions of multistate mark-recapture models and facilitate inference from ecological systems where sampling effort is highly constrained. (c) 2012 elsevier b.v. all rights reserved.
methods;IPM, density-dependence;estimating the strength of density dependence in the presence of observation errors using integrated population models;"bayesian; demographic parameters; density dependence; identifiability; observation error; population growth rate";ECOLOGICAL MODELLING;"ABADI F;GIMENEZ O;JAKOBER H;STAUBER W;ARLETTAZ R;SCHAUB M";assessing the strength of density dependence is crucial for understanding population dynamics, but its estimation is difficult. because estimates of population size and demographic parameters usually include errors due to imperfect detection, estimations of the strength of density dependence will be biased if obtained with conventional methods and lack statistical power to detect density dependence. we propose a bayesian integrated population model to study density dependence. the model allows assessing the effect of density both on the population growth rate as well as the demographic parameters while accounting for imperfect detection. we studied the performance of this model using simulation and illustrate its use with data on red-backed shrikes lanius collurio. our simulation results showed that the strength of density dependence is identifiable and it was estimated with higher precision using the integrated population model than the conventional regression model. as expected, the conventional regression model tended to overestimate density dependence at the population level whereas underestimates at the demographic level, but the bias was small. the analysis of the red-backed shrike data revealed negative density dependence at the population level most likely mediated by a density-dependent decline in adult survival. this work highlights the potential of integrated population models in assessing density dependence and its practical application in population studies. (c) 2012 elsevier b.v. all rights reserved.
methods;NA;people born in the middle east but residing in the netherlands: invariant population size estimates and the role of active and passive covariates;"population size estimation; capture-recapture; collapsibility; multiple record-systems estimation; missing data; structural zeros";ANNALS OF APPLIED STATISTICS;"VAN DER HEIJDEN PGM;WHITTAKER J;CRUYFF M;BAKKER B;VAN DER VLIET R";"including covariates in loglinear models of population registers improves population size estimates for two reasons. first, it is possible to take heterogeneity of inclusion probabilities over the levels of a covariate into account; and second, it allows subdivision of the estimated population by the levels of the covariates, giving insight into characteristics of individuals that are not included in any of the registers. the issue of whether or not marginalizing the full table of registers by covariates over one or more covariates leaves the estimated population size estimate invariant is intimately related to collapsibility of contingency tables [biometrika 70 (1983) 567-578]. we show that, with information from two registers, population size invariance is equivalent to the simultaneous collapsibility of each margin consisting of one register and the covariates. we give a short path characterization of the loglinear model which describes when marginalizing over a covariate leads to different population size estimates. covariates that are collapsible are called passive, to distinguish them from covariates that are not collapsible and are termed active. we make the case that it can be useful to include passive covariates within the estimation model, because they allow a finer description of the population in terms of these covariates. as an example we discuss the estimation of the population size of people born in the middle east but residing in the netherlands."
methods;Distance sampling, data combination, random effects;estimating abundance of cryptic but trappable animals using trapping point transects: a case study for key largo woodrats;"abundance estimation; capture-recapture; distance sampling; key largo; neotoma floridana smalli; rodents; small mammals; woodrat";METHODS IN ECOLOGY AND EVOLUTION;"POTTS JM;BUCKLAND ST;THOMAS L;SAVAGE A";1. obtaining robust abundance or density estimates is problematic for many rare or cryptic species. we combine elements of capturerecapture and distance sampling, to develop a method called trapping point transects (tpt), and we applied this method to estimate the abundance of the endangered key largo woodrat (neotoma floridana smalli). 2. trapping point transects requires two separate surveys to be held concurrently in space and time. in the main survey, the encounter rate (number of animals caught per trap per session) is measured. in the trial survey, animals whose locations are known prior to opening traps are used to estimate the detection function g(r) (the probability of capturing an animal given it is distance r from a trap when it is set), so the effective trapping area in the main survey can be estimated. it is assumed animals in the trial survey are a representative sample of all animals in the population. individual heterogeneity in trappability is accommodated using random effects in g(r). 3. performance of two tpt estimators was assessed by simulation. generally, when underlying capture probabilities were high [g(0) = 0.8] and between-individual variation was small, modest survey effort (360 trap nights in the trial survey) generated little bias in estimated abundance (c. 5%). uncertainty and relative bias in population estimates increased with decreasing capture probabilities and increasing between-individual variation. survey effort required to obtain unbiased estimates was also investigated. 4. given the challenges of working with cryptic, sparse or nocturnal species, we tested the validity of this method to estimate the abundance of the key largo woodrats between 2008 and 2011. 5. trapping point transects was found to be an effective monitoring method yielding annual estimates of the extant wild population of 693, 248, 78 and 256 animals, with cvs of 0.45, 0.55, 0.82 and 0.43, respectively. the tpt method could be adapted to a range of species that are otherwise very difficult to monitor.
methods;sex-ratio;estimating adult sex ratios from bird mist netting data;"adult sex ratio; bayesian analysis; capture probability; detectability; mark-recapture";METHODS IN ECOLOGY AND EVOLUTION;"AMRHEIN V;SCAAR B;BAUMANN M;MINERY N;BINNERT JP;KORNER NIEVERGELT F";"1. it is increasingly acknowledged that skewed adult sex ratios (asrs) may play an important role in ecology, evolution and conservation of animals. 2. in birds, published estimates on asrs mostly rely on mist netting data. however, previous studies suggested that mist nets or other trap types provide biased estimates on sex ratios, with males being more susceptible to capture than females. 3. we used data from a constant effort site ringing scheme to show how sex ratios that are corrected for sex- and year-specific capture probabilities can be directly estimated by applying capturerecapture analysis, for example, in a bayesian framework. 4. when capture data were pooled from the 19 years of study, we found that in the blackbird (turdus merula) and the blackcap (sylvia atricapilla), the observed proportions of males were 57% and 55%, respectively. however, when the observed annual proportions of males were corrected for the sex-specific capture probabilities, the proportions of males did not clearly differ from 50% in most study years, and thus, the apparent male-bias in the asrs almost completely disappeared. 5. we propose that published estimates on asrs in birds should be re-evaluated if based solely on observed sex ratios from mist netting studies. 6. we further propose that data from national bird ringing schemes and in particular from constant effort site ringing programs can provide valuable information on asrs, if analysed using capturerecapture models. we discuss important assumptions of those models; for example, movements that may differ between sexes should be taken into account, as well as the occurrence of transient individuals that do not hold breeding territories within a study site."
methods;Random effects, covariates;simple estimation and test procedures in capture-mark-recapture mixed models;"capture-mark-recapture; environmental covariates; glmm; mixed models; population dynamics; random effects";BIOMETRICS;"LEBRETON JD;CHOQUET R;GIMENEZ O";the need to consider in capture-recapture models random effects besides fixed effects such as those of environmental covariates has been widely recognized over the last years. however, formal approaches require involved likelihood integrations, and conceptual and technical difficulties have slowed down the spread of capturerecapture mixed models among biologists. in this article, we evaluate simple procedures to test for the effect of an environmental covariate on parameters such as time-varying survival probabilities in presence of a random effect corresponding to unexplained environmental variation. we show that the usual likelihood ratio test between fixed models is strongly biased, and tends to detect too often a covariate effect. permutation and analysis of deviance tests are shown to behave properly and are recommended. permutation tests are implemented in the latest version of program e-surge. our approach also applies to generalized linear mixed models.
methods;R package, age, data combination, left truncation, right censoring;basta: an r package for bayesian estimation of age-specific survival from incomplete mark-recapture/recovery data with covariates;"bayesian inference; capture-recapture; capture-recovery; free software; long-term individual-based data sets; r project; survival analysis";METHODS IN ECOLOGY AND EVOLUTION;"COLCHERO F;JONES OR;REBKE M";1. understanding age-specific survival in wild animal populations is crucial to the study of population dynamics and is therefore an essential component of several fields including evolution, management and conservation. 2. we present bayesian survival trajectory analysis (basta), a free open-source software package for estimating age-specific survival from capturerecapture/recovery data under a bayesian framework. 3. the method copes with low recapture probabilities, unknown ages (e.g. because of left-truncation) and unknown ages at death (e.g. because of right-censoring). it estimates survival and detection parameters as well as the unknown birth and death times (i.e. latent states) while allowing users to test a range of survival models. in addition, the effect of continuous or categorical covariates can be evaluated. 4. this tool facilitates the analysis of age patterns of survival in long-term animal studies and will enable researchers to robustly infer the effect of covariates, even with large amounts of missing data.
methods;heterogeneity, HMM;assessing individual heterogeneity using model selection criteria: how many mixture components in capture-recapture models?;"capture-recapture; classification; individual heterogeneity; information criteria; mixture models; simulation experiment";METHODS IN ECOLOGY AND EVOLUTION;"CUBAYNES S;LAVERGNE C;MARBOUTIN E;GIMENEZ O";"1. capturerecapture mixture models are important tools in evolution and ecology to estimate demographic parameters and abundance while accounting for individual heterogeneity. a key step is to select the correct number of mixture components i) to provide unbiased estimates that can be used as reliable proxies of fitness or ingredients in management strategies and ii) classify individuals into biologically meaningful classes. however, there is no consensus method in the statistical literature for selecting the number of components. 2. in ecology, most studies rely on the akaike information criterion (aic) and the bayesian information criterion (bic) that has recently gained attention in ecology. the integrated completed likelihood criterion (icl; ieee transactions on pattern analysis and machine intelligence, 2000, 22, 719) was specifically developed to favour well-separated components, but its use has never been investigated in ecology. 3. we compared the performance of aic, bic and icl for selecting the number of components with regard to a) bias and accuracy of survival and detection estimates and b) success in selecting the true number of components using extensive simulations and data on wolf (canis lupus) that were used for management through survival and abundance estimation. 4. bias in survival and detection estimates was <0.02 for both aic and bic, and more than 0.09 for icl, while mean square error was <0.05 for all criteria. as expected, bias increased as heterogeneity increased. success rates of aic and bic in selecting the true number of components were better than icl (68% for aic, 58% for bic, and 16% for icl). as the degree of heterogeneity increased, aic (and bic in a lesser extent) overestimated the number of components, while icl often underestimated this number. for the wolf study, the 2-class model was selected by bic and icl, while aic could not decide between the 2- and 3-class models. 5. we recommend using aic or bic when the aim is to estimate parameters. regarding classification, we suggest taking the classification quality into account by using icl in conjunction with bic, pending further work to adapt its penalty term for capturerecapture data."
methods;Covariates, path analysis;exploring causal pathways in demographic parameter variation: path analysis of mark-recapture data;"atlantic puffin; bayesian inference; causal modelling; cormack-jolly-seber model; environmental covariates; survival estimation; winbugs";METHODS IN ECOLOGY AND EVOLUTION;"GIMENEZ O;ANKER NILSSEN T;GROSBOIS V";1. inference about demographic parameters of animal and plant natural populations is important to evaluate the consequences of global changes on populations. investigating the factors driving their variation over space and time allows evaluating the relative importance of biotic and abiotic variables in shaping the dynamics of a population. although numerous studies have identified the factors possibly affecting population dynamics, they have barely formally determined the routes by which these different factors are related to demographic parameters. 2. we focus on mark-recapture (mr) models that provide unbiased estimators of demographic parameters, while explicitly coping with imperfect detection inherent to wild populations. mrmodels allow estimating the effect of covariates on demographic parameters and testing their significance in a regression-like framework. however, these models can only detect correlations and do not inform on causal pathways (e. g. direct vs. indirect effects) in the relationships between demographic parameters and the factors possibly explaining their variability. 3. we develop an integrated model to perform path analysis (pa) of mr data, to examine causal relationships among several (including demographic) variables. this approach is implemented in a bayesian framework usingmarkov chain monte carlo. 4. to motivate our developments, we analyse 17 years of mark-recapture data from atlantic puffins (fratercula arctica), to investigate the mechanisms through which environmental conditions have an impact on puffins' adult survival. using our pa-based mr modelling approach, we found that local climatic conditions had an indirect and lagged impact on puffin survival through their influence on local abundance of herring. besides, we found no evidence for any lagged effect through an alternative unknown pathway (e. g. abundance of another resource). 5. our method allows elucidating pathways through which environmental, trophic or densitydependent factors influence demographic parameters, while accounting for detectability < 1. this is a critical step to understand the interactions of a species with its environment and to predict the impacts of global change on its viability.
methods;Batch marking, HMM;inference on partially observed quasi-stationary markov chains with applications to multistate population models;"arnason-schwarz model; batch marking; capture-recapture; little penguin; (eudyptula minor); quasi-stationary markov chains; transition probabilities";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"STOKLOSA J;DANN P;HUGGINS R";if the full capture histories of captured individuals are available, inferences on multistate open population models may be conducted using the well known arnason-schwarz model. however, data of this detail is not always available. it is well known that inference on the transition probabilities of a markov chain may be conducted using aggregate data and we extend this approach to aggregate data on multistate open population models. we show that for parameters to be identifiable we need to augment the aggregate data and we achieve this by batch marking a cohort of individuals according to their initial state, so that the batch marking augments the aggregate data. model performance is examined by conducting several simulation studies and the model is applied to a real data set where full capture histories are available so it may be compared with the arnason-schwarz estimates. this article has supplementary material online.
methods;Survey design, species richest;when can we ignore the problem of imperfect detection in comparative studies?;"biodiversity monitoring; capture-mark-recapture; comparative studies; detection probability; nonparametric estimator; population size; sampling design; simulations; type i error";METHODS IN ECOLOGY AND EVOLUTION;"ARCHAUX F;HENRY PY;GIMENEZ O";1. numbers of individuals or species are often recorded to test for variations in abundance or richness between treatments, habitat types, ecosystem management types, experimental treatments, time periods, etc. however, a difference in mean detectability among treatments is likely to lead to the erroneous conclusion that mean abundance differs among treatments. no guidelines exist to determine the maximum acceptable difference in detectability. 2. in this study, we simulated count data with imperfect detectability for two treatments with identical mean abundance (n) and number of plots (n(plots)) but different mean detectability (p). we then estimated the risk of erroneously concluding that n differed between treatments because the difference in p was ignored. the magnitude of the risk depended on p, nand nplots. 3. our simulations showed that even small differences in p can dramatically increase this risk. a detectability difference as small as 4-8% can lead to a 50-90% risk of erroneously concluding that a significant difference in n exists among treatments with identical n = 50 and n(plots) = 50. yet, differences in p of this magnitude among treatments or along gradients are commonplace in ecological studies. 4. fortunately, simple methods of accounting for imperfect detectability prove effective at removing detectability difference between treatments. 5. considering the high sensitivity of statistical tests to detectability differences among treatments, we conclude that accounting for detectability by setting up a replicated design, applied to at least part of the design scheme and analysing data with appropriate statistical tools, is always worthwhile when comparing count data (abundance, richness).
methods;heterogeneity, assumptions;bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity;"bias; capture heterogeneity; mark-recapture; maximum population growth rate; population growth rate; population model; survival";METHODS IN ECOLOGY AND EVOLUTION;"FLETCHER D;LEBRETON JD;MARESCOT L;SCHAUB M;GIMENEZ O;DAWSON S;SLOOTEN E";1. markrecapture studies are often used to estimate adult survival probability , which is an important demographic parameter for long-lived species, as it can have a large impact on the population growth rate. we consider the impact of variation in capture probability among individuals (capture heterogeneity) on the estimation of ? from a markrecapture study and thence on estimation of the asymptotic population growth rate . 2. we review the mechanisms by which capture heterogeneity arises, methods of allowing for it in the analysis, and use simulation to assess the power of detecting three types of capture heterogeneity (two-group heterogeneity, trap-response and temporary emigration) using standard mark-recapture lack-of-fit tests. 3. we use simulation to assess the bias that can arise in the estimation of phi from a mark-recapture study when we do not allow for capture heterogeneity. using a generic population model, we assess the effect this bias has on estimation of lambda. 4. we use our results on the power of the lack-of-fit tests, together with a measure of the size of the bias relative to the standard error of the estimate of phi, to assess which situations might lead to an important level of undetected bias. our results suggest that undetected bias is not likely to be an issue when there is trap-response, owing to the lack-of-fit tests having sufficient power to detect any trap-response that could lead to non-negligible bias. for two-group heterogeneity, the worst bias generally occurs when the difference between the capture probabilities for the two groups is moderate and both capture probabilities are low. for temporary emigration, the worst bias generally occurs when the rate of emigration and the capture probability are both low. 5. we illustrate the issues for conservation management using data from studies of hector's dolphin (cephalorhynchus hectori) in new zealand and wolves (canis lupus) in france. 6. previous studies have suggested that capture heterogeneity will generally lead to a relatively small bias in the estimate of phi. however, given the high sensitivity of the asymptotic population growth rate to adult survival, a small bias in phi might lead to nontrivial bias in the estimate of lambda.
methods;SCR, opportunistic;spatial capture-recapture models for search-encounter data;"bayesian analysis; data augmentation; density estimation; distance sampling; hierarchical models; population size; search-encounter data; spatial capture-recapture; spatially explicit capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;"ROYLE JA;KERY M;GUELAT J";1. spatial capturerecapture models make use of auxiliary data on capture location to provide density estimates for animal populations. previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. here, we develop a class of models for ` search-encounter' data, i. e. for detections of recognizable individuals in continuous space, not restricted to trap locations. in our hierarchical model, detection probability is related to the average distance between individual location and the survey path. the locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. we provide a bayesian analysis of the model in winbugs, and develop a custommcmcalgorithm in ther language. 3. the model is applied to simulated data and to territory mapping data for the willow tit from the swiss breeding bird survey mhb. while the observed density was 15 territories per nominal 1 km 2 plot of unknown effective sample area, the model produced a density estimate of 21 12 territories per square km(95% posterior interval: 17-26). 4. spatial capture-recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture-recapture models.
methods;NA;an integrated model of habitat and species occurrence dynamics;"ambystoma maculatum; amphibians; detection; habitat modelling; multi-state occupancy; occupancy dynamics; species occurrence; spotted salamander; vernal pools";METHODS IN ECOLOGY AND EVOLUTION;"MACKENZIE DI;BAILEY LL;HINES JE;NICHOLS JD";1. relationships between animal populations and their habitats are well known and commonly acknowledged to be important by animal ecologists, conservation biologists and wildlife managers. such relationships are most commonly viewed as static, such that habitat at time t is viewed as a determinant of animals present at that same time, t, or sometimes as a determinant of animal population or occurrence dynamics (e.g. between t and t+1). 2. here, we motivate interest in simultaneous dynamics of both habitat and occupancy state (e. g. species presence or absence) and develop models to estimate parameters that describe the dynamics of such systems. 3. the models permit inference about transition probabilities for both habitat and focal species occupancy, such that habitat transitions may influence focal species transitions and vice versa. 4. example analyses using data from salamanders in the eastern united states are presented for (i) the special case in which habitat is characterized as either suitable or unsuitable and (ii) the more general case in which different habitat states are expected to influence occupancy dynamics in a less extreme manner (occupancy is possible in the various habitat states). 5. we believe that the integrated inference methods presented here will be useful for a variety of ecological and conservation investigations and attain special relevance in the face of habitat dynamics driven by such factors as active management, land use changes and climate change.
methods;covariates;smoothing population size estimates for time-stratified mark-recapture experiments using bayesian p-splines;"atlantic salmon; bayesian inference; hierarchical model; mark-recapture; openbugs; penalized spline; stratified petersen";BIOMETRICS;"BONNER SJ;SCHWARZ CJ";petersen-type markrecapture experiments are often used to estimate the number of fish or other animals in a population moving along a set migration route. a first sample of individuals is captured at one location, marked, and returned to the population. a second sample is then captured farther along the route, and inferences are derived from the numbers of marked and unmarked fish found in this second sample. data from such experiments are often stratified by time (day or week) to allow for possible changes in the capture probabilities, and previous methods of analysis fail to take advantage of the temporal relationships in the stratified data. we present a bayesian, semiparametric method that explicitly models the expected number of fish in each stratum as a smooth function of time. results from the analysis of historical data from the migration of young atlantic salmon (salmo salar) along the conne river, newfoundland, and from a simulation study indicate that the new method provides more precise estimates of the population size and more accurate estimates of uncertainty than the currently available methods.
methods;heterogeneity, covariates;heterogeneous capture-recapture models with covariates: a partial likelihood approach for closed populations;"closed population; generalized additive models; generalized linear mixed models; simulation-extrapolation";BIOMETRICS;"STOKLOSA J;HWANG WH;WU SH;HUGGINS R";in practice, when analyzing data from a capturerecapture experiment it is tempting to apply modern advanced statistical methods to the observed capture histories. however, unless the analysis takes into account that the data have only been collected from individuals who have been captured at least once, the results may be biased. without the development of new software packages, methods such as generalized additive models, generalized linear mixed models, and simulationextrapolation cannot be readily implemented. in contrast, the partial likelihood approach allows the analysis of a capturerecapture experiment to be conducted using commonly available software. here we examine the efficiency of this approach and apply it to several data sets.
methods;Misidentification, photo-id;estimating survival in photographic capture-recapture studies: overcoming misidentification error;"accuracy; bias; capture-recapture; connochaetes taurinus; cormack-jolly-seber; open-population models; photographic identification; precision; survival; wildebeest";METHODS IN ECOLOGY AND EVOLUTION;"MORRISON TA;YOSHIZAKI J;NICHOLS JD;BOLGER DT";1. for many species, noninvasive photographic identification offers a powerful and costeffective method for estimating demographic parameters and testing ecological hypotheses in large populations. however, this technique is prone to misidentification errors that can severely bias capturerecapture estimates. 2. we present a simple ad hoc data conditioning technique that minimizes bias in survival estimates across all rates of misidentification. we use simulated data sets to characterize trade-offs in bias, precision and accuracy of survival estimators for a range of misidentification probabilities, sampling intensities, survival rates and population sizes using this conditional approach. 3. misidentification errors resulted in mean survival estimates that were negatively biased by as much as -24.9% when errors were ignored. applying the conditional approach resulted in very low levels of bias across parameter space. however, the main cost of conditioning is a loss of precision, which was particularly severe at low sampling intensities. overall, the conditional approach was superior to the nonconditional approach [in terms of root mean square error (rmse) of survival estimates] in 51% of the parameter combinations that we explored. 4. we apply the data conditioning technique to a 3-sample capture-recapture data set compiled from 2551 images of a migratory wildebeest, connochaetes taurinus, population in northern tanzania. we estimate the false rejection rate (i.e., the probability of failing to match two photographs of the same individual) using a test set of ` known-identity'individuals. with this information, we compare survival estimates derived from conditioned data ((phi) over cap = 0.698 +/- 0.176), unconditioned data ((phi) over cap = 0.706 +/- 0.121) and simulated data to illustrate some of the key considerations for deciding whether to apply a conditional approach to a photographic data set. 5. these analyses demonstrate that ignoring misidentification error can lead to substantial bias in survival estimates. when sampling intensity and misclassification error rates are both relatively high, use of our conditioned data approach is preferred and yields survival estimates with lower rmse. however, when sampling intensity and misclassification error are both small, the standard approach using unconditioned data yields smaller rmse.
methods;assumptions;testing the effectiveness of capture mark recapture population estimation techniques using a computer simulation with known population size;"capture-mark-recapture; population; simulation; small mammal";ECOLOGICAL MODELLING;"REES SG;GOODENOUGH AE;HART AG;STAFFORD R";estimation of small mammal population sizes is important for monitoring ecosystem condition and for conservation. here, we test the accuracy of standard methods of population size estimation using capture-mark-recapture (cmr) on a simulated population of agents. the use of a computer simulation allows complete control of population sizes and behaviors, thereby avoiding assumptions that may be violated in real populations. we find that the recommended protocol for cmr sampling, using uniformly distributed traps, consistently overestimates population sizes by as much as 100% when studies are conducted over only two trapping periods. more than 20 trapping periods are required before this method, or that of placing traps randomly, gives an accurate estimation of population size (i.e., within a 95% confidence limit of the actual value). non-random sampling, by placing traps on runways used by small mammals, produces the most accurate, and least variable, estimates of population. however, we show that around 10 trapping periods are still required to produce an accurate population estimate using this method. given that most real populations do not comply with the 'ideal' assumptions made by cmr, we suggest that population estimates based on cmr may be fundamentally flawed, and recommend that protocols for cmr population estimation methods may need revising. (c) 2011 elsevier b.v. all rights reserved.
methods;Genetic assignment, robust design;augmenting superpopulation capture-recapture models with population assignment data;"assignment procedures; capture-recapture; genetic assignment test; immigration; kangaroo rat; resampling approach; robust design; superpopulation";BIOMETRICS;"WEN Z;POLLOCK K;NICHOLS J;WASER P";ecologists applying capture-recapture models to animal populations sometimes have access to additional information about individuals' populations of origin (e. g., information about genetics, stable isotopes, etc.). tests that assign an individual's genotype to its most likely source population are increasingly used. here we show how to augment a superpopulation capture-recapture model with such information. we consider a single superpopulation model without age structure, and split each entry probability into separate components due to births in situ and immigration. we show that it is possible to estimate these two probabilities separately. we first consider the case of perfect information about population of origin, where we can distinguish individuals born in situ from immigrants with certainty. then we consider the more realistic case of imperfect information, where we use genetic or other information to assign probabilities to each individual's origin as in situ or outside the population. we use a resampling approach to impute the true population of origin from imperfect assignment information. the integration of data on population of origin with capture-recapture data allows us to determine the contributions of immigration and in situ reproduction to the growth of the population, an issue of importance to ecologists. we illustrate our new models with capture-recapture and genetic assignment data from a population of banner-tailed kangaroo rats dipodomys spectabilis in arizona.
methods;Distance sampling, data combination, heterogeneity;point-based mark-recapture distance sampling;"avian surveys; detection bias; distance sampling; double-observer methods; golden-cheeked warbler; mark-recapture; point independence; unmodeled heterogeneity";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"LAAKE JL;COLLIER BA;MORRISON ML;WILKINS RN";avian surveys using point sampling for abundance estimation have either focused on distance sampling or more commonly mark-recapture to correct for detection bias. combining mark-recapture and distance sampling (mrds) has become an effective tool for line transects, but it has been largely ignored in point sampling literature. we describe mrds and show that the previously published methods for point sampling are special cases. using simulated data and golden-cheeked warbler (dendroica chrysoparia) survey data from texas, we demonstrate large differences in abundance estimates resulting from different independence assumptions. data and code are provided in supplementary materials.
methods;Social network, social species;estimating dyad association probability under imperfect and heterogeneous detection;"animal behaviour analysis; association index; individual detection; dyad association probability; information theory; likelihood based model; cephalorhynchus commersonii";ECOLOGICAL MODELLING;"KLAICH MJ;KINAS PG;PEDRAZA SN;COSCARELLA MA;CRESPO EA";in animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. in terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. however, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (psi) accounting for p < 1 and possibly varying between associated and not associated individuals. the proposed likelihood based model allows for both individual and dyadic missing observations. in addition, the model can easily be extended to incorporate covariate information for modeling p and psi. a simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. the application of the proposed approach is illustrated using a real data set collected from a population of commerson's dolphin (cephalorhynchus commersonii) in patagonia argentina. finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies. (c) 2011 elsevier b.v. all rights reserved.
methods;HMM, senesence;a semi-markov model to assess reliably survival patterns from birth to death in free-ranging populations;"continuous function; cormack-jolly-seber model; long-lived species; mixture; nonlinear model; reduce additive weibull; siler";METHODS IN ECOLOGY AND EVOLUTION;"CHOQUET R;VIALLEFONT A;ROUAN L;GAANOUN K;GAILLARD JM";1. semi-markov models explicitly define the distribution of waiting time duration and have been used as a convenient framework for modelling the time spent in one physiological state in previous biological studies. 2. here, we focus on the modelling of the time spent within a life-cycle stage (e. g. juvenile, adult and old) by individuals over their lifetime from capture-mark-recapture data, which are commonly used to estimate demographic parameters in free-ranging populations. 3. we propose a parametric model (1) with a semi-markov state, (2) associated to a hazard function and (3) accounting for imperfect detection to assess reliably survival patterns from birth to death. 4. these models indeed outperform models with a linear trend or a quadratic form, often selected as the best model for survival in capture-recapture studies. 5. moreover, our approach offers the first opportunity to estimate statistically rather than visually the age of the onset of actuarial senescence, associated with confidence intervals. 6. the application of this new approach to the detailed long-term study of survival in roe deer at trois fontaines (france) illustrates the relevance of semi-markov models to assess survival patterns from birth to death.
methods;Data combination, photo-id, genetic tagging;a new method for estimating animal abundance with two sources of data in capture-recapture studies;"abundance; humpback whales; joint modelling; jolly-seber model; open population; two-source jolly-seber model";METHODS IN ECOLOGY AND EVOLUTION;"MADON B;GIMENEZ O;MCARDLE B;BAKER CS;GARRIGUE C";1. mark-recapture studies are often used to estimate population size based on a single source of individual identification data such as natural markings or artificial tags. however, with the development of molecular ecology, multiple sources of identification can be obtained for some species and combining them to obtain population size estimates would certainly provide better information about abundance than each survey can provide alone. 2. we propose an extension of the jolly-seber model to infer abundance by combining two sources of capture-recapture data. the need to merge both sources of data was motivated by studies of humpback whales in which both photo-identification and dna from skin biopsy samples are often collected. as whales are not necessarily available by both sampling methods on any given occasion, they can appear twice in the combined data set if no combined sampling ever occurred during the survey, i.e. being photographed and genotyped on the same occasion. our model thus combines the two sources of information by estimating the possible overlap. monte carlo simulations are used to assess the properties of the present estimator that is then used to estimate the size of the humpback whale population in new caledonia. the new open-population estimator is also compared with classic closed-population estimators incorporating either temporal and/or individual heterogeneity in the capture probability: the purpose was to evaluate which approach (closed or open population) was the least biased for an open population with individual heterogeneous capture probabilities. 3. when all assumptions are met, the estimator is unbiased as long as the probability of being double-tagged (e. g. photographed and biopsied on the same occasion) on every occasion is above 0 2. 4. the humpback whale case study in new caledonia shows that our two-source jolly-seber (tsjs) estimator could be more efficient in estimating population size than models based only on one type of data. for monitoring purposes, the proposed method provides an efficient alternative to the existing approaches and a productive direction for future work to deal with multiple sources of data to estimate abundance. 5. r-codes formatting the data and implementing the tsjs model are provided in resource s5.
methods;NA;theory put into practice: an r implementation of the infinite-dimensional model;"evolution; function valued trait; infinite-dimensional model; growth trajectory; phenotypic variation; selection";ECOLOGICAL MODELLING;"KUPARINEN A;BYORKLUND M";the infinite dimensional model (idm) is an approach that has been developed for the analyses of phenotypic variation in function valued traits such as growth trajectories and continuous reaction norms. this model is particularly suited for the analysis of the potential and the constraints for growth to evolve under selection on body size. despite of its applicability to a broad range of study systems idm has only been applied in a handful of studies, as it is mathematically demanding for scientists not familiar with quantitative genetics methods. here, we present a user-friendly r implementation of idm, demonstrate its performance with growth data on nine-spined stickleback (pungitius pungitius). in addition to rearing experiments, individual based size-at-age trajectories are often measured in wild in mark-recapture studies or estimated retrospectively from scales or bones. therefore, our r implementation of idm should be applicable to many studies conducted in wild and in a lab, and be useful by making the methodologically challenging idm approach more easily accessible also in the fields where quantitative genetics methods are less standardly used. (c) 2011 elsevier b.v. all rights reserved.
methods;zero-truncated, chao;population size estimation based upon ratios of recapture probabilities;"chao-bunge estimator; katz distribution; species problem; negative binomial distribution; weighted linear regression; zero-truncation";ANNALS OF APPLIED STATISTICS;"ROCCHETTI I;BUNGE J;BOHNING D";"estimating the size of an elusive target population is of prominent interest in many areas in the life and social sciences. our aim is to provide an efficient and workable method to estimate the unknown population size, given the frequency distribution of counts of repeated identifications of units of the population of interest. this counting variable is necessarily zero-truncated, since units that have never been identified are not in the sample. we consider several applications: clinical medicine, where interest is in estimating patients with adenomatous polyps which have been overlooked by the diagnostic procedure; drug user studies, where interest is in estimating the number of hidden drug users which are not identified; veterinary surveillance of scrapie in the uk, where interest is in estimating the hidden amount of scrapie; and entomology and microbial ecology, where interest is in estimating the number of unobserved species of organisms. in all these examples, simple models such as the homogenous poisson are not appropriate since they do not account for present and latent heterogeneity. the poisson-gamma (negative binomial) model provides a flexible alternative and often leads to well-fitting models. it has a long history and was recently used in the development of the chao-bunge estimator. here we use a different property of the poisson-gamma model: if we consider ratios of neighboring poisson-gamma probabilities, then these are linearly related to the counts of repeated identifications. also, ratios have the useful property that they are identical for truncated and untruncated distributions. in this paper we propose a weighted logarithmic regression model to estimate the zero frequency counts, assuming a gamma-poisson distribution for the counts. a detailed explanation about the chosen weights and a goodness of fit index are presented, along with extensions to other distributions. to evaluate the proposed estimator, we applied it to the benchmark examples mentioned above, and we compared the results with those obtained through the chao-bunge and other estimators. the major benefits of the proposed estimator are that it is defined under mild conditions, whereas the chao-bunge estimator fails to be well defined in several of the examples presented; in cases where the chao-bunge estimator is defined, its behavior is comparable to the proposed estimator in terms of bias and mse as a simulation study shows. furthermore, the proposed estimator is relatively insensitive to inclusion or exclusion of large outlying frequencies, while sensitivity to outliers is characteristic of most other methods. the implications and limitations of such methods are discussed."
methods;covariates, missing data;full open population capture-recapture models with individual covariates;"capture-recapture; demographic parameters; hierarchical modeling; individual covariates; jags/bugs";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"SCHOFIELD MR;BARKER RJ";traditional analyses of capture-recapture data are based on likelihood functions that explicitly integrate out all missing data. we use a complete data likelihood (cdl) to show how a wide range of capture-recapture models can be easily fitted using readily available software jags/bugs even when there are individual-specific time-varying covariates. the models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. the use of a cdl means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. this approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. we illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable. supplemental materials for this article are available online.
methods;Multiple marks, tag loss;estimating demographic parameters for capture-recapture data in the presence of multiple mark types;"mark-recapture; mark-loss; halichoerus grypus; multiple mark types; integrated analysis";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"SMOUT S;KING R;POMEROY P";in mark-recapture studies, various techniques can be used to uniquely identify individual animals, such as ringing, tagging or photo-identification using natural markings. in some long-term studies more than one type of marking procedure may be implemented during the study period. in these circumstances, ignoring the different mark types can produce biased survival estimates since the assumption that the different mark types are equally catchable (homogeneous capture probability across mark types) may be incorrect. we implement an integrated approach where we simultaneously analyse data obtained using three different marking techniques, assuming that animals can be cross-classified across the different mark types. we discriminate between competing models using the aic statistic. this technique also allows us to estimate both relative mark-loss probabilities and relative recapture efficiency rates for the different marking methods. we initially perform a simulation study to explore the different biases that can be introduced if we assume a homogeneous recapture probability over mark type, before applying the method to a real dataset. we make use of data obtained from an intensive long-term observational study of uk female grey seals (halichoerus grypus) at a single breeding colony, where three different methods are used to identify individuals within a single study: branding, tagging and photo-identification based on seal coat pattern or pelage.
methods;Record linkage;a hierarchical bayesian approach to record linkage and population size problems;"capture-recapture methods; conditional independence; gibbs sampling; metropolis-hastings; record linkage";ANNALS OF APPLIED STATISTICS;"TANCREDI A;LISEO B";we propose and illustrate a hierarchical bayesian approach for matching statistical records observed on different occasions. we show how this model can be profitably adopted both in record linkage problems and in capture-recapture setups, where the size of a finite population is the real object of interest. there are at least two important differences between the proposed model-based approach and the current practice in record linkage. first, the statistical model is built up on the actually observed categorical variables and no reduction (to 0-1 comparisons) of the available information takes place. second, the hierarchical structure of the model allows a two-way propagation of the uncertainty between the parameter estimation step and the matching procedure so that no plug-in estimates are used and the correct uncertainty is accounted for both in estimating the population size and in performing the record linkage. we illustrate and motivate our proposal through a real data example and simulations.
methods;Individual growth;a new method for estimating growth transition matrices;"growth; length-based models; mark-recapture; transition matrices";BIOMETRICS;HILLARY RM;the vast majority of population models work using age or stage not length but there are many cases where animals cannot be aged sensibly or accurately. for these cases length-based models form the logical alternative but there has been little work done to develop and compare different methods of estimating growth transition matrices to be used in such models. this article demonstrates how a consistent bayesian framework for estimating growth parameters and a novel method for constructing length transition matrices accounts for variation in growth in a clear and consistent manner and avoids potential subjective choices required using more established methods. the inclusion of the resultant growth uncertainty in population assessment models and the potential impact on management decisions is also addressed.
methods;Model selection, memory;multistate mark-recapture model selection using score tests;"branta canadensis; canada goose; mark-recapture; memory model; model selection";BIOMETRICS;"MCCREA RS;MORGAN BJT";although multistate mark-recapture models are recognized as important, they lack a simple model-selection procedure. this article proposes and evaluates a step-up approach to select appropriate models for multistate mark-recapture data using score tests. only models supported by the data require fitting, so that over-complicated model structures with too many parameters do not need to be considered. typically only a small number of models are fitted, and the procedure is also able to identify parameter-redundant and near-redundant models. the good performance of the technique is demonstrated using simulation, and the approach is illustrated on a three-region canada goose data set. in this case, it identifies a new model that is much simpler than the best model previously considered for this application.
methods;Age, senescence, covariates;capture-recapture smooth estimation of age-specific survival probabilities in animal populations;"k-fold cross-validation; generalized cross-validation; penalized likelihood; senescence; age-structured populations";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;VIALLEFONT A;yielding sound estimates of survival according to age in wild populations where senescence or other age-related variations may occur is very important to management decision makers, and remains challenging. this paper proposes to use penalized maximum likelihood to obtain smooth estimates of annual survival probabilities across age in populations of wild animals followed by capture-recapture. we propose to use two different types of smoothing penalties, and we use nu-fold cross-validation to select the best value of the tuning parameter for the intensity of smoothing. we then assess the accuracy of the method by a simulation study with two different shapes of the relationship between age and survival, and we conclude that a careful use of this method provides reliable noise-free estimates of age-specific annual survival. we apply this procedure to the motivating data from a population of roe deer known to exhibit a marked decrease of survival with age, and we compare our results with those previously published on this population.
methods;Misidentification, genetic tagging, photo-id;modeling misidentification errors that result from use of genetic tags in capture-recapture studies;"closed population models; genotype; population size estimate; natural tags; photographic identification; misidentification";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"YOSHIZAKI J;BROWNIE C;POLLOCK KH;LINK WA";misidentification of animals is potentially important when naturally existing features (natural tags) such as dna fingerprints (genetic tags) are used to identify individual animals. for example, when misidentification leads to multiple identities being assigned to an animal, traditional estimators tend to overestimate population size. accounting for misidentification in capture-recapture models requires detailed understanding of the mechanism. using genetic tags as an example, we outline a framework for modeling the effect of misidentification in closed population studies when individual identification is based on natural tags that are consistent over time (non-evolving natural tags). we first assume a single sample is obtained per animal for each capture event, and then generalize to the case where multiple samples (such as hair or scat samples) are collected per animal per capture occasion. we introduce methods for estimating population size and, using a simulation study, we show that our new estimators perform well for cases with moderately high capture probabilities or high misidentification rates. in contrast, conventional estimators can seriously overestimate population size when errors due to misidentification are ignored.
methods;Tag loss, heterogeneity, multiple marks, HMM;a capture-recapture model with double-marking, live and dead encounters, and heterogeneity of reporting due to auxiliary mark loss;"e-surge; mixture of information; multi-event; recoveries; tag loss";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"JUILLET C;CHOQUET R;GAUTHIER G;PRADEL R";capture-recapture (cr) models assume marked individuals remain at risk of capture, which may not be true if individuals lose their mark or emigrate definitively from the study area. using a double-marking protocol, with a main and auxiliary mark, and both live encounters and dead recoveries at a large scale, partially frees cr models from this assumption. however, the auxiliary mark may fall off and its presence is often not mentioned when dead individuals are reported. we propose a new model to deal with heterogeneity of detection and uncertainty of the presence of an auxiliary mark in a multi-event framework. our general model, based on a double-marking protocol, uses information from physical captures/recaptures, distant observations and main mark recoveries from dead animals. we applied our model to a 13-year data set of a harvested species, the greater snow goose. we obtained seasonal survival estimates for adults of both sexes. survival estimates differed between models where the presence of the auxiliary mark upon recovery was ignored versus those where the presence was accounted for. in the multi-event framework, seasonal survival estimates are no longer biased because the heterogeneity due to the presence of an auxiliary mark is accounted for in the estimation of recovery rates. note: an illustration of the implementation of our model in e-surge is available online.
methods;heterogeneity;individual heterogeneity in recapture probability and survival estimates in cheetah;"acinonyx jubatus; capture-mark-recapture; carnivores; continuous reproduction; cormack-jolly-seber; serengeti";ECOLOGICAL MODELLING;"OLIVER LJ;MORGAN BJT;DURANT SM;PETTORELLI N";accurate estimates of demographic parameters are key for understanding and predicting population dynamics and for providing insights for effective wildlife management. up until recently, no suitable methodology has been available to estimate survival probabilities of species with asynchronous reproduction and a high level of individual variation in capture probabilities. the present work develops a capture-mark-recapture model for cheetahs in the serengeti national park, tanzania, which (a) deals with continuous reproduction, (b) takes into account the high level of individual heterogeneity in capture probabilities and (c) is spatially explicit. results show that (1) our approach, which is an extensive modification of the cormack-jolly-seber model, provides a lower female adult survival estimate and a higher male adolescent survival estimate than previous approaches to estimate cheetah survival in the area, (2) using sighting location alone is not sufficient to capture the individual variation in resighting probabilities for both sexes, and (3) precision in estimated survival probabilities is generally increased. species which are individually recognizable, wide-ranging and/or where individuals differ substantially in sightability are particularly appropriate to our modelling approach, and our methodology would thus be appropriate for a wide number of species to provide more accurate estimates of survival. (c) 2010 elsevier b.v. all rights reserved.
methods;Multispecies, random effects;a capture-recapture model for exploring multi-species synchrony in survival;"adult survival; atlantic puffin; bayesian models; common guillemot; environmental covariates; interspecific synchronisation; partition of variance; random effects; razorbill; winbugs";METHODS IN ECOLOGY AND EVOLUTION;"LAHOZ MONFORT JJ;MORGAN BJT;HARRIS MP;WANLESS S;FREEMAN SN";1. although recent decades have seen much development of statistical methods to estimate demographical parameters such as reproduction, and survival and migration probabilities, the focus is usually the estimation of parameters for individual species. this is despite the fact that several species may live in close proximity, sometimes competing for the same resources. there is therefore a great need for new methods that enable a better integration of demographical data, e.g. the study of synchrony between sympatric species, which are subject to common environmental stochasticity and potentially similar biotic interactions. 2. we propose a mark-recapture statistical model that uses random effect terms for studying synchrony in a demographical parameter at a multi-species level, adapting a framework initially developed to study multi-site synchrony to this novel situation. the model allows us to divide between-year variance in a demographical parameter into a 'synchronous' component, common to all species considered, and species-specific 'asynchronous' components, as well as to estimate the proportion of each component accounted for by environmental covariates. 3. we demonstrate the method with data from three colonially breeding auk species that share resources during the breeding season at the isle of may, scotland. mark-resight information has been collected since 1984 for atlantic puffins fratercula arctica, common guillemots uria aalge and razorbills alca torda marked as breeding adults. we explore the relationship between synchrony in the species' survival and two environmental covariates. 4. most of the between-year variation was synchronous to the three species, and the same environmental covariates acted simultaneously as synchronising and desynchronising agents of adult survival, possibly through different indirect causation paths. 5. synthesis and applications. the model proposed allows the investigation of multi-species synchrony and asynchrony in adult survival, as well as the role of environmental covariates in generating them. it provides insight into whether sympatric species respond similarly or differently to changes in their environment, and helps to disentangle the sources of these differences. the estimated indices of synchrony/asynchrony can facilitate the generation of further hypotheses about similarities/differences in these species' ecology, such as the potential overlap of wintering areas. the method is readily applicable to other species, ecosystems and demographical parameters.
methods;covariates, missing data;continuous covariates in mark-recapture-recovery analysis: a comparison of methods;"bayesian inference; imputation; individual covariates; mark-recapture-recovery; missing covariates; time-varying continuous covariates; trinomial model";BIOMETRICS;"BONNER SJ;MORGAN BJT;KING R";p>time varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. we examine three methods to incorporate time varying, individual covariates of the survival probabilities into the analysis of data from mark-recapture-recovery experiments: deterministic imputation, a bayesian imputation approach based on modeling the joint distribution of the covariate and the capture history, and a conditional approach considering only the events for which the associated covariate data are completely observed (the trinomial model). after describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of soay sheep (ovis aries) on the isle of hirta, scotland. simulations based on these results are then used to make further comparisons. we conclude that both the trinomial model and bayesian imputation method perform best in different situations. if the capture and recovery probabilities are all high, then the trinomial model produces precise, unbiased estimators that do not depend on any assumptions regarding the distribution of the covariate. in contrast, the bayesian imputation method performs substantially better when capture and recovery probabilities are low, provided that the specified model of the covariate is a good approximation to the true data-generating mechanism.
methods;IPM;multi-site integrated population modelling;"great cormorant; kalman filter; mark-recapture data; phalacrocorax carbo; recruitment; state-space models";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"MCCREA RS;MORGAN BJT;GIMENEZ O;BESBEAS P;LEBRETON JD;BREGNBALLE T";we examine the performance of a method of integrated population modelling for the joint analysis of different types of demographic data on individuals that exist in, and move between, different sites. the value of the approach is demonstrated by a simulation study which shows substantial improvement in parameter estimation when site-specific census data are combined with demographic data. the multivariate normal approximation to a multi-state mark-recapture likelihood is evaluated, and the performance of a diagonal variance-covariance matrix for the approximation is also examined. the work is motivated by a study of great cormorants. analysis of the cormorant data suggests that breeders survive better than non-breeders, and also that probabilities of recruitment to breeding have been declining over time for all the colonies of the study. supplementary material, including notes on the computation of standard errors and extended simulation results, are available online.
methods;SCR;estimating population size using capture-recapture encounter histories created from point-coordinate locations of animals;"abundance estimation; animal locations; athene cunicularia; burrowing owl; capture-recapture; point-coordinate capture-recapture; point location; space use; spatially explicit capture-recapture";METHODS IN ECOLOGY AND EVOLUTION;"MANNING JA;GOLDBERG CS";1. estimating population size is a fundamental objective of many animal monitoring programmes. capture-recapture methods are often used to estimate population size from repeated sampling of uniquely marked animals, but capturing and marking animals can be cost prohibitive and affect animal behaviours, which can bias population estimates. 2. we developed a method to construct spatially explicit capture-recapture encounter histories from locations of unmarked animals for estimating population size with conventional capture-recapture models. prior estimates of the maximum distance individuals move in the population is used to set a summary statistic and process subsequent capture-recapture survey data. animal locations are recorded as point coordinates during survey occasions, and the parameter of interest is abundance of individual activity centres. 3. we applied this method to data from a point-coordinate capture-recapture survey of burrowing owls athene cunicularia in the imperial valley of california, usa. we also used simulations to examine the utility of this technique for additional species with variable detection probabilities, levels of home range overlap and distributions of activity centres within a survey area. 4. the estimates from empirical and simulation studies were precise and unbiased when detection probabilities were high and territorial overlap was low. 5. this method of estimating population size from point locations fills a gap in non-invasive census and long-term monitoring methods available for conspicuous species and provides accurate estimates of burrowing owl territory abundance. the method requires high detection probabilities, low levels of home range overlap and that individuals use activity centres. we believe that these requirements can be met, with suitable survey protocols, for numerous songbird and reptile species.
methods;heterogeneity, mixture;open capture-recapture models with heterogeneity: ii. jolly-seber model;"abundance; capture-recapture; finite mixture model; heterogeneity; jolly-seber; maximum likelihood; non-parametric mle; open populations; schwarz-arnason";BIOMETRICS;"PLEDGER S;POLLOCK KH;NORRIS JL";estimation of abundance is important in both open and closed population capture-recapture analysis, but unmodeled heterogeneity of capture probability leads to negative bias in abundance estimates. this article defines and develops a suite of open population capture-recapture models using finite mixtures to model heterogeneity of capture and survival probabilities. model comparisons and parameter estimation use likelihood-based methods. a real example is analyzed, and simulations are used to check the main features of the heterogeneous models, especially the quality of estimation of abundance, survival, recruitment, and turnover. the two major advances in this article are the provision of realistic abundance estimates that take account of heterogenetiy of capture, and an appraisal of the amount of overestimation of survival arising from conditioning on the first capture when heterogeneity of survival is present.
methods;heterogeneity, mark-resighting;bayesian analysis of abundance for binomial sighting data with unknown number of marked individuals;"individual heterogeneity; mark-resight; marking and sighting; markov chain monte carlo; population size";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"MCCLINTOCK BT;HOETING JA";"the mark-resight method for estimating the size of a closed population can in many circumstances be a less expensive and less invasive alternative to traditional mark-recapture. despite its potential advantages, one major drawback of traditional mark-resight methodology is that the number of marked individuals in the population available for resighting needs to be known exactly. in real field studies, this can be quite difficult to accomplish. here we develop a bayesian model for estimating abundance when sighting data are acquired from distinct sampling occasions without replacement, but the exact number of marked individuals is unknown. by first augmenting the data with some fixed number of individuals comprising a marked ""super population,"" the problem may then be reformulated in terms of estimating the proportion of this marked super population that was actually available for resighting. this then allows the data for the marked population available for resighting to be modeled as random realizations from a binomial logit-normal distribution. we demonstrate the use of our model to estimate the new zealand robin (petroica australis) population size in a region of fiordland national park, new zealand. we then evaluate the performance of the proposed model relative to other estimators via a series of simulation experiments. we generally found our model to have advantages over other models when sample sizes are smaller with individually heterogeneous resighting probabilities. due to limited budgets and the inherent variability between individuals, this is a common occurrence in mark-resight population studies. winbugs and r code to carry out these analyses is available from http://www.stat.colostate.edu/similar to jah/software."
methods;Batch marking, GEE;analysis of an extended batch marking experiment using estimating equations;"mark-recapture experiments; open population; population size; pseudo-likelihood; survival probability";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"HUGGINS R;WANG Y;KEARNS J";estimating equations give a flexible method of inference when the likelihood is either intractable or is not fully specified. here we consider a batch marking experiment where the full likelihood is complex and without resorting to devices such as the computationally intensive expectation-maximization (em) algorithm is intractable. however, a pseudo-likelihood that yields tractable estimating equations may be easily constructed and its effectiveness is demonstrated via simulation studies. the methodology was applied to study the abundance of oriental weatherloach in a waterbody in south-eastern australia. the programs and data set used in this article are available in the online supplements.
methods;mixture, heterogeneity;capture recapture estimation using finite mixtures of arbitrary dimension;"bayesian model averaging; capture recapture; closed populations; heterogeneity; mixture distribution; reversible jump mcmc";BIOMETRICS;"ARNOLD R;HAYAKAWA Y;YIP P";"reversible.jump marko' chain monte carlo (rjmcmc) methods are used to fit bayesian capture recapture models incorporating heterogeneity in individuals and samples. heterogeneity in capture probabilities comes from finite mixtures and/or fixed sample effects allowing for interactions. estimation by rjmcmc allows automatic model selection and/or model averaging. priors on the parameters stabilize the estimates and produce realistic credible intervals for population size for overparameterized models; in contrast to likelihood-based methods. to demonstrate the approach we analyze the standard snowshoe hare and cottontail rabbit data sets from ecology, a reliability testing data set."
methods;GEE, measurement error, heterogeneity, covariates;a measurement error model for heterogeneous capture probabilities in mark-recapture experiments: an estimating equation approach;"horvitz-thompson; mark-recapture; measurement error; population size";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"HUGGINS R;HWANG WH";logistic models for capture probabilities that depend on covariates are effective if the covariates can be measured exactly. if there is measurement error so that a surrogate for the covariate is observed rather than the covariate itself, simple adjustments may be made if the parameters of joint distribution of the covariate and the surrogate are known. here we consider the case when a surrogate is observed whenever an individual is captured and the parameters must also be estimated from the data. an estimating equation regression calibration approach is developed and it is illustrated on a real dataset where the surrogate is an individual bird's wing-length, which varies from occasion to occasion. this article has supplementary material online.
methods;Data aggregation, sparse data;improving estimates of abundance by aggregating sparse capture-recapture data;"abundance estimation; data aggregation; mark-recapture; program capture; program mark; population parameters";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"LITT AR;STEIDL RJ";"inferences about abundance often are based on unadjusted counts of individuals observed, in part, because of the large amount of data required to generate reliable estimates of abundance. where capture-recapture data are sparse, aggregating data across multiple sample elements by pooling species, locations, and sampling periods increases the information available for modeling detection probability, a necessary step for estimating abundance reliably. the process of aggregating sample elements involves balancing trade-offs related to the number of aggregated elements; although larger aggregates increase the amount of information available for estimation, they often require more complex models. we describe a heuristic approach for aggregating data for studies with multiple sample elements, use simulated data to evaluate the efficacy of aggregation, and illustrate the approach using data from a field study. aggregating data systematically improved reliability of model selection and increased accuracy of abundance estimates while still providing estimates of abundance for each original sample unit, an important benefit necessary to maintain the design and sampling structure of a study. within the framework of capture-recapture sampling, aggregating data improves estimates of abundance and increases the reliability of subsequent inferences made from sparse data. additional tables and datasets may be found in the online supplements."
methods;PGR, heterogeneity, tag loss, mixture;estimating population growth rate from capture-recapture data in presence of capture heterogeneity;"coefficient of proportionality; losses on capture; maximum likelihood; mixture models; multinomial models; peromyscus maniculatus";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"PRADEL R;CHOQUET R;LIMA M;MERRITT J;CRESPIN L";the direct estimation and modeling of population growth rate from capture-recapture data has now seen a number of applications. however, the original model cannot accommodate heterogeneous capture probabilities. while studying a population of small mammals peromyscus maniculatus, we became concerned that the peak of population size may be estimated too late in the year because of heterogeneous catchability. hence, we developed a variation of the original model with a finite number of catchability classes. the results obtained with the new model are more in agreement with the known biology of this population. a bibliographic appendix and computer code are available online.
methods;Tradeoffs, LRS;predicting life-history traits for female new zealand sea lions, phocarctos hookeri: integrating short-term mark-recapture data and population modeling;"auckland islands; fisheries interactions; multi-state mark-recapture model; population dynamics";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"CHILVERS BL;WILKINSON IS;MACKENZIE DI";the trade-off between survival and reproduction by individuals is central to understanding life-history parameters of a species. few mammal species have life-history information from long-term research. instead, demographic models are commonly utilized to investigate an individual's life-history strategy, species dynamics, and population trends. this research investigates age-related survival and reproductive performance of adult female new zealand (nz) sea lions (phocarctos hookeri), using multi-state mark-recapture data from known-age branded individuals over five years. the mark-recapture analysis was integrated with a population model to predict the lifetime reproductive output of female nz sea lions. the integration of an analysis of short-term datasets with population modeling allows for the prediction of life-history parameters of long lived animals when long-term information is not available. while such approaches involve some caveats, it provides a framework for investigating population dynamics and is preferential to unsubstantiated assumptions. this technique can lead to better design and implementation of conservation management for long lived species. base code is provided in the online supplement.
methods;Robust design, temporary emigration, tradeoffs;unbiased survival estimates and evidence for skipped breeding opportunities in females;"boreal toad; capture-recapture; colorado; multi-state open robust design; survival; temporary emigration";METHODS IN ECOLOGY AND EVOLUTION;"MUTHS E;SCHERER RD;LAMBERT BA";"1. estimates of demographic parameters for females, in many organisms, are sparse. this is particularly worrisome as more and more species are faced with high extinction probabilities and conservation increasingly depends on actions dictated by complex predictive models that require accurate estimates of demographic parameters for each sex and species. 2. this study assesses demographic parameters, specifically temporary emigration and survival, for females, a class that has been difficult to investigate historically because of lack of data. amphibians provide a particularly good example because there is global concern about amphibian decline; yet most demographic parameter estimates are based on data from males, which we show can lead to erroneous conclusions. 3. we use 10 years of capture-recapture data from boreal toads (bufo boreas) and the multi-state open robust design model to provide evidence for the occurrence of skipped breeding opportunities (i.e. temporary emigration) in females. this is the first time that the open robust design model has been applied to an analysis of an amphibian population that we are aware of. 4. we determined that the transition from breeder to non-breeder is obligate and the probability of a non-breeder remaining a non-breeder is 64%; thus, temporary emigration is first-order markovian in nature, where breeding probability is dependent on the previous year's activity, i.e. if a female did not breed in year one, there is a 36% chance that she will breed in year two. with temporary emigration accounted for, we estimated between-year female survival at 87%. 5. establishing the occurrence of temporary emigration not only reduces bias in estimates of survival probabilities but also provides information about expected breeding attempts by females, a critical element in understanding the ecology of an organism and the impacts of outside stressors and conservation actions."
methods;NA;a primer on the application of markov chains to the study of wildlife disease dynamics;"epidemiological model; house finch carpodacus mexicanus; markov process; mycoplasma gallisepticum; wildlife disease";METHODS IN ECOLOGY AND EVOLUTION;"ZIPKIN EF;JENNELLE CS;COOCH EG";1. for wildlife researchers, disease specialists and policy analysts unfamiliar with the mathematical/statistical language of disease models, translation of probability statements into meaningful terms for disease research and control may be challenging. markov chain models are powerful tools, applicable to the study of disease dynamics that allow straightforward calculations of easily interpretable metrics of interest including probabilities of infection/recovery, expected times to initial infection, duration of illness and life expectancies for susceptible and infected individuals. 2. we present the basic principles and assumptions behind markov chain modelling with an intuitive interpretation of parameter estimates and a step-by-step guide (including software code) for implementing this approach in the study of wildlife diseases. we also include an explanation of the estimation process necessary to implement markov chain modelling (i.e. estimating the probability of state transitions between consecutive time steps) from typical survey data. 3. we demonstrate the usefulness and ease of calculation of markov chains through an example using a house finch carpodacus mexicanus-mycoplasma gallisepticum (mg) system. our results show how semi-weekly transition estimates of susceptible and infected individuals can be used to estimate a wide array of seasonal disease-associated metrics. 4. markov chain modelling can provide a basic understanding of parameters estimated from wildlife disease studies, and can aid in understanding the implications of disease on wildlife populations and in evaluation of control measures. we envision this paper serving as an entry point into the extensive literature and potential applications of markov chains in epidemiological modelling.
methods;NA;double-observer line transect methods: levels of independence;"distance sampling; double-observer methods; full independence; limiting independence; line transect sampling; point independence";BIOMETRICS;"BUCKLAND ST;LAAKE JL;BORCHERS DL";"p>double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. the resulting data supplement conventional distance sampling data with two-sample mark-recapture data. like conventional mark-recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. unlike conventional mark-recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. this knowledge can be used to diagnose unmodeled heterogeneity in the mark-recapture component of the data. by modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. at one extreme, full independence is assumed, as in the petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. we show how this framework can be used to provide more reliable analysis of double-observer line transect data. we test the methods by simulation, and by analysis of a dataset for which true abundance is known. we illustrate the approach through analysis of minke whale sightings data from the north sea and adjacent waters."
methods;Misidentification, genetic tagging, photo-id;uncovering a latent multinomial: analysis of mark-recapture data with misidentification;"dna fingerprints; mark-recapture; misidentification; multinomial; natural tags; null space";BIOMETRICS;"LINK WA;YOSHIZAKI J;BAILEY LL;POLLOCK KH";natural tags based on dna fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. however, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using bayesian methods. we present a general framework for bayesian analysis of categorical data arising from a latent multinomial distribution. although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. suppose that observed frequencies f are a known linear transformation f = a'x of a latent multinomial variable x with cell probability vector pi = pi(theta). given that full conditional distributions [theta vertical bar x] can be sampled, implementation of gibbs sampling requires only that we can sample from the full conditional distribution [x vertical bar f, theta], which is made possible by knowledge of the null space of a'. we illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks.
methods;persistence;persistence models for mark-recapture;"mark-recapture; persistence; markov chain; great copper butterfly; wetland snails";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"RAMSEY FL;SEVERNS PM";the stable of models available for analyzing mark-recapture data (otis et al. wild momogr 66:135, 1978) includes those having behavioral characteristics, time variation, heterogeneity, along with combinations of those characteristics. this paper proposes use of a series of models based on the persistence model of ramsey and usner (biometrics 59:331-339, 2003). we show that persistence can be modeled in combination with behavior and with time variation. we apply the persistence model to situations in which capture occasions are not equally-spaced in time. two case studies illustrate the use of these extended persistence models.
methods;Photo-id, misidentification;population estimates from aerial photographic surveys of naturally and variably marked bowhead whales;"abundance; bootstrap; capture-recapture; confidence curve; event history; false negatives; photo-identification";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"SCHWEDER T;SADYKOVA D;RUGH D;KOSKI W";abundance, mortality, and population growth of bowhead whales (balaena mysticetus) are estimated from captures of 4,894 putatively different individuals obtained from 10 years of systematic photographic surveys conducted during the spring migration when most of the bering-chukchi-beaufort population of bowheads migrates past point barrow, alaska. a stringent matching protocol designed to prevent false positive matches of the naturally, but variably marked individuals, led to 42 resightings between years. the flip side of this stringency is a presence of false negatives, i.e., some true recaptures are not recognized as such. the problem of false negatives is addressed by modeling the capture process and the matching process. the captures of an individual are assumed to follow a poisson process with intensity depending stochastically on the individual whale and on the year. the probability of successfully matching a capture to a previous capture is estimated by logistic regression on the degree of marking and image quality. individuals are recruited by the pella-tomlinson population model, and their mortality rate is assumed to be constant. the point estimate of yearly growth rate is 3.2%, and bowhead abundance in 2001 is estimated to be 8,250, similar to previous estimates.
methods;IPM;an integrated population model from constant effort bird-ringing data;"bayesian approach; capture-recapture; constant effort sites scheme; emigration; sedge warbler acrocephalus schoenobaenus; transients";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"CAVE VM;KING R;FREEMAN SN";"data from annual bird-ringing programs, in which catch effort is standardized, are routinely used to index abundance, productivity, and adult survival. efficient models have been developed for each. such monitoring schemes, based on ringing across a number of sites, are perhaps unique in providing this combination of demographic information and make the data particularly amenable to an integrated approach to population modeling. we develop a bayesian approach and a deterministic population model uniting abundance, productivity, and survival. the method is applied to sedge warbler acrocephalus schoenobaenus data from the british trust for ornithology's constant effort sites scheme. the possibility of ""transient"" birds needs to be incorporated within this analysis. we demonstrate how current methodology can efficiently be extended to use additional data from multiple within year recaptures when controlling for transience. supplemental materials for this article are available online."
methods;migration, memory, telemetry;using multistate mark-recapture methods to model adult salmonid migration in an industrialized river;"chinook salmon; memory effects; multistate model; radiotelemetry; release-recapture";ECOLOGICAL MODELLING;"BUCHANAN RA;SKALSKI JR";"a multistate mark-recapture (msmr) model of the adult salmonid migration through the lower columbia river and into the snake river was developed, designed for radiotelemetry detections at dams and tributary mouths. the model focuses on upstream-directed travel, with states determined from observed fish movement patterns indicating directed upstream travel, downstream travel (fallback), and use of non-natal tributaries. the model was used to analyze telemetry data from 846 migrating adult spring-summer chinook salmon (oncorhynchus tshawytscha) tagged in 1996 at bonneville dam on the columbia river. we used the model to test competing hypotheses regarding delayed effects of fallback at dams and visits to tributaries, and to define and estimate migration summary measures. tagged fish had an average probability of 0.755 ((se) over cap = 0.018) of ending migration at a tributary or upstream of lower granite dam on the snake river, and a probability of 0.245 ((se) over cap = 0.018) of unaccountable loss (i.e., mortality or mainstem spawning) between the release site downstream of bonneville dam and lower granite dam. the highest probability of unaccountable loss (0.092; (se) over cap = 0.012) was in the reach between bonneville dam and the dalles dam. study fish used the tributaries primarily as exits from the hydrosystem, and visits to non-natal tributaries had no significant effect on subsequent movement upriver (p = 0.4245). however, fallback behavior had a small effect on subsequent tributary entry and exit (p = 0.0530), with fish using tributaries as resting areas after reascending bonneville dam after fallback. the spatial msmr model developed here can be adapted to address additional questions about the interaction of migrating organisms with their environment. or for the study of migrations in other river systems. (c) 2009 elsevier b.v. all rights reserved."
methods;NA;measuring and modeling the seasonal changes of an urban green treefrog (hyla cinerea) population;"anuran population; capture-mark-recapture; statistical population estimates; stage structured model; model-to-data fits; extinction probability; intra-annual fluctuations";ECOLOGICAL MODELLING;"ACKLEH AS;CARTER J;COLE L;NGUYEN T;MONTE J;PETTIT C";"green treefrogs (hyla cinerea) were captured, marked, measured and released at an urban study site in lafayette, la, during the 2004 and 2005 breeding seasons. a statistical method based on a generalization of the hypergeometric distribution was used to derive weekly time-series estimates of the population sizes. to describe the population dynamics, a stage structured mathematical model was developed and compared to time-series obtained from the weekly population estimates study using a least-squares approach. two fitting experiments were done: (1) using uniform distribution for the birth rate during the breeding season; (2) using a birth rate distributed according to weekly data on frog calling intensity. although both model-to-data fits look very promising during the years 2004 and 2005 and result in similar inherent survivorship rates for the tadpoles, juvenile and adult frogs, the fit that uses the calling data predicts a lower number of tadpoles and frogs in the long term than the one that uses uniform birth distribution. the parameter estimates resulting from these fitting experiments are used in the context of stochastic simulations to derive extinction and persistence probabilities for this population. due to the oscillatory dynamics (with high amplitude) evidenced by the capture-recapture data and corroborated by the model, it is suggested that anuran monitoring efforts should take into account the natural intra-annual variation in population size. (c) 2009 elsevier b.v. all rights reserved."
methods;NA;comparing species assemblages via species accumulation curves;"binomial mixtures; species richness";BIOMETRICS;"MAO CX;LI J";p>comparing species assemblages given incidence-based data is of importance in ecological studies, often done by a visual inspection of estimated species accumulation curves or by an ad hoc use of 95% pointwise confidence bands of these curves. it is shown that comparing species assemblages is a challenging problem. a chi 2 test is proposed. an adjustment using an eigenvalue decomposition is proposed to overcome computational difficulties. the bootstrap method is also suggested to approximate the distribution of the proposed test statistic. the eigenvalue adjusted (eva) chi 2 test and the eva-bootstrap test are assessed by a simulation study. both the eva-chi 2 and the eva-bootstrap tests are applied to a study that involves two woody seedling species assemblages.
methods;IPM, tag loss, temporary emigration;integrated data analysis in the presence of emigration and mark loss;"bayesian inference; combined data; common guillemot (uria aalge); markov chain monte carlo (mcmc); population dynamics; state-space model";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"REYNOLDS TJ;KING R;HARWOOD J;FREDERIKSEN M;HARRIS MP;WANLESS S";integrated data analyses are becoming increasingly common in studies of wild animal populations where two or more separate sources of data contain information about common parameters. these types of analyses provide robust parameter estimates which fully reflect all available information, as well as estimates of parameters that would be unidentifiable in a separate analysis. in this article we present an integrated bayesian analysis of four long-term datasets (counts, two mark-recapture-recovery time series, and productivity) relating to a colony of common guillemots (uria aalge) on the isle of may, southeast scotland. a complication when considering the dynamics of populations of this kind is the unobservable emigration of immature animals. in the analysis of mark-recapture-recovery data, the rate of emigration is frequently confounded with that of mark loss and it is only possible to estimate the product of these parameters. by combining all available data for the isle of may guillemots in an integrated population model, we are able to estimate these parameters separately and thus obtain improved estimates of prerecruitment emigration.
methods;Species richness;a nonparametric lower bound for the number of species shared by multiple communities;"abundance data; community overlap; diversity indices; replicated incidence data; shared species; similarity";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"PAN HY;CHAO A;FOISSNER W";in biological and ecological statistical inference, it is practically useful to provide a lower bound for species richness in a community. chao (1984, 1989) derived a non-parametric lower bound for species richness in a single community. however, there have been no lower bounds proposed in the literature for the number of species shared by multiple communities. based on sample species abundance or replicated incidence records from each of the n communities, we derive in this article a nonparametric approach to constructing a lower bound for the number of species shared by n (n >= 2) communities. the approach is valid for all types of species abundance distributions (for abundance data) or species detection probabilities (for replicated incidence data). variance estimators for the proposed lower bounds are obtained by using typical asymptotic theory. simulation results are reported to examine the performance of the lower bounds. replicated incidence data of ciliate species collected in three areas from namibia, southwest africa, are used for illustration. we also briefly discuss the application of the proposed method to estimate the size of a shared population (i.e., the number of individuals in the intersection of multiple populations) based on capture-recapture data from each population.
methods;Genetic tagging, uncertainty, non-identification;incorporating genotype uncertainty into mark-recapture-type models for estimating abundance using dna samples;"allelic dropout; bayesian inference; mark recapture; markov chain monte carlo; microsatellite; noninvasive genetic sampling; population estimation";BIOMETRICS;"WRIGHT JA;BARKER RJ;SCHOFIELD MR;FRANTZ AC;BYROM AE;GLEESON DM";p>sampling dna noninvasively has advantages for identifying animals for uses such as mark-recapture modeling that require unique identification of animals in samples. although it is possible to generate large amounts of data from noninvasive sources of dna, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. a major source of error is allelic dropout, which is failure of dna amplification at one or more loci. this has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. if errors go undetected and the genotypes are naively used in mark-recapture models, significant overestimates of population size can occur. to avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. it is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. we describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. application is illustrated using data from the european badger (meles meles).
methods;continuous, multilist;a multilevel model for continuous time population estimation;"closed population; continuous time; hierarchical model; population estimation";BIOMETRICS;"SUTHERLAND JM;CASTELLUCCIO P;SCHWARZ CJ";"p>statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). as multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. however, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. existing methods are based on time-to-event analyses with a second step of estimating population size. we propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. a bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. the proposed method is applied to the motivating data to illustrate the methods' applicability. then, a small simulation study explores the performance of the estimator under a variety of conditions. finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation."
methods;Missing data, HMM, covariates, hierarchical;flexible hierarchical mark-recapture modeling for open populations using winbugs;"bayesian; hierarchical modeling; winbugs";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"SCHOFIELD MR;BARKER RJ;MACKENZIE DI";"hierarchical mark-recapture models offer three advantages over classical mark-recapture models: (i) they allow expression of complicated models in terms of simple components; (ii) they provide a convenient way of modeling missing data and latent variables in a way that allows expression of relationships involving latent variables in the model; (iii) they provide a convenient way of introducing parsimony into models involving many nuisance parameters. expressing models using the complete data likelihood we show how many of the standard mark-recapture models for open populations can be readily fitted using the software winbugs. we include examples that illustrate fitting the cormack-jolly-seber model, multi-state and multi-event models, models including auxiliary data, and models including density dependence."
methods;persistence;trap mortality in mark-recapture studies;"mark-recapture; trap mortality; removal; behavior; persistence; likelihood inference";ENVIRONMENTAL AND ECOLOGICAL STATISTICS;"RAMSEY FL;JOHNSTON A";"when animals die in traps in a mark-recapture study, straightforward likelihood inferences are possible in a class of models. the class includes m-0, m-t, and m-b as reported by white et al. (los alamos national laboratory, la-8787-nerp, pp 235, 1982), those that do not involve heterogeneity. we include three markov chain ""persistence"" models and show that they provide good fits in a trapping study of deer mice in the cascade-siskiyou national monument of southern oregon where trapping mortality was high."
methods;memory, HMM;a general framework for modeling memory in capture-recapture data;"canada geese (branta canadensis); hidden markov model; hybrid symbolic-numeric method; memory model; multievent model; parameter redundancy; transients";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"ROUAN L;CHOQUET R;PRADEL R";"in classical multisite capture-recapture (cr) models, the probability of moving to a new location depends only on the current site occupied. yet, it is known that some species, such as canada geese (branta canadensis), have a strong tendency to return to sites visited earlier during their life. to account for this ""phenomenon of memory,"" several authors have considered cr models in which transition probabilities depend not only on the current location of the individuals but also on the sites previously visited. in this article, we clarify the differences between these previous ""memory"" models and provide a general framework for the study of memory using cr data. we illustrate this study with the reanalysis of the movements of canada geese among three wintering sites. this article has supplementary material online."
methods;NA;modelling dispersal with diffusion and habitat selection: analytical results for highly fragmented landscapes;"dispersal; random walk; edge-mediate behavior; diffusion model; conditional occupancy times; metapopulation";ECOLOGICAL MODELLING;"ZHENG CZ;PENNANEN J;OVASKAINEN O";quantifying dispersal is crucial both for understanding ecological population dynamics, and for gaining insight into factors that affect the genetic structure of populations. the role of dispersal becomes pronounced in highly fragmented landscapes inhabited by spatially structured populations. we consider a landscape consisting of a set of habitat patches surrounded by unsuitable matrix, and model dispersal by assuming that the individuals follow a random walk with parameters that may be specific to the habitat type. we allow for spatial variation in patch quality, and account for edge-mediated behavior, the latter meaning that the individuals bias their movement towards the patches when close to an edge between a patch and the matrix. we employ a diffusion approximation of the random walk model to derive analytical expressions for various characteristics of the dispersal process. for example, we derive formulae for the time that an individual is expected to spend in its current patch i, and for the time that it will spend in the matrix, both conditional on the individual hitting next a given patch j before hitting any of the other patches or dying. the analytical formulae are based on the assumptions that the landscape is infinitely large, that the patches are circularly shaped, and that the patches are small compared to interpatch distances. we evaluate the effect of these assumptions by comparing the analytical results to numerical results in a real patch network that violates all of the three assumptions. we then consider a landscape that fulfills the assumptions, and show that in this case the analytical results are in a very good agreement with the numerical results. the results obtained here allow the construction of computationally efficient dispersal models that can be used as components of metapopulation models. (c) 2009 elsevier b.v. all rights reserved.
methods;SSM, multispecies;modelling predation by transient leopard seals for an ecosystem-based management of southern ocean fisheries;"bioenergetics; data augmentation; dispersal; food consumption; hydrurga leptonyx; mark-recapture; mcmc; prey choice; state-space model; sub-antarctic";ECOLOGICAL MODELLING;"FORCADA J;MALONE D;ROYLE JA;STANILAND IJ";"correctly quantifying the impacts of rare apex marine predators is essential to ecosystem-based approaches to fisheries management, where harvesting must be sustainable for targeted species and their dependent predators. this requires modelling the uncertainty in such processes as predator life history, seasonal abundance and movement, size-based predation, energetic requirements, and prey vulnerability. we combined these uncertainties to evaluate the predatory impact of transient leopard seals on a community of mesopredators (seals and penguins) and their prey at south georgia, and assess the implications for an ecosystem-based management. the mesopredators are highly dependent on antarctic krill and icefish, which are targeted by regional fisheries. we used a state-space formulation to combine (1) a mark-recapture open-population model and individual identification data to assess seasonally variable leopard seal arrival and departure dates, numbers, and residency times; (2) a size-based bioenergetic model; and (3) a size-based prey choice model from a diet analysis. our models indicated that prey choice and consumption reflected seasonal changes in leopard seal population size and structure, size-selective predation and prey vulnerability. a population of 104 (90-125) leopard seals, of which 64% were juveniles, consumed less than 2% of the antarctic fur seal pup production of the area (50% of total ingested energy, ie), but ca. 12-16% of the local gentoo penguin population (20% ie). antarctic krill (28% ie) were the only observed food of leopard seal pups and supplemented the diet of older individuals. direct impacts on krill and fish were negligible, but the ""escapement"" due to leopard seal predation on fur seal pups and penguins could be significant for the mackerel icefish fishery at south georgia. these results suggest that: (1) rare apex predators like leopard seals may control, and may depend on, populations of mesopredators dependent on prey species targeted by fisheries: and (2) predatory impacts and community control may vary throughout the predator's geographic range, and differ across ecosystems and management areas, depending on the seasonal abundance of the prey and the predator's dispersal movements. this understanding is important to integrate the predator needs as natural mortality of its prey in models to set prey catch limits for fisheries. reliable estimates of the variability of these needs are essential for a precautionary interpretation in the context of an ecosystem-based management. (c) 2009 elsevier b.v. all rights reserved."
methods;covariates, multilist;a covariate adjustment for zero-truncated approaches to estimating the size of hidden and elusive populations;"population size estimation; capture-recapture; estimation under model misspecification; truncated poisson and binomial likelihood; elusive population";ANNALS OF APPLIED STATISTICS;"BOHNING D;VAN DER HEUDEN PGM";in this paper we consider the estimation of population size from one-source capture-recapture data, that is, a list in which individuals can potentially be found repeatedly and where the question is how many individuals are missed by the list. as a typical example, we provide data from a drug user study in bangkok from 2001 where the list consists of drug users who repeatedly contact treatment institutions. drug users with 1, 2, 3, ... contacts occur, but drug users with zero contacts are not present, requiring the size of this group to be estimated. statistically, these data can be considered as stemming from a zero-truncated count distribution, we revisit an estimator for the population size suggested by zelterman that is known to be robust under potential unobserved heterogeneity. we demonstrate that the zelterman estimator can be viewed as a maximum likelihood estimator for a locally truncated poisson likelihood which is equivalent to a binomial likelihood. this result allows the extension of the zelterman estimator by means of logistic regression to include observed heterogeneity in the form of covariates. we also review ail estimator proposed by chao and explain why we are not able to obtain similar results for this estimator. the zelterman estimator is applied in two case studies, the first a drug user study from bangkok, the second an illegal immigrant study in the netherlands. our results suggest the new estimator should be used, in particular, if substantial unobserved heterogeneity is present.
methods;mixture, heterogeneity;on comparison of mixture models for closed population capture-recapture studies;"bias decomposition; boundary problems; intrinsic bias";BIOMETRICS;"MAO CX;YOU N";"a mixture model is a natural choice to deal with individual heterogeneity in capture-recapture studies. pledger (2000, biometrics 56, 434-442; 2005, biometrics 61, 868-876) advertised the use of the two-point mixture model. dorazio and royle (2003, biometrics 59, 351-364; 2005, biometrics 61, 874-876) suggested that the beta-binomial model has advantages. the controversy is related to the nonidentifiability of the population size (ink, 2003, biometrics 59, 1123-1130) and certain boundary problems. the total bias is decomposed into an intrinsic bias, an approximation bias, and an estimation bias. we propose to assess the approximation bias, the estimation bias, and the variance, with the intrinsic bias excluded when comparing different estimators. the boundary problems in both models and their impacts are investigated. real epidemiological and ecological examples are analyzed."
methods;covariates, distance sampling;a gamma-shaped detection function for line-transect surveys with mark-recapture and covariate data;"contour transect; distance sampling; double-count; horvitz-thompson; population estimate";JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS;"BECKER EF;QUANG PX";we have developed a procedure for estimating animal population size from aerial survey data collected simultaneously by two observers on the same sighting platform. we used a line transect sample design where transects follow elevation contours in mountainous terrain. because our 10 data sets from aerial line transect surveys, conducted over a terrestrial environment, consistently show unimodal detection shapes, we chose a gamma-shaped detection function that is unimodal and admits covariates. we fit models separately to data from each observer, and then used all of the data to estimate the probabilities at the apex of the detection curves. we used a horvitz-thompson estimator to estimate the population size. we illustrate our procedure on a recently collected brown bear data set. the programs and data set used in this work are available in the online supplements.
methods;Mark-resighting, heterogeneity;estimating abundance using mark-resight when sampling is with replacement or the number of marked individuals is unknown;"bowden's estimator; individual heterogeneity; mark-recapture; population size; prairie dog; program mark; program noremark";BIOMETRICS;"MCCLINTOCK BT;WHITE GC;ANTOLIN MF;TRIPP DW";although mark-resight methods can often be a less expensive and less invasive means for estimating abundance in long-term population monitoring programs, two major limitations of the estimators are that they typically require sampling without replacement and/or the number of marked individuals available for resighting to be known exactly. these requirements can often be difficult to achieve. here we address these limitations by introducing the poisson log and zero-truncated poisson log-normal mixed effects models (pne and zpne, respectively). the generalized framework of the models allow the efficient use of covariates in modeling resighting rate and individual heterogeneity parameters, information-theoretic model selection and multimodel inference, and the incorporation of individually unidentified marks. both models may be implemented using standard statistical computing software, but they have also been added to the mark-recapture freeware package program mark. we demonstrate the use and advantages of (z) pne using black-tailed prairie dog data recently collected in colorado. we also investigate the expected relative performance of the models in simulation experiments. compared to other available estimators, we generally found (z) pne to be more precise with little or no loss in confidence interval coverage. with the recent introduction of the logit-normal mixed effects model and (z) pne, a more flexible and efficient framework for mark-resight abundance estimation is now available for the sampling conditions most commonly encountered in these studies.
methods;Hierarchical;estimation of rates of births, deaths, and immigration from mark-recapture data;"births; immigration; mark-recapture; model checking; survival; tits";BIOMETRICS;"O HARA RB;LAMPILA S;ORELL M";the analysis of mark-recapture data is undergoing a period of development and expansion. here we contribute to that by presenting a model which includes both births and immigration, as well as the usual deaths. data come from a long-term study of the willow tit (parus montanus), where we can assume that all births are recorded, and hence immigrants can also be identified as birds captured as adults for the first time. we model the rates of immigration, birth rate per parent, and death rates of juveniles and adults. using a hierarchical model allows us to incorporate annual variation in these parameters. the model is fitted to the data using markov chain monte carlo, as a bayesian analysis. in addition to the model fitting, we also check several aspects of the model fit, in particular whether survival varies with age or immigrant status, and whether capture probability is affected by previous capture history. the latter check is important, as independence of capture histories is a key assumption that simplifies the model considerably. here we find that the capture probability depends strongly on whether the individual was captured in the previous year.
ecological;heterogeneity, assumptions;revisiting the effect of capture heterogeneity on survival estimates in capture-mark-recapture studies: does it matter?;NA;PLOS ONE;"ABADI F;BOTHA A;ALTWEGG R";recently developed capture-mark-recapture methods allow us to account for capture heterogeneity among individuals in the form of discrete mixtures and continuous individual random effects. in this article, we used simulations and two case studies to evaluate the effectiveness of continuously distributed individual random effects at removing potential bias due to capture heterogeneity, and to evaluate in what situation the added complexity of these models is justified. simulations and case studies showed that ignoring individual capture heterogeneity generally led to a small negative bias in survival estimates and that individual random effects effectively removed this bias. as expected, accounting for capture heterogeneity also led to slightly less precise survival estimates. our case studies also showed that accounting for capture heterogeneity increased in importance towards the end of study. though ignoring capture heterogeneity led to a small bias in survival estimates, such bias may greatly impact management decisions. we advocate reducing potential heterogeneity at the sampling design stage. where this is insufficient, we recommend modelling individual capture heterogeneity in situations such as when a large proportion of the individuals has a low detection probability (e.g. in the presence of floaters) and situations where the most recent survival estimates are of great interest (e.g. in applied conservation).
ecological;IPM, assumptions;an assessment of integrated population models: bias, accuracy, and violation of the assumption of independence;"accuracy; bayesian; capture-recapture; dependent data; independent data; individual-based model; leslie matrix model; population survey data; reproductive success; state-space model";ECOLOGY;"ABADI F;GIMENEZ O;ARLETTAZ R;SCHAUB M";understanding population dynamics requires accurate estimates of demographic rates. integrated population models combine demographic and survey data into a single, comprehensive analysis and provide more coherent estimates of vital rates. integrated population models rely on the assumption that different data sets are independent, which is frequently violated in practice. moreover, the precision that call be gained using integrated modeling compared to conventional modeling is only known from empirical studies. the present study used simulation methods to assess how the violation of the assumption of independence affects the statistical properties of the parameter estimators. further, the gains in precision and accuracy from the model were explored under varying sample sizes. for capture-recapture, population survey, and reproductive success, we generated independent and dependent data that were analyzed with integrated and conventional models. we found only a minimal impact of the violation of the assumption of independence on the parameter estimates. furthermore, we observed an overall gain in precision and accuracy when all three data sets were analyzed simultaneously. this was particularly pronounced when the sample size was small. these findings contribute to clearing the way for the application of integrated population models in practice.
ecological;immigration, IPM;estimation of immigration rate using integrated population models;"athene noctua; bayesian; capture-recapture; identifiability; population counts; reproductive success; survival; state-space model";JOURNAL OF APPLIED ECOLOGY;"ABADI F;GIMENEZ O;ULLRICH B;ARLETTAZ R;SCHAUB M";p>1. the dynamics of many populations is strongly affected by immigrants. however, estimating and modelling immigration is a real challenge. in the past, several methods have been developed to estimate immigration rate but they either require strong assumptions or combine in a piecewise manner the results from separate analyses. in most methods the effects of covariates cannot be modelled formally. 2. we developed a bayesian integrated population model which combines capture-recapture data, population counts and information on reproductive success into a single model that estimates and models immigration rate, while directly assessing the impact of environmental covariates. 3. we assessed parameter identifiability by comparing posterior distributions of immigration rates under varying priors, and illustrated the application of the model with long term demographic data of a little owl athene noctua population from southern germany. we further assessed the impact of environmental covariates on immigration. 4. the resulting posterior distributions were insensitive to different prior distributions and dominated by the observed data, indicating that the immigration rate was identifiable. average yearly immigration into the little owl population was 0 center dot 293 (95% credible interval 0 center dot 183-0 center dot 418), which means that ca 0 center dot 3 female per resident female entered the population every year. immigration rate tended to increase with increasing abundance of voles, the main prey of little owls. 5.synthesis and applications. the means to estimate and model immigration is an important step towards a better understanding of the dynamics of geographically open populations. the demographic estimates obtained from the developed integrated population model facilitate population diagnoses and can be used to assess population viability. the structural flexibility of the model should constitute a useful tool for wildlife managers and conservation ecologists.
ecological;multilist;estimating the completeness of death registration: an empirical method;NA;PLOS ONE;"ADAIR T;LOPEZ AD";introduction many national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. this paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level. methods random-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970 +/- 2015 using the global burden of disease 2015 database. predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. models were developed separately for males, females and both sexes. findings all variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (r-squared 0.85). mean error is highest at medium levels of observed completeness. the models show quite close agreement between predicted and observed completeness for populations outside the dataset. there is high concordance with the hybrid death distribution method in brazilian states. uncertainty in the under-five mortality rate, assessed using the dataset and in colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level. conclusions the method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. the method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. the method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.
ecological;GEE, random effects, heterogeneity;estimation of capture probabilities using generalized estimating equations and mixed effects approaches;"closed population; generalized linear models; generalized linear mixed models; heterogeneity; population size estimation";ECOLOGY AND EVOLUTION;"AKANDA MAS;ALPIZAR JARA R";modeling individual heterogeneity in capture probabilities has been one of the most challenging tasks in capture-recapture studies. heterogeneity in capture probabilities can be modeled as a function of individual covariates, but correlation structure among capture occasions should be taking into account. a proposed generalized estimating equations (gee) and generalized linear mixed modeling (glmm) approaches can be used to estimate capture probabilities and population size for capture-recapture closed population models. an example is used for an illustrative application and for comparison with currently used methodology. a simulation study is also conducted to show the performance of the estimation procedures. our simulation results show that the proposed quasi-likelihood based on gee approach provides lower se than partial likelihood based on either generalized linear models (glm) or glmm approaches for estimating population size in a closed capture-recapture experiment. estimator performance is good if a large proportion of individuals are captured. for cases where only a small proportion of individuals are captured, the estimates become unstable, but the gee approach outperforms the other methods.
ecological;Closed kin,  GOF;statistical test for detecting overdispersion in offspring number based on kinship information;"close kin mark recapture; negative binomial distribution; overdispersion; relatedness; sweepstakes reproductive success";POPULATION ECOLOGY;AKITA T;"in this paper, we develop a theory of a new statistic that tests overdispersion in offspring number on the basis of exactly known kinship relationships. the statistic utilizes the sample size and the number of kinship pairs found in a sample, specially the number of mother-offspring (mo) and maternal-half-sibling (mhs) pairs. given a sufficiently large sample size, the statistic proposed in this paper approximately follows a standard-normal distribution under non-overdispersed conditions (poisson's variance). we found that (1) the value of the statistic reasonably indicates whether reproduction is overdispersed at the 5% significance level; (2) the power of the statistic is determined primarily by the balance between the degree of overdispersion and the sample size; (3) in many cases, if the number of kinship pairs can be approximated by a normal distribution, false-positive and false-negative situations can be avoided. the proposed method can detect moderate-weak levels of overdispersion that produce few mhs pairs in a sample because the effect of the population size (which determines the number of detected mhs pairs) is canceled by the detection of the number of mo pairs. once the kinship determination procedure is established, this indirect measurement will be readily applicable to species even with weak overdispersion, expanding the available opportunities for understanding how overdispersion in offspring number affects ecological processes."
ecological;IPM;integrated population models facilitate ecological understanding and improved management decisions;"band recovery; fecundity; harvest surveys; integrated population models; survival; waterfowl management";JOURNAL OF WILDLIFE MANAGEMENT;"ARNOLD TW;CLARK RG;KOONS DN;SCHAUB M";"integrated population models (ipms) represent a formal statistical methodology for combining multiple data sets such as population counts, band recoveries, and fecundity estimates into a single unified analysis with dual objectives: better estimating population size, trajectory, and vital rates; and formally describing the ecological processes that generated these patterns. although ipms have been used in population ecology and fisheries management, their use in wildlife management has been limited. data sets available for north american waterfowl are unprecedented in terms of time span (> 60 years) and geographic coverage, and are especially well-suited for development of ipms that could improve the understanding of population ecology and help guide future harvest and habitat management decisions. in this overview, we illustrate 3 potential benefits of ipms: integration of multiple data sources (i.e., population counts, mark-recapture data, and fecundity estimates), increased precision of parameter estimates, and ability to estimate missing demographic parameters by reanalyzing results from a historical study of canvasbacks (aythya valisineria). drawing from our own published and unpublished work, we demonstrate how ipms could be used to identify the critical vital rates that have had the greatest influence on population change in lesser scaup (aythya affinis), evaluate potential mechanisms of harvest compensation for american black ducks (anas rubripes), or prioritize the most appropriate places to conduct habitat management to benefit northern pintails (anas acuta). integrated population models provide a powerful platform for evaluating alternative hypotheses about population regulation and they have potential to advance the understanding of wildlife ecology and help managers make ecologically based decisions. (c) 2017 the wildlife society."
ecological;Identifiability, assumptions;bias, precision, and parameter redundancy in complex multistate models with unobservable states;"albatross; identifiability; mark-recapture; parameter redundancy; robust design; salamanders; temporary emigration";ECOLOGY;"BAILEY LL;CONVERSE SJ;KENDALL WL";"multistate mark-recapture models with unobservable states can yield unbiased estimators of survival probabilities in the presence of temporary emigration (i.e., in cases where some individuals are temporarily unavailable for capture). in addition, these models permit the estimation of transition probabilities between states, which may themselves be of interest; for example, when only breeding animals are available for capture. however, parameter redundancy is frequently a problem in these models, yielding biased parameter estimates and influencing model selection. using numerical methods, we examine complex multistate mark-recapture models involving two observable and two unobservable states. this model structure was motivated by two different biological systems: one involving island-nesting albatross, and another involving pond-breeding amphibians. we found that, while many models are theoretically identifiable given appropriate constraints, obtaining accurate and precise parameter estimates in practice can be difficult. practitioners should consider ways to increase detection probabilities or adopt robust design sampling in order to improve the properties of estimates obtained from these models. we suggest that investigators interested in using these models explore both theoretical identifiability and possible near-singularity for likely parameter values using a combination of available methods."
ecological;heterogeneity, covariates, random effects;modeling sighting heterogeneity and abundance in spatially replicated multiple-observer surveys;"bayesian analysis; density; individual covariates; koala; multiple-observer; phascolarctos cinereus; population size; random effects";JOURNAL OF WILDLIFE MANAGEMENT;"BARKER RJ;FORSYTH DM;WOOD M";failure to account for dependencies among observers in multiple-observer capture-recapture studies will lead to biased inference yet methods that account for dependencies are poorly developed. we combined hierarchical capture-recapture models and finite sampling theory to infer population abundance of koala (phascolarctos cinereus) in the 5,812-ha mount eccles national park, southeastern australia. we used plot replication to build a bayesian hierarchical model of koala sightings by multiple observers. given the number of koala present on a plot, we modeled counts of koala by a multinomial log-linear model with interaction terms to account for between-observer dependencies. we modeled total numbers using a poisson log-linear model with covariate and random effects. we estimated the number of koala as 1.9 koala/ha (95% ci = 1.6, 2.4). we found evidence of between-observer interactions, highlighting the importance of having at least 3 observers in multiple observer studies. (c) 2014 the wildlife society.
ecological;Individual growth, SSM;combining capture-recapture data and known ages allows estimation of age-dependent survival rates;"age; bayesian; individual growth; otoliths; state-space; survival";ECOLOGY AND EVOLUTION;"BIRD T;LYON J;WOTHERSPOON S;TODD C;TONKIN Z;MCCARTHY M";in many animal populations, demographic parameters such as survival and recruitment vary markedly with age, as do parameters related to sampling, such as capture probability. failing to account for such variation can result in biased estimates of population-level rates. however, estimating age-dependent survival rates can be challenging because ages of individuals are rarely known unless tagging is done at birth. for many species, it is possible to infer age based on size. in capture-recapture studies of such species, it is possible to use a growth model to infer the age at first capture of individuals. we show how to build estimates of age-dependent survival into a capture-mark-recapture model based on data obtained in a capture-recapture study. we first show how estimates of age based on length increments closely match those based on definitive aging methods. in simulated analyses, we show that both individual ages and age-dependent survival rates estimated from simulated data closely match true values. with our approach, we are able to estimate the age-specific apparent survival rates of murray and trout cod in the murray river, australia. our model structure provides a flexible framework within which to investigate various aspects of how survival varies with age and will have extensions within a wide range of ecological studies of animals where age can be estimated based on size.
ecological;Data combination;improving abundance estimation by combining capture-recapture and occupancy data: example with a large carnivore;"abundance; bayesian approach; camera-trapping; lynx lynx; population size; presence signs; site-occupancy";JOURNAL OF APPLIED ECOLOGY;"BLANC L;MARBOUTIN E;GATTI S;ZIMMERMANN F;GIMENEZ O";1. abundance is a key quantity for conservation and management strategies but remains challenging to assess in the field. capture-recapture (cr) methods are often used to estimate abundance while correcting for imperfect detection, but these methods are costly. occupancy, sometimes considered as a surrogate for abundance, is estimated through the collection of presence/absence data and is less costly while allowing gathering of information at a large spatial scale. 2. building on the recent pieces of work on the combination of different data sources, we showed how abundance data can be complemented by presence/absence data and can be analysed conjointly to improve abundance estimates. our approach relies on a hierarchical model that makes explicit the link between the abundance and occupancy state variables while formally accounting for imperfect detection. 3. we used a population of eurasian lynx in france monitored via camera traps and a collection of presence signs as an illustration of our approach. 4. synthesis and applications. we combined capture-recapture and occupancy data and demonstrated that we can efficiently improve abundance estimates. our method can be used by managers when estimates of trends in abundance lack power due to sparse data collected during an intensive survey, by simply integrating data collected during non-systematic survey. furthermore, combining these two sampling procedures makes full use of all available data and allows the development of conservation and management strategies based on precise abundance estimates. overall, the combination of different data sources in an integrated statistical framework has great potential, especially for elusive species. we combined capture-recapture and occupancy data and demonstrated that we can efficiently improve abundance estimates. our method can be used by managers when estimates of trends in abundance lack power due to sparse data collected during an intensive survey, by simply integrating data collected during non-systematic survey. furthermore, combining these two sampling procedures makes full use of all available data and allows the development of conservation and management strategies based on precise abundance estimates. overall, the combination of different data sources in an integrated statistical framework has great potential, especially for elusive species.
ecological;IPM;using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics;"bayesian; brown bear; hierarchical modeling; integrated population model; kodiak island; ursus arctos";ECOLOGY AND EVOLUTION;"BLED F;BELANT JL;VAN DAELE LJ;SVOBODA N;GUSTINE D;HILDERBRAND G;BARNES VG";"current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. we present a general integrated population model (ipm), specifically designed for brown bears (ursus arctos), using three common data types for bear (u. spp.) populations: repeated counts, capture-mark-recapture, and litter size. we considered factors affecting ecological and observation processes for these data. we assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. we then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on kodiak island, alaska, usa. the ipm provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. for the kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. we detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. survival probabilities increased from cubs to yearlings to dependent young 2years old and decreased with litter size. linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. ipms can also reduce data collection efforts by sharing information among agencies and management units. our approach responds to an increasing need in bear populations' management and can be readily adapted to other large carnivores."
ecological;Data combination, telemetry;accounting for tagging-to-harvest mortality in a brownie tag-recovery model by incorporating radio-telemetry data;"auxiliary data; brownie model; harvest rate; hunter behavior; joint model; known-fate; survival rate; tag recovery";ECOLOGY AND EVOLUTION;"BUDERMAN FE;DIEFENBACH DR;CASALENA MJ;ROSENBERRY CS;WALLINGFORD BD";"the brownie tag-recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the brownie estimator to be negatively biased. alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. we developed a joint model to estimate harvest and annual survival rates that combines known-fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward-tagged animals in a brownie tag-recovery model. we evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. tagging-to-harvest survival rates from >20 individuals with radio transmitters combined with 50-100 reward tags resulted in an unbiased and precise estimator of harvest rates. in addition, the joint model can test whether transmitters affect an individual's probability of being harvested. we illustrate application of the model using data from wild turkey, meleagris gallapavo, to estimate harvest rates, and data from white-tailed deer, odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. the joint known-fate tag-recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. in addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior."
ecological;SSM, random effects;a hierarchical bayesian approach to multi-state mark-recapture: simulations and applications;"departure; mark; migration; movement; random effects; songbird; state-space; stopover; survival; winbugs";JOURNAL OF APPLIED ECOLOGY;"CALVERT AM;BONNER SJ;JONSEN ID;FLEMMING JM;WALDE SJ;TAYLOR PD";mark-recapture models are valuable for assessing diverse demographic and behavioural parameters, yet the precision of traditional estimates is often constrained by sparse empirical data. bayesian inference explicitly recognizes estimation uncertainty, and hierarchical bayes has proven particularly useful for dealing with sparseness by combining information across data sets. we developed a general hierarchical bayesian multi-state mark-recapture model, tested its performance on simulated data sets and applied it to real ecological data on stopovers by migratory birds. our hierarchical model performed well in terms of both precision and accuracy of parameters when tested with simulated data of varying quality (sample size, capture and survivorship probabilities). it also provided more precise and accurate parameter estimates than a non-hierarchical model when data were sparse. a specific version of the model, designed for estimation of daily transience and departure of migratory birds at a mid-route stopover, was applied to 11 years of autumn migration data from atlantic canada. hierarchical estimates of departure and transience were more precise than those derived from parallel non-hierarchical and frequentist methods, and indicated that inter-annual variability in parameters suggested by these other methods was largely due to sampling error. synthesis and applications. estimates of demographic parameters, often derived from mark-recapture studies, provide the basis for evaluating the status of species at risk, for developing conservation and management strategies and for evaluating the results of current protocols. the hierarchical bayesian multi-state mark-recapture model presented here permits partitioning of complex parameter variation across space or time, and the simultaneous analysis of multiple data sets results in a marked increase in the precision of estimates derived from sparse capture data. its structural flexibility should make it a valuable tool for conservation ecologists and wildlife managers.
ecological;Dispersal, HMM;estimating dispersal in spatiotemporally variable environments using multievent capture-recapture modeling;"capture-recapture; dispersal; dynamic landscapes; metapopulation; multi-event models; survival";ECOLOGY;"CAYUELA H;PRADEL R;JOLY P;BONNAIRE E;BESNARD A";dispersal is a key process in ecological and evolutionary dynamics. spatiotemporal variation in habitat availability and characteristics has been suggested to be one of the main cause involved in dispersal evolution and has a strong influence on metapopulation dynamics. in recent decades, the study of dispersal has led to the development of capture-recapture (cr) models that allow movement between sites to be quantified, while handling imperfect detection. for studies involving numerous recapture sites, lagrange et al. (2014) proposed a multievent cr model that allows dispersal to be estimated while omitting site identity by distinguishing between individuals that stay and individuals that move. more recently, cayuela et al. (2017) extended this model to allow survival and dispersal probabilities to differ for the different types of habitat represented by several sites within a study area. yet in both of these modeling systems, the state of sites is assumed to be static over time, which is not a realistic assumption in dynamic landscapes. for that purpose, we generalized the multievent cr model proposed by cayuela et al. (2017) to allow the estimation of dispersal, survival and recapture probabilities when a site may appear or disappear over time (model 1) or when the characteristics of a site fluctuate over space and time (model 2). this paper first presents these two new modeling systems, and then provides an illustration of their efficacy and usefulness by applying them to simulated cr data and data collected on two metapopulations of amphibians. model 1 was tested using cr data recorded on a metapopulation of yellow-bellied toads (bombina variegata). in this first empirical case, we examined whether the drying-out dynamics of ponds and the past dispersal status of an individual might affect dispersal behavior. our study revealed that the probability of facultative dispersal (i.e., from a pond group that remained available/flooded) fluctuated between years and was higher in individuals that had previously dispersed. model 2 was tested using cr data collected on a metapopulation of great crested newts (triturus cristatus). in this second empirical example, we investigated whether the density of alpine newts (ichthyosaura alpestris), a potential competitor, might affect the dispersal and survival of the crested newt. our study revealed that the departure rate was lower in ponds with a high density of heterospecifics than in ponds with a low density of heterospecifics at both inter-annual and intra-annual scales. moreover, annual survival was slightly higher in ponds with a high density of heterospecifics. overall, our findings indicate that these multievent cr models provide a highly flexible means of modeling dispersal in dynamic landscapes.
ecological;heneterogeneity, GOF, random effects;use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates;"bayesian inference; dynamic heterogeneity; fixed heterogeneity; individual variation; leptonychotes weddellii; model checking; posterior predictive checking; state-space models";ECOLOGY AND EVOLUTION;"CHAMBERT T;ROTELLA JJ;HIGGS MD";the investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark-recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of tuljapurkar et al. (ecology letters, 12, 93, 2009) on dynamic heterogeneity. model-selection approaches based on information criteria or bayes factors have been used to address this question. here, we suggest that, in addition to model-selection, model-checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. posterior predictive checking is a straightforward and flexible approach for performing model checks in a bayesian framework that is based on comparisons of observed data to model-generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. if discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. we illustrate this approach using analyses of vital rates with long-term mark-recapture data for weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest.
ecological;Temporary emigration, N-mixture;inference about density and temporary emigration in unmarked populations;"chestnut-sided warbler; dendroica pensylvanica; detection probability; hierarchical models; n-mixture model; population density; spot-mapping; temporary emigration; unmarked populations; white mountain national forest; usa";ECOLOGY;"CHANDLER RB;ROYLE JA;KING DI";"few species are distributed uniformly in space, and populations of mobile organisms are rarely closed with respect to movement, yet many models of density rely upon these assumptions. we present a hierarchical model allowing inference about the density of unmarked populations subject to temporary emigration and imperfect detection. the model can be fit to data collected using a variety of standard survey methods such as repeated point counts in which removal sampling, double-observer sampling, or distance sampling is used during each count. simulation studies demonstrated that parameter estimators are unbiased when temporary emigration is either ""completely random"" or is determined by the size and location of home ranges relative to survey points. we also applied the model to repeated removal sampling data collected on chestnut-sided warblers (dendroica pensylvancia) in the white mountain national forest, usa. the density estimate from our model, 1.09 birds/ha, was similar to an estimate of 1.11 birds/ha produced by an intensive spot-mapping effort. our model is also applicable when processes other than temporary emigration affect the probability of being available for detection, such as in studies using cue counts. functions to implement the model have been added to the r package unmarked."
ecological;Disease ecology, HMM, heterogeneity, mixture;estimating transitions between states using measurements with imperfect detection: application to serological data;"black-legged kittiwake; borrelia burdgorferi; continuous measurement; eco-epidemiology; hidden markov model; immunological assay; mixture; rissa tridactyla; stable-isotope ratio";ECOLOGY;"CHOQUET R;CARRIE C;CHAMBERT T;BOULINIER T";classifying the states of an individual and quantifying transitions between states are crucial while modeling animal behavior, movement, and physiologic status. when these states are hidden or imperfectly known, it is particularly convenient to relate them to appropriate quantitative measurements taken on the individual. this task is, however, challenging when quantitative measurements are not available at each sampling occasion. for capture-recapture data, various ways of incorporating such non-discrete information have been used, but they are either ad hoc and/or use a fraction of the available information by relying on a priori thresholds to assign individual states. here we propose assigning discrete states based on a continuous measurement, and then modeled survival and transition probabilities based on these assignments. the main advantage of this new approach is that a more informative use of the non-discrete information is done. as an illustrative working example, we applied this approach to eco-epidemiological data collected across a series of years in which individuals of a long-lived seabird, the black-legged kittiwake (rissa tridactyla), could either be visually detected or physically recaptured and blood sampled for subsequent immunological analyses. we discuss how this approach opens many perspectives in eco-epidemiology, but also more broadly, in population ecology.
ecological;SCR, clustered sampling;comparing clustered sampling designs for spatially explicit estimation of population density;"bear; cluster; density; mark-recapture; simulation; spatially explicit; ursus";POPULATION ECOLOGY;CLARK JD;spatially explicit capture-recapture methods do not assume that animals have equal access to sampling devices (e.g., detectors), which allows for gaps in the sampling extent and nonuniform (e.g., clustered) sampling designs. however, the performance (i.e., relative root mean squared error [rrmse], confidence interval coverage, relative bias and relative standard error) of clustered detector arrays has not been thoroughly evaluated. i used simulations to evaluate the performance of various detector and cluster spacings, cluster configurations (i.e., number of detectors arranged in a square grid), sampling extents and number of sampling occasions for estimating population density, the relationship between detection rate and distance to a detector from the animal's center of activity (sigma) and base detection rates, using american black bears (ursus americanus) as a case study. my simulations indicated that a wide range of detector configurations can provide reliable estimates if spacing between detectors in clusters is 1 sigma and 3 sigma. a number of cluster configurations and occasion lengths produced estimates that were unbiased, resulted in good spatial coverage, and were relatively precise. moreover, increasing the duration of sampling, establishing large study areas, increasing detection rates and spacing clusters so that cross-cluster sampling of individuals can occur could help ameliorate deficiencies in the detector layout. these results have application for a wide array of species and sampling methods (e.g., dna sampling, camera trapping, mark-resight and search-encounter) and suggest that clustered sampling can significantly reduce the effort necessary to provide reliable estimates of population density across large spatial extents that previously would have been infeasible with nonclustered sampling designs.
ecological;N-mixture, distance sampling;accounting for imperfect detection of groups and individuals when estimating abundance;"abundance; aerial surveys; distance sampling; double observer; grouped animals; mark-recapture-distance-sampling; n-mixture models";ECOLOGY AND EVOLUTION;"CLEMENT MJ;CONVERSE SJ;ROYLE JA";"if animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; mrds). when animals reside in groups, however, the assumption of independent detection is violated. in this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. however, this assumption is often unsupported. we introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. the estimator combines an mrds model with an n-mixture model to account for imperfect detection of individuals. the new mrds-nmix model requires the same data as an mrds model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. we extend the model to situations in which detection of individuals within groups declines with distance. we simulated 12 data sets and used bayesian methods to compare the performance of the new mrds-nmix model to an mrds model. abundance estimates generated by the mrds-nmix model exhibited minimal bias and nominal coverage levels. in contrast, mrds abundance estimates were biased low and exhibited poor coverage. many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. we believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size."
ecological;age, covariates, left-truncation, right-censoring;bayesian inference on age-specific survival for censored and truncated data;"age-specific survival; bayesian inference; capture-recapture; recovery data; maximum likelihood";JOURNAL OF ANIMAL ECOLOGY;"COLCHERO F;CLARK JS";1. traditional estimation of age-specific survival and mortality rates in vertebrates is limited to individuals with known age. although this subject has been studied extensively using effective capturerecapture and capturerecovery models, inference remains challenging because of large numbers of incomplete records (i.e. unknown age of many individuals) and because of the inadequate duration of the studies. 2. here, we present a hierarchical model for capture-recapture/recovery (crr) data sets with large proportions of unknown times of birth and death. the model uses a bayesian framework to draw inference on population-level age-specific demographic rates using parametric survival functions and applies this information to reconstruct times of birth and death for individuals with unknown age. 3. we simulated a set of crr data sets with varying study span and proportions of individuals with known age, and varying recapture and recovery probabilities. we used these data sets to compare our method to a traditional crr model, which requires knowledge of individual ages. subsequently, we applied our method to a subset of a long-term crr data set on soay sheep. 4. our results show that this method performs better than the common crr model when sample sizes are low. still, our model is sensitive to the choice of priors with low recapture probability and short studies. in such cases, priors that overestimate survival perform better than those that underestimate it. also, the model was able to estimate accurately ages at death for soay sheep, with an average error of 0.94 years and to identify differences in mortality rate between sexes. 5. although many of the problems in the estimation of age-specific survival can be reduced through more efficient sampling schemes, most ecological data sets are still sparse and with a large proportion of missing records. thus, improved sampling needs still to be combined with statistical models capable of overcoming the unavoidable limitations of any fieldwork. we show that our approach provides reliable estimates of parameters and unknown times of birth and death even with the most incomplete data sets while being flexible enough to accommodate multiple recapture probabilities and covariates.
ecological;HMM, disease ecology;multistate capture-recapture analysis under imperfect state observation: an application to disease models;"capture-recapture; disease model; misclassification; multievent model; multistate model; transition probability; unobservable states";JOURNAL OF APPLIED ECOLOGY;"CONN PB;COOCH EG";multistate capture-recapture models are frequently used to estimate the survival and state transition parameters needed to parameterize stage-structured population models, tools that are important for conservation and management. typically, such models assume that all encountered individuals can be assigned to a particular state without error or ambiguity, a requirement which is difficult to meet in practice. model extensions to relax this assumption would increase the richness of ecological data sets available for estimating life-history and stage-transition parameters with multistate models. one relatively common analytical approach when confronted with ambiguity in state determination is to censor all encounters where the state of an animal cannot be ascertained. here, we present an alternative approach, which uses a hidden markov (or multievent) modelling framework that can incorporate data from encounters of unknown state. using simulation, we show that our approach leads to estimators of state-specific survival and transition probabilities that are more precise, and sometimes considerably so, than methods based on censoring. we demonstrate our approach using field data from a study of the dynamics of conjunctivitis in the house finch carpodacus mexicanus muller. a fundamental challenge in modelling disease dynamics involves the estimation of the rates of entry and exit from one or more disease states, which can be complicated when disease state is uncertain. we show that incorporating data from unknown states made substantial improvements to parameter precision. synthesis and applications. missing or incomplete records are an unfortunate but common feature of many ecological field studies, often diminishing the quality and quantity of data. our approach of treating state as a hidden markov process allows such records to be used, increasing the precision of survival and state transition parameters in multistate mark-recapture studies. our approach is more general than other approaches in the literature, and does not require specialized sampling designs or ancillary information to inform state assignment. we suggest that ecologists consider using this modelling approach instead of censoring records whenever state information is missing.
ecological;N-mixture;time-for-space substitution in n-mixture modeling and population monitoring;"hierarchical models; monitoring; population dynamics; population size; species decline; trend";JOURNAL OF WILDLIFE MANAGEMENT;"COSTA A;ONETO F;SALVIDIO S";"population size is a fundamental state variable in ecology, and the analysis of temporal variation in abundance (i.e., detection of trends) is a prime objective in wildlife monitoring. however, population abundance cannot be directly observed because part of the population remains undetected and methods that account for imperfect detection are often not used. capture-mark-recapture approaches give reliable estimates of abundance but are time- and effort-consuming. in the last decade, the application of hierarchical, or n-mixture, models that use repeated counts of unmarked animals seem to give great advantages in the estimation of population size. hierarchical models require repeated surveys at multiple sites, but sometimes only data obtained for a single site in successive years are available. we applied the time-for-space substitution implemented within the n-mixture modeling framework to estimate population size and evaluate the dynamics of the endangered european leaf-toed gecko (euleptes europaea) surveyed >20 years. we compared these results with capture-mark-recapture estimates obtained from the same population and over the same time period. estimates and trends were comparable and both methods indicated similar population declines; moreover, n-mixture modeling indicated temperature affected detection. therefore, the application of the time-for-space substitution in hierarchical modeling seems valuable and may be useful in species monitoring and conservation. (c) 2019 the wildlife society."
ecological;heterogeneity, N-mixture, assumptions;estimating abundance and population trends when detection is low and highly variable: a comparison of three methods for the hermann's tortoise;"abundance estimation; activity modeling; capturerecapture; distance sampling; hermann's tortoise; n-mixture; power analysis; radiotelemetry; testudo hermanni";JOURNAL OF WILDLIFE MANAGEMENT;"COUTURIER T;CHEYLAN M;BERTOLERO A;ASTRUC G;BESNARD A";assessing population trends is a basic prerequisite to carrying out adequate conservation strategies. selecting an appropriate method to monitor animal populations can be challenging, particularly for low-detection species such as reptiles. this study compares 3 detection-corrected abundance methods (capturerecapture, distance sampling, and n-mixture) used to assess population size of the threatened hermann's tortoise. we used a single dataset of 432 adult tortoise observations collected at 118 sampling sites in the plaine des maures, southeastern france. we also used a dataset of 520 tortoise observations based on radiotelemetry data collected from 10 adult females to estimate and model the availability (g0) needed for distance sampling. we evaluated bias for n-mixture and capturerecapture, by using simulations based on different values of detection probabilities. finally, we conducted a power analysis to estimate the ability of the 3 methods to detect changes in hermann's tortoise abundances. the abundance estimations we obtained using distance sampling and n-mixture models were respectively 1.75 and 2.19 times less than those obtained using the capturerecapture method. our results indicated that g0 was influenced by temperature variations and can differ for the same temperature on different days. simulations showed that the n-mixture models provide unstable estimations for species with detection probabilities <0.5, whereas capturerecapture estimations were unbiased. power analysis showed that none of the 3 methods were precise enough to detect slow population changes. we recommend that great care should be taken when implementing monitoring designs for species with large variation in activity rates and low detection probabilities. although n-mixture models are easy to implement, we would not recommend using them in situations where the detection probability is very low at the risk of providing biased estimates. among the 3 methods allowing estimation of tortoise abundances, capturerecapture should be preferred to assess population trends. (c) 2013 the wildlife society.
ecological;Reporting rates, migration, tag loss;estimating exploitation rates of migrating yellowtail flounder (limanda ferruginea) using multistate mark-recapture methods incorporating tag loss and variable reporting rates;NA;CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES;"COWEN L;WALSH SJ;SCHWARZ CJ;CADIGAN N;MORGAN J";multistate mark-recapture models can be used to model migration through stratification of the study area into states (location). however, the incorporation of both tag loss and reporting rates is new to the multistate paradigm. we develop a migration model for fish that incorporates tag loss and reporting rates but has as its primary purpose the modelling of exploitation and natural mortality rates. this model is applied to a 2000-2004 yellowtail flounder (limanda ferruginea) tagging study on the grand bank of newfoundland, canada. we found that exploitation rates varied over both location and years, ranging from 0.000 to 0.047. migration into the centre of the grand bank (state 2) was three times higher than migration out. the estimate of the instantaneous annual natural mortality rate was 0.256, which is equivalent to an annual survival rate of 0.880. we describe how these mortality estimates will be quite valuable in specifying an assessment model for this stock.
ecological;Batch marking;a comparison of abundance estimates from extended batch-marking and jolly-seber-type experiments;"abundance; batch mark; mark-recapture; open population";ECOLOGY AND EVOLUTION;"COWEN LLE;BESBEAS P;MORGAN BJT;SCHWARZ CJ";"little attention has been paid to the use of multi-sample batch-marking studies, as it is generally assumed that an individual's capture history is necessary for fully efficient estimates. however, recently, huggins etal. () present a pseudo-likelihood for a multi-sample batch-marking study where they used estimating equations to solve for survival and capture probabilities and then derived abundance estimates using a horvitz-thompson-type estimator. we have developed and maximized the likelihood for batch-marking studies. we use data simulated from a jolly-seber-type study and convert this to what would have been obtained from an extended batch-marking study. we compare our abundance estimates obtained from the crosbie-manly-arnason-schwarz (cmas) model with those of the extended batch-marking model to determine the efficiency of collecting and analyzing batch-marking data. we found that estimates of abundance were similar for all three estimators: cmas, huggins, and our likelihood. gains are made when using unique identifiers and employing the cmas model in terms of precision; however, the likelihood typically had lower mean square error than the pseudo-likelihood method of huggins etal. (). when faced with designing a batch-marking study, researchers can be confident in obtaining unbiased abundance estimators. furthermore, they can design studies in order to reduce mean square error by manipulating capture probabilities and sample size."
ecological;SSM, structural équation modelling, covariates;testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling;"capture-recapture models; evolutionary ecology; individual heterogeneity; life history trade-offs; selection gradient analyses; state-space models; structural equation models";ECOLOGY;"CUBAYNES S;DOUTRELANT C;GREGOIRE A;PERRET P;FAIVRE B;GIMENEZ O";studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. it is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. to cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (cr-sem) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. we employ markov chain monte carlo sampling in a bayesian framework to (1) estimate model parameters, (2) implement a model selection procedure to evaluate competing hypotheses about causal mechanisms, and (3) assess the fit of models to data using posterior predictive checks. we illustrate the value of our approach using two case studies on wild bird populations. we first show that cr-sem can be useful to quantify the action of selection on a set of phenotypic traits with an analysis of selection gradients on morphological traits in common blackbirds (turdus merula). in a second case study on blue tits (cyanistes caeruleus), we illustrate the use of cr-sem to study evolutionary trade-offs in the wild, while accounting for varying environmental conditions.
ecological;HMM, dependence;a multievent approach to estimating pair fidelity and heterogeneity in state transitions;"great tit; heterogeneous recapture rates; multievent mark-recapture modeling; survival";ECOLOGY AND EVOLUTION;"CULINA A;LACHISH S;PRADEL R;CHOQUET R;SHELDON BC";fidelity rates of pair-bonded individuals are of considerable interest to behavioral and population biologists as they can influence population structure, mating rates, population productivity, and gene flow. estimates of fidelity rates calculated from direct observations of pairs in consecutive breeding seasons may be biased because (i) individuals that are not seen are assumed to be dead, (ii) variation in the detectability of individuals is ignored, and (iii) pair status must be known with certainty. this can lead to a high proportion of observations being ignored. this approach also restricts the way variation in fidelity rates for different types of individuals, or the covariation between fidelity and other vital rates (e.g., survival) can be analyzed. in this study, we develop a probabilistic multievent capture-mark-recapture (mecmr) modeling framework for estimating pair fidelity rates that accounts for imperfect detection rates and capture heterogeneity, explicitly incorporates uncertainty in the assessment of pair status, and allows estimates of state-dependent survival and fidelity rates to be obtained simultaneously. we demonstrate the utility of our approach for investigating patterns of fidelity in pair-bonded individuals, by applying it to 30years of breeding data from a wild population of great tits parus major linnaeus. results of model selection supported state-dependent recapture, survival, and fidelity rates. recapture rates were higher for individuals breeding with their previous partner than for those breeding with a different partner. faithful birds that were breeding with the same partner as in the previous breeding season (i.e., at t-1) experienced substantially higher survival rates (between t and t+1) and were also more likely to remain faithful to their current partner (i.e., to remain in the faithful state at t+1). first year breeders were more likely to change partner than older birds. these findings imply that traditional estimates, which do not account for state-dependent parameters, may be both inaccurate and biased, and hence, inferences based on them may conceal important biological effects. this was demonstrated in the analysis of simulated capture histories, which showed that our mecmr model was able to estimate state-dependant survival and pair fidelity rates in the face of varying state-dependant recapture rates robustly, and more accurately, than the traditional method. in addition, this new modeling approach provides a statistically rigorous framework for testing hypothesis about the causes and consequences of fidelity to a partner for natural populations. the novel modeling approach described here can readily be applied, either in its current form or via extension, to other populations and other types of dyadic interactions (e.g., between nonpaired individuals, such as parent-offspring relationships, or between individuals and locations, such as nest-site fidelity).
ecological;SCR, bioacoustic;bird population density estimated from acoustic signals;"acoustic localization; bird counting; density estimation; microphone array; passive acoustic methods; sound attenuation; spatially explicit capture-recapture";JOURNAL OF APPLIED ECOLOGY;"DAWSON DK;EFFORD MG";"p>1. many animal species are detected primarily by sound. although songs, calls and other sounds are often used for population assessment, as in bird point counts and hydrophone surveys of cetaceans, there are few rigorous methods for estimating population density from acoustic data. 2. the problem has several parts - distinguishing individuals, adjusting for individuals that are missed, and adjusting for the area sampled. spatially explicit capture-recapture (secr) is a statistical methodology that addresses jointly the second and third parts of the problem. we have extended secr to use uncalibrated information from acoustic signals on the distance to each source. 3. we applied this extension of secr to data from an acoustic survey of ovenbird seiurus aurocapilla density in an eastern us deciduous forest with multiple four-microphone arrays. we modelled average power from spectrograms of ovenbird songs measured within a window of 0 center dot 7 s duration and frequencies between 4200 and 5200 hz. 4. the resulting estimates of the density of singing males (0 center dot 19 ha-1 se 0 center dot 03 ha-1) were consistent with estimates of the adult male population density from mist-netting (0 center dot 36 ha-1 se 0 center dot 12 ha-1). the fitted model predicts sound attenuation of 0 center dot 11 db m-1 (se 0 center dot 01 db m-1) in excess of losses from spherical spreading. 5.synthesis and applications. our method for estimating animal population density from acoustic signals fills a gap in the census methods available for visually cryptic but vocal taxa, including many species of bird and cetacean. the necessary equipment is simple and readily available; as few as two microphones may provide adequate estimates, given spatial replication. the method requires that individuals detected at the same place are acoustically distinguishable and all individuals vocalize during the recording interval, or that the per capita rate of vocalization is known. we believe these requirements can be met, with suitable field methods, for a significant number of songbird species."
ecological;HMM, dispersal;assessing the effect on survival of natal dispersal using multistate capture-recapture models;"dispersal costs; european hare; france; lepus europaeus; multistate capture-recapture models; natal dispersal; recoveries; telemetry";ECOLOGY;"DEVILLARD S;BRAY Y";despite their crucial importance in understanding and modeling of the evolution of natal dispersal, it is still difficult to reliably estimate the costs of natal dispersal. we have developed a multistate capture-recapture model, mixing telemetry and recoveries, to simultaneously estimate natal dispersal probability, survival probability of dispersers vs. philopatric individuals, and the proportions of individuals dying from different causes. by applying this model to the european hare (lepus europaeus), we show that dispersing juveniles suffer from a considerably higher mortality rate during their first post-weaning year compared to philopatric juveniles, due to both hunters and predators. we emphasize the usefulness and reliability of our model in the broader context of studies of natal dispersal costs, as well as the evolutionary and management implications of such a dispersal cost in declining european hare populations.
ecological;Data combination, telemetry;increased flexibility for modeling telemetry and nest-survival data using the multistate framework;"black duck; known fate; lynx; mallard; mark-recapture; multistate; nest survival; survival; telemetry";JOURNAL OF WILDLIFE MANAGEMENT;"DEVINEAU O;KENDALL WL;DOHERTY PF;SHENK TM;WHITE GC;LUKACS PM;BURNHAM KP";although telemetry is one of the most common tools used in the study of wildlife, advances in the analysis of telemetry data have lagged compared to progress in the development of telemetry devices. we demonstrate how standard known-fate telemetry and related nest-survival data analysis models are special cases of the more general multistate framework. we present a short theoretical development, and 2 case examples regarding the american black duck and the mallard. we also present a more complex lynx data analysis. although not necessary in all situations, the multistate framework provides additional flexibility to analyze telemetry data, which may help analysts and biologists better deal with the vagaries of real-world data collection. (c) 2014 the wildlife society.
ecological;N-mixture, disease ecology;disease-structured n-mixture models: a practical guide to model disease dynamics using count data;"bayesian; dail-madsen model; disease ecology; emerging infectious diseases; generalized n-mixture model; hierarchical models; host-pathogen interaction; mark-recapture models; multistate models; occupancy model";ECOLOGY AND EVOLUTION;"DIRENZO GV;CHE CASTALDO C;SAUNDERS SP;GRANT EHC;ZIPKIN EF";obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. while multistate mark-recapture models can produce high-quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark-recapture techniques. recently developed n-mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. n-mixture models are a type of state-space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). the analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. we review the challenges of modeling disease dynamics and describe how n-mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. we also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease-structured n-mixture models, and the integration of other data types with count data to inform disease dynamics. managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. at a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.
ecological;SCR, single-catch;a spatially explicit capture-recapture estimator for single-catch traps;"density estimation; single-catch trap likelihood; spatially explicit capture-recapture; statistical methods";ECOLOGY AND EVOLUTION;"DISTILLER G;BORCHERS DL";single-catch traps are frequently used in live-trapping studies of small mammals. thus far, a likelihood for single-catch traps has proven elusive and usually the likelihood for multicatch traps is used for spatially explicit capture-recapture (secr) analyses of such data. previous work found the multicatch likelihood to provide a robust estimator of average density. we build on a recently developed continuous-time model for secr to derive a likelihood for single-catch traps. we use this to develop an estimator based on observed capture times and compare its performance by simulation to that of the multicatch estimator for various scenarios with nonconstant density surfaces. while the multicatch estimator is found to be a surprisingly robust estimator of average density, its performance deteriorates with high trap saturation and increasing density gradients. moreover, it is found to be a poor estimator of the height of the detection function. by contrast, the single-catch estimators of density, distribution, and detection function parameters are found to be unbiased or nearly unbiased in all scenarios considered. this gain comes at the cost of higher variance. if there is no interest in interpreting the detection function parameters themselves, and if density is expected to be fairly constant over the survey region, then the multicatch estimator performs well with single-catch traps. however if accurate estimation of the detection function is of interest, or if density is expected to vary substantially in space, then there is merit in using the single-catch estimator when trap saturation is above about 60%. the estimator's performance is improved if care is taken to place traps so as to span the range of variables that affect animal distribution. as a single-catch likelihood with unknown capture times remains intractable for now, researchers using single-catch traps should aim to incorporate timing devices with their traps.
ecological;SCR, estimation;bayes and empirical bayes estimators of abundance and density from spatial capture-recapture data;NA;PLOS ONE;DORAZIO RM;in capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. in these circumstances spatially explicit capture-recapture (secr) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. in this paper i propose two bayesian secr models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. in formulating these models i used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. this separation of ecological and observational processes allowed me to derive a formal connection between bayes and empirical bayes estimators of population abundance that has not been established previously. i showed that this connection applies to every poisson point-process model of secr data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. to illustrate results of both classical and bayesian methods of analysis, i compared bayes and empirical bayes esimates of abundance and density using recaptures from simulated and real populations of animals. real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. in the datasets i analyzed, classical and bayesian methods provided similar - and often identical - inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.
ecological;SCR, point process, continuous;a hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders;NA;PLOS ONE;"DORAZIO RM;KARANTH KU";motivation several spatial capture-recapture (scr) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. instead most scr models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. both practices make inefficient use of potentially important information in the data. model and data analysis we developed a hierarchical scr model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. we illustrated this scr model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the nagarahole tiger reserve in india. we also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data. benefits our approach provides three important benefits: first, it exploits all of the information in scr data obtained using continuous-time recorders. second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. we illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. our continuous-time scr model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. we plan to extend this model to estimate the population dynamics of animals detected during multiple years of scr surveys.
ecological;telemetry, bioacoustics, data combination;integrating acoustic telemetry into mark-recapture models to improve the precision of apparent survival and abundance estimates;"cjs; js; popan; broadnose sevengill sharks; capture-recapture; population estimation";OECOLOGIA;"DUDGEON CL;POLLOCK KH;BRACCINI JM;SEMMENS JM;BARNETT A";"capture-mark-recapture models are useful tools for estimating demographic parameters but often result in low precision when recapture rates are low. low recapture rates are typical in many study systems including fishing-based studies. incorporating auxiliary data into the models can improve precision and in some cases enable parameter estimation. here, we present a novel application of acoustic telemetry for the estimation of apparent survival and abundance within capture-mark-recapture analysis using open population models. our case study is based on simultaneously collecting longline fishing and acoustic telemetry data for a large mobile apex predator, the broadnose sevengill shark (notorhynchus cepedianus), at a coastal site in tasmania, australia. cormack-jolly-seber models showed that longline data alone had very low recapture rates while acoustic telemetry data for the same time period resulted in at least tenfold higher recapture rates. the apparent survival estimates were similar for the two datasets but the acoustic telemetry data showed much greater precision and enabled apparent survival parameter estimation for one dataset, which was inestimable using fishing data alone. combined acoustic telemetry and longline data were incorporated into jolly-seber models using a monte carlo simulation approach. abundance estimates were comparable to those with longline data only; however, the inclusion of acoustic telemetry data increased precision in the estimates. we conclude that acoustic telemetry is a useful tool for incorporating in capture-mark-recapture studies in the marine environment. future studies should consider the application of acoustic telemetry within this framework when setting up the study design and sampling program."
ecological;continuous, dispersal;estimating disperser abundance using open population models that incorporate data from continuous detection passive integrated transponder arrays;NA;CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES;"DZUL MC;YACKULIC CB;KORMAN J";autonomous passive integrated transponder (pit) tag antenna systems continuously detect individually marked organisms at one or more fixed points over long time periods. estimating abundance using data from autonomous antennas can be challenging because these systems do not detect unmarked individuals. here we pair pit antenna data from a tributary with mark-recapture sampling data in a mainstem river to estimate the number of fish moving from the mainstem to the tributary. we then use our model to estimate abundance of non-native rainbow trout (oncorhynchus mykiss) that move from the colorado river to the little colorado river, the latter of which is important spawning and rearing habitat for federally endangered humpback chub (gila cypha). we estimate that 226 rainbow trout (95% confidence interval: 127-370) entered the little colorado river from october 2013 to april 2014. we discuss the challenges of incorporating detections from autonomous pit antenna systems into mark-recapture population models, particularly in regards to using information about spatial location to estimate movement and detection probabilities.
ecological;covariates;incorporating temporal heterogeneity in environmental conditions into a somatic growth model;NA;CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES;"DZUL MC;YACKULIC CB;KORMAN J;YARD MD;MUEHLBAUER JD";"evaluating environmental effects on fish growth can be challenging because environmental conditions may vary at relatively fine temporal scales compared with sampling occasions. here we develop a bayesian state-space growth model to evaluate effects of monthly environmental data on growth of fish that are observed less frequently (e. g., from mark-recapture data where time between captures can range from months to years). weassess effects of temperature, turbidity, food availability, flow variability, and trout abundance on subadult humpback chub (gila cypha) growth in two rivers, the colorado river (cr) and the little colorado river (lcr), and we use out-of-sample prediction to rank competing models. environmental covariates explained a high proportion of the variation in growth in both rivers; however, the best growth models were river-specific and included either positive temperature and turbidity duration effects (cr) or positive temperature and food availability effects (lcr). our approach to analyzing environmental controls on growth should be applicable in other systems where environmental data vary over relatively short time scales compared with animal observations."
ecological;SCR, assumptions, telemetry;non-circular home ranges and the estimation of population density;"anisotropic detection function; density estimation; home range; non-circularity; radiotelemetry; spatially explicit capture-recapture; study design; telemetry-scaled non-spatial estimator";ECOLOGY;EFFORD MG;spatially explicit capture-recapture (secr) models have emerged as one solution to the problem of estimating the population density of mobile and cryptic animals. spatial models embody assumptions regarding the spatial distribution of individuals and the spatial detection process. the detection process is modeled in secr as a radial decline in detection probability with distance from the activity center of each individual. this would seem to require that home ranges are circular. the robustness of secr when home ranges are not circular has been the subject of conflicting statements. ivan et al. previously compared the secr density estimator to a telemetry-scaled non-spatial estimator. i suggest that the apparent non-robustness of secr in their study was a simulation artefact. new simulations of elliptical home ranges establish that the secr density estimator is largely robust to non-circularity when detectors are spread in two dimensions, but may be very biased if the detector array is linear and home ranges align with the array. transformation to isotropy reduces bias from designs of intermediate dimension, such as hollow square arrays. possible alignment of home ranges should be considered when designing detector arrays.
ecological;SCR, survey design;estimation of population density by spatially explicit capture-recapture analysis of data from area searches;"area search; bayesian analysis; data augmentation; flat-tailed horned lizard; maximum likelihood; polygons; population density; spatially explicit capture-recapture; transects";ECOLOGY;EFFORD MG;"the recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. this report presents new likelihood-based spatially explicit capture-recapture (secr) methods for such data. the methods are shown to be at least as robust in simulations as an equivalent bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. it is recommended on the basis of simulation that plots for secr should be at least as large as the home range of the target species. the r package ""secr"" may be used to fit the models. the likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. it is also shown how the method may be adapted for detections along a linear transect."
ecological;SCR, genetic tagging;population density estimated from locations of individuals on a passive detector array;"acoustic census methods; area search; camera trap; fecal dna; maximum likelihood; microphone array; passive detector array; population density; proximity detector; signal strength; spatially explicit capture-recapture";ECOLOGY;"EFFORD MG;DAWSON DK;BORCHERS DL";"the density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture-recapture. likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement (""proximity"" detectors such as camera traps and hair snags). as originally proposed, these methods require multiple sampling intervals. we show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. this considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. acoustic detection can be defined as occurring when received signal strength exceeds a threshold. we suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. while binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small."
ecological;SCR, assumptions, genetic tagging;compensatory heterogeneity in spatially explicit capture-recapture data;"carnivores; density estimates; dna; effective sampling area; grizzly bear; heterogeneity; home range; secr; sex differences; single-detector sampling area; spatially explicit capture-recapture; ursus arctos";ECOLOGY;"EFFORD MG;MOWAT G";spatially explicit capture-recapture methods, used widely to estimate the abundance of large carnivores, allow for movement within home ranges during sampling. probability of detection is a decreasing function of distance from the home range center, with one parameter for magnitude and another for spatial scale. sex-based and other differences in home range size potentially cause heterogeneity in individual detection and bias in estimates of density. the two parameters of detection have hitherto been treated as independent, but we suggest that an inverse relation is expected when detection probability depends on time spent near the detector. variation in the spatial scale of detection is then compensated by reciprocal variation in the magnitude parameter. we define a net measure of detection (single-detector sampling area, a(0)), and show by simulation that its coefficient of variation (cv) is a better predictor of bias than the cv of either component or the sum of their squared cvs. in an example using the grizzly bear ursus arctos, the estimated sex variation in a(0) was small despite large variation in each component. from the simulations, the relative bias of density estimates was generally negligible (<5%) when cv(a(0)) < 30%. parameterization of the detection model in terms of a(0) and spatial scale can be more parsimonious and significantly aids the biological interpretation of detection parameters.
ecological;SCR, assumptions, camera trapping;evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras;"bayesian; density; mark-recapture; monitoring; remote camera; spatial count model; spatially explicit; visual marking";JOURNAL OF APPLIED ECOLOGY;"EVANS MJ;RITTENHOUSE TAG";1. remote cameras have become a promising, cost-effective tool for monitoring wildlife populations. yet, for species where individuals are indistinguishable, remote cameras' ability to provide robust and precise density estimates has been limited without the use of invasive marking. 2. using the american black bear as a model species, we evaluated methods for estimating wildlife densities using remote camera detections of unmarked individuals against estimates from spatial capture-recapture (scr) models using individual detections. we also tested the effect of incorporating varying proportions of marked individuals on model accuracy and precision. 3. spatial count (sc) models using unmarked individuals produced estimates of bear density within 0.6% of those from scr. we extended sc models to incorporate variation in density as a function of land use/land cover, and identified identical relationships between variation in bear densities and housing density as obtained using scr. incorporating individual detection data from simultaneous non-invasive genetic sampling lead to more precise, but biased estimates. 4. synthesis and applications. our results identify contexts in which camera count data can be used as an alternative to spatial capture-recapture (scr) when individual identification is prohibitive. spatial count models provided an accurate, but less precise replication of spatial capture-recapture density estimates and may provide consistent insights into spatial variation in density. mixed samples of camera counts and auxiliary individual detections are likely to be of limited use, but fitting spatial count models to populations with partial visual markings could improve their precision.
ecological;Camera trapping, assumptions;a critique of density estimation from camera-trap data;"abundance estimation; camera trap; capture-recapture; closed population model; density estimation";JOURNAL OF WILDLIFE MANAGEMENT;"FOSTER RJ;HARMSEN BJ";densities of elusive terrestrial mammals are commonly estimated from camera-trap data. typically, this is a 2-step process involving 1) fitting conventional closed population capturerecapture models to estimate abundance, and 2) using ad hoc methods to determine the effective trapping area. the methodology needs to be accurate, robust, and reliable when results are used to guide wildlife management. we critically review 47 published studies and discuss the problems associated with contemporary population estimates of elusive species from camera-trap data. in particular we discuss 1) individual identification, 2) sample size and capture probability, 3) camera location and spacing, 4) the size of the study area, and 5) ad hoc density estimation from the calculation of an effective trapping area. we also discuss the recently developed spatially explicit capturerecapture (secr) models as an alternative approach that does not require the intermediate step of estimating an effective trapping area. we recommend 1) greater transparency in study design and quality of the data, 2) greater rigor when reviewing manuscripts, and 3) that more attention is given to the survey design to ensure data are of sufficient quality for analysis. (c) 2011 the wildlife society.
ecological;SCR, camera-trapping;spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies;"andes mountains; argentina; bayesian analysis; camera trapping; data augmentation; hierarchical model; jolly-seber model; pampas cats; spatial capture-recapture; trapping arrays";ECOLOGY;"GARDNER B;REPPUCCI J;LUCHERINI M;ROYLE JA";we develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. such spatial capture-recapture data arise from studies based on camera trapping, dna sampling, and other situations in which a spatial array of devices records encounters of unique individuals. we integrate an individual-based formulation of a jolly-seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. we adopt a bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program winbugs. the model was motivated by a camera trapping study of pampas cats leopardus colocolo from argentina, which we present as an illustration of the model in this paper. we provide estimates of density and the first quantitative assessment of vital rates for the pampas cat in the high andes. the precision of these estimates is poor due likely to the sparse data set. unlike conventional inference methods which usually rely on asymptotic arguments, bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.
ecological;SCR, genetic tagging;hierarchical models for estimating density from dna mark-recapture studies;"abundance; adirondacks, new york, usa; bayesian analysis; bears; dna sampling; hair-snare trapping; hierarchical model; spatial capture-recapture; ursus americanus";ECOLOGY;"GARDNER B;ROYLE JA;WEGAN MT";genetic sampling is increasingly used as a tool by wildlife biologists and managers to estimate abundance and density of species. typically, dna is used to identify individuals captured in an array of traps ( e. g., baited hair snares) from which individual encounter histories are derived. standard methods for estimating the size of a closed population can be applied to such data. however, due to the movement of individuals on and off the trapping array during sampling, the area over which individuals are exposed to trapping is unknown, and so obtaining unbiased estimates of density has proved difficult. we propose a hierarchical spatial capture-recapture model which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to ( via movement) and detection by traps. detection probability is modeled as a function of each individual's distance to the trap. we applied this model to a black bear ( ursus americanus) study conducted in 2006 using a hair-snare trap array in the adirondack region of new york, usa. we estimated the density of bears to be 0.159 bears/km(2), which is lower than the estimated density (0.410 bears/km(2)) based on standard closed population techniques. a bayesian analysis of the model is fully implemented in the software program winbugs.
ecological;SCR, assumptions, dispersal;state space and movement specification in open population spatial capture-recapture models;"camera-trapping; dispersal; markovian movement; population dynamics; tigers; transience";ECOLOGY AND EVOLUTION;"GARDNER B;SOLLMANN R;KUMAR NS;JATHANNA D;KARANTH KU";"with continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. one powerful way to estimate population size and dynamics is through capture-recapture methods. spatial capture (scr) models for open populations make efficient use of capture-recapture data, while being robust to design changes. relatively few studies have implemented open scr models, and to date, very few have explored potential issues in defining these models. we develop a series of simulation studies to examine the effects of the state-space definition and between-primary-period movement models on demographic parameter estimation. we demonstrate the implications on a 10-year camera-trap study of tigers in india. the results of our simulation study show that movement biases survival estimates in open scr models when little is known about between-primary-period movements of animals. the size of the state-space delineation can also bias the estimates of survival in certain cases.we found that both the state-space definition and the between-primary-period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). in general, we suggest that open scr models can provide an efficient and flexible framework for long-term monitoring of populations; however, in many cases, realistic modeling of between-primary-period movements is crucial for unbiased estimates of survival and density."
ecological;SCR, assumptions;evaluating the potential biases in carnivore capture-recapture studies associated with the use of lure and varying density estimation techniques using photographic-sampling data of the malagasy civet;"baiting; buffer; luring; mmdm; population; spatially-explicit";POPULATION ECOLOGY;"GERBER BD;KARPANTY SM;KELLY MJ";estimating density of elusive carnivores with capture-recapture analyses is increasingly common. however, providing unbiased and precise estimates is still a challenge due to uncertainties arising from the use of (1) bait or lure to attract animals to the detection device and (2) ad hoc boundary-strip methods to compensate for edge effects in area estimation. we used photographic-sampling data of the malagasy civet fossa fossana collected with and without lure to assess the effects of lure and to compare the use of four density estimators which varied in methods of area estimation. the use of lure did not affect permanent immigration or emigration, abundance and density estimation, maximum movement distances, or temporal activity patterns of malagasy civets, but did provide more precise population estimates by increasing the number of recaptures. the spatially-explicit capture-recapture (secr) model density estimates +/- se were the least precise as they incorporate spatial variation, but consistent with each other (maximum likelihood-secr = 1.38 +/- a 0.18, bayesian-secr = 1.24 +/- a 0.17 civets/km(2)), whereas estimates relying on boundary-strip methods to estimate effective trapping area did not incorporate spatial variation, varied greatly and were generally larger than secr model estimates. estimating carnivore density with ad hoc boundary-strip methods can lead to overestimation and/or increased uncertainty as they do not incorporate spatial variation. this may lead to inaction or poor management decisions which may jeopardize at-risk populations. in contrast, secr models free researchers from making subjective decisions associated with boundary-strip methods and they estimate density directly, providing more comparable and valuable population estimates.
ecological;HMM, age;estimating survival in the apennine brown bear accounting for uncertainty in age classification;"apennine brown bear; hair-snagging; multievent models; non-invasive genetic sampling; small populations; survival";POPULATION ECOLOGY;"GERVASI V;BOITANI L;PAETKAU D;POSILLICO M;RANDI E;CIUCCI P";"for most rare and elusive species, estimating age-specific survival is a challenging task, although it is an important requirement to understand the drivers of population dynamics, and to inform conservation actions. apennine brown bears ursus arctos marsicanus are a small, isolated population under a severe risk of extinction, for which the main demographic mechanisms underlying population dynamics are still unknown, and population trends have not been formally assessed. we present a 12-year analysis of their survival rates using non-invasive genetic sampling data collected through four different sampling techniques. by using multi-event capture-recapture models, we estimated survival probabilities for two broadly defined age classes (cubs and older individuals), even though the age of the majority of sampled bears was unknown. we also applied the pradel model to provide a preliminary assessment of population trend during the study period. survival was different between cubs [i center dot = 0.51, 95% ci (0.22, 0.79)], adult males [i center dot = 0.85, 95% ci (0.76, 0.91)] and adult females [i center dot = 0.92, 95% ci (0.87, 0.95)], no temporal variation in survival emerged, suggesting that bear survival remained substantially stable throughout the study period. the pradel analysis of population trend yielded an estimate of lambda = 1.009 [se = 0.018; 95% ci (0.974, 1.046)]. our results indicate that, despite the status of full legal protection, the basically stable demography of this relict population is compatible with the observed lack of range expansion, and that a relatively high cub mortality could be among the main factors depressing recruitment and hence population growth."
ecological;Covariates;dealing with many correlated covariates in capture-recapture models;"animal demography; population dynamics; principal-component capture-recapture model; snow petrel; survival estimation";POPULATION ECOLOGY;"GIMENEZ O;BARBRAUD C";capture-recapture models for estimating demographic parameters allow covariates to be incorporated to better understand population dynamics. however, high-dimensionality and multicollinearity can hamper estimation and inference. principal component analysis is incorporated within capture-recapture models and used to reduce the number of predictors into uncorrelated synthetic new variables. principal components are selected by sequentially assessing their statistical significance. we provide an example on seabird survival to illustrate our approach. our method requires standard statistical tools, which permits an efficient and easy implementation using standard software.
ecological;Random effects, covariates;individual heterogeneity in studies on marked animals using numerical integration: capture-recapture mixed models;"capture-recapture mixed models; cr2m; european dippers; finite mixture models; generalized linear mixed models; likelihood-ratio test; mark-recapture models; random effects; sociable weavers; survival estimation; winbugs";ECOLOGY;"GIMENEZ O;CHOQUET R";in conservation and evolutionary ecology, quantifying and accounting for individual heterogeneity in vital rates of open populations is of particular interest. individual random effects have been used in capture-recapture models, adopting a bayesian framework with markov chain monte carlo (mcmc) to carry out estimation and inference. as an alternative, we show how numerical integration via the gauss-hermite quadrature (ghq) can be efficiently used to approximate the capture-recapture model likelihood with individual random effects. we compare the performance of the two approaches (mcmc vs. ghq) and finite mixture models using two examples, including data on european dippers and sociable weavers. besides relying on standard statistical tools, ghq was found to be faster than mcmc simulations. our approach is implemented in program e-surge. overall, capture-recapture mixed models (cr2ms), implemented either via a ghq approximation or mcmc simulations, have potential important applications in population biology.
ecological;SSM;estimating individual fitness in the wild using capture-recapture data;"delifing; growth rate; imperfect detection; lifetime reproductive success; mark-recapture; state-space models";POPULATION ECOLOGY;"GIMENEZ O;GAILLARD JM";the concept of darwinian fitness is central in evolutionary ecology, and its estimation has motivated the development of several approaches. however, measuring individual fitness remains challenging in empirical case studies in the wild. measuring fitness requires a continuous monitoring of individuals from birth to death, which is very difficult to get in part because individuals may or may not be controlled at each reproductive event and recovered at death. imperfect detection hampers keeping track of mortality and reproductive events over the whole lifetime of individuals. we propose a new statistical approach to estimate individual fitness while accounting for imperfect detection. based on hidden process modelling of longitudinal data on marked animals, we show that standard metrics to quantify fitness, namely lifetime reproductive success, individual growth rate and lifetime individual contribution to population growth, can be extended to cope with imperfect detection inherent to most monitoring programs in the wild. we illustrate our approach using data collected on individual roe deer in an intensively monitored population.
ecological;covariates, random effects;modeling survival at multi-population scales using mark-recapture data;"atlantic puffin; bayesian modeling; demography; deviance information criterion; environmental forcing; fratercula arctica; mixed model; multi-population scale; sea surface temperature; synchronization; winbugs";ECOLOGY;"GROSBOIS V;HARRIS MP;ANKER NILSSEN T;MCCLEERY RH;SHAW DN;MORGAN BJT;GIMENEZ O";the demography of vertebrate populations is governed in part by processes operating at large spatial scales that have synchronizing effects on demographic parameters over large geographic areas, and in part, by local processes that generate fluctuations that are independent across populations. we describe a statistical model for the analysis of individual monitoring data at the multi-population scale that allows us to (1) split up temporal variation in survival into two components that account for these two types of processes and (2) evaluate the role of environmental factors in generating these two components. we derive from this model an index of synchrony among populations in the pattern of temporal variation in survival, and we evaluate the extent to which environmental factors contribute to synchronize or desynchronize survival variation among populations. when applied to individual monitoring data from four colonies of the atlantic puffin (fratercula arctica), 67% of between-year variance in adult survival was accounted for by a global spatial-scale component, indicating substantial synchrony among colonies. local sea surface temperature (sst) accounted for 40% of the global spatial-scale component but also for an equally large fraction of the local-scale component. sst thus acted at the same time as both a synchronizing and a desynchronizing agent. between-year variation in adult survival not explained by the effect of local sst was as synchronized as total between-year variation, suggesting that other unknown environmental factors acted as synchronizing agents. our approach, which focuses on demographic mechanisms at the multi-population scale, ideally should be combined with investigations of population size time series in order to characterize thoroughly the processes that underlie patterns of multi-population dynamics and, ultimately, range dynamics.
ecological;covariates;covariate and multinomial: accounting for distance in movement in capture-recapture analyses;"covariate; dependent estimates; link function; multinomial logit; transformations; variance-covariance matrix";ECOLOGY AND EVOLUTION;"GUERY L;ROUAN L;DESCAMPS S;BETY J;FERNANDEZ CHACON A;GILCHRIST G;PRADEL R";many biological quantities cannot be measured directly but rather need to be estimated from models. estimates from models are statistical objects with variance and, when derived simultaneously, covariance. it is well known that their variance-covariance (vc) matrix must be considered in subsequent analyses. although it is always preferable to carry out the proposed analyses on the raw data themselves, a two-step approach cannot always be avoided. this situation arises when the parameters of a multinomial must be regressed against a covariate. the delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. implementing the delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. this paper proposes a how-to guide for calculating the correct vc matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. for illustrative purpose, we focus on variables calculated in capture-recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances.
ecological;Social species, assumptions;effects of social organization, trap arrangement and density, sampling scale, and population density on bias in population size estimation using some common mark-recapture estimators;NA;PLOS ONE;"GUPTA M;JOSHI A;VIDYA TNC";mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. however, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the asian elephant. in the specific case of asian elephants, doubts have been raised about the accuracy of population size estimates. more importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. we developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by popan, robust design, and robust design with detection heterogeneity. in the present study, we ran simulations with biological, demographic and ecological parameters relevant to asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. we collected capture history data from the simulations, and used those data to test for bias in population size estimation. social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. popan clearly outperformed the two robust design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. social organization did not have a major effect on bias for these parameter combinations at which popan gave more or less unbiased population size estimates. therefore, the effect of social organization on bias in population estimation could be removed by using popan with specific parameter combinations, to obtain population size estimates in a social species.
ecological;Social species;a new mark-recapture approach for abundance estimation of social species;NA;PLOS ONE;"HICKEY JR;SOLLMANN R";accurate estimates of population abundance are a critical component of species conservation efforts in order to monitor the potential recovery of populations. capture-mark-recapture (cmr) is a widely used approach to estimate population abundance, yet social species moving in groups violate the assumption of cmr approaches that all individuals in the population are detected independently. we developed a closed cmr model that addresses an important characteristic of group-living species-that individual-detection probability typically is conditional on group detection. henceforth termed the two-step model, this approach first estimates group-detection probability and then-conditional on group detection-estimates individual-detection probability for individuals within detected groups. overall abundance is estimated assuming that undetected groups have the same average group size as detected groups. we compared the performance of this two-step cmr model to a conventional (one-step) closed cmr model that ignored group structure. we assessed model sensitivity to variation in both group- and individual-detection probability. both models returned overall unbiased estimates of abundance, but the one-step model returned deceptively narrow bayesian confidence intervals (bci) that failed to encompass the correct population abundance an average 52% of the time. contrary, under the two-step model, ci coverage was on average 96%. both models had similar root mean squared errors (rmse), except for scenarios with low group detection probability, where the two-step model had much lower rmse. for illustration with a real data set, we applied the two-step and regular model to non-invasive genetic capture-recapture data of mountain gorillas (gorilla beringei beringei). as with simulations, abundance estimates under both models were similar, but the two-step model estimate had a wider confidence interval. results support using the two-step model for species living in constant groups, particularly when group detection probability is low, to reduce risk of bias and adequately portray uncertainty in abundance estimates. important sources of variation in detection need to be incorporated into the two-step model when applying it to field data.
ecological;SSM, data combination;wanted dead or alive: a state-space mark-recapture-recovery model incorporating multiple recovery types and state uncertainty;NA;CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES;"HOSTETTER NJ;GARDNER B;EVANS AF;CRAMER BM;PAYTON Q;COLLIS K;ROBY DD";we developed a state-space mark-recapture-recovery model that incorporates multiple recovery types and state uncertainty to estimate survival of an anadromous fish species. we apply the model to a dataset of outmigrating juvenile steelhead trout (oncorhynchus mykiss (walbaum, 1792)) tagged with passive integrated transponders, recaptured during outmigration, and recovered on bird colonies in the columbia river basin (2008-2014). recoveries on bird colonies are often ignored in survival studies because the river reach of mortality is often unknown, which we model as a form of state uncertainty. median outmigration survival from release to the lower river (river kilometre 729 to 75) ranged from 0.27 to 0.35, depending on year. recovery probabilities were frequently >= 0.20 in the first river reach following tagging, indicating that one out of five fish that died in that reach was recovered on a bird colony. integrating dead recovery data provided increased parameter precision, estimation of where birds consumed fish, and survival estimates across larger spatial scales. more generally, these modeling approaches provide a flexible framework to integrate multiple sources of tag recovery data into mark-recapture studies.
ecological;Tag loss, multiple marks, data combination;accounting for tag loss and its uncertainty in a mark-recapture study with a mixture of single and double tags;NA;TRANSACTIONS OF THE AMERICAN FISHERIES SOCIETY;"HYUN SY;REYNOLDS JH;GALBREATH PF";unless accounted for in the estimation, tag loss will cause mark-recapture methods to overestimate the true abundance of a closed population and to underestimate the associated uncertainty. current methods of accounting for tag loss require all marked individuals to be double-tagged. we present a new model that fully accounts for tag loss and allows for the use of a mixture of single-and double-tagged individuals, thus simplifying implementation in the field. treating abundance, tag loss rate, and capture probabilities as free parameters, we estimated those parameters and their uncertainty by using maximum likelihood. whereas existing methods assume that a double-tagged animal does not lose both tags, the new model allows the animal to lose both tags. the new model's performance was assessed and compared with that of other estimators (modified petersen and seber-felton) via simulation. as expected, estimates from the new model were less biased and more precise than estimates from the other models. the model was used to estimate the abundance of the kokanee oncorhynchus nerka population in the metolius river, oregon, during 2007. abundance was estimated at 102,970 fish ((se) over cap = 8,930), tag loss rate was estimated at 0.27 ((se) over cap =0.05), the capture probability for the first sample (tagging) was 0.03 ((se) over cap = 0.00), and the capture probability for the second sample (recovery) was 0.11 ((se) over cap = 0.01). the new model uses all of the information from single-and double-tag data, provides unbiased abundance estimates in the presence of tag loss for a closed population, and has less-stringent field requirements that make it easier to employ than other methods.
ecological;telemetry;using auxiliary telemetry information to estimate animal density from capture-recapture data;"density; geographic closure; mark-recapture; telemetry";ECOLOGY;"IVAN JS;WHITE GC;SHENK TM";estimation of animal density is fundamental to ecology, and ecologists often pursue density estimates using grids of detectors (e.g., cameras, live traps, hair snags) to sample animals at a study site. however, under such a framework, reliable estimates can be difficult to obtain because animals move on and off of the site during the sampling session (i.e., the site is not closed geographically). generally, practitioners address lack of geographic closure by inflating the area sampled by the detectors based on the mean distance individuals moved between trapping events or invoking hierarchical models in which animal density is assumed to be a spatial point process, and detection is modeled as a declining function of distance to a detector. we provide an alternative in which lack of geographic closure is sampled directly using telemetry, and this auxiliary information is used to compute estimates of density based on a modified huggins closed-capture estimator. contrary to other approaches, this method is free from assumptions regarding the distribution and movement of animals on the landscape, the stationarity of their home ranges, and biases induced by abnormal movements in response to baited detectors. the estimator is freely available in program mark.
ecological;SCR, assumptions;using simulation to compare methods for estimating density from capture-recapture data;"closure; density; geographic closure; mean maximum distance moved; simulation; spatially explicit capture-recapture; telemetry; trapping grid";ECOLOGY;"IVAN JS;WHITE GC;SHENK TM";estimation of animal density is fundamental to wildlife research and management, but estimation via mark-recapture is often complicated by lack of geographic closure of study sites. contemporary methods for estimating density using mark-recapture data include (1) approximating the effective area sampled by an array of detectors based on the mean maximum distance moved (mmdm) by animals during the sampling session, (2) spatially explicit capture-recapture (secr) methods that formulate the problem hierarchically with a process model for animal density and an observation model in which detection probability declines with distance from a detector, and (3) a telemetry estimator (telem) that uses auxiliary telemetry information to estimate the proportion of animals on the study site. we used simulation to compare relative performance (percent error) of these methods under all combinations of three levels of detection probability (0.2, 0.4, 0.6), three levels of occasions (5, 7, 10), and three levels of abundance (10, 20, 40 animals). we also tested each estimator using five different models for animal home ranges. telem performed best across most combinations of capture probabilities, sampling occasions, true densities, and home range configurations, and performance was unaffected by home range shape. secr outperformed mmdm estimators in nearly all comparisons and may be preferable to telem at low capture probabilities, but performance varied with home range configuration. mmdm estimators exhibited substantial positive bias for most simulations, but performance improved for elongated or infinite home ranges.
ecological;Mark-resighting, telemetry, incomplete identification;generalized spatial mark-resight models with incomplete identification: an application to red fox density estimates;"camera trapping; generalized spatial mark-resight; incomplete identification; mark; red fox; telemetry";ECOLOGY AND EVOLUTION;"JIMENEZ J;CHANDLER R;TOBAJAS J;DESCALZO E;MATEO R;FERRERAS P";1. the estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. spatially explicit capture-recapture (scr) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera-trap sampling. 2. generalized spatial mark-resight (gen-smr) is a recently developed scr extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. however, in many cases, it is not possible to read the marks in camera-trap pictures, even though individuals can be recognized as marked. we present a new extension of gen-smr that allows for this type of incomplete identification. 3. we used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. we demonstrate the model's performance in estimating red fox (vulpes vulpes) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. according to the simulations, accuracy increases with the number of marked individuals (m), but is less sensitive to changes in individual identification rate (delta). in our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation sd: 0.32) and 0.28 (sd: 0.06) individuals/km(2), in high and low density, with an identification rate of 0.21 and 0.91, respectively. 4. this extension of gen-smr is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.
ecological;Data entry error, assumptions;an evaluation of data entry error and proofing methods for fisheries data;NA;TRANSACTIONS OF THE AMERICAN FISHERIES SOCIETY;"JOHNSON CL;TEMPLE GM;PEARSONS TN;WEBSTER TD";reducing data entry error has the potential to improve estimates produced by fisheries practitioners. however, the frequencies of data entry error and evaluations of the recommended protocols for dealing with data entry error have rarely been presented in fisheries-related literature. the objectives of our study were to determine the magnitude of data entry error in a typical fisheries data set, what kind of errors occurred most often. and how those errors might affect commonly generated estimates of abundance, size structure, and species richness. we evaluated four methods of data entry into proofing: (1) a single entry, (2) read-aloud proofing.(3)double-entry proofing, and (4) field use of it personal digital assistant (pda). we determined the quality of the data after the use of each method and compared common fisheries estimates derived front each with estimates generated front standardized data. total error discovered in the data set averaged 0.79 +/- 0.22 % (mean +/- sd) and consisted of 44.1% field-related error and 55.9% data entry errors. we found thin numbers of known errors remaining in the data were significantly lower when proofing, methods were ticked. abundance estimates derived front it single data entry were significantly different front those derived front data thin had undergone proofing. however, the magnitude of the difference (2.22%) was less than our limit of acceptable error and far less than the mean confidence interval of the estimates themselves (60.91%). further, no differences were detected in mark-recapture abundance estimates, estimates of size, or estimates of species richness. this suggests thin for most common fisheries estimates, a single entry of data or single, entry using a pda is sufficient. we subsequently found thin the use of automated en-or checking helped to ensure an acceptable level of data quality without the time and expense of more traditional error-checking methods.
ecological;HMM, robust design, heterogeneity, mixture;estimating parameters of hidden markov models based on marked individuals: use of robust design data;"capture-recapture; closed robust design; hidden markov models; manatees; misclassification; mixtures; multi-event; multistate model; stage structure; state uncertainty; trichechus manatus latirostrus";ECOLOGY;"KENDALL WL;WHITE GC;HINES JE;LANGTIMM CA;YOSHIZAKI J";development and use of multistate mark-recapture models, which provide estimates of parameters of markov processes in the face of imperfect detection, have become common over the last 20 years. recently, estimating parameters of hidden markov models, where the state of an individual can be uncertain even when it is detected, has received attention. previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. we provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. these models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. we apply our model to expected-value data, and to data from a study of florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. we have also implemented these models in program mark. this general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within-primary-period parameters (e. g., state structure) and between-primary-period parameters (e.g., state transition probabilities).
ecological;N-mixture;trend estimation in populations with imperfect detection;"abundance; binomial mixture model; detectability; glm; hierarchical model; lizard; metapopulation design; monitoring; trend";JOURNAL OF APPLIED ECOLOGY;"KERY M;DORAZIO RM;SOLDAAT L;VAN STRIEN A;ZUIDERWIJK A;ROYLE JA";p>1. trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. here, we extend these models to open populations and illustrate them using sand lizard lacerta agilis counts from the national dutch reptile monitoring scheme. 2. our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. we used winbugs to fit the model to lizard counts from 208 transects with 1-10 (mean 3) replicate surveys during each spring 1994-2005. 3. our state model for abundance contained two independent log-linear poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. the observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. lizard populations increased in both regions but more steeply on the coast. detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 june. interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. the proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% cri 35-151). 5.synthesis and applications. open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. we emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.
ecological;dispersal, HMM;estimating dispersal among numerous sites using capture-recapture data;"capture-recapture; dispersal; memory model; multi-event model; multisite model; site fidelity; southern quebec; canada; tachycineta bicolor; tree swallow";ECOLOGY;"LAGRANGE P;PRADEL R;BELISLE M;GIMENEZ O";dispersal affects processes as diverse as habitat selection, population growth, and gene flow. inference about dispersal and its variation is thus crucial for assessing population and evolutionary dynamics. two approaches are generally used to estimate dispersal in free-ranging animals. first, multisite capture recapture models estimate movement rates among sites while accounting for survival and detection probabilities. this approach, however, is limited in the number of sites that can be considered. second, diffusion models estimate movements within discrete habitat using a diffusion coefficient, resulting in a continuous processing of space. however, this approach has been rarely used because of its mathematical and implementation complexity. here, we develop a multi-event capture recapture approach that circumvents the issue of too many sites while being relatively simple to be implemented in existing software. moreover, this new approach allows the quantifying of memory effects, whereby the decision of dispersing or not on a given year impacts the survival or dispersal likelihood of the following year. we illustrate our approach using a long-term data set on the breeding ecology of a declining passerine in southern quebec, canada, the tree swallow (tachycineta bicolor).
ecological;Survey design;exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival;"data thinning; hidden parameters; individual covariates; integrated population model; juvenile survival; long-term monitoring; mark-recapture-recovery; productivity; survey design; uria aalge";JOURNAL OF APPLIED ECOLOGY;"LAHOZ MONFORT JJ;HARRIS MP;MORGAN BJT;FREEMAN SN;WANLESS S";1. long-term monitoring programmes often involve substantial input of skilled staff time. in mark-recapture studies, considerable effort is usually devoted to both marking and recapturing/resighting individuals. given increasing budgetary constraints, it is essential to streamline field protocols to minimize data redundancy while still achieving targets such as detecting trends or ecological effects. 2. we evaluated different levels of field effort investment in marking and resighting individuals by resampling existing mark-recapture-recovery data to construct plausible scenarios of changes in field protocols. we demonstrate the method with 26years data from a common guillemot uria aalge monitoring programme at a major north sea colony. we also assess the impact of stopping the ringing of chicks on our ability to study population demography using integrated population models (ipm) fitted to data including information on breeding adults. different data sets were removed artificially to explore the ability to compensate for missing data. 3. current ringing effort at this colony appears adequate but resighting effort could be halved while still maintaining the capacity to monitor first-year survival and detect the effect of hatch date on survival prospects. 4. the ipm appears robust for estimating survival, productivity or abundance of the breeding population, but has limited capacity to recover year-specific first-year survival when chick data are omitted. if productivity were not monitored, the inclusion of chick data would be essential to estimate it, albeit imprecisely. 5. synthesis and applications. post-study evaluation can help streamline existing long-term environmental monitoring programmes. to our knowledge, this study is the first use of data thinning of existing mark-recapture-recovery data to identify potential field effort reductions. we also highlight how alternative monitoring scenarios can be evaluated with integrated population models when data are collected on different aspects of demography and abundance. when effort reduction is necessary, both approaches provide decision-support tools for adjusting field protocols to collect demographic data. the framework has broad applicability to other taxa and demographic parameters, provided suitable long-term data are available, and we discuss its use in different contexts. post-study evaluation can help streamline existing long-term environmental monitoring programmes. to our knowledge, this study is the first use of data thinning of existing mark-recapture-recovery data to identify potential field effort reductions. we also highlight how alternative monitoring scenarios can be evaluated with integrated population models when data are collected on different aspects of demography and abundance. when effort reduction is necessary, both approaches provide decision-support tools for adjusting field protocols to collect demographic data. the framework has broad applicability to other taxa and demographic parameters, provided suitable long-term data are available, and we discuss its use in different contexts.
ecological;Survey design;designing cost-effective capture-recapture surveys for improving the monitoring of survival in bird populations;"survey design; optimisation; statistical power; cost efficiency, stage-structured population";BIOLOGICAL CONSERVATION;"LIEURY N;DEVILLARD S;BESNARD A;GIMENEZ O;HAMEAU O;PONCHON C;MILLON A";population monitoring traditionally relies on population counts, accounting or not for the issue of detectability. however, this approach does not permit to go into details on demographic processes. therefore, capture-recapture (cr) surveys have become popular tools for scientists and practitioners willing to measure survival response to environmental change or conservation actions. however, cr surveys are expensive and their design is often driven by the available resources, without estimation about the level of precision they provide for detecting changes in survival, despite optimising resource allocation in wildlife monitoring is increasingly important. investigating how cr surveys could be optimised by manipulating resource allocation among different design components is therefore critically needed. we have conducted a simulation experiment exploring the statistical power of a wide range of cr survey designs to detect changes in the survival rate of birds. cr surveys differ in terms of number of breeding pairs monitored, number of offspring and adults marked, resighting effort and survey duration. we compared open-nest (on) and nest-box (nb) monitoring types, using medium- and long-lived model species. increasing survey duration and number of pairs monitored increased statistical power. long survey duration can provide accurate estimations for long-lived birds even for small population size (15 pairs). a cost-benefit analysis revealed that for long-lived on species, ringing as many chicks as possible appears as the most effective survey component, unless a technique for capturing breeding birds at low cost is available to compensate for reduced local recruitment. for medium-lived nb species, focusing the nb rounds at a period that maximises the chance to capture breeding females inside nest-boxes is more rewarding than ringing all chicks. we show that integrating economic costs is crucial when designing cr surveys and discuss ways to improve efficiency by reducing duration to a time scale compatible with management and conservation issues.
ecological;N-mixture, assumptions;on the robustness of n-mixture models;"abundance estimation; bayesian p-value; count data; detection probability; n-mixture model; robustness";ECOLOGY;"LINK WA;SCHOFIELD MR;BARKER RJ;SAUER JR";n-mixture models provide an appealing alternative to mark-recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. there is, however, a cost to using the n-mixture models: inference is very sensitive to the model's assumptions. we consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. these three examples show that small violations of assumptions can lead to large biases in estimation. the violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. in cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.
ecological;Stopover, heterogeneity, mixture;accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the luannan coast of bohai bay, china;"heterogeneity; jolly-seber; mark-recapture; migration; population size; red knot; state-space model; stopover duration";ECOLOGY AND EVOLUTION;"LOK T;HASSELL CJ;PIERSMA T;PRADEL R;GIMENEZ O";to successfully perform their long-distance migrations, migratory birds require sites along their migratory routes to rest and refuel. monitoring the use of so-called stopover and staging sites provides insights into (a) the timing of migration and (b) the importance of a site for migratory bird populations. a recently developed bayesian superpopulation model that integrates mark-recapture data and ring density data enabled the estimation of stopover timing, duration, and population size. yet, this model did not account for heterogeneity in encounter (p) and staying (phi) probabilities. here we extended the integrated superpopulation model by implementing finite mixtures to account for heterogeneity in p and phi. we used simulations and real data (from 2009-2016) on red knots calidris canutus, mostly of the subspecies piersmai, staging in bohai bay, china, during spring migration to (a) show the importance of accounting for heterogeneity in encounter and staying probabilities to get unbiased estimates of stopover timing, duration, and numbers of migratory birds at staging sites and (b) get accurate stopover parameter estimates for a migratory bird species at a key staging site that is threatened by habitat destruction. our simulations confirmed that heterogeneity in p affected stopover parameter estimates more than heterogeneity in phi, especially when most birds had low p. bias was particularly severe when most birds had both low phi and p. bias was largest for population size, intermediate for stopover duration and negligible for stopover timing. a total of 50,000-100,000 red knots were estimated to annually stop for 5-9 days in bohai bay between 10 and 30 may. this shows the key importance of this staging site for this declining species. there were no clear changes in stopover parameters over time, although stopover population size was substantially lower in 2016 than in preceding years. our study shows the importance of accounting for heterogeneity in both encounter and staying probabilities for accurately estimating stopover duration and population size and provides an appropriate modeling framework.
ecological;SSM, frailty, random effects, age, covariates;frailty in state-space models: application to actuarial senescence in the dipper;"actuarial senescence; bayesian; cinclus cinclus; dipper; frailty; individual heterogeneity; state-space models; survival";ECOLOGY;"MARZOLIN G;CHARMANTIER A;GIMENEZ O";senescence, a decrease in life history traits with age, is a within-individual process. the lack of suitable methods to deal with individual heterogeneity has long impeded progress in exploring senescence in wild populations. analyses of survival senescence are additionally complicated by the often neglected issue of imperfect detectability. to deal with both these issues, we developed state-space models to analyze capture-mark-recapture data while accounting for individual heterogeneity by incorporating random effects. we illustrated our approach by applying it to 29 years of data on breeding females in a dipper (cinclus cinclus) population. we highlighted patterns of age-related variation in annual survival by statistical comparisons of piecewise linear, quadratic, gompertz, and weibull survival models. the gompertz model was ranked first in our set. it provided strong evidence for actuarial senescence with an onset of senescence estimated at about 2.3 years. the probability for this model to involve a frailty was 0.15, and the probability to involve an individual latent effect in detection was about 0.4. the estimated mean age at first reproduction was 1.2 years. the general case model described here in detail should encourage the reanalysis of actuarial senescence in cases where imperfect detection or individual heterogeneity is suspected.
ecological;N-mixture, stopover, mixture, citizen science;monitoring abundance and phenology in (multivoltine) butterfly species: a novel mixture model;"ukbms; stopover duration; parameter redundancy; common blue butterfly; super-population; normal mixtures; sample counts";JOURNAL OF APPLIED ECOLOGY;"MATECHOU E;DENNIS EB;FREEMAN SN;BRERETON T";data from 'citizen science' surveys are increasingly valuable in identifying declines in widespread species, but require special attention in the case of invertebrates, with considerable variation in number, seasonal flight patterns and, potentially, voltinism. there is a need for reliable and more informative methods of inference in such cases. we focus on data consisting of sample counts of individuals that are not uniquely identifiable, collected at one or more sites. arrival or emergence and departure or death of individuals take place during the study. we introduce a new modelling approach, which borrows ideas from the 'stopover' capture-recapture literature, that permits the estimation of parameters of interest, such as mean arrival times and relative abundance, or in some cases, absolute abundance, and the comparison of these between sites. the model is evaluated using an extensive simulation study which demonstrates that the estimates for the parameters of interest obtained by the model are reliable, even when the data sets are sparse, as is often the case in reality. when applied to data for the common blue butterfly polyommatus icarus at a large number of sites, the results suggest that mean emergence times, as well as the relative sizes of the broods, are linked to site northing, and confirm field experience that the species is bivoltine in the south of the uk but practically univoltine in the north. synthesis and applications. our proposed 'stopover' model is parameterized with biologically informative constituents: times of emergence, survival rate and relative brood sizes. estimates of absolute or relative abundance that can be obtained alongside these underlying variables are robust to the presence of missing observations and can be compared in a statistically rigorous framework. these estimates are direct indices of abundance, rather than 'sightings', implicitly adjusted for the possible presence of repeat sightings during a season. at the same time, they provide indices of change in demographic and phenological parameters that may be of use in identifying the factors underlying population change. the model is widely applicable and this will increase the utility of already valuable and influential long-standing surveys in monitoring the effects of environmental change on phenology or abundance.
ecological;R package, multiple marks;"multimark: an r package for analysis of capture-recapture data consisting of multiple ""noninvasive"" marks";"bayesian multimodel inference; capture-recapture; cormack-jolly-seber; latent multinomial; mark-recapture; markov chain monte carlo; multiple lists; population size";ECOLOGY AND EVOLUTION;MCCLINTOCK BT;i describe an open-source r package, multimark, for estimation of survival and abundance from capture-mark-recapture data consisting of multiple noninvasive marks. noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. multimark provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left- and right-sided photographs of bilaterally asymmetrical individuals). the package is currently capable of fitting open population cormack-jolly-seber (cjs) and closed population abundance models with up to two mark types using bayesian markov chain monte carlo (mcmc) methods. multimark can also be used for bayesian analyses of conventional capture-recapture data consisting of a single-mark type. some package features include (1) general model specification using formulas already familiar to most r users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved mcmc algorithm that is computationally faster and more efficient than previously proposed methods, (4) bayesian multimodel inference using reversible jump mcmc, and (5) data simulation capabilities for power analyses and assessing model performance. i demonstrate use of multimark using left- and right-sided encounter histories for bobcats (lynx rufus) collected from remote single-camera stations in southern california. in this example, there is evidence of a behavioral effect (i.e., trap happy response) that is otherwise indiscernible using conventional single-sided analyses. the package will be most useful to ecologists seeking stronger inferences by combining different sources of mark-recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. addressing deficiencies in currently available software, multimark also provides a user-friendly interface for performing bayesian multimodel inference using capture-recapture data consisting of a single conventional mark or multiple noninvasive marks.
ecological;Photo-id;integrated modeling of bilateral photo-identification data in mark-recapture analyses;"abundance; latent multinomial; mark-resight; noninvasive capture-recapture; photo-identification; remote camera trapping";ECOLOGY;"MCCLINTOCK BT;CONN PB;ALONSO RS;CROOKS KR";when natural marks provide sufficient resolution to identify individual animals, noninvasive sampling using cameras has a number of distinct advantages relative to traditional mark-recapture methods. however, analyses from photo-identification records often pose additional challenges. for example, it is often unclear how to link left- and right-side photos to the same individual, and previous studies have primarily used data from just one side for statistical inference. here we describe how a recently developed statistical method can be adapted for integrated mark-recapture analyses using bilateral photo-identification records. the approach works by assuming that the true encounter history for each animal is a latent (unobserved) realization from a multinomial distribution. based on the type of photo encounter (e.g., right, left, or both sides), the recorded (observed) encounter histories can only arise from certain combinations of these latent histories. in this manner, the approach properly accounts for uncertainty about the true number of distinct animals observed in the study. using a markov chain monte carlo sampling procedure, we conduct a small simulation study to show that this approach has reasonable properties and outperforms other methods. we further illustrate our approach by estimating population size from bobcat photo-identification records. although motivated by bilateral photo-identification records, we note that the proposed methodology can be used to combine and jointly analyze other types of mark-recapture data (e.g., photo and dna records).
ecological;Citizen science, telemetry;endangered florida panther population size determined from public reports of motor vehicle collision mortalities;"abundance; capture-recapture; citizen science; dead recovery; human-wildlife ecology; imperfect detection; mark-resight; puma concolor coryi; risk of collision; telemetry";JOURNAL OF APPLIED ECOLOGY;"MCCLINTOCK BT;ONORATO DP;MARTIN J";reliably estimating the abundance of rare or elusive animals is notoriously difficult. an archetypical example is the endangered florida panther, whose conservation status is intrinsically linked to population size, but for which reliable abundance information is lacking across its range. this is due not only to the inherent difficulty of sampling a rare and elusive species over a large geographic area, but also because of restricted scientific access to private land. human interactions with wildlife are a regular occurrence, and interactions with non-scientists constitute an important and underutilized source of information about species distribution and abundance. for example, motor vehicle collisions with florida panthers are recurrent on the vast network of roads within the public and private lands comprising its range in southern florida, usa. capitalizing on a tendency for the public to report collisions with species of concern to wildlife officials, we describe a novel methodology using public reports along with routine telemetry monitoring data to produce the first statistically defensible population estimates for the florida panther across its entire breeding range. in essence, our approach uses traffic volume and road density to estimate the probability of motor vehicle collision mortality from telemetered animals and models counts reported by the public accordingly. despite low motor vehicle collision mortality probabilities, our methodology achieved abundance estimates of reasonable precision (29% cv) that was similar to that of previous panther studies using conventional approaches on much smaller study areas. while recovery criteria require establishment of three distinct populations of 240 florida panthers, we found this single population may never have exceeded 150 individuals from 2000 to 2012.synthesis and applications. by extracting critical demographic information from underutilized aspects of human-wildlife ecology, our citizen-based approach can cost less than conventional alternatives and could conceivably be used for long-term population monitoring of other species over broad geographic areas, for example from reports of avian wind farm collisions, beached whales or marine mammal boat strikes. an additional benefit is that it can be applied to historical data sets of carcass recovery programmes, in our case permitting abundance estimation over a 13-year period. by extracting critical demographic information from underutilized aspects of human-wildlife ecology, our citizen-based approach can cost less than conventional alternatives and could conceivably be used for long-term population monitoring of other species over broad geographic areas, for example from reports of avian wind farm collisions, beached whales or marine mammal boat strikes. an additional benefit is that it can be applied to historical data sets of carcass recovery programmes, in our case permitting abundance estimation over a 13-year period.
ecological;mark-resighting, robust design;a less field-intensive robust design for estimating demographic parameters with mark-resight data;"capture-recapture; cormack-jolly-seber; marking and sighting; multistate; new zealand robin; petroica australis; population size; program noremark; temporary emigration";ECOLOGY;"MCCLINTOCK BT;WHITE GC";the robust design has become popular among animal ecologists as a means for estimating population abundance and related demographic parameters with mark-recapture data. however, two drawbacks of traditional mark-recapture are financial cost and repeated disturbance to animals. mark-resight methodology may in many circumstances be a less expensive and less invasive alternative to mark-recapture, but the models developed to date for these data have overwhelmingly concentrated only on the estimation of abundance. here we introduce a mark-resight model analogous to that used in mark-recapture for the simultaneous estimation of abundance, apparent survival, and transition probabilities between observable and unobservable states. the model may be implemented using standard statistical computing software, but it has also been incorporated into the freeware package program mark. we illustrate the use of our model with mainland new zealand robin (petroica australis) data collected to ascertain whether this methodology may be a reliable alternative for monitoring endangered populations of a closely related species inhabiting the chatham islands. we found this method to be a viable alternative to traditional mark-recapture when cost or disturbance to species is of particular concern in long-term population monitoring programs.
ecological;SCR, bioacoustic;counting chirps: acoustic monitoring of cryptic frogs;"acoustic array; acoustic spatially explicit capture-recapture; anurans; call density; non-invasive sampling; population monitoring; sensor networks; signal strength; time of arrival; triangulation";JOURNAL OF APPLIED ECOLOGY;"MEASEY GJ;STEVENSON BC;SCOTT T;ALTWEGG R;BORCHERS DL";1. global amphibian declines have resulted in a vital need for monitoring programmes that follow population trends. monitoring using advertisement calls is ideal as choruses are undisturbed during data collection. however, methods currently employed by managers frequently rely on trained observers and/or do not provide density data on which to base trends. 2. this study explores the utility of monitoring using acoustic spatially explicit capture-recapture (ascr) with time of arrival (toa) and signal strength (ss) as a quantitative monitoring technique to measure call density of a threatened but visually cryptic anuran, the cape peninsula moss frog arthroleptella lightfooti. 3. the relationships between temporal and climatic variables (date, rainfall, temperature) and a. lightfooti call density at three study sites on the cape peninsula, south africa, were examined. acoustic data, collected from an array of six microphones over 4 months during the winter breeding season, provided a time series of call density estimates. 4. model selection indicated that call density was primarily associated with seasonality fitted as a quadratic function. call density peaked mid-breeding season. at the main study site, the lowest recorded mean call density (0.160 calls m(-2) min(-1)) occurred in may and reached its peak mid-july (1.259 calls m(-2) min(-1)). the sites differed in call density, but also the effective sampling area. 5. synthesis and applications. the monitoring technique, acoustic spatially explicit capture-recapture (ascr), quantitatively estimates call density of calling animals without disturbing them or their environment. in addition, time of arrival (toa) and signal strength (ss) data significantly add to the accuracy of call localization, which in turn increases precision of call density estimates without the need for specialist field staff. this technique appears ideally suited to aid the monitoring of visually cryptic, acoustically active species.
ecological;Camera trapping;a novel method to improve individual animal identification based on camera-trapping data;"adjusted rand index; animal natural marks; bobcat; camera-trapping; individual identification; jasper ridge biological preserve; lynx rufus";JOURNAL OF WILDLIFE MANAGEMENT;"MENDOZA E;MARTINEAU PR;BRENNER E;DIRZO R";we present a novel method to improve individual identification of animals based on camera-trapping data. the method combines computer tools and human visual recognition to help multiple users to reach identification agreement. application of this method to a bobcat (lynx rufus) picture database from the jasper ridge biological preserve resulted in a progressive increase in identification agreement between 2 users, as measured by the adjusted rand index (ari). an initial ari value of 0.28 increased to a final value of 0.84 (1 maximum agreement). in contrast, comparisons involving random picture groupings consistently rendered low ari values (<= 0.05). the numbers of individuals named by the 2 users decreased from initial values of 46 and 43 to final values of 25 and 29, respectively. the tool presented here will help researchers and wildlife managers to identify individual mammals and monitor populations. (c) 2011 the wildlife society.
ecological;REM, camera-trapping;estimating animal density without individual recognition using information derivable exclusively from camera traps;"animal movement; camera trap; density estimation; distance sampling; individual recognition; population monitoring; random encounter model; rest model; trapping rate; ungulates";JOURNAL OF APPLIED ECOLOGY;"NAKASHIMA Y;FUKASAWA K;SAMEJIMA H";1. efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. camera trapping is an increasingly popular tool in this area of wildlife research, with further potential arising from technological improvements, such as video-recording functions that allow for behavioural observation of animals. this information may be useful in the estimation of animal density, even without individual recognition. although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established. 2. we developed a likelihood-based model: the random encounter and staying time (rest) model. it is an extension of the random encounter model by rowcliffe et al. (j appl ecol 45: 1228, 2008). the rest model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or bayesian approach. we tested the reliability and feasibility of the rest model using monte carlo simulations. we also applied the approach in the african rainforest and compared the results with those of a line-transect survey. 3. the simulations showed that the rest model provided unbiased estimates of animal density. even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. however, the rest model was vulnerable to unsynchronized activity patterns among individuals. moreover, it is necessary to use a camera model with a fast and reliable infrared sensor and to set the camera trap's parameters appropriately (i.e. video length, delay period). the field survey showed that the staying time of two ungulate species in the african rainforest exhibited good fit with a temporal parametric distribution, and the rest model provided density estimates consistent with those of a line-transect survey. 4. synthesis and applications. the random encounter and staying time model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. careful application of the random encounter and staying time model provides the potential to estimate density of many ground-dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.
ecological;N-mixture, heterogeneity, covariates;partitioning detectability components in populations subject to within-season temporary emigration using binomial mixture models;NA;PLOS ONE;"O DONNELL KM;THOMPSON FR;SEMLITSCH RD";"detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. in this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. the state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. we illustrate our model's potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders plethodon serratus. we fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3-5 surveys each spring and fall 2010-2012. our models generated similar parameter estimates to standard binomial mixture models. aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). by explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. we stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability."
ecological;mark-resighting, data combination, telemetry;camera traps and mark-resight models: the value of ancillary data for evaluating assumptions;"ancillary data; camera traps; detection bias; mark-resight; population estimation; procyon lotor; raccoon";JOURNAL OF WILDLIFE MANAGEMENT;"PARSONS AW;SIMONS TR;POLLOCK KH;STOSKOPF MK;STOCKING JJ;O CONNELL AF";"unbiased estimators of abundance and density are fundamental to the study of animal ecology and critical for making sound management decisions. capture-recapture models are generally considered the most robust approach for estimating these parameters but rely on a number of assumptions that are often violated but rarely validated. mark-resight models, a form of capture-recapture, are well suited for use with noninvasive sampling methods and allow for a number of assumptions to be relaxed. we used ancillary data from continuous video and radio telemetry to evaluate the assumptions of mark-resight models for abundance estimation on a barrier island raccoon (procyon lotor) population using camera traps. our island study site was geographically closed, allowing us to estimate real survival and in situ recruitment in addition to population size. we found several sources of bias due to heterogeneity of capture probabilities in our study, including camera placement, animal movement, island physiography, and animal behavior. almost all sources of heterogeneity could be accounted for using the sophisticated mark-resight models developed by mcclintock et al. (2009b) and this model generated estimates similar to a spatially explicit mark-resight model previously developed for this population during our study. spatially explicit capture-recapture models have become an important tool in ecology and confer a number of advantages; however, non-spatial models that account for inherent individual heterogeneity may perform nearly as well, especially where immigration and emigration are limited. non-spatial models are computationally less demanding, do not make implicit assumptions related to the isotropy of home ranges, and can provide insights with respect to the biological traits of the local population. (c) 2015 the wildlife society."
ecological;SCR, assumptions;simulation-based validation of spatial capture-recapture models: a case study using mountain lions;NA;PLOS ONE;"PATERSON JT;PROFFITT K;JIMENEZ B;ROTELLA J;GARROTT R";spatial capture-recapture (scr) models have improved the ability to estimate densities of rare and elusive animals. however, scr models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of scr-based estimates. we used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from scr models using spatially unstructured sampling. to assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. we then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. a correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. we demonstrated that density estimates from scr models using spatially unstructured sampling are reliable when sufficient information is provided. accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.
ecological;IPM, dispersal;studying dispersal at the landscape scale: efficient combination of population surveys and capture-recapture data;"black-headed gull; capture-recapture; central france; chroicocephalus ridibundus; colony size; e-surge; kalman filter; larus spp.; leslie matrix; metapopulation; spatially structured population";ECOLOGY;"PERON G;CROCHET PA;DOHERTY PF;LEBRETON JD";researchers often rely on capture-mark-recapture (cmr) data to study animal dispersal in the wild. yet their spatial coverage often does not encompass the entire dispersal range of the study individuals, sometimes producing misleading results. information contained in population surveys and variation in population spatial structure can be used to overcome this issue. we build an integrated model in a multisite context in which cmr data are only collected at a subset of sites, but numbers of breeding pairs are counted at all sites. in a black-headed gull chroicocephalus ridibundus population, the integrated-modeling approach induces an increase in precision for the demographic parameters of interest (variances, on average, were decreased by 20%) and provides a more precise extrapolation of results from the cmr data to the whole population. patterns of condition-dependent dispersal are therefore made easier to detect, and we obtain evidence for colony-size dependence in recruitment, dispersal, and breeding success. these results suggest that first-time breeders disperse to small colonies in order to recruit earlier. the exchange of experienced breeders between colonies appears as a main determinant of the observed variation in colony sizes.
ecological;covariates, random effects, spatial;nonparametric spatial regression of survival probability: visualization of population sinks in eurasian woodcock;"bayesian state-space modeling; bivariate radial spline; capture-mark-recapture; capturerecovery; eurasian woodcock; generalized additive models (gam); hunting management; leslie matrix; population growth rate; scolopax rusticola; smooth function; sustainability of wildlife exploitation";ECOLOGY;"PERON G;FERRAND Y;GOSSMANN F;BASTAT C;GUENEZAN M;GIMENEZ O";both evolutionary ecologists and wildlife managers make inference based on how fitness and demography vary in space. spatial variation in survival can be difficult to assess in the wild because (1) multisite study designs are not well suited to populations that are continuously distributed across a large area and (2) available statistical models accounting for detectability less than 1.0 do not easily cope with geographical coordinates. here we use penalized splines within a bayesian state-space modeling framework to estimate and visualize survival probability in two dimensions. the approach is flexible in that no parametric form for the relationship between survival and coordinates need be specified a priori. to illustrate our method, we study a game species, the eurasian woodcock scolopax rusticola, based on band recovery data (5000 individuals) collected over a >50 000-km(2) area in west-central france with contrasted habitats and hunting pressures. we find that spatial variation in survival probability matches an index of hunting pressure and creates a mosaic of population sources and sinks. such analyses could provide guidance concerning the spatial management of hunting intensity or could be used to identify pathways of spatial variation in fitness, for example, to study adaptation to changing landscape and climate.
ecological;IPM, multispecies;integrated modeling of communities: parasitism, competition, and demographic synchrony in sympatric ducks;"breeding success; canvasback aythya vaselineria; capture-mark-recapture; competition; density regulation; functional redundancy; neutral theory; niche theory; parasitism; redhead aythya americana; survival; waterfowl breeding population and habitat survey";ECOLOGY;"PERON G;KOONS DN";functionally similar species often co-occur within an ecosystem, and they can compete for or facilitate each other's access to resources. the coupled dynamics of such species play an important role in shaping biodiversity and an ecosystem's resilience to perturbations. here we study two congeneric north american ducks: redhead aythya americana and canvasback a. vaselineria. both are largely sympatric during the breeding season, and in addition to competition, facultative parasitic egg-laying can lead to interspecific density dependence. using multi-population integrated models, we combined capture-recovery data, population surveys, and age ratio data in order to simultaneously estimate the mechanistic drivers of fecundity, survival, and population dynamics for both species. canvasback numbers positively affected redhead fecundity, whereas redhead numbers negatively affected canvasback fecundity, as expected due to parasitism. this interaction was modulated by wetland habitat availability in a way that matched the observation that redhead hens parasitize canvasback nests under all conditions but exhibit typical nesting behavior more frequently during years with numerous ponds. once these effects of density and habitat were statistically controlled for, we found high levels of interspecific synchrony in both fecundity and survival (respectively, 75% and 49% of remaining variation). thus, both neutral and non-neutral mechanisms affected the dynamics of these functionally similar species. in this and other systems, our method can be used to test hypotheses about species coexistence and to gain insights into the demographic drivers of community dynamics.
ecological;IPM;integrated population models: powerful methods to embed individual processes in population dynamics models;"data integration for population models special feature; density-dependence; eco-evolutionary feedback; heterogeneity; individual quality; integral projection model; integrated population model; population model; structured population";ECOLOGY;"PLARD F;FAY R;KERY M;COHAS A;SCHAUB M";population dynamics models have long assumed that populations are composed of a restricted number of groups, where individuals in each group have identical demographic rates and where all groups are similarly affected by density-dependent and -independent effects. however, individuals usually vary tremendously in performance and in their sensitivity to environmental conditions or resource limitation, such that individual contributions to population growth will be highly variable. recent efforts to integrate individual processes in population models open up new opportunities for the study of eco-evolutionary processes, such as the density-dependent influence of environmental conditions on the evolution of morphological, behavioral, and life-history traits. we review recent advances that demonstrate how including individual mechanisms in models of population dynamics contributes to a better understanding of the drivers of population dynamics within the framework of integrated population models (ipms). ipms allow for the integration in a single inferential framework of different data types as well as variable population structure including sex, social group, or territory, all of which can be formulated to include individual-level processes. through a series of examples, we first show how ipms can be beneficial for getting more accurate estimates of demographic traits than classic matrix population models by including basic population structure and their influence on population dynamics. second, the integration of individual- and population-level data allows estimating density-dependent effects along with their inherent uncertainty by directly using the population structure and size to feedback on demography. third, we show how ipms can be used to study the influence of the dynamics of continuous individual traits and individual quality on population dynamics. we conclude by discussing the benefits and limitations of ipms for integrating data at different spatial, temporal, and organismal levels to build more mechanistic models of population dynamics.
ecological;Data combination, assumptions, camera-trapping;testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry;"camera trap; capture rate; data consistency; detection probability; fisher; home range; pekania pennanti; sierra nevada; telemetry; wildlife monitoring";ECOLOGY AND EVOLUTION;"POPESCU VD;DE VALPINE P;SWEITZER RA";wildlife data gathered by different monitoring techniques are often combined to estimate animal density. however, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. we used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2years of data for fishers (pekania pennanti) as a case study. we evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. the number of telemetry relocations within 250 and 500m from camera traps predicted detection probability well. for the same number of relocations, females were more likely to be detected during the first year. during the second year, all fishers were more likely to be detected during the fall/winter season. models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best akaike information criterion (aic), suggesting that telemetry and camera traps provide consistent information on space use. given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. although methods that combine data types (spatially explicit capture-recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. however, differences between the 2years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data.
ecological;HMM;modeling trap-awareness and related phenomena in capture-recapture studies;NA;PLOS ONE;"PRADEL R;SANZ AGUILAR A";trap-awareness and related phenomena whereby successive capture events are not independent is a feature of the majority of capture-recapture studies. this phenomenon was up to now difficult to incorporate in open population models and most authors have chosen to neglect it although this may have damaging consequences. focusing on the situation where animals exhibit a trap response at the occasion immediately following one where they have been trapped but revert to their original naive state if they are missed once, we show that trap-dependence is more naturally viewed as a state transition and is amenable to the current models of capture-recapture. this approach has the potential to accommodate lasting or progressively waning trap effects.
ecological;SCR;a spatial capture-recapture model to estimate fish survival and location from linear continuous monitoring arrays;NA;CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES;"RAABE JK;GARDNER B;HIGHTOWER JE";we developed a spatial capture-recapture model to evaluate survival and activity centres (i.e., mean locations) of tagged individuals detected along a linear array. our spatially explicit version of the cormack-jolly-seber model, analyzed using a bayesian framework, correlates movement between periods and can incorporate environmental or other covariates. we demonstrate the model using 2010 data for anadromous american shad (alosa sapidissima) tagged with passive integrated transponders (pit) at a weir near the mouth of a north carolina river and passively monitored with an upstream array of pit antennas. the river channel constrained migrations, resulting in linear, one-dimensional encounter histories that included both weir captures and antenna detections. individual activity centres in a given time period were a function of the individual's previous estimated location and the river conditions (i.e., gage height). model results indicate high within-river spawning mortality (mean weekly survival = 0.80) and more extensive movements during elevated river conditions. this model is applicable for any linear array (e. g., rivers, shorelines, and corridors), opening new opportunities to study demographic parameters, movement or migration, and habitat use.
ecological;SCR, mark-resighting, presence-absence;estimating population density from presence-absence data using a spatially explicit model;"abundance; bayesian analysis; camera traps; detection; non-detection; mcmc; red fox; spatial model; vulpes vulpes";JOURNAL OF WILDLIFE MANAGEMENT;"RAMSEY DSL;CALEY PA;ROBLEY A";presence-absence (detection/non-detection) data are routinely collected in wildlife studies where identification of individuals is impossible or impractical and where the detection method may be able to detect only the presence of an individual rather than a count (e.g., track or scat surveys). estimating population density from presence-absence data usually is assumed to be difficult or impossible unless certain restrictive assumptions are made or supplementary information is collected. recently, chandler and royle (2013) presented an extension of a spatially explicit capture-recapture model that estimates population density from spatially replicated counts in unmarked populations. we extended the model of chandler and royle (2013) to situations where only presence-absence data can be collected. the model assumes that individuals can be detected at multiple sample units, producing spatially correlated detections. a spatially explicit model of the detection process is then fit to the correlated detection data using bayesian methods. we report on the performance of the model using simulation and illustrate its use with a practical example estimating the abundance and density of red foxes (vulpes vulpes) from remote camera surveys in the grampians national park in southeastern australia. results from simulations suggest the model produces unbiased estimates of density if device spacing is less than the radial length of a typical home range and the number of encounter occasions is high (i.e., at least 10). application of the model to camera detection data from foxes in the grampians national park resulted in an estimated density of 0.22foxes/km(2) (95% ci: 0.16-0.53). for this dataset, precision of the density and detection parameter estimates were increased by the use of an informative prior distribution for the home-range-scale parameter. the current model should apply widely to a range of sampling situations that result in spatially correlated detection/non-detection data such as bait take, scat surveys, tracking stations, and chew cards, to name a few. (c) 2015 the wildlife society.
ecological;survey design;planning for success: identifying effective and efficient survey designs for monitoring;"cost analysis; monte carlo simulation; population trend; sample size; statistical power; study planning";BIOLOGICAL CONSERVATION;"REYNOLDS JH;THOMPSON WL;RUSSELL B";selecting a survey design to detect change through time in an ecological resource requires balancing the speed with which a given level of change can be detected against the cost of monitoring. planning studies allow one to assess these tradeoffs and identify the optimal design choices for a specific scenario of change. however, such studies seldom are conducted. even worse, they seem least likely to be undertaken when they offer the most insight - when survey methods and monitoring designs are complex and not well captured by simple statistical models. this may be due to limited technical capacity within management agencies. without such planning, managers risk a potentially severe waste of monitoring resources on ineffective and inefficient monitoring, and institutions will remain ignorant of the true costs of information and the potential efficiency gains afforded by a moderate increase in technical capacity. we discuss the importance of planning studies, outline their main components, and illustrate the process through an investigation of competing designs for monitoring for declining brown bear (ursus arctos) densities in southwestern alaska. the results provide guidance on how long monitoring must be sustained before any change is likely to be detected (under a scenario of rather strong true decline), the optimal designs for detecting a change, and a tradeoff where accepting a delay of 2 years in detecting the change could reduce the monitoring cost by almost 50%. this report emphasizes the importance of planning studies for guiding monitoring decisions. published by elsevier ltd.
ecological;survey design, SCR;sampling design and analytical advances allow for simultaneous density estimation of seven sympatric carnivore species from camera trap data;"camera trap; spatially explicit models; multispecies; population density; sampling design";BIOLOGICAL CONSERVATION;"RICH LN;MILLER DAW;MUNOZ DJ;ROBINSON HS;MCNUTT JW;KELLY MJ";population density is a fundamental parameter needed to assess wildlife populations but is difficult to obtain given species are often wide-ranging and elusive. photographic capture-recapture techniques do not require direct observations and thus, have become a common approach for estimating wildlife densities. to date, however, these studies have typically focused on single species. our research explores study design- and analytical-based approaches for expanding photographic capture-recapture studies to assess multiple species simultaneously. we developed a hybrid-sampling scheme that varied inter-camera distances and used simulations to test the efficacy of this design versus a systematically spaced grid in estimating densities of species with varied space use. through simulations we found the hybrid design facilitated density estimates for a wider range of species with little or no cost in accuracy for most species. we implemented a hybrid camera design across a 1154-km(2) area in northern botswana to estimate densities of lions, spotted hyenas, leopards, wild dogs, servals, civets, and aardwolves. we estimated densities of these small- to wide-ranging carnivores, where all or some portion of the population was individually identifiable, using spatially explicit capture-recapture and mark-resight models. mean estimates ranged from 1.2 (95% ci = 0.72-1.99) lions to 10.1 (95% ci = 8.69-11.63) spotted hyenas/100 km(2) and provided empirical information needed for the conservation of these species in botswana. our research demonstrates how photographic capture-recapture studies can be expanded to estimate the densities of multiple species versus just a single species within a community, thus increasing the conservation value of this globally implemented approach.
ecological;SCR, survey design;spatial capture-recapture design and modelling for the study of small mammals;NA;PLOS ONE;"ROMAIRONE J;JIMENEZ J;LUQUE LARENA JJ;MOUGEOT F";"spatial capture-recapture modelling (scr) is a powerful analytical tool to estimate density and derive information on space use and behaviour of elusive animals. yet, scr has been seldom applied to the study of ecologically keystone small mammals. here we highlight its potential and requirements with a case study on common voles (microtus arvalis). first, we address mortality associated with live-trapping, which can be high in small mammals, and must be kept minimal. we designed and tested a nest box coupled with a classic sherman trap and show that it allows a 5-fold reduction of mortality in traps. second, we address the need to adjust the trapping grid to the individual home range to maximize spatial recaptures. in may-june 2016, we captured and tagged with transponders 227 voles in a 1.2-ha area during two monthly sessions. using a bayesian scr with a multinomial approach, we estimated: (1) the baseline detection rate and investigated variation according to sex, time or behaviour (aversion/attraction after a previous capture); (2) the parameter sigma that describes how detection probability declines as a function of the distance to an individual's activity centre, and investigated variation according to sex; and (3) density and population sex-ratio. we show that reducing the maximum distance between traps from 12 to 9.6m doubled spatial recaptures and improved model predictions. baseline detection rate increased over time (after overcoming a likely aversion to entering new odourless traps) and was greater for females than males in june. the sigma parameter of males was twice that of females, indicating larger home ranges. density estimates were of 142.92 +/- 38.50 and 168.25 +/- 15.79 voles/ha in may and june, respectively, with 2-3 times more females than males. we highlight the potential and broad applicability that scr offers and provide specific recommendations for using it to study small mammals like voles."
ecological;unmarked;estimating individual survival using territory occupancy data on unmarked animals;"bayesian analysis; capture recapture; common birds census; robust design; state-space models; territory colonization; territory fidelity; winbugs";JOURNAL OF APPLIED ECOLOGY;"ROTH T;AMRHEIN V";p>1. survival estimation forms the basis of much ecological research, and usually requires data on marked animals. in population studies of territorial animals, however, data are often collected on animal territory occupancy without identification of individuals. previously, these data could not be used to estimate demographic parameters such as survival. 2. we developed a hierarchical site-occupancy model for estimating survival from territory occupancy data without individual identification. we defined survival as the probability that an individual occupying a territory survives until the next reproductive period and settles in the same territory again. to evaluate our model, we used simulated data as well as real data from a long-term study on nightingales luscinia megarhynchos, from which mark-recapture data and territory occupancy data were available. 3. when applied to simulated data sets on territory occupancy, with parameter settings that are typical for different monitoring programmes (i.e. 10 years duration, three or eight visits per season, and 55 or 200 territories surveyed), our model yielded unbiased estimates of survival if the probability of detecting an occupied territory during a single visit was p = 0 center dot 5 or p = 0 center dot 7. 4. when applied to the data on nightingale territory occupancy, estimates of survival from our model were very similar to the estimates obtained from a traditional mark-recapture model (cormack-jolly-seber model) applied to the ringing data from the same nightingale population. 5.synthesis and applications. data collection for mark-recapture analysis is usually invasive and labour intensive, and suitable data are rarely available from large-scale monitoring programmes covering entire regions or countries. applying our model to territory occupancy data from such monitoring programmes could make large amounts of data available for research on animal demography.
ecological;SCR, connectivity;spatial capture-recapture models for jointly estimating population density and landscape connectivity;"animal movement; ecological distance; landscape connectivity; least-cost path; resistance surface; spatial capture-recapture";ECOLOGY;"ROYLE JA;CHANDLER RB;GAZENSKI KD;GRAVES TA";"population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. recently developed spatial capture-recapture (scr) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. rather, all applications of scr models have used encounter probability models based on the euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. in this paper we devise encounter probability models based on ""ecological distance,"" i.e., the least-cost path between traps and activity centers, which is a function of both euclidean distance and animal movement behavior in resistant landscapes. we integrate least-cost path models into a likelihood-based estimation scheme for spatial capture-recapture models in order to estimate population density and parameters of the least-cost encounter probability model. therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture-recapture data. furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive scr model."
ecological;SCR, dispersal, assumptions;spatial capture-recapture models allowing markovian transience or dispersal;"animal movement; density estimation; dispersal; spatial capture-recapture; spatially explicit capture-recapture; transience";POPULATION ECOLOGY;"ROYLE JA;FULLER AK;SUTHERLAND C";spatial capture-recapture (scr) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, dna sampling and other methods which produce spatially explicit individual encounter information. one of the core assumptions of scr models is that individuals possess home ranges that are spatially stationary during the sampling period. for many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. in this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary scr models when dispersal or transience is present in the population. then, using both simulated and real data, we demonstrate that such models can easily be described in the bugs language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture-recapture data. we find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. thus, use of ordinary scr models to make inferences about density is feasible, but interpretation of scr model parameters in relation to movement should be avoided. instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.
ecological;SCR, camera-trapping;bayesian inference in camera trapping studies for a class of spatial capture-recapture models;"abundance; bayesian analysis; binomial point process; camera trapping; carnivore surveys; data augmentation; density estimation; hierarchical model; markov chain monte carlo; spatial capture-recapture; tigers; trapping array; trapping grid";ECOLOGY;"ROYLE JA;KARANTH KU;GOPALASWAMY AM;KUMAR NS";we develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. the model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. we suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. we show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. we adopt a bayesian framework for inference under these models using a formulation based on data augmentation. we apply the models to camera trapping data on tigers from the nagarahole reserve, india, collected over 48 nights in 2006. for this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.
ecological;SCR;a hierarchical model for estimating density in camera-trap studies;"bayesian analysis; camera trapping; carnivore surveys; density estimation; hierarchical model; markov chain monte carlo; point process; spatial capture-recapture; tigers; trapping grid";JOURNAL OF APPLIED ECOLOGY;"ROYLE JA;NICHOLS JD;KARANTH KU;GOPALASWAMY AM";estimating animal density using capture-recapture data from arrays of detection devices such as camera traps has been problematic due to the movement of individuals and heterogeneity in capture probability among them induced by differential exposure to trapping. we develop a spatial capture-recapture model for estimating density from camera-trapping data which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to and detection by traps. we adopt a bayesian approach to analysis of the hierarchical model using the technique of data augmentation. the model is applied to photographic capture-recapture data on tigers panthera tigris in nagarahole reserve, india. using this model, we estimate the density of tigers to be 14.3 animals per 100 km(2) during 2004. synthesis and applications. our modelling framework largely overcomes several weaknesses in conventional approaches to the estimation of animal density from trap arrays. it effectively deals with key problems such as individual heterogeneity in capture probabilities, movement of traps, presence of potential 'holes' in the array and ad hoc estimation of sample area. the formulation, thus, greatly enhances flexibility in the conduct of field surveys as well as in the analysis of data, from studies that may involve physical, photographic or dna-based 'captures' of individual animals.
ecological;PVA;developing population models with data from marked individuals;"population viability analysis; stage-structured demographic models; survival; fecundity; density-dependence; monitoring avian productivity and survivorship (maps)";BIOLOGICAL CONSERVATION;"RYU HY;SHOEMAKER KT;KNEIP E;PIDGEON AM;HEGLUND PJ;BATEMAN BL;THOGMARTIN WE;AKCAKAYA HR";population viability analysis (pva) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density dependence, environmental stochasticity, and specification of uncertainties. developing a fully specified population model from commonly available data sources - notably, mark-recapture studies - remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. we present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark-recapture dataset. unlike standard mark-recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. we apply this approach to 9 bird species and demonstrate the feasibility of using data from the monitoring avian productivity and survivorship (maps) program. bias-correction factors for raw estimates of survival and fecundity derived from mark recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of pva. this method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern. (c) 2016 elsevier ltd. all rights reserved.
ecological;HMM;use of hidden markov capture-recapture models to estimate abundance in the presence of uncertainty: application to the estimation of prevalence of hybrids in animal populations;"anthropogenic introgression; capture-recapture; hidden markov models; hybridization; multievent models; prevalence; viterbi algorithm";ECOLOGY AND EVOLUTION;"SANTOSTASI NL;CIUCCI P;CANIGLIA R;FABBRI E;MOLINARI L;REGGIONI W;GIMENEZ O";"estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. the raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. first, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. we developed a hidden markov (also known as multievent) capture-recapture model to estimate prevalence in free-ranging populations accounting for imperfect detectability and uncertainty in individual's classification. we carried out a simulation study to compare naive and model-based estimates of prevalence and assess the performance of our model under different sampling scenarios. we then illustrate our method with a real-world case study of estimating the prevalence of wolf (canis lupus) and dog (canis lupus familiaris) hybrids in a wolf population in northern italy. we showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. model-based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. we also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. our results underline the importance of a model-based approach to obtain unbiased estimates of prevalence of different population segments. our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals)."
ecological;HMM, recruitment;estimating recruitment and survival in partially monitored populations;"calonectris diomedea; capture-recapture; demography; dispersal; experimental design; multi-event; partial monitoring; population modelling; scopoli's shearwater; vital rates";JOURNAL OF APPLIED ECOLOGY;"SANZ AGUILAR A;IGUAL JM;ORO D;GENOVART M;TAVECCHIA G";in evolutionary and ecological studies, demographic parameters are commonly derived from detailed information collected on a limited number of individuals or in a confined sector of the breeding area. this partial monitoring is expected to underestimate survival and recruitment processes because individuals marked in a monitored location may move to or recruit in an unobservable site. we formulate a multi-event capture-recapture model using e-surge software which incorporates additional information on breeding dispersal and the proportion of monitored sites to obtain unbiased estimates of survival and recruitment rates. using simulated data, we assessed the biases in recruitment, survival and population growth rate when monitoring 10-90% of the whole population in a short- and a long-lived species with low breeding dispersal. finally, we illustrate the approach using real data from a long-term monitoring program of a colony of scopoli's shearwaters calonectris diomedea. we found that demographic parameters estimated without considering the proportion of the area monitored were generally underestimated. these biases caused a substantial error in the estimated population growth rate, especially when a low proportion of breeding individuals were monitored. the proposed capture-recapture model successfully corrected for partial monitoring and provided robust demographic estimates. synthesis and applications. in many cases, animal breeding populations can only be monitored partially. consequently, recruitment and immature survival are underestimated, but the extent of these biases depends on the proportion of the area that remains undetected and the degree of breeding dispersal. we present a new method to obtain robust and unbiased measures of survival and recruitment processes from capture-recapture data. the method can be applied to any monitored population regardless of the type of nests (e.g. artificial or natural) or breeding system (e.g. colonial or territorial animals), and it only relies on an estimate of the proportion of the monitored area. the unbiased estimates obtained by this method can be used to improve the reliability of predictions of demographic population models for species' conservation and management.
ecological;Spatial;modeling spatial variation in avian survival and residency probabilities;"capture-recapture; car model; cormack-jolly-seber; hylocichla mustelina; maps program; mist netting; residency; spatial autoregressive model; spatial ecology; survival estimation; transient model; wood thrush";ECOLOGY;"SARACCO JF;ROYLE JA;DESANTE DF;GARDNER B";the importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. yet little attention has been paid to spatial modeling of vital rates. here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. we apply the model to data collected on a declining bird species, wood thrush (hylocichla mustelina), as part of a broad-scale bird-banding network, the monitoring avian productivity and survivorship (maps) program. the wood thrush analysis showed variability in both phi and pi among years and across space. spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. we found broad-scale spatial patterning in wood thrush phi and pi that lend insight into population trends and can direct conservation and research. the spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.
ecological;Data combination, N-mixture;estimating demographic parameters using a combination of known-fate and open n-mixture models;"canis lupus; detection probability; gates of the arctic national park; alaska; usa; integrated model; known-fate models; mark-resight data; n-mixture models; recruitment; survival; wolves";ECOLOGY;"SCHMIDT JH;JOHNSON DS;LINDBERG MS;ADAMS LG";accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. known-fate data from marked individuals are commonly used to estimate survival rates, whereas n-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. however, a joint approach combining the strengths of both analytical tools has not been developed. here we develop an integrated model combining known-fate and open n-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. we demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (canis lupus). simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. we provide implementations in both the bugs language and an r package.
ecological;Batch marking;integrating batch marks and radio tags to estimate the size of a closed population with a movement model;"batch-marking; capture-recapture; movement";ECOLOGY AND EVOLUTION;"SCHWARZ CJ;COPE S;FRATTON G";movement models require individually identifiable marks to estimate the movement rates among strata. but they are relatively expensive to apply and monitor. batch marks can be readily applied, but individual animal movements cannot be identified. we describe a method to estimate population size in a stratified population when movement takes place among strata and animals are marked with a combination of batch and individually identifiable tags. a hierarchical model with bayesian inference is developed that pools information across segments on the detection efficiency based on radio-tagged fish and also uses the movement of the radio-tagged fish to impute the movement of the batch-marked fish to provide estimates of the population size on a segment and river level. the batch marks provide important information to help estimate the movement rates, but contribute little to the overall estimate of the population size. in this case, the approximate equal catchability among strata in either sample obviates the need for stratification.
ecological;SSM;assessing whether mortality is additive using marked animals: a bayesian state-space modeling approach;"bayesian inference; cause-specific mortalities; compensatory mortality; depensatory mortality; mark-recapture; mixture of information; multistate models; ring-recoveries; wild boar";ECOLOGY;"SERVANTY S;CHOQUET R;BAUBET E;BRANDT S;GAILLARD JM;SCHAUB M;TOIGO C;LEBRETON JD;BUORO M;GIMENEZ O";whether different sources of mortality are additive, compensatory, or depensatory is a key question in population biology. a way to test for additivity is to calculate the correlation between cause-specific mortality rates obtained from marked animals. however, existing methods to estimate this correlation raise several methodological issues. one difficulty is the existence of an intrinsic bias in the correlation parameter. although this bias can be formally expressed, it requires knowledge about natural survival without any competing mortality source, which is difficult to assess in most cases. another difficulty lies in estimating the true process correlation while properly accounting for sampling variation. using a bayesian approach, we developed a state-space model to assess the correlation between two competing sources of mortality. by distinguishing the mortality process from its observation through dead recoveries and live recaptures, we estimated the process correlation. to correct for the intrinsic bias, we incorporated experts' opinions on natural survival. we illustrated our approach using data on a hunted population of wild boars. mortalities were not additive and natural mortality increased with hunting mortality likely as a consequence of non-controlled mortality by crippling loss. our method opens perspectives for wildlife management and for the conservation of endangered species.
ecological;SCR, assumptions;how does spatial study design influence density estimates from spatial capture-recapture models?;NA;PLOS ONE;"SOLLMANN R;GARDNER B;BELANT JL";when estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (scr) models present an advance over non-spatial models by accounting for individual movement. while these models should be more robust to changes in trapping designs, they have not been well tested. here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for scr models. we analysed black bear data collected with 123 hair snares with an scr model accounting for differences in detection and movement between sexes and across the trapping occasions. to see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the south of the array. additionally, we simulated and analysed data under a suite of trap designs and home range sizes. in the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. black bear density was approximately 10 individuals per 100 km(2). our simulation study showed that scr models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. scr models performed well across a range of spatial trap setups and animal movements. contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. this renders scr models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.
ecological;mark-resighting, data combination, telemetry;a spatial mark-resight model augmented with telemetry data;"abundance; bayesian statistics; camera trapping; density; mark-resight; metropolis-within-gibbs sampler; population estimation; procyon lotor; raccoons; spatial capture-recapture; telemetry";ECOLOGY;"SOLLMANN R;GARDNER B;PARSONS AW;STOCKING JJ;MCCLINTOCK BT;SIMONS TR;POLLOCK KH;O CONNELL AF";abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. however, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. we developed a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. incorporating telemetry data allows us to inform model parameters related to movement and individual location. our model also allows <100% individual identification of marked individuals. we implemented the model in a bayesian framework, using a custom-made metropolis-within-gibbs markov chain monte carlo algorithm. as an example, we applied this model to a mark-resight study of raccoons (procyon lotor) on south core banks, a barrier island in cape lookout national seashore, north carolina, usa. we estimated a population of 186.71 +/- 14.81 individuals, which translated to a density of 8.29 +/- 0.66 individuals/km(2) (mean +/- sd). the model presented here will have widespread utility in future applications, especially for species that are not naturally marked.
ecological;camera-trapping, assumptions;risky business or simple solution - relative abundance indices from camera-trapping;"density estimation; detection bias; medium to large sized mammals; individual identification; population monitoring";BIOLOGICAL CONSERVATION;"SOLLMANN R;MOHAMED A;SAMEJIMA H;WILTING A";camera-traps are a widely applied to monitor wildlife populations. for individually marked species, capture-recapture models provide robust population estimates, but for unmarked species, inference is often based on relative abundance indices (rai, number of records per trap effort), although these do not account for imperfect and variable detection. we use a simulation study and empirical camera-trapping data to illustrate how ecological and sampling-related factors can bias rais. our simulations showed that (1) differences in detection between species led to bias in rat ratios toward the more detectable species, especially at low detection levels, (2) species with larger home ranges were photographed more often, inflating rais, (3) species specific responses to different types of trap setup biased rat ratios, and (4) changes in detection over time blurred true population trends inferred from rais. empirical data for leopard cats prionailurus bengalensis and common palm civets paradoxurus hermaphroditus showed that traps set up along roads led to higher rais than off-road traps, but targeting roads increased detection more for leopard cats than for common palm civets. comparing rais of sunda clouded leopards neofelis diardi and leopard cats with spatial capture-recapture based density estimates across sites, rais did not reflect differences in density. analytical options for estimating density from camera-trapping data of unmarked populations are limited. consequently, we fear that rais will continue to be applied. this is alarming, since these measures often form the basis for conservation and management decisions. we suggest considering alternative analytical and survey methods, especially when dealing with threatened species. (c) 2012 elsevier ltd. all rights reserved.
ecological;heterogeneity, assumptions, HMM, temporary emigration;to breed or not: a novel approach to estimate breeding propensity and potential trade-offs in an arctic-nesting species;"breeding propensity; chen caerulescens atlantica; cost of reproduction; greater snow goose; heterogeneity; multi-event models; reproduction; reproduction strategy; survival; temporary emigration";ECOLOGY;"SOUCHAY G;GAUTHIER G;PRADEL R";breeding propensity, i.e., the probability that a mature female attempts to breed in a given year, is a critical demographic parameter in long-lived species. life-history theory predicts that this trait should be affected by reproductive trade-offs so that the probability of future reproduction should depend on the current reproductive investment. however, breeding propensity is one of the most difficult parameters to estimate because nonbreeders are often absent from the breeding area, thereby requiring the inclusion of unobservable states in the analysis. we developed a new methodological approach by integrating a robust design sampling scheme within the multi-event capture-recapture framework. our new model accounted for uncertainty in state assignation while allowing for departure of individuals between secondary sampling occasions. we applied this model to a long-term data set of female greater snow geese (chen caerulescens atlantica) to estimate breeding propensity and to investigate potential reproductive costs. we combined resightings during the nesting stage and recapture at the end of the breeding season to estimate breeding propensity and nesting success, and added recoveries to improve survival probability estimates. we found that both breeding propensity and nesting success depended upon breeding status in the previous year, though not survival. successful breeders had a lower breeding propensity than failed breeders in the following year, but a higher nesting success. individuals absent from the breeding colony had a low breeding propensity, but a high nesting success the following year. our results suggest a cost of reproduction on breeding propensity in the next year, but once females decide to breed, nesting success is likely driven by individual quality. an added benefit of our model is that, unlike previous models with unobservable states, all parameters were identifiable when survival and breeding probabilities were fully state dependent. our new multi-event framework is a flexible tool that can be applied to a large range of species to estimate breeding propensity and to investigate reproductive trade-offs.
ecological;SCR, assumptions;trap configuration and spacing influences parameter estimates in spatial capture-recapture models;NA;PLOS ONE;"SUN CC;FULLER AK;ROYLE JA";an increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. we simulated black bear (ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. we varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). we explored trap spacing and number of traps per cluster by varying the number of traps. the clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. however, performance differences between trap configurations diminished as home range size increased. our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. while spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.
ecological;HMM, dispersal;modelling survival and breeding dispersal to unobservable nest sites;"blue penguin; breeding-site fidelity; capture-recapture; merging information; multi-event model; seabirds";WILDLIFE RESEARCH;"TAVECCHIA G;SANZ AGUILAR A;CANNELL B";context demographic parameters in wildlife populations are typically estimated by monitoring a limited number of individuals in observable sites and assuming that these are representative of the whole population. if individuals permanently disperse to unobservable breeding sites, recruitment and immature survival are expected to be negatively biased and breeding-site fidelity cannot be investigated. aims to develop a method to obtain unbiased estimated of survival, recruitment and breeding dispersal when individuals can move to, or recruit in, unobservable sites. methods we used the flexibility of multi-event capture-recapture models to estimate dispersal and recruitment to unobservable sites, merging observations made at two sites within the same breeding locations. we illustrated the model with data on little penguin (eudyptula minor) breeding in artificial as well as in natural nests. natural nests are unknown or inaccessible and birds in these sites remain unobservable. encounters at beaches surrounding the colony suggested that marked animals can permanently move to unobservable nests. we built the multi-event model considering two possible states of the individuals (alive breeding in a nest box and alive in a natural nest) and three types of observations (encountered at a nest only, encountered at the beach only and encountered at both places). this model ensured that the breeding dispersal to unobservable places became estimable. key results results indicate that the estimated survival was 8% higher than when recaptures at artificial nests were analysed alone. also, fidelity to artificial nests was 12% lower than to natural nests. this might reflect the greater availability of natural sites or, alternatively, a heterogeneity between these two types of nest. conclusions we obtained an estimate of local survival of little penguins breeding at penguin island that incorporates the permanent migration to unobservable sites and found an asymmetric dispersion towards natural nests. implication our conclusions suggest a need for more careful treatment of data derived from artificial sites alone, as demographic parameters might be underestimated if animals prefer natural breeding sites or if they are in greater proportion compared with artificial ones. the analytical approach presented can be applied to many biological systems, when animals might move into inaccessible or unobservable breeding sites.
ecological;SCR, data combination;data integration for inference about spatial processes: a model-based approach to test and account for data inconsistency;NA;PLOS ONE;"TENAN S;PEDRINI P;BRAGALANTI N;GROFF C;SUTHERLAND C";recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry). despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration. using data collected on the reintroduced brown bear population in the italian alps, a population of conservation importance, we combined data from three sources: traditional spatial capture-recapture data, telemetry data, and opportunistic data. we developed a fully integrated spatial capture-recapture (scr) model that included a model-based test for data consistency to first compare model estimates using different combinations of data, and then, by acknowledging data-type differences, evaluate parameter consistency. we demonstrate that opportunistic data lend itself naturally to integration within the scr framework and highlight the value of opportunistic data for improving inference about space use and population size. this is particularly relevant in studies of rare or elusive species, where the number of spatial encounters is usually small and where additional observations are of high value. in addition, our results highlight the importance of testing and accounting for inconsistencies in spatial information from structured and unstructured data so as to avoid the risk of spurious or averaged estimates of space use and consequently, of population size. our work supports the use of a single modeling framework to combine spatially-referenced data while also accounting for parameter consistency.
ecological;PGR;assessing the effect of density on population growth when modeling individual encounter data;"audouin's gull; capture-recapture; gibbs variable selection; open population estimation; population dynamics; pradel model; rate of population change; temporal symmetry model";ECOLOGY;"TENAN S;TAVECCHIA G;ORO D;PRADEL R";the relative role of density-dependent and density-independent variation in vital rates and population size remains largely unsolved. despite its importance to the theory and application of population ecology, and to conservation biology, quantifying the role and strength of density dependence is particularly challenging. we present a hierarchical formulation of the temporal symmetry approach, also known as the pradel model, that permits estimation of the strength of density dependence from capture-mark-reencounter data. a measure of relative population size is built in the model and serves to detect density dependence directly on population growth rate. the model is also extended to account for temporal random variability in demographic rates, allowing estimation of the temporal variance of population growth rate unexplained by density dependence. we thus present a model-based approach that enable to test and quantify the effect of density-dependent and density-independent factors affecting population fluctuations in a single modeling framework. more generally, we use this modeling framework along with simulated and empirical data to show the value of including density dependence when modeling individual encounter data without the need for auxiliary data.
ecological;SCR, detection dogs;a framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs;"bayesian; density; fisher; martes pennanti; scat detector dogs; winbugs";JOURNAL OF WILDLIFE MANAGEMENT;"THOMPSON CM;ROYLE JA;GARNER JD";wildlife management often hinges upon an accurate assessment of population density. although undeniably useful, many of the traditional approaches to density estimation such as visual counts, livetrapping, or markrecapture suffer from a suite of methodological and analytical weaknesses. rare, secretive, or highly mobile species exacerbate these problems through the reality of small sample sizes and movement on and off study sites. in response to these difficulties, there is growing interest in the use of non-invasive survey techniques, which provide the opportunity to collect larger samples with minimal increases in effort, as well as the application of analytical frameworks that are not reliant on large sample size arguments. one promising survey technique, the use of scat detecting dogs, offers a greatly enhanced probability of detection while at the same time generating new difficulties with respect to non-standard survey routes, variable search intensity, and the lack of a fixed survey point for characterizing non-detection. in order to account for these issues, we modified an existing spatially explicit, capturerecapture model for camera trap data to account for variable search intensity and the lack of fixed, georeferenced trap locations. we applied this modified model to a fisher (martes pennanti) dataset from the sierra national forest, california, and compared the results (12.3?fishers/100?km2) to more traditional density estimates. we then evaluated model performance using simulations at 3 levels of population density. simulation results indicated that estimates based on the posterior mode were relatively unbiased. we believe that this approach provides a flexible analytical framework for reconciling the inconsistencies between detector dog survey data and density estimation procedures. (c) 2011 the wildlife society.
ecological;SCR, dispersal;estimating brownian motion dispersal rate, longevity and population density from spatially explicit mark-recapture data on tropical butterflies;"brownian motion; dispersal; effective trapping area; mark-recapture; survival; tropical butterflies";JOURNAL OF ANIMAL ECOLOGY;"TUFTO J;LANDE R;RINGSBY TH;ENGEN S;SAETHER BE;WALLA TR;DEVRIES PJ";1. we develop a bayesian method for analysing markrecapture data in continuous habitat using a model in which individuals movement paths are brownian motions, life spans are exponentially distributed and capture events occur at given instants in time if individuals are within a certain attractive distance of the traps. 2. the joint posterior distribution of the dispersal rate, longevity, trap attraction distances and a number of latent variables representing the unobserved movement paths and time of death of all individuals is computed using gibbs sampling. 3. an estimate of absolute local population density is obtained simply by dividing the poisson counts of individuals captured at given points in time by the estimated total attraction area of all traps. our approach for estimating population density in continuous habitat avoids the need to define an arbitrary effective trapping area that characterized previous markrecapture methods in continuous habitat. 4. we applied our method to estimate spatial demography parameters in nine species of neotropical butterflies. path analysis of interspecific variation in demographic parameters and mean wing length revealed a simple network of strong causation. larger wing length increases dispersal rate, which in turn increases trap attraction distance. however, higher dispersal rate also decreases longevity, thus explaining the surprising observation of a negative correlation between wing length and longevity.
ecological;continuous;integrated survival analysis using an event-time approach in a bayesian framework;"charadrius montanus; continuous time; detection probability; event time; hazard rate; mountain plover; simulation; survival; unknown fate";ECOLOGY AND EVOLUTION;"WALSH DP;DREITZ VJ;HEISEY DM";event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. however, these techniques have traditionally relied on knowing failure times. this has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. to address these limitations, we developed an integrated approach within a bayesian framework to estimate hazard rates in the face of unknown fates. we combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. this provides the foundation for our integrated model and permits necessary parameter estimation. we provide the bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (charadrius montanus) chicks. traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. simulations revealed biases of posterior mean estimates were minimal ( 4.95%), and posterior distributions behaved as expected with rmse of the estimates decreasing as sample sizes, detection probability, and survival increased. we determined mortality hazard rates for plover chicks were highest at <5days old and were lower for chicks with larger birth weights and/or whose nest was within agricultural habitats. based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.
ecological;PVA;estimating extinction risk with minimal data;"extinction; modeling; risk assessment; conservation; occupancy; cormack-jolly-seber models";BIOLOGICAL CONSERVATION;"WEISS LEHMAN C;DAVIES KF;CLEMENTS C;MELBOURNE BA";anthropogenic pressures on the global biome are causing widespread species declines and extinctions. assessing the extinction risk faced by individual species is a critical first step in combating this trend. however, we lack high quality demographic data to do so for the vast majority of plant and animal species. we present an efficient modeling approach to estimate extinction risk based on a statistical framework from the mark-recapture literature. we assessed the model's performance using a combination of simulated data, results from a protist microcosm experiment, and data from a long-term, large-scale habitat fragmentation experiment in southeastern australia. simulation experiments showed the model is robust to missing data as well as biological processes not included explicitly in the model's assumptions. fitting the model to data from the protist experiment yielded accurate predictions of the regional extinction dynamics observed in the system, even with a relatively low level of replication. finally, the model was able to accurately predict the observed dynamics in the habitat fragmentation experiment. the model provides a robust and accurate method to evaluate a species' extinction risk. since it only requires presence/absence data, applies to a wide range of survey designs, and allows for observational uncertainty and missing data, it can be applied to many datasets that existing models cannot accommodate. for these reasons, the model should be useful in conservation settings.
ecological;heterogeneity, random effects;population abundance estimation with heterogeneous encounter probabilities using numerical integration;"abundance estimation; capture-mark-reencounter; huggins estimator; individual heterogeneity; m-h; numerical integration; program mark";JOURNAL OF WILDLIFE MANAGEMENT;"WHITE GC;COOCH EG";"estimation of population abundance is a common problem in wildlife ecology and management. capture-mark-reencounter (cmr) methods using marked animals are a standard approach, particularly in recent history with the development of innovative methods of marking using camera traps or dna samples. however, estimates of abundance from multiple encounters of marked individuals are biased low when individual heterogeneity of encounter probabilities is not accounted for in the estimator. we evaluated the operating characteristics of the huggins logit-normal estimator through computer simulations, using gaussian-hermite quadrature to model individual encounter heterogeneity. we simulated individual encounter data following a factorial design with 2 levels of sampling occasions (t=5, 10), 3 levels of abundance (n=100, 500, 1,000), 4 levels of median detection probabilities (p=0.1, 0.2, 0.4, 0.6) for each sampling occasion (on the probability scale), and 4 levels of individual heterogeneity (sigma(p)=0, 0.5, 1, 2; on the logit normal scale), resulting in a design space consisting of 96 simulation scenarios (2x3x4x4). for each scenario, we performed 1,000 simulations using the huggins estimators m-t, m-0, m-tre, and m-0re, where the re subscript corresponds to the random effects model. as expected, the m-t and m-0 estimators were biased when individual heterogeneity was present but unbiased for sigma(p)=0 data. the estimators for m-tre and m-0re were biased high for n=100 and median p0.2 but showed little bias elsewhere. the bias is attributed to the occasional sets of data that result in a low overall detection probability and a resulting highly skewed sampling distribution of this result is confirmed in that the median of the sampling distributions was only slightly biased high. the random effects estimators performed poorly for sigma(p)=0 data, mainly because a log link function forces the estimate of sigma(p)>0. however, the fletcher statistic provided useful evidence to evaluate sigma(p)>0, as did likelihood ratio tests of the null hypothesis sigma(p)=0. generally, confidence interval coverage of n appears close to the nominal 95% expected when the estimator is not biased. (c) 2017 the wildlife society. estimates of population abundance from multiple encounters of marked individuals are biased low when individual heterogeneity of encounter probabilities is not accounted for in the estimator. through computer simulations, we found that the huggins logit-normal estimator with gaussian-hermite quadrature to integrate out individual random effects eliminates major bias of the abundance estimates with an adequate number of sampling occasions and reasonable detection probabilities."
ecological;mark-resighting, robust design, point process, telemetry;generalized spatial mark-resight models with an application to grizzly bears;"banff national park; carnivore; grizzly bear; hierarchical model; point process models; population density; remote camera; spatial capture-recapture; spatial mark-resight; telemetry";JOURNAL OF APPLIED ECOLOGY;"WHITTINGTON J;HEBBLEWHITE M;CHANDLER RB";1. the high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. recently developed spatial mark-resight (smr) models were proposed as a cost-effective alternative because they only require a single marking event. however, existing smr models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. this assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. 2. we developed a generalized smr model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. 3. our simulation study demonstrated that conventional smr models produce biased density estimates with low credible interval coverage (cic) when marked and unmarked animals had differing spatial distributions. in contrast, generalized smr models produced unbiased density estimates with correct cic in all scenarios. 4. we applied our smr model to grizzly bear (ursus arctos) data where the marking process occurred along a transportation route through banff and yoho national parks, canada. twenty-two grizzly bears were trapped, fitted with radiocollars and then detected along with unmarked bears on 214 remote cameras. closed population density estimates (posterior median1 sd) averaged from 2012 to 2014 were much lower for conventional smr models (7.41.0 bears per 1,000km(2)) than for generalized smr models (12.4 +/- 1.5). when compared to previous dna-based estimates, conventional smr estimates erroneously suggested a 51% decline in density. conversely, generalized smr estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. 5. synthesis and applications. mark-resight studies often cost less than capture-recapture studies, but require that marked and unmarked animals have equal encounter rates. generalized spatial mark-resight models relax this assumption by including sub-models for both the marking and resighting processes. they produce unbiased density estimates even when marked and unmarked animals have differing spatial distributions and encounter rates. they thus provide a cost-effective and widely applicable approach for estimating the density of wildlife populations.
ecological;telemetry;a novel approach for estimating densities of secretive species from road-survey and spatial-movement data;"abundance estimation; behaviour; heterodon simus; method; radio-telemetry; southern hognose snake";WILDLIFE RESEARCH;"WILLSON JA;PITTMAN SE;BEANE JC;TUBERVILLE TA";context. accurate estimates of population density are a critical component of effective wildlife conservation and management. however, many snake species are so secretive that their density cannot be determined using traditional methods such as capture-mark-recapture. thus, the status of most terrestrial snake populations remains completely unknown. aim. we developed a novel simulation-based technique for estimating density of secretive snakes that combined behavioural observations of snake road-crossing behaviour (crossing speed), effort-corrected road-survey data, and simulations of spatial movement patterns derived from radio-telemetry, without relying on mark recapture. methods. we used radio-telemetry data to parameterise individual-based movement models that estimate the frequency with which individual snakes cross roads and used information on survey vehicle speed and snake crossing speed to determine the probability of detecting a snake, given that it crosses the road transect during a survey. snake encounter frequencies during systematic road surveys were then interpreted in light of detection probabilities and simulation model results to estimate snake densities and to assess various factors likely to affect abundance estimates. we demonstrated the broad applicability of this approach through a case study of the imperiled southern hognose snake (heterodon simus) in the north carolina (usa) sandhills. key results. we estimated that h. simus occurs at average densities of 0.17 ha(-1) in the north carolina sandhills and explored the sensitivity of this estimate to assumptions and variation in model parameters. conclusions. our novel method allowed us to generate the first abundance estimates for h. simus. we found that h. simus exists at low densities relative to congeners and other mid-sized snake species, raising concern that this species may not only have declined in geographic range, but may also occur at low densities or be declining in their strongholds, such as the north carolina sandhills.
ecological;Robust design, temporary emigration;ecological and methodological factors affecting detectability and population estimation in elusive species;"aquatic snakes; behavioral responses; detection probability; mark-recapture; minnow trap; nerodia fasciata; population estimation; robust design; seminatrix pygaea; temporary emigration";JOURNAL OF WILDLIFE MANAGEMENT;"WILLSON JD;WINNE CT;TODD BD";although mark-recapture methods are among the most powerful tools for monitoring wildlife populations, the secretive nature of some species requires a comprehensive understanding of the factors that affect capture probability to maximize accuracy and precision of population parameter estimates (e.g., population size and survivorship). here, we used aquatic snakes as a case study in applying rigorous mark-recapture methods to estimate population parameters for secretive species. specifically, we used intensive field sampling and robust design mark-recapture analyses in program mark to test specific hypotheses about ecological and methodological factors influencing detectability of two species of secretive aquatic snakes, the banded watersnake (nerodia fasciata), and the black swamp snake (seminatrix pygaea). we constructed a candidate set of a priori mark-recapture models incorporating various combinations of time-and sex-varying capture and recapture probabilities, behavioral responses to traps (i.e., trap-happiness or trap-shyness), and temporary emigration, and we ranked models for each species using akaike's information criterion. for both banded watersnakes and black swamp snakes we found strong support for time-varying capture and recapture probabilities and strong trap-happy responses, factors that can bias population estimation if not accommodated in the models. we also found evidence of sex-dependent temporary emigration in black swamp snakes. our study is among the first comprehensive assessments of factors affecting detectability in snakes and provides a framework for studies aimed at monitoring populations of other secretive species. (c) 2011 the wildlife society.
ecological;Misidentification;effects of photo and genotype-based misidentification error on estimates of survival, detection and state transition using multistate survival models;NA;PLOS ONE;"WINIARSKI KJ;MCGARIGAL K";we simulated multistate capture histories (chs) by varying state survival (phi), detection (p) and transition (psi), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (fre), a common misidentification error, resulting from the failure to match samples of the same individual. we then fit a multistate model and estimated accuracy, bias and precision of state-specific phi, p and psi to better understand the effects of fre on different simulation scenarios. as expected, phi, and p, decreased in accuracy with fre, with lower accuracy when chs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). accuracy of. estimates were robust to fre except in those ch scenarios simulated using low sample size. the effect of fre on bias was not consistent among parameters and differed by ch scenario. as expected, phi was negatively biased with increased fre (except for the low. low p ch scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p ch scenarios were more negatively biased). state transition was relatively unbiased, except for the low p ch scenarios simulated with a low sample size, which were positively biased with fre, and high p ch scenarios simulated with a low sample size. the effect of fre on precision was not consistent among parameters and differed by scenario and sample size. precision of phi decreased with fre and was lowest with the low phi low p ch scenarios. precision of p estimates also decreased with fre under all scenarios, except the low phi high p ch scenarios. however, precision of psi increased with fre, except for those ch scenarios simulated with a low sample size. our results demonstrate how fre leads to loss of accuracy in parameter estimates in a multistate model with the exception of. when estimated using an adequate sample size.
ecological;Multispecies, interactions;inferring species interactions through joint mark-recapture analysis;"bayesian; competition; density dependence; gila cypha; interspecific; intraspecific; mark-recapture; oncorhynchus mykiss; predation";ECOLOGY;"YACKULIC CB;KORMAN J;YARD MD;DZUL M";introduced species are frequently implicated in declines of native species. in many cases, however, evidence linking introduced species to native declines is weak. failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. for many species, mark-recapture analysis is the more rigorous form of demographic analysis. however, to our knowledge, there are no mark-recapture models that allow for joint modeling of interacting species. here, we introduce a two-species mark-recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. we use a simulation study to explore bias and choose an approach to model selection. we then use the model to investigate species interactions between endangered humpback chub (gila cypha) and introduced rainbow trout (oncorhynchus mykiss) in the colorado river between 2009 and 2016. in particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. we also project the long-term effects of different rainbow trout abundances on adult humpback chub abundances. our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. when the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. the general approach presented here for combining mark-recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.
ecological;Misidentification, camera trapping;modeling misidentification errors in capture-recapture studies using photographic identification of evolving marks;"capture-recapture; closed-population models; evolving tags; misidentification; natural tags; photographic identification; population size estimate";ECOLOGY;"YOSHIZAKI J;POLLOCK KH;BROWNIE C;WEBSTER RA";misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture-recapture study. photographic identification (photoid) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non-evolving natural tags) and those related to changes in natural marks (evolving natural tags). the conventional methods for analysis of capture-recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. we introduce methods for estimating population size based on this model. using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (< 0.2) or low misidentification rates (< 2.5%).
ecological;Data combination, N-mixture, IPM;integrating count and detection-nondetection data to model population dynamics;"dail-madsen model; detection probability; integrated population model; n-mixture model; occupancy; unmarked data";ECOLOGY;"ZIPKIN EF;ROSSMAN S;YACKULIC CB;WIENS JD;THORSON JT;DAVIS RJ;GRANT EHC";there is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. current work on this topic primarily focuses on combining capture-recapture data from marked individuals with other data types into integrated population models. yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. we present a modeling framework for integrating detection-nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. the observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. we examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection-nondetection data under a variety of parameter values and survey configurations. we also provide an empirical example of the model by combining long-term detection-nondetection data (1995-2014) with newly collected count data (2015-2016) from a growing population of barred owl (strix varia) in the pacific northwest to examine the factors influencing population abundance over time. our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. this approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
ecological;NA;inferences about population dynamics from count data using multistate models: a comparison to capture-recapture approaches;NA;ECOLOGY AND EVOLUTION;"ZIPKIN EF;SILLETT TS;GRANT EHC;CHANDLER RB;ROYLE JA";NA
ecological;N-mixture, IPM;modeling structured population dynamics using data from unmarked individuals;"desmognathus fuscus; detection probability; n-mixture model; northern dusky salamander; stage-structured models; state-space models";ECOLOGY;"ZIPKIN EF;THORSON JT;SEE K;LYNCH HJ;GRANT EHC;KANNO Y;CHANDLER RB;LETCHER BH;ROYLE JA";the study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. traditionally, these estimates have only been available through approaches that rely on intensive mark-recapture data. we extended recently developed n-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. we present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. we apply our modeling framework to a population of northern dusky salamanders (desmognathus fuscus) in the mid-atlantic region (usa) and find that the population is unexpectedly declining. our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark-recapture) and extensive (e.g., counts) data sources.