begoniasociety/research.html at master · seltmann/begoniasociety · GitHub

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<p><br /></p>
<div id="research-areas" class="section level2">
<h2>Research Areas</h2>
<p><img align="right" src="https://seltmann.github.io/begoniasociety/images/Rplot01.png" width="400">
My principal research interests are to digitally capture biodiversity
information using materials from natural history collections and to
employ these data to analyze, model, and decode patterns of
environmental, morphological, and community change. The patterns provide
insights into the classification of organisms, the evolution of
phenotype, and the ecological function of traits. Each specimen is an
object that contains many associated pieces of information. This
information includes the occurrence of the specimen in space, time, or
other parameters, its internal and external anatomy, DNA, stable isotope
and chemical signatures, associated publications, images, and the
individuals who studied or collected the organisms. Natural history
collections comprise libraries of millions of biological specimens,
ranging from preserved pollinating bees to dinosaur remnants. These
collections can shed light on environmental change and morphological
evolution over long periods, illuminate how species are lost, and how
biological knowledge has evolved. As a leader and key contributor to
significant specimen digitization and data aggregation efforts, I have
helped initiate, produce, and advance important regional and global
datasets. My research efforts are also guided by the recognition that
collections serve as invaluable hubs for research on specific taxa and
are integral to our global cultural heritage.</p>
<p>Much of my research addresses fundamental questions in Hymenoptera
systematics and biodiversity data sharing. However, my research also
addresses interactions and communities of organisms and thus includes
investigations involving other insects and kingdoms, including plants,
birds, and reptiles. Currently, we are investigating trait evolution
through new computational tools, new methods for sharing biodiversity
data, and new efforts for describing insect-plant interactions and
distributions for conservation.
<img align="left" src="https://seltmann.github.io/begoniasociety/research_files/figure-html/globi-1.png" width="400"></p>
</div>
<div id="biocollections" class="section level2">
<h2>Biocollections</h2>
<p>Central to my research are natural history collections - massive sets
of biological specimens that are housed in museums, tissue collections,
herbaria, botanical gardens, and zoos, and that have been compiled over
several centuries. Together, they constitute our biodiversity libraries.
These collections have broad cultural significance, and more
importantly, constitute enormous primary resources that support
fundamental research in the biological sciences. These libraries of
millions of biological specimens, ranging from preserved pollinating
bees to dinosaur remnants, shed light on the past, and illuminate ways
that the climate has changed, that species are lost, and how biological
knowledge has evolved. They also constitute voucher repositories for
specimens used in research and identification of organisms of all
varieties.</p>
</div>
<div
id="investigating-traits-and-morphology-via-biodiversity-data-science-and-computer-vision"
class="section level2">
<h2>Investigating Traits and Morphology via Biodiversity Data Science
and Computer Vision</h2>
<p>A key research emphasis in my lab is to develop and apply new
computational imaging and data science tools for fundamental
investigations in morphology, anatomy, and phenotype. This research area
is exemplified by a major NSF initiative I am leading, <i>Extending
Anthophila research through image and trait digitization
(Big-Bee)</i><sup>1</sup>. A cornerstone of this 13-institution effort
is the high-resolution imaging of more than one million specimens,
representing more than 5000 bee species, in 2D and 3D modalities. These
data and novel computational tools we are developing in the project will
enable quantitative analyses of morphological features and will furnish
new automated tools that aid identification. Recent advancements in
computer vision and machine learning are transforming many fields of
science, including entomology<sup>2</sup>. These emerging computational
methods are already yielding striking results, including impressive
capacities for sorting organisms<sup>3</sup> and quantifying
abundances.</p>
<p>My research group is investigating multiple computer vision
techniques (image segmentation, feature identification) and machine
learning methods (data augmentation, transfer learning) for the
quantification of bee traits, the analysis of variation between species
or populations, and the development of new morphological characters for
identification and systematic study. A simple but highly effective
example from our current work involves the computational quantification
of bee pilosity (Fig 1), a trait that may affect thermal tolerance and
the ability of a species to adapt to changing climate conditions. Bee
hairs have also evolved for carrying pollen, and clades differ markedly
in pilosity patterns. However, hair patterns, like many morphological
structures, are variable and difficult to quantify in a repeatable and
scalable manner and thus exemplify the potential value that
computational tools for trait analysis may contribute.</p>
<p>In addition to investigating new character systems, my research group
is developing scalable, geographic-specific specimen classifiers using
training data from images captured from natural history collections. The
overarching vision of this effort is to develop methods for creating
individualized models that can be used as “keys” that can be trained to
encode expert domain knowledge of taxonomists or researchers.</p>
<p><img align="right" src="https://seltmann.github.io/begoniasociety/images/segmentation.jpg"></p>
</div>
<div id="advancing-biodiversity-data-and-knowledge-sharing"
class="section level2">
<h2>Advancing Biodiversity Data and Knowledge Sharing</h2>
<p>Access to biological information is dramatically reshaping the kinds
of research questions we can address and raising questions about how we
generate and manage biological data. Through my research, I examine
processes through which we capture information for research in digital
formats, use and reuse large datasets, publish and share biological
data, preserve at-risk information, and use this information in
scientific activities. My work is consequently aligned with major
research initiatives in biodiversity that seek to enable researchers to
access and exploit biodiversity data troves through global computing
systems and new technologies. Presently, I am working with a global
network of collaborators on the Extended Specimen Concept<sup>4</sup>, a
now widely accepted concept that I co-authored for the future of data
sharing. This initiative is working to promote and interconnect emerging
multimodal specimen datasets, including 3D image models<sup>5</sup>,
environmental data, and trait data (as in the Global Bee Interaction
Database<sup>6</sup>). One example of my research activity in this
overarching area is the publication “A semantically enriched taxonomic
revision of <i>Gryonoides</i> Dodd, 1920 (Hymenoptera, Scelionidae),
with a review of the hosts of Teleasinae.” (J. Hymenoptera Res., 2021).
This work exploits data sharing of examined material and translation
into PhenoScript, a standardized language for linking morphology with
ontology to explicitly describe characters.
<img align="right" src="https://seltmann.github.io/begoniasociety/images/SCI.jpg" width="300"></p>
</div>
<div id="insect-distributions-and-conservation" class="section level2">
<h2>Insect Distributions and Conservation</h2>
<p>A further research focus is aimed at understanding and mitigating
insect biodiversity loss. Understanding such declines requires
characterizing the species distributions of insects and their host
plants. My group investigates these questions by analyzing camera trap
data and through biodiversity data-driven analyses of distributions. We
complement these methods with basic fieldwork to enable us to evaluate
hypotheses via ground-truth data. Presently, my collaborators and I are
developing new techniques for analyzing co-occurrences of plants and
bees from collection data, which make it possible to identify shifts or
phenological mismatches. Our ongoing work includes taxonomic evaluations
of bees on Santa Cruz Island, California, to produce a checklist for
this scientifically valuable island ecosystem<sup>7</sup>. Species
checklists are a key product of systematic research and also furnish
valuable resources for informing predictions of multi-species occupancy
models, ecological networks, and other community-level classifications.
I am complementing these activities by developing new computer vision
approaches for automating the identification and prediction of new
species (Fig. 3). The methods we are developing for research can be
applied to important conservation challenges, and our research engages
state and federal agencies, including USFW and USGS. We have applied
these methods in recent characterizations of the insects associated with
the endangered California lupine plant<sup>8</sup> and in a genome
skimming analysis of herbarium specimens aimed at characterizing
populations of an estuarine seablite (Madroño Journal, Accepted,
2023).</p>
</div>
<div id="references" class="section level2">
<h2>References</h2>
<ol style="list-style-type: decimal">
<li>Katja C. Seltmann (2021). Extending Anthophila research through
image and trait digitization (Big-Bee) proposal. UC Santa Barbara:
Cheadle Center for Biodiversity and Ecological Restoration. Retrieved
from <a href="https://escholarship.org/uc/item/2vm761mv"
class="uri">https://escholarship.org/uc/item/2vm761mv</a></li>
<li>Høye, Toke T., Johanna Ärje, Kim Bjerge, Oskar LP Hansen, Alexandros
Iosifidis, Florian Leese, Hjalte MR Mann, Kristian Meissner, Claus
Melvad, and Jenni Raitoharju. “Deep learning and computer vision will
transform entomology.” Proceedings of the National Academy of Sciences
118, no. 2 (2021): e2002545117.</li>
<li>Spiesman, Brian J., Claudio Gratton, Richard G. Hatfield, William H.
Hsu, Sarina Jepsen, Brian McCornack, Krushi Patel, and Guanghui Wang.
“Assessing the potential for deep learning and computer vision to
identify bumble bee species from images.” Scientific reports 11, no. 1
(2021): 1-10.</li>
<li>James Lendemer, Barbara Thiers, Anna K Monfils, Jennifer Zaspel,
Elizabeth R Ellwood, Andrew Bentley, Katherine LeVan, John Bates, David
Jennings, Dori Contreras, Laura Lagomarsino, Paula Mabee, Linda S Ford,
Robert Guralnick, Robert E Gropp, Marcy Revelez, Neil Cobb, Katja
Seltmann, M Catherine 5. Aime, The Extended Specimen Network: A Strategy
to Enhance US Biodiversity Collections, Promote Research and Education,
BioScience, Volume 70, Issue 1, January 2020, Pages 23–30, <a
href="https://doi.org/10.1093/biosci/biz140"
class="uri">https://doi.org/10.1093/biosci/biz140</a></li>
<li>Katja C. Seltmann, Jorrit H. Poelen, UCSB Natural History
Collections, &amp; Big Bee Community. (2023). Halictus ligatus
Halictidae UCSB-IZC00036803 [Data set]. Zenodo. <a
href="https://doi.org/10.5281/zenodo.7510912"
class="uri">https://doi.org/10.5281/zenodo.7510912</a></li>
<li>Katja C. Seltmann. (2022). Global Bee Interaction Data (v2.02) [Data
set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7315159"
class="uri">https://doi.org/10.5281/zenodo.7315159</a></li>
<li>Katja C. Seltmann, David Dewey, Lynn McLaren, &amp; Charlie Thrift.
(2022). Native and non-native bees (Anthophila) of Santa Cruz Island: An
annotated checklist (0.03). Zenodo. <a
href="https://doi.org/10.5281/zenodo.7216845"
class="uri">https://doi.org/10.5281/zenodo.7216845</a></li>
<li>C. I. Motta, J. C. Luong, K. C. Seltmann. Plant–arthropod
interactions of an endangered California lupine. Ecology and Evolution.
2022 Mar;12(3):e8688.</li>
</ol>
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