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Ecology Module

Community ecology analysis: diversity indices, ordination, species abundance distributions, and functional trait analysis.

Architecture

graph TD
    subgraph "Ecology Module"
        AC[analysis/community.py] --> |calculate_diversity| DIV[Shannon, Simpson, Richness]

        AO[analysis/ordination.py] --> |pcoa| PCO[Principal Coordinates]
        AO --> |nmds| NM[Non-metric MDS]
        AO --> |cca| CC[Canonical Correspondence]

        AI[analysis/indicators.py] --> |indval| IV[Indicator Species]
        AI --> |permanova| PA[PERMANOVA]
        AI --> |anosim| AN[ANOSIM]
        AI --> |simper| SP[SIMPER]

        AF[analysis/functional.py] --> |functional_richness| FR[FRic, FEve, FDiv]
        AF --> |raos_quadratic_entropy| RQ[Rao's Q]

        AM[analysis/macroecology.py] --> |fit_logseries| SAD[SAD Models]
        AM --> |species_area_power| SAR[Species-Area]

        PH[phylogenetic/diversity.py] --> PD[Phylogenetic Diversity]

        VZ[visualization/] --> VP[Community Plots]
    end
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Key Capabilities

Diversity Indices

from metainformant.ecology.analysis.community import calculate_diversity

# Shannon diversity for multiple communities
shannon = calculate_diversity([[10, 20, 30, 40], [5, 5, 5, 85]], method="shannon")
simpson = calculate_diversity([[10, 20, 30, 40]], method="simpson")

Ordination

Function Module Description
pcoa analysis.ordination Principal Coordinates Analysis (Gower double-centering)
nmds analysis.ordination Non-metric MDS (Kruskal stress minimization)
cca analysis.ordination Canonical Correspondence Analysis
distance_matrix analysis.ordination Bray-Curtis, Jaccard, Euclidean, Canberra distances
procrustes analysis.ordination Procrustes rotation for ordination comparison

Hypothesis Testing and Indicators

Function Module Description
indval analysis.indicators Indicator species analysis (Dufrene-Legendre)
permanova analysis.indicators Permutational MANOVA on distance matrices
anosim analysis.indicators Analysis of similarities
simper analysis.indicators Similarity percentages decomposition
multivariate_dispersion analysis.indicators Beta-dispersion (Anderson)

Functional Diversity

Function Module Description
functional_richness analysis.functional FRic from convex hull volume
functional_evenness analysis.functional FEve from minimum spanning tree
functional_divergence analysis.functional FDiv weighted divergence
raos_quadratic_entropy analysis.functional Rao's quadratic entropy
functional_diversity_suite analysis.functional All indices in one call

Submodules

Module Purpose
analysis/ Community diversity, ordination, indicators, functional traits, macroecology
phylogenetic/ Phylogenetic diversity metrics
traits/ Trait-based ecology: CWM, functional richness (FRic), evenness (FEve), divergence (FDiv), Rao's Q
visualization/ Community ecology plots

Quick Start

from metainformant.ecology.analysis.community import calculate_diversity
from metainformant.ecology.analysis.ordination import distance_matrix, pcoa
from metainformant.ecology.analysis.indicators import permanova
from metainformant.ecology.analysis.functional import functional_diversity_suite

# Compute Bray-Curtis distances between communities
abundances = [[10, 20, 0, 5], [12, 18, 3, 7], [0, 5, 30, 15]]
dist = distance_matrix(abundances, method="bray_curtis")

# PCoA ordination
coords = pcoa(dist, n_components=2)

# PERMANOVA test for group differences
result = permanova(dist, group_labels=["forest", "forest", "grassland"])

# Full functional diversity suite
fd = functional_diversity_suite(trait_matrix=traits, abundances=abund)

Integration

  • DNA -- Phylogenetic diversity metrics use tree structures from metainformant.dna.phylogeny
  • Information -- Shannon entropy connects community diversity to information theory
  • Simulation -- Agent-based ecological models via metainformant.simulation

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