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A hierarchical Bayesian approach for modeling changes in species composition

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  • Dias, Filipe S.
  • Betancourt, Michael
  • Rodríguez-González, Patricia María
  • de Água, Luís Borda

Abstract

Understanding the factors associated with changes in species composition is a critical issue in ecology. A typical modeling approach consists of calculating beta diversity indices between pairs of sites using beta diversity indices and assessing the relationship between those indices and environmental covariates. Beta diversity indices are paired comparisons, which means that indices calculated with the same sample are not independent. The most common solution is to transform community data and environmental covariates into distance matrices, perform row and column permutations, and calculate correlations (e.g., Mantel tests and Generalized Dissimilarity Modeling). Here we introduce a novel hierarchical Bayesian approach that deals with this dependence by including two varying intercepts, one per sample, that capture the heterogeneity corresponding to that sample. Our modeling approach is highly flexible and customizable, adding novel features that are not available in any existing software packages. It allows the relationship between beta diversity indices and covariates to change across different regions by including varying intercepts and slopes and provides a simple path for performing model validation and model improvement.

Suggested Citation

  • Dias, Filipe S. & Betancourt, Michael & Rodríguez-González, Patricia María & de Água, Luís Borda, 2021. "A hierarchical Bayesian approach for modeling changes in species composition," EcoEvoRxiv sn5jr, Center for Open Science.
    • Handle: RePEc:osf:ecoevo:sn5jr
    DOI: 10.31219/osf.io/sn5jr
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