BetaBayes—A Bayesian Approach for Comparing Ecological Communities

• Main Author: Dias, Filipe S.
• Publication Date: 2022
• Other Authors: Betancourt, M., Rodriguez-Gonzalez, Patrícia Maria, Borda-de-Água, Luís
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.5/26012
• topic: beta diversity; community similarity; pairwise comparisons; Bradley–Terry models; Panama

Ecological communities change because of both natural and human factors. Distinguishing between the two is critical to ecology and conservation science. One of the most common approaches for modelling species composition changes is calculating beta diversity indices and then relating index changes to environmental changes. The main difficulty with these analyses is that beta diversity indices are paired comparisons, which means indices calculated with the same community are not independent. Mantel tests and generalised dissimilarity modelling (GDM) are two of the most commonly used statistical procedures for analysing such data, employing randomisation tests to consider the data’s dependence. Here, we introduce a Bayesian model-based approach called BetaBayes that explicitly incorporates the data dependence. This approach is based on the Bradley– Terry model, which is a widely used approach for modelling paired comparisons that involves building a standard regression model containing two varying intercepts, one for each community involved in the beta diversity index, that capture their respective contributions. We used BetaBayes to analyse a famous dataset collected in Panama that contains information on multiple 1 ha plots from the rain forests of Panama. We calculated the Bray–Curtis index between all pairs of plots, analysed the relationship between the index and two covariates (geographic distance and elevation), and compared the results of BetaBayes with those from the Mantel test and GDM. BetaBayes has two distinctive features. The first is its flexibility, which allows the user to quickly change it to fit the data structure; namely, by adding varying effects, incorporating spatial autocorrelation, and modelling complex nonlinear relationships. The second is that it provides a clear path for performing model validation and model improvement. BetaBayes avoids hypothesis testing, instead focusing on recreating the data generating process and quantifying all the model configurations that are consistent with the observed data