Ecological communities change due to 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 covariates 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 Modeling are the most commonly used statistical procedures for analysing such data, dealing with the datas dependence using randomisation tests. Here we introduce a model-based approach called BetaBayes that requires first modelling the process that results in Beta diversity indices and then quantifying which model configurations are consistent with the observed data. This approach is based on Bradley-Terry model and consists of fitting a regression model containing two varying intercepts that capture the dependence in the data. We demonstrate the use of this approach by analysing a famous dataset collected in Panama that contains information on multiple 1-ha plots from the rain forests of Panama. Several studies have analysed this dataset using Mantel tests and Generalised Dissimilarity Modeling. We calculated the Bray-Curtis index between all pairs of plots and analysed the relationship between the index and two covariates, geographic distance and elevation. We compared the results of BetaBayes with those from the methods mentioned above. We show that BetaBayes provides a step towards consistently modelling community composition changes and discussing possible extensions and future directions.
Biodiversity, Biology, Ecology and Evolutionary Biology, Life Sciences, Physical Sciences and Mathematics, Statistics and Probability
Beta diversity, community similarity, Mantel tests, riparian vegetation, Sorensen index
Published: 2021-04-08 12:34
Last Updated: 2022-07-10 11:49
CC-By Attribution-ShareAlike 4.0 International