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Abstract/Summary
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AIM: Modelling the response of β‐diversity (i.e., the turnover in species composition among sites) to environmental variation has wide‐ranging applications, including informing conservation planning, understanding community assembly and forecasting the impacts of climate change. However, modelling β‐diversity is challenging, especially for multiple diversity facets (i.e., taxonomic, functional and phylogenetic diversity), and current methods have important limitations. Here, we present a new approach for predicting the response of multifaceted β‐diversity to the environment, called Multifaceted Biodiversity Modelling (MBM). We illustrate the approach using both a plant diversity dataset from the French Alps and a set of simulated data. We also provide an implementation via an R package. LOCATION: French Alps. METHODS: For both the French Alps and the simulated communities, we compute β‐diversity indices (e.g., Sørensen dissimilarity, mean functional/phylogenetic pairwise distance) among site pairs. We then apply Gaussian process regression, a flexible nonlinear modelling technique, to predict β‐diversity in response to environmental distance among site pairs. For comparison, we also perform similar analyses using Generalized Dissimilarity Modelling (GDM), a well‐established method for modelling β‐diversity in response to environmental distance. RESULTS: In the Alps, we observed a general increase in taxonomic (TD) and functional (FD) β‐diversity (i.e., site pairs were more different from each other) as the climatic distance between site pairs increased. GDM performed better for TD and FD when fitting to calibration data, whereas MBM performed better for both when predicting to a validation dataset. For phylogenetic β‐diversity, MBM outperformed GDM in predicting the observed decrease in phylogenetic β‐diversity with increasing climatic distance.
MAIN CONCLUSIONS: Multifaceted Biodiversity Modelling provides a flexible new approach that expands our capacity to model multiple facets of β‐diversity. Advantages of MBM over existing methods include simpler assumptions, more flexible modelling, potential to consider multiple facets of diversity across a range of diversity indices, and robust uncertainty estimation.
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