Multifaceted biodiversity modelling at macroecological scales using Gaussian processes

Abstract

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.

Uncertainty in ensembles of global biodiversity scenarios

“…Since those approaches do not rely on stacking individual SDM, they are less prone to bias coming from aggregating models with different quality 20 . However, modelling α- and β-diversity explicitly is not as straightforward than modelling individual species 21 . Similar analyses than the one proposed here but with community-level modelling approaches will be interesting to understand whether they are less influential on projection outputs than SDMs.…”

Climate extremes, variability, and trade shape biogeographical patterns of alien species

“…However, evaluation of the uncertainty of overall model fit is unfortunately not available for GDM yet. Consequently, we used the multifaceted biodiversity modelling (MBM) approach based on Gaussian processes to generate the 95% confidence intervals of the overall model fit and qualitatively compared it to the model fit with GDM ( Talluto et al 2018 ). We then evaluated model fits for MBM and GDM using a root mean square prediction error (RMSE) test with smaller values indicating better performance ( Talluto et al 2018 ).…”

“…Consequently, we used the multifaceted biodiversity modelling (MBM) approach based on Gaussian processes to generate the 95% confidence intervals of the overall model fit and qualitatively compared it to the model fit with GDM ( Talluto et al 2018 ). We then evaluated model fits for MBM and GDM using a root mean square prediction error (RMSE) test with smaller values indicating better performance ( Talluto et al 2018 ). The MBM analyses were conducted using the mbm package ( Talluto et al 2018 ) in R. MBM analyses showed similar trends with GDM for changes in species compositional dissimilarity with environmental distance ( Supplementary Figure S4 ).…”

“…We then evaluated model fits for MBM and GDM using a root mean square prediction error (RMSE) test with smaller values indicating better performance ( Talluto et al 2018 ). The MBM analyses were conducted using the mbm package ( Talluto et al 2018 ) in R. MBM analyses showed similar trends with GDM for changes in species compositional dissimilarity with environmental distance ( Supplementary Figure S4 ). RMSE values for both the GDM and MBM models were very small (0.051–0.070), suggesting a good fit to the data ( Supplementary Table S5 ).…”

EcoPhyloMapper: An r package for integrating geographical ranges, phylogeny and morphology

“…With a growing appreciation that evolutionary history, morphology and species richness are interconnected and all play a role in shaping the geographical distributions of species pools at differing spatial scales (Kraft et al, 2007; Ricklefs, 2004), there has been an increasing interest in quantifying and exploring biodiversity across multiple axes (Huang et al, 2012; Mazel et al, 2014; Nakamura et al, 2020; Talluto et al, 2018). Morphological data, both size and shape, lend themselves well to the analysis of multi‐faceted biodiversity, as they are metric and the complexity of shape data often captures information about multiple dimensions of ecological adaptations.…”