Abstract
Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.
Highlights
Understanding and predicting the distribution of species across space and time is one of the central questions in ecology (Thuiller et al, 2013)
Recent statistical advances yield to Joint Species Distribution Models (JSDMs) (Pollock et al, 2014; Warton et al, 2015; Clark et al, 2017; Ovaskainen et al, 2017b), which are multivariate extensions of generalized linear regression models (GLM)
We provide a formal description of our model, which is an extension of the model in Taylor-Rodriguez et al (2017) developed to reduce the dimensionality of the inference in JSDMs, in the particular framework of Generalized Joint Attribute Modeling (GJAM) (Clark et al, 2017)
Summary
Understanding and predicting the distribution of species across space and time is one of the central questions in ecology (Thuiller et al, 2013). The estimated relationship between species and the environment allows to infer the environmental niche of the species and to predict its distribution for new environmental conditions, either in space or time, or in both (Guisan and Thuiller, 2005; Merow et al, 2014; Guisan et al, 2017). Clustering Species With Residual Covariances in JSDMs assemblages (a technique commonly called stacked SDM (sSDM), see Ferrier and Guisan, 2006; Calabrese et al, 2014), they were meant to model and predict the distribution of individual species. In JSDMs, the regression coefficients are related to the response of species to the environment, as in SDMs, while the correlation among the residuals describe the pairwise-species dependencies not explained by the environment
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