Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models - Bystrova, Daria; Poggiato, Giovanni; Bektaş, Billur; Arbel, Julyan; Clark, James S.; Guglielmi, Alessandra; Thuiller, Wilfried (Frontiers in Ecology and Evolution, 2021)
Modelling 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 reduces the dimension of the model, facilitates the visualization and analysis of the residual covariance matrix, and builds groups of species that share the same associations with other species. We applied our methodology to a case study of plant communities in a mountain regional of park 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.