Spatiotemporal joint species distribution modelling: A basis function approach

Abstract

Abstract We introduce community‐level basis function models (CBFMs) as an approach for spatiotemporal joint distribution modelling. CBFMs can be viewed as related to spatiotemporal latent variable models, where the latent variables are replaced by a set of pre‐specified spatiotemporal basis functions which are common across species. In a CBFM, the coefficients that link the basis functions to each species are treated as random slopes. As such, the CBFM can be formulated to have a similar structure to a generalised additive model. This allows us to adapt existing techniques to fit CBFMs efficiently. CBFMs can be used for a variety of reasons, such as inferring patterns of habitat use in space and time, understanding how residual covariation between species varies spatially and/or temporally, and spatiotemporal predictions of species‐ and community‐level quantities. A simulation study and an application to data from a bottom trawl survey conducted across the U.S. Northeast shelf show that CBFMs can achieve similar and sometimes better predictive performance compared to existing approaches for spatiotemporal joint species distribution modelling, while being computationally more scalable.

Keywords

• community‐level modelling
• environmental filtering
• fixed rank kriging
• latent variable model
• random effects
• spatial statistics