Data from: Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

A primary goal of ecology is to understand the fundamental processes
underlying the geographic distributions of species. Two major strands of
ecology – habitat modelling and community ecology – approach this problem
differently. Habitat modellers often use species distribution models
(SDMs) to quantify the relationship between species’ and their
environments without considering potential biotic interactions. Community
ecologists, on the other hand, tend to focus on biotic interactions and,
in observational studies, use co‐occurrence patterns to identify
ecological processes. Here, we describe a joint species distribution model
(JSDM) that integrates these distinct observational approaches by
incorporating species co‐occurrence data into a SDM. JSDMs estimate
distributions of multiple species simultaneously and allow decomposition
of species co‐occurrence patterns into components describing shared
environmental responses and residual patterns of co‐occurrence. We provide
a general description of the model, a tutorial and code for fitting the
model in R. We demonstrate this modelling approach using two case studies:
frogs and eucalypt trees in Victoria, Australia. Overall, shared
environmental correlations were stronger than residual correlations for
both frogs and eucalypts, but there were cases of strong residual
correlation. Frog species generally had positive residual correlations,
possibly due to the fact these species occurred in similar habitats that
were not fully described by the environmental variables included in the
JSDM. Eucalypt species that interbreed had similar environmental responses
but had negative residual co‐occurrence. One explanation is that
interbreeding species may not form stable assemblages despite having
similar environmental affinities. Environmental and residual correlations
estimated from JSDMs can help indicate whether co‐occurrence is driven by
shared environmental responses or other ecological or evolutionary process
(e.g. biotic interactions), or if important predictor variables are
missing. JSDMs take into account the fact that distributions of species
might be related to each other and thus overcome a major limitation of
modelling species distributions independently.