Department of Biostatistics

Species distribution models usually attempt to explain presence-absenceor abundance of a species at a site in terms of the environmental features (socalledabiotic features) present at the site. Historically, such models have consideredspecies individually. However, it is well-established that species interactto influence presence-absence and abundance (envisioned as biotic factors). Asa result, there has been substantial recent interest in joint species distributionmodels with various types of response, e.g., presence-absence, continuous andordinal data. Such models incorporate dependence between species response asa surrogate for interaction.The challenge we address here is how to accommodate such modeling in thecontext of a large number of species (e.g., order 102) across sites numbering on theorder of 102 or 103 when, in practice, only a few species are found at any observedsite. Again, there is some recent literature to address this; we adopt a dimensionreduction approach. The novel wrinkle we add here is spatial dependence. Thatis, we have a collection of sites over a relatively small spatial region so it isanticipated that species distribution at a given site would be similar to that at a nearby site. Specifically, we handle dimension reduction through Dirichletprocesses, enabling clustering of species, joined with spatial dependence acrosssites through Gaussian processes.We use both simulated data and a plant communities dataset for the CapeFloristic Region (CFR) of South Africa to demonstrate our approach. The latterconsists of presence-absence measurements for 639 tree species at 662 locations.Through both data examples we are able to demonstrate improved predictiveperformance using the foregoing specification.