Spatial Joint Species Distribution Modeling using Dirichlet Processes.

Published

Journal Article

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

Full Text

Duke Authors

Cited Authors

  • Shirota, S; Gelfand, AE; Banerjee, S

Published Date

  • January 2019

Published In

Volume / Issue

  • 29 / 3

Start / End Page

  • 1127 - 1154

PubMed ID

  • 31555038

Pubmed Central ID

  • 31555038

Electronic International Standard Serial Number (EISSN)

  • 1996-8507

International Standard Serial Number (ISSN)

  • 1017-0405

Digital Object Identifier (DOI)

  • 10.5705/ss.202017.0482

Language

  • eng