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Spatial Joint Species Distribution Modeling using Dirichlet Processes.

Publication ,  Journal Article
Shirota, S; Gelfand, AE; Banerjee, S
Published in: Statistica Sinica
January 2019

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.

Duke Scholars

Published In

Statistica Sinica

DOI

EISSN

1996-8507

ISSN

1017-0405

Publication Date

January 2019

Volume

29

Issue

3

Start / End Page

1127 / 1154

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0199 Other Mathematical Sciences
  • 0104 Statistics
 

Citation

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Shirota, S., Gelfand, A. E., & Banerjee, S. (2019). Spatial Joint Species Distribution Modeling using Dirichlet Processes. Statistica Sinica, 29(3), 1127–1154. https://doi.org/10.5705/ss.202017.0482
Shirota, Shinichiro, Alan E. Gelfand, and Sudipto Banerjee. “Spatial Joint Species Distribution Modeling using Dirichlet Processes.Statistica Sinica 29, no. 3 (January 2019): 1127–54. https://doi.org/10.5705/ss.202017.0482.
Shirota S, Gelfand AE, Banerjee S. Spatial Joint Species Distribution Modeling using Dirichlet Processes. Statistica Sinica. 2019 Jan;29(3):1127–54.
Shirota, Shinichiro, et al. “Spatial Joint Species Distribution Modeling using Dirichlet Processes.Statistica Sinica, vol. 29, no. 3, Jan. 2019, pp. 1127–54. Epmc, doi:10.5705/ss.202017.0482.
Shirota S, Gelfand AE, Banerjee S. Spatial Joint Species Distribution Modeling using Dirichlet Processes. Statistica Sinica. 2019 Jan;29(3):1127–1154.

Published In

Statistica Sinica

DOI

EISSN

1996-8507

ISSN

1017-0405

Publication Date

January 2019

Volume

29

Issue

3

Start / End Page

1127 / 1154

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0199 Other Mathematical Sciences
  • 0104 Statistics