Joint species distribution modeling: Dimension reduction using Dirichlet processes
Publication
, Journal Article
Taylor-Rodríguez, D; Kaufeld, K; Schliep, EM; Clark, JS; Gelfand, AE
Published in: Bayesian Analysis
January 1, 2017
Species distribution models are used to evaluate the variables that affect the distribution and abundance of species and to predict biodiversity. Historically, such models have been fitted to each species independently. While independent models can provide useful information regarding distribution and abundance, they ignore the fact that, after accounting for environmental covariates, residual interspecies dependence persists. With stacking of individual models, misleading behaviors, may arise. In particular, individual models often imply too many species per location.
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Published In
Bayesian Analysis
DOI
EISSN
1931-6690
ISSN
1936-0975
Publication Date
January 1, 2017
Volume
12
Issue
4
Start / End Page
939 / 967
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics
Citation
APA
Chicago
ICMJE
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Taylor-Rodríguez, D., Kaufeld, K., Schliep, E. M., Clark, J. S., & Gelfand, A. E. (2017). Joint species distribution modeling: Dimension reduction using Dirichlet processes. Bayesian Analysis, 12(4), 939–967. https://doi.org/10.1214/16-BA1031
Taylor-Rodríguez, D., K. Kaufeld, E. M. Schliep, J. S. Clark, and A. E. Gelfand. “Joint species distribution modeling: Dimension reduction using Dirichlet processes.” Bayesian Analysis 12, no. 4 (January 1, 2017): 939–67. https://doi.org/10.1214/16-BA1031.
Taylor-Rodríguez D, Kaufeld K, Schliep EM, Clark JS, Gelfand AE. Joint species distribution modeling: Dimension reduction using Dirichlet processes. Bayesian Analysis. 2017 Jan 1;12(4):939–67.
Taylor-Rodríguez, D., et al. “Joint species distribution modeling: Dimension reduction using Dirichlet processes.” Bayesian Analysis, vol. 12, no. 4, Jan. 2017, pp. 939–67. Scopus, doi:10.1214/16-BA1031.
Taylor-Rodríguez D, Kaufeld K, Schliep EM, Clark JS, Gelfand AE. Joint species distribution modeling: Dimension reduction using Dirichlet processes. Bayesian Analysis. 2017 Jan 1;12(4):939–967.
Published In
Bayesian Analysis
DOI
EISSN
1931-6690
ISSN
1936-0975
Publication Date
January 1, 2017
Volume
12
Issue
4
Start / End Page
939 / 967
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics