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A Gibbs sampler for Bayesian analysis of site-occupancy data
Article first published online: 20 AUG 2012
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Methods in Ecology and Evolution
Volume 3, Issue 6, pages 1093–1098, December 2012
How to Cite
Dorazio, R. M. and Rodríguez, D. T. (2012), A Gibbs sampler for Bayesian analysis of site-occupancy data. Methods in Ecology and Evolution, 3: 1093–1098. doi: 10.1111/j.2041-210X.2012.00237.x
- Issue published online: 11 DEC 2012
- Article first published online: 20 AUG 2012
- Received 27 April 2012; accepted 5 July 2012 Handling Editor: Nigel Yoccoz
- Markov chain Monte Carlo;
- probit regression;
- proportion of area occupied;
- species distribution model;
- species occurrence
1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately.
2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements.
3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.