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A Gibbs sampler for Bayesian analysis of site‐occupancy data
Author(s) -
Dorazio Robert M.,
Rodríguez Daniel Taylor
Publication year - 2012
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2012.00237.x
Subject(s) - occupancy , gibbs sampling , markov chain monte carlo , bayesian probability , statistics , markov chain , computer science , bayesian inference , mathematics , ecology , biology
Summary 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.