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Of bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research

Journal of Wildlife Management

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Abstract

Markov chain Monte Carlo (MCMC) is a statistical innovation that allows researchers to fit far more complex models to data than is feasible using conventional methods. Despite its widespread use in a variety of scientific fields, MCMC appears to be underutilized in wildlife applications. This may be due to a misconception that MCMC requires the adoption of a subjective Bayesian analysis, or perhaps simply to its lack of familiarity among wildlife researchers. We introduce the basic ideas of MCMC and software BUGS (Bayesian inference using Gibbs sampling), stressing that a simple and satisfactory intuition for MCMC does not require extraordinary mathematical sophistication. We illustrate the use of MCMC with an analysis of the association between latent factors governing individual heterogeneity in breeding and survival rates of kittiwakes (Rissa tridactyla). We conclude with a discussion of the importance of individual heterogeneity for understanding population dynamics and designing management plans.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Of bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research
Series title:
Journal of Wildlife Management
Volume
66
Issue:
2
Year Published:
2002
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
277-291
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of Wildlife Management
First page:
277
Last page:
291
Number of Pages:
15