Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

Of Bugs and Birds: Markov Chain Monte Carlo for Hierarchical Modeling in Wildlife Research

William A. Link, Emmanuelle Cam, James D. Nichols and Evan G. Cooch
The Journal of Wildlife Management
Vol. 66, No. 2 (Apr., 2002), pp. 277-291
Published by: Wiley on behalf of the Wildlife Society
DOI: 10.2307/3803160
Stable URL: http://www.jstor.org/stable/3803160
Page Count: 15
  • Download ($42.00)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
Of Bugs and Birds: Markov Chain Monte Carlo for Hierarchical Modeling in Wildlife Research
Preview not available

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.

Page Thumbnails

  • Thumbnail: Page 
277
    277
  • Thumbnail: Page 
278
    278
  • Thumbnail: Page 
279
    279
  • Thumbnail: Page 
280
    280
  • Thumbnail: Page 
281
    281
  • Thumbnail: Page 
282
    282
  • Thumbnail: Page 
283
    283
  • Thumbnail: Page 
284
    284
  • Thumbnail: Page 
285
    285
  • Thumbnail: Page 
286
    286
  • Thumbnail: Page 
287
    287
  • Thumbnail: Page 
288
    288
  • Thumbnail: Page 
289
    289
  • Thumbnail: Page 
290
    290
  • Thumbnail: Page 
291
    291