WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods.
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The computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided.
Modeling Demographic Processes in Marked Populations 885-918 2008
Modeling Demographic Processes in Marked Populations
The original publication is available at www.springerlink.com
This paper was presented at the EURING 2007 Technical Meeting, January 14-21, Dunedin, New Zealand. It has been submitted for publication in the conference proceedings, which will appear as a special issue of Environmental and Ecological Statistics.The zip file contains accompanying code in WinBUGS
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