WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods

O Giminez, S J Bonner, Ruth King, R A Parker, S P Brooks, L E Jamieson, V Grosbois, B J T Morgan, Len Thomas

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationModeling Demographic Processes in Marked Populations
EditorsD L Thomson, E G Cooch, M J Conroy
ISBN (Electronic)978-0-387-78151-8
ISBN (Print)978-0-387-78150-1
Publication statusPublished - 2009

Publication series

NameEnvironmental and Ecological Statistics


  • Bayesian statistics
  • density dependence
  • distance sampling
  • external covariates
  • hierarchical modelling
  • line transect
  • mark-recapture
  • random effects
  • reversible jump MCMC
  • spline smoothing
  • state-space model
  • survival estimation
  • Density dependence
  • Distance sampling
  • External covariates
  • Hierarchical modeling
  • Line transect
  • Mark-recapture
  • Random effects
  • Reversible jump MCMC
  • Spline smoothing
  • State-space model
  • Survival estimation


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