A Bayesian Capture-Recapture Population Model With Simultaneous Estimation of Heterogeneity

Ross Corkrey, Steve Brooks, David Lusseau, Kim Parsons, John W. Durban, Philip S. Hammond, Paul M. Thompson

Research output: Contribution to journalArticle

20 Citations (Scopus)


We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel features fo the mdoel include simultaneous estimation of time-varying survival and capture probability distributions, estimation of heterogeneity effects for survival and capture, use of separate data to inflate the number of identified animals to the total abundance, and integration of spearate observations of the same animals from right and left side photographs. A Bayesian approach using Markov chain Monte CArlo methods allows for uncertainty in measurement and parameters, and simulations confirm the model's validity.

Original languageEnglish
Pages (from-to)948-960
Number of pages13
JournalJournal of the American Statistical Association
Issue number483
Publication statusPublished - 2008


  • abundance
  • logits
  • photo-identification
  • survival
  • trends
  • bottle-nosed dolphins
  • chain Monte-Carlo
  • photographic identification
  • unequal catchability
  • probabilities vary
  • closed poulation
  • mark-recapture
  • survival rates
  • size


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