Bayesian methods for hierarchical distance sampling models

Cornelia Sabrina Oedekoven, Stephen Terrence Buckland, Monique Lea MacKenzie, Ruth King, Kristine O. Evans, L. W. Burger

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
3 Downloads (Pure)


The few distance sampling studies that use Bayesian methods typically consider only line transect sampling with a half-normal detection function. We present a Bayesian approach to analyse distance sampling data applicable to line and point transects, exact and interval distance data and any detection function possibly including covariates affecting detection probabilities. We use an integrated likelihood which combines the detection and density models. For the latter, densities are related to covariates in a log-linear mixed effect Poisson model which accommodates correlated counts. We use a Metropolis-Hastings algorithm for updating parameters and a reversible jump algorithm to include model selection for both the detection function and density models. The approach is applied to a large-scale experimental design study of northern bobwhite coveys where the interest was to assess the effect of establishing herbaceous buffers around agricultural fields in several states in the US on bird densities. Results were compared with those from an existing maximum likelihood approach that analyses the detection and density models in two stages. Both methods revealed an increase of covey densities on buffered fields. Our approach gave estimates with higher precision even though it does not condition on a known detection function for the density model.
Original languageEnglish
Pages (from-to)219-239
JournalJournal of Agricultural, Biological and Environmental Statistics
Issue number2
Early online date19 Feb 2014
Publication statusPublished - Jun 2014


  • Designed experiments
  • Hazard-rate detection function
  • Heterogeneity in detection probabilities
  • Metropolis–Hastings update
  • Point transect sampling


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