Bayesian Methods for Hierarchical Distance Sampling Models View Full Text


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Article Info

DATE

2014-06

AUTHORS

C. S. Oedekoven, S. T. Buckland, M. L. Mackenzie, R. King, K. O. Evans, L. W. Burger

ABSTRACT

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. More... »

PAGES

219-239

References to SciGraph publications

  • 2004-06. Spatial models for line transect sampling in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2005-09. Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2009-12. Analyzing designed experiments in distance sampling in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2009. WinBUGS for Population Ecologists: Bayesian Modeling Using Markov Chain Monte Carlo Methods in MODELING DEMOGRAPHIC PROCESSES IN MARKED POPULATIONS
  • 2011-03. On parametric estimation of population abundance for line transect sampling in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13253-014-0167-0

    DOI

    http://dx.doi.org/10.1007/s13253-014-0167-0

    DIMENSIONS

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