Bayesian methods for hierarchical distance sampling models
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
Oedekoven , C S , Buckland , S T , MacKenzie , M L , King , R , Evans , K O & Burger , L W 2014 , ' Bayesian methods for hierarchical distance sampling models ' , Journal of Agricultural, Biological and Environmental Statistics , vol. 19 , no. 2 , pp. 219-239 . https://doi.org/10.1007/s13253-014-0167-0
Journal of Agricultural, Biological and Environmental Statistics
© 2014. International Biometric Society. This is the author’s version of a work that was accepted for publication in Journal of Agricultural, Biological and Environmental Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The final publication is available at Springer via http://dx.doi.org/10.1007/s13253-014-0167-0
DescriptionCornelia S. Oedekoven was supported by a studentship jointly funded by the University of St Andrews and EPSRC (EPSRC grant EP/C522702/1), through the National Centre for Statistical Ecology.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.