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dc.contributor.authorOedekoven, Cornelia Sabrina
dc.contributor.authorBuckland, Stephen Terrence
dc.contributor.authorMacKenzie, Monique Lea
dc.contributor.authorKing, Ruth
dc.contributor.authorEvans, Kristine O.
dc.contributor.authorBurger, L. W.
dc.identifier.citationOedekoven , 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 .
dc.identifier.otherORCID: /0000-0002-5610-7814/work/61978858
dc.identifier.otherORCID: /0000-0002-9939-709X/work/73701098
dc.identifier.otherORCID: /0000-0002-8505-6585/work/74509976
dc.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.en
dc.description.abstractThe 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.
dc.relation.ispartofJournal of Agricultural, Biological and Environmental Statisticsen
dc.subjectDesigned experimentsen
dc.subjectHazard-rate detection functionen
dc.subjectHeterogeneity in detection probabilitiesen
dc.subjectMetropolis–Hastings updateen
dc.subjectPoint transect samplingen
dc.subjectQA Mathematicsen
dc.titleBayesian methods for hierarchical distance sampling modelsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. St Andrews Sustainability Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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