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dc.contributor.authorBuckland, Stephen Terrence
dc.contributor.authorOedekoven, Cornelia Sabrina
dc.contributor.authorBorchers, David Louis
dc.date.accessioned2015-09-07T11:10:02Z
dc.date.available2015-09-07T11:10:02Z
dc.date.issued2016-03
dc.identifier.citationBuckland , S T , Oedekoven , C S & Borchers , D L 2016 , ' Model-based distance sampling ' , Journal of Agricultural, Biological and Environmental Statistics , vol. 21 , no. 1 , pp. 58-75 . https://doi.org/10.1007/s13253-015-0220-7en
dc.identifier.issn1085-7117
dc.identifier.otherPURE: 214296895
dc.identifier.otherPURE UUID: a38ed61e-7873-4b42-994f-9f13b1acbe2d
dc.identifier.otherScopus: 84958040638
dc.identifier.otherWOS: 000374695400004
dc.identifier.otherORCID: /0000-0002-5610-7814/work/61978857
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842477
dc.identifier.otherORCID: /0000-0002-9939-709X/work/73701091
dc.identifier.urihttp://hdl.handle.net/10023/7410
dc.descriptionCSO was part-funded by EPSRC/NERC Grant EP/1000917/1.en
dc.description.abstractConventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal abundance in the study region, given estimated probabilities of detection. In recent years, there has been increasing interest in fully model-based methods. Model-based methods are less robust for estimating animal abundance than conventional methods, but offer several advantages: they allow the analyst to explore how animal density varies by habitat or topography; abundance can be estimated for any sub-region of interest; they provide tools for analysing data from designed distance sampling experiments, to assess treatment effects. We develop a common framework for model-based distance sampling, and show how the various model-based methods that have been proposed fit within this framework.
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofJournal of Agricultural, Biological and Environmental Statisticsen
dc.rightsCopyright © The Author(s) 2015. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectDistance samplingen
dc.subjectLine transect samplingen
dc.subjectModel-based inferenceen
dc.subjectPoint transect samplingen
dc.subjectQA Mathematicsen
dc.subjectNDASen
dc.subjectBDCen
dc.subject.lccQAen
dc.titleModel-based distance samplingen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
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.identifier.doihttps://doi.org/10.1007/s13253-015-0220-7
dc.description.statusPeer revieweden


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