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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 . DOI: 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.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.en
dc.language.isoeng
dc.relation.ispartofJournal of Agricultural, Biological and Environmental Statisticsen
dc.rightsThis 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.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.contributor.institutionUniversity of St Andrews. Centre for Higher Education Researchen
dc.identifier.doihttp://dx.doi.org/10.1007/s13253-015-0220-7
dc.description.statusPeer revieweden


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