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dc.contributor.authorBuckland, Stephen Terrence
dc.contributor.authorLaake, Jeffrey L.
dc.contributor.authorBorchers, David Louis
dc.date.accessioned2011-07-22T11:00:10Z
dc.date.available2011-07-22T11:00:10Z
dc.date.issued2010-03
dc.identifier401968
dc.identifierf929d6e1-1769-4ac0-a560-532a9648a864
dc.identifier000275727200020
dc.identifier77949660683
dc.identifier.citationBuckland , S T , Laake , J L & Borchers , D L 2010 , ' Double-observer line transect methods : levels of independence ' , Biometrics , vol. 66 , no. 1 , pp. 169-177 . https://doi.org/10.1111/j.1541-0420.2009.01239.xen
dc.identifier.issn0006-341X
dc.identifier.otherstandrews_research_output: 22395
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842437
dc.identifier.otherORCID: /0000-0002-9939-709X/work/73701012
dc.identifier.urihttps://hdl.handle.net/10023/1928
dc.description.abstractDouble-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark–recapture data. Like conventional mark–recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark–recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark–recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters.
dc.format.extent9
dc.format.extent372411
dc.language.isoeng
dc.relation.ispartofBiometricsen
dc.subjectDistance samplingen
dc.subjectDouble-observer methodsen
dc.subjectFull independenceen
dc.subjectLimiting independenceen
dc.subjectLine transect samplingen
dc.subjectPoint independenceen
dc.subjectQA Mathematicsen
dc.subjectSDG 14 - Life Below Wateren
dc.subject.lccQAen
dc.titleDouble-observer line transect methods : levels of independenceen
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.identifier.doihttps://doi.org/10.1111/j.1541-0420.2009.01239.x
dc.description.statusPeer revieweden
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=77949660683&partnerID=8YFLogxKen


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