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dc.contributor.authorBorchers, David Louis
dc.contributor.authorCox, Martin James
dc.date.accessioned2016-11-28T10:30:14Z
dc.date.available2016-11-28T10:30:14Z
dc.date.issued2017-06-15
dc.identifier.citationBorchers , D L & Cox , M J 2017 , ' Distance sampling detection functions : 2D or not 2D? ' , Biometrics , vol. 73 , no. 2 , pp. 593-602 . https://doi.org/10.1111/biom.12581en
dc.identifier.issn1541-0420
dc.identifier.otherPURE: 246904402
dc.identifier.otherPURE UUID: 7330b93c-0ded-46c8-a91c-9961d98010a3
dc.identifier.otherBibtex: urn:cb4f442bb524434728b85a9908bc76a0
dc.identifier.otherScopus: 84995473156
dc.identifier.otherWOS: 000403478400022
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842418
dc.identifier.urihttp://hdl.handle.net/10023/9885
dc.descriptionMJC was funded by Australian Research Council grant FS110200057.en
dc.description.abstractConventional distance sampling (CDS) methods assume that animals are uniformly distributed in the vicinity of lines or points. But when animals move in response to observers before detection, or when lines or points are not located randomly, this assumption may fail. By formulating distance sampling models as survival models, we show that using time to first detection in addition to perpendicular distance (line transect surveys) or radial distance (point transect surveys) allows estimation of detection probability, and hence density, when animal distribution in the vicinity of lines or points is not uniform and is unknown. We also show that times to detection can provide information about failure of the CDS assumption that detection probability is 1 at distance zero. We obtain a maximum likelihood estimator of line transect survey detection probability and effective strip half-width using times to detection, and we investigate its properties by simulation in situations where animals are nonuniformly distributed and their distribution is unknown. The estimator is found to perform well when detection probability at distance zero is 1. It allows unbiased estimates of density to be obtained in this case from surveys in which there has been responsive movement prior to animals coming within detectable range. When responsive movement continues within detectable range, estimates may be biased but are likely less biased than estimates from methods that assuming no responsive movement. We illustrate by estimating primate density from a line transect survey in which animals are known to avoid the transect line, and a shipboard survey of dolphins that are attracted to it.
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofBiometricsen
dc.rights© 2016, The International Biometric Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/biom.12581en
dc.subjectg(0)=1en
dc.subjectLine transecten
dc.subjectPoint transecten
dc.subjectRemoval methoden
dc.subjectResponsive movementen
dc.subjectSurvival analysisen
dc.subjectGE Environmental Sciencesen
dc.subjectDASen
dc.subjectBDCen
dc.subject.lccGEen
dc.titleDistance sampling detection functions : 2D or not 2D?en
dc.typeJournal articleen
dc.description.versionPostprinten
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.Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1111/biom.12581
dc.description.statusPeer revieweden


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