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Distance sampling detection functions : 2D or not 2D?
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dc.contributor.author | Borchers, David Louis | |
dc.contributor.author | Cox, Martin James | |
dc.date.accessioned | 2016-11-28T10:30:14Z | |
dc.date.available | 2016-11-28T10:30:14Z | |
dc.date.issued | 2017-06-15 | |
dc.identifier.citation | Borchers , 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.12581 | en |
dc.identifier.issn | 1541-0420 | |
dc.identifier.other | PURE: 246904402 | |
dc.identifier.other | PURE UUID: 7330b93c-0ded-46c8-a91c-9961d98010a3 | |
dc.identifier.other | Bibtex: urn:cb4f442bb524434728b85a9908bc76a0 | |
dc.identifier.other | Scopus: 84995473156 | |
dc.identifier.other | WOS: 000403478400022 | |
dc.identifier.other | ORCID: /0000-0002-3944-0754/work/72842418 | |
dc.identifier.uri | https://hdl.handle.net/10023/9885 | |
dc.description | MJC was funded by Australian Research Council grant FS110200057. | en |
dc.description.abstract | Conventional 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.extent | 10 | |
dc.language.iso | eng | |
dc.relation.ispartof | Biometrics | en |
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.12581 | en |
dc.subject | g(0)=1 | en |
dc.subject | Line transect | en |
dc.subject | Point transect | en |
dc.subject | Removal method | en |
dc.subject | Responsive movement | en |
dc.subject | Survival analysis | en |
dc.subject | GE Environmental Sciences | en |
dc.subject | DAS | en |
dc.subject | BDC | en |
dc.subject | R2C | en |
dc.subject.lcc | GE | en |
dc.title | Distance sampling detection functions : 2D or not 2D? | en |
dc.type | Journal article | en |
dc.description.version | Postprint | en |
dc.contributor.institution | University of St Andrews. School of Mathematics and Statistics | en |
dc.contributor.institution | University of St Andrews. Marine Alliance for Science & Technology Scotland | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | https://doi.org/10.1111/biom.12581 | |
dc.description.status | Peer reviewed | en |
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