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dc.contributor.authorStevenson, Ben C.
dc.contributor.authorBorchers, David L.
dc.contributor.authorFewster, Rachel M.
dc.identifier.citationStevenson , B C , Borchers , D L & Fewster , R M 2019 , ' Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations ' , Biometrics , vol. 75 , no. 1 , pp. 326-336 .
dc.identifier.otherPURE: 255284445
dc.identifier.otherPURE UUID: f11adcc5-d9ec-43fe-87b7-fbb6874fe5fa
dc.identifier.otherWOS: 000471347100030
dc.identifier.otherScopus: 85063807572
dc.identifier.otherWOS: 000471347100030
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842425
dc.descriptionThis work was funded by a joint EPSRC/NERC PhD grant (No. EP/1000917/1), by the EPSRC through a Doctoral Prize Fellowship, and by the Royal Society of New Zealand through Marsden grant 14-UOA-155.en
dc.description.abstractCapture‐recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern‐day wildlife surveys detect animals without physical capture—visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture‐recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high‐definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.
dc.rights© 2019, 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 as such may differ slightly from the final published version. The final published version of this work is available at
dc.subjectNeyman-scott processen
dc.subjectPalm intensityen
dc.subjectSpatial capture-recaptureen
dc.subjectThomas processen
dc.subjectUnmanned aerial vehiclesen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.titleCluster capture-recapture to account for identification uncertainty on aerial surveys of animal populationsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Pure Mathematicsen
dc.contributor.institutionUniversity of St Andrews. 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.description.statusPeer revieweden

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