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dc.contributor.authorBorchers, David L.
dc.contributor.authorNightingale, Peter
dc.contributor.authorStevenson, Ben C.
dc.contributor.authorFewster, Rachel M.
dc.identifier.citationBorchers , D L , Nightingale , P , Stevenson , B C & Fewster , R M 2022 , ' A latent capture history model for digital aerial surveys ' , Biometrics , vol. 78 , no. 1 , pp. 274-285 .
dc.identifier.otherRIS: urn:428988FAA89FCBA967968305909A938A
dc.identifier.otherORCID: /0000-0002-3944-0754/work/86986831
dc.descriptionFunding: This work was part-funded by the Royal Society of New Zealand Marsden grant UOA-1418, Leverhulme grant RF-2018-213\9 and EPSRC IAA grant ‘High Definition digital aerial survey software’.en
dc.description.abstractWe anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark‐recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark‐recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method “Latent Capture‐history Enumeration” (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed. We compare the LCE method with the recently developed cluster capture‐recapture method (CCR), which uses a Palm likelihood approximation, providing the first comparison of CCR with maximum likelihood. The LCE estimator has slightly lower variance, more so as sample size increases, and close to nominal coverage probabilities. Both methods are approximately unbiased. We illustrate with semisynthetic data from a harbor porpoise survey.
dc.subjectAvailability biasen
dc.subjectDouble-observer surveyen
dc.subjectLine transecten
dc.subjectMovement modelen
dc.subjectPoisson processen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.titleA latent capture history model for digital aerial surveysen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews. Pure Mathematicsen
dc.description.statusPeer revieweden

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