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A latent capture history model for digital aerial surveys

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Borchers_2020_Biometrics_Latent_capture_AAM.pdf (788.0Kb)
Date
01/03/2022
Author
Borchers, David L.
Nightingale, Peter
Stevenson, Ben C.
Fewster, Rachel M.
Funder
EPSRC
Grant ID
XAP001
Keywords
Availability bias
Double-observer survey
Line transect
Mark-recapture
Movement model
Poisson process
QA Mathematics
QH301 Biology
DAS
MCC
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Abstract
We 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.
Citation
Borchers , 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 . https://doi.org/10.1111/biom.13403
Publication
Biometrics
Status
Peer reviewed
DOI
https://doi.org/10.1111/biom.13403
ISSN
0006-341X
Type
Journal article
Rights
Copyright © 2020 The International Biometric Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted 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.13403.
Description
Funding: 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’.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/21299

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