A latent capture history model for digital aerial surveys.
Poisson process
availability bias
double-observer survey
line transect
mark-recapture
movement model
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
revised:
03
11
2020
received:
26
06
2020
accepted:
10
11
2020
pubmed:
21
11
2020
medline:
5
4
2022
entrez:
20
11
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
274-285Informations de copyright
© 2020 The International Biometric Society.
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