Incorporating animal movement into distance sampling
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Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (<5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (Stenella attenuata) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys
Glennie , R , Buckland , S T , Langrock , R , Gerrodette , T , Ballance , L , Chivers , S & Scott , M 2020 , ' Incorporating animal movement into distance sampling ' , Journal of the American Statistical Association , vol. Latest Articles , pp. 1-9 . https://doi.org/10.1080/01621459.2020.1764362
Journal of the American Statistical Association
Copyright 2017 the Author(s). This paper is currently (27 September 2017) under peer-review at a journal. Please check with the authors for a later version before citing.Copyright © 2020 American Statistical Association. 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/10.1080/01621459.2020.1764362
DescriptionR. Glennie gratefully acknowledges the Carnegie Trust for funding his work on this research project.
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