Incorporating animal movement into distance sampling

Richard Glennie, Stephen Terrence Buckland, Roland Langrock, Tim Gerrodette, Lisa Ballance, Susan Chivers, Michael Scott

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

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
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJournal of the American Statistical Association
VolumeLatest Articles
Early online date8 Jun 2020
DOIs
Publication statusE-pub ahead of print - 8 Jun 2020

Keywords

  • Abundance
  • Continuous-time
  • Diffusion
  • Hidden Markov model

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