Skip to content

Research at St Andrews

Incorporating animal movement into distance sampling

Research output: ResearchArticle

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, responsive or non-responsive to the observer, can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for non-responsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with independently obtained animal telemetry data.A detection probability that depends on the entire unobserved path the animal travels is derived in continuous space-time. The intractable integration overall possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (less than 5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%.The method is applied to a line transect survey of spotted dolphins (Stenella attenuata attenuata) in the eastern tropical Pacific.
Close

Details

Original languageEnglish
Number of pages9
JournalJournal of the American Statistical Association
StateSubmitted - 8 Sep 2017

    Research areas

  • Abundance, Distance sampling, Animal movement

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. The effect of animal movement on line transect estimates of abundance

    Glennie, R., Buckland, S. T. & Thomas, L. 23 Mar 2015 In : PLoS One. 10, 3, e0121333

    Research output: Research - peer-reviewArticle

  2. Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds

    Oedekoven, C. S., Elston, D. A., Harrison, P. J., Brewer, M. J., Buckland, S. T., Johnston, A., Foster, S. & Pearce-Higgins, J. W. Dec 2017 In : Methods in Ecology and Evolution. 8, 12, p. 1690-1702

    Research output: Research - peer-reviewArticle

  3. Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

    Yuan, Y., Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B., Buckland, S. T., Rue, H. & Gerrodette, T. Dec 2017 In : Annals of Applied Statistics. 11, 4, p. 2270-2297

    Research output: Research - peer-reviewArticle

  4. Distance sampling with camera traps

    Howe, E. J., Buckland, S. T., Després-Einspenner, M-L. & Kühl, H. Nov 2017 In : Methods in Ecology and Evolution. 8, 11, p. 1558-1565

    Research output: Research - peer-reviewArticle

  5. The number and distribution of polar bears in the western Barents Sea

    Aars, J., Marques, T. A., Lone, K., Andersen, M., Wiig, Ø., Fløystad, I. M. B., Hagen, S. B. & Buckland, S. T. 9 Oct 2017 In : Polar Research. 36, 15 p., 1374125

    Research output: Research - peer-reviewArticle

Related by journal

  1. Joint Bayesian modeling of binomial and rank data for primate cognition

    Barney, B. J., Amici, F., Aureli, F., Call, J. & Johnson, V. E. 3 Apr 2015 In : Journal of the American Statistical Association. 110, 510, p. 573-582 10 p.

    Research output: Research - peer-reviewArticle

  2. A Bayesian capture-recapture population model with simultaneous estimation of heterogeneity.

    Corkrey, R., Brooks, S., Lusseau, D., Parsons, K., Durban, J. W., Hammond, P. S. & Thompson, P. M. Sep 2008 In : Journal of the American Statistical Association. 103, p. 948-960 13 p.

    Research output: Research - peer-reviewArticle

ID: 251041173