MULTISITE MARK-RECAPTURE FOR CETACEANS: POPULATION ESTIMATES WITH BAYESIAN MODEL AVERAGING

Authors

  • J. W. Durban,

    1. University of Aberdeen, School of Biological Sciences, Lighthouse Field Station, Cromarty, Ross-Shire IV11 8YJ, United Kingdom
    Search for more papers by this author
    • 1

      Current address: National Marine Mammal Laboratory, Alaska Fisheries Science Center, 7600 Sand Point Way NE, Seattle, Washington 98115–6349, U.S.A.; e-mail: john.durban@noaa.gov.

  • D. A. Elston,

    1. Biomathematics and Statistics Scotland, Environmental Modelling Unit, The Macaulay Institute, Aberdeen AB15 8QH, United Kingdom
    Search for more papers by this author
  • D. K. Ellifrit,

    1. Center for Whale Research, 355 Smugglers Cove Road, Friday Harbor, Washington 98250, U.S.A.
    Search for more papers by this author
  • E. Dickson,

    1. Whale and Dolphin Conservation Society, Moray Firth Wildlife Centre, Spey Bay, Moray IV32 7PJ, United Kingdom P. S. Hammond
    Search for more papers by this author
  • P. S. Hammond,

    1. NERC Sea Mammal Research Unit, Gatty Marine Laboratory, School of Biology, University of St Andrews, St Andrews, Fife KY16 8LB, United Kingdom
    Search for more papers by this author
  • P. M. Thompson

    1. University of Aberdeen, School of Biological Sciences, Lighthouse Field Station, Cromarty, Ross-Shire IV11 8YJ, United Kingdom
    Search for more papers by this author

Abstract

Mark-recapture techniques are widely used to estimate the size of wildlife populations. However, in cetacean photo-identification studies, it is often impractical to sample across the entire range of the population. Consequently, negatively biased population estimates can result when large portions of a population are unavailable for photographic capture. To overcome this problem, we propose that individuals be sampled from a number of discrete sites located throughout the population's range. The recapture of individuals between sites can then be presented in a simple contingency table, where the cells refer to discrete categories formed by combinations of the study sites. We present a Bayesian framework for fitting a suite of log-linear models to these data, with each model representing a different hypothesis about dependence between sites. Modeling dependence facilitates the analysis of opportunistic photo-identification data from study sites located due to convenience rather than by design. Because inference about population size is sensitive to model choice, we use Bayesian Markov chain Monte Carlo approaches to estimate posterior model probabilities, and base inference on a model-averaged estimate of population size. We demonstrate this method in the analysis of photographic mark-recapture data for bottlenose dolphins from three coastal sites around NE Scotland.

Ancillary