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Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources

Author

Listed:
  • Soumen Dey

    (Indian Statistical Institute, Bangalore Centre)

  • Mohan Delampady

    (Indian Statistical Institute, Bangalore Centre)

  • Ravishankar Parameshwaran

    (Centre for Wildlife Studies
    Wildlife Conservation Society, India Program)

  • N. Samba Kumar

    (Centre for Wildlife Studies
    Wildlife Conservation Society, India Program)

  • Arjun Srivathsa

    (Centre for Wildlife Studies
    Wildlife Conservation Society, India Program
    School of Natural Resources and Environment, University of Florida
    University of Florida)

  • K. Ullas Karanth

    (Centre for Wildlife Studies
    Wildlife Conservation Society, India Program
    National Centre for Biological Sciences, Tata Institute of Fundamental Research)

Abstract

Estimating animal distributions and abundances over large regions is of primary interest in ecology and conservation. Specifically, integrating data from reliable but expensive surveys conducted at smaller scales with cost-effective but less reliable data generated from surveys at wider scales remains a central challenge in statistical ecology. In this study, we use a Bayesian smoothing technique based on a conditionally autoregressive (CAR) prior distribution and Bayesian regression to address this problem. We illustrate the utility of our proposed methodology by integrating (i) abundance estimates of tigers in wildlife reserves from intensive photographic capture–recapture methods, and (ii) estimates of tiger habitat occupancy from indirect sign surveys, conducted over a wider region. We also investigate whether the random effects which represent the spatial association due to the CAR structure have any confounding effect on the fixed effects of the regression coefficients.

Suggested Citation

  • Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:2:d:10.1007_s13253-017-0276-7
    DOI: 10.1007/s13253-017-0276-7
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    References listed on IDEAS

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