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From distance sampling to spatial capture–recapture

Author

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  • David L. Borchers

    (The Observatory, University of St Andrews)

  • Tiago A. Marques

    (The Observatory, University of St Andrews
    Faculdade de Ciências da Universidade de Lisboa)

Abstract

Distance sampling and capture–recapture are the two most widely used wildlife abundance estimation methods. capture–recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture–recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals’ locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals’ locations and movements, new methods for integrating spatial capture–recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when “capture” consists of detection by a remote device like a camera trap or microphone.

Suggested Citation

  • David L. Borchers & Tiago A. Marques, 2017. "From distance sampling to spatial capture–recapture," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 475-494, October.
  • Handle: RePEc:spr:alstar:v:101:y:2017:i:4:d:10.1007_s10182-016-0287-7
    DOI: 10.1007/s10182-016-0287-7
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    References listed on IDEAS

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    1. Shirley Pledger, 2000. "Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures," Biometrics, The International Biometric Society, vol. 56(2), pages 434-442, June.
    2. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, June.
    3. Kenneth F Kellner & Robert K Swihart, 2014. "Accounting for Imperfect Detection in Ecology: A Quantitative Review," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.
    4. Stephen T. Buckland, 1992. "Fitting Density Functions with Polynomials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 63-76, March.
    5. S. T. Buckland & C. S. Oedekoven & D. L. Borchers, 2016. "Model-Based Distance Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 58-75, March.
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    Cited by:

    1. Roland Langrock & David L. Borchers, 2017. "Guest editors’ introduction to the special issue on “Ecological Statistics”," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 345-347, October.
    2. Charlotte Warembourg & Monica Berger-González & Danilo Alvarez & Filipe Maximiano Sousa & Alexis López Hernández & Pablo Roquel & Joe Eyerman & Merlin Benner & Salome Dürr, 2020. "Estimation of free-roaming domestic dog population size: Investigation of three methods including an Unmanned Aerial Vehicle (UAV) based approach," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-24, April.

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