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Species occupancy estimation and imperfect detection: shall surveys continue after the first detection?

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  • Gurutzeta Guillera-Arroita

    (University of Melbourne)

  • José J. Lahoz-Monfort

    (University of Melbourne)

Abstract

Species occupancy, the proportion of sites occupied by a species, is a state variable of interest in ecology. One challenge in its estimation is that detection is often imperfect in wildlife surveys. As a consequence, occupancy models that explicitly describe the observation process are becoming widely used in the discipline. These models require data that are informative about species detectability. Such information is often obtained by conducting repeat surveys to sampling sites. One strategy is to survey each site a predefined number of times, regardless of whether the species is detected. Alternatively, one can stop surveying a site once the species is detected and reallocate the effort saved to surveying new sites. In this paper we evaluate the merits of these two general design strategies under a range of realistic conditions. We conclude that continuing surveys after detection is beneficial unless the cumulative probability of detection at occupied sites is close to one, and that the benefits are greater when the sample size is small. Since detectability and sample size tend to be small in ecological applications, our recommendation is to follow a strategy where at least some of the sites continue to be sampled after first detection.

Suggested Citation

  • Gurutzeta Guillera-Arroita & José J. Lahoz-Monfort, 2017. "Species occupancy estimation and imperfect detection: shall surveys continue after the first detection?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 381-398, October.
  • Handle: RePEc:spr:alstar:v:101:y:2017:i:4:d:10.1007_s10182-017-0292-5
    DOI: 10.1007/s10182-017-0292-5
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    References listed on IDEAS

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    1. Byron J. T. Morgan & David J. Revell & Stephen N. Freeman, 2007. "A Note on Simplifying Likelihoods for Site Occupancy Models," Biometrics, The International Biometric Society, vol. 63(2), pages 618-621, June.
    2. J. Andrew Royle, 2006. "Site Occupancy Models with Heterogeneous Detection Probabilities," Biometrics, The International Biometric Society, vol. 62(1), pages 97-102, March.
    3. P. Besbeas & S. N. Freeman & B. J. T. Morgan & E. A. Catchpole, 2002. "Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters," Biometrics, The International Biometric Society, vol. 58(3), pages 540-547, September.
    4. Alan H Welsh & David B Lindenmayer & Christine F Donnelly, 2013. "Fitting and Interpreting Occupancy Models," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-21, January.
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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. Newman, Kevin D. & Nelson, Jenny L. & Durkin, Louise K. & Cripps, Jemma K. & McCarthy, Michael A., 2022. "An analytical solution for optimising detections when accounting for site establishment costs," Ecological Modelling, Elsevier, vol. 473(C).
    3. Hee-Young Kim & Christian H. Weiß & Tobias A. Möller, 2018. "Testing for an excessive number of zeros in time series of bounded counts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 689-714, December.

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