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Ignoring Imperfect Detection in Biological Surveys Is Dangerous: A Response to ‘Fitting and Interpreting Occupancy Models'

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  • Gurutzeta Guillera-Arroita
  • José J Lahoz-Monfort
  • Darryl I MacKenzie
  • Brendan A Wintle
  • Michael A McCarthy

Abstract

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

Suggested Citation

  • Gurutzeta Guillera-Arroita & José J Lahoz-Monfort & Darryl I MacKenzie & Brendan A Wintle & Michael A McCarthy, 2014. "Ignoring Imperfect Detection in Biological Surveys Is Dangerous: A Response to ‘Fitting and Interpreting Occupancy Models'," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-14, July.
  • Handle: RePEc:plo:pone00:0099571
    DOI: 10.1371/journal.pone.0099571
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    Citations

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    Cited by:

    1. 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.
    2. Kelly M O’Connor & Lucas R Nathan & Marjorie R Liberati & Morgan W Tingley & Jason C Vokoun & Tracy A G Rittenhouse, 2017. "Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-12, April.
    3. Emily B Dennis & Byron J T Morgan & Stephen N Freeman & Martin S Ridout & Tom M Brereton & Richard Fox & Gary D Powney & David B Roy, 2017. "Efficient occupancy model-fitting for extensive citizen-science data," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
    4. Wen-Han Hwang & Rachel V. Blakey & Jakub Stoklosa, 2020. "Right-Censored Mixed Poisson Count Models with Detection Times," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 112-132, March.

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