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Accounting for Imperfect Detection in Ecology: A Quantitative Review

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  • Kenneth F Kellner
  • Robert K Swihart

Abstract

Detection in studies of species abundance and distribution is often imperfect. Assuming perfect detection introduces bias into estimation that can weaken inference upon which understanding and policy are based. Despite availability of numerous methods designed to address this assumption, many refereed papers in ecology fail to account for non-detection error. We conducted a quantitative literature review of 537 ecological articles to measure the degree to which studies of different taxa, at various scales, and over time have accounted for imperfect detection. Overall, just 23% of articles accounted for imperfect detection. The probability that an article incorporated imperfect detection increased with time and varied among taxa studied; studies of vertebrates were more likely to incorporate imperfect detection. Among articles that reported detection probability, 70% contained per-survey estimates of detection that were less than 0.5. For articles in which constancy of detection was tested, 86% reported significant variation. We hope that our findings prompt more ecologists to consider carefully the detection process when designing studies and analyzing results, especially for sub-disciplines where incorporation of imperfect detection in study design and analysis so far has been lacking.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0111436
    DOI: 10.1371/journal.pone.0111436
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    References listed on IDEAS

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    1. 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.
    2. Robert M. Dorazio, 2012. "Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors," Biometrics, The International Biometric Society, vol. 68(4), pages 1303-1312, December.
    3. 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.
    4. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
    5. Peter Guttorp & Walter W. Piegorsch & B. J. Reich & B. Gardner, 2014. "A spatial capture‐recapture model for territorial species," Environmetrics, John Wiley & Sons, Ltd., vol. 25(8), pages 630-637, December.
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    Cited by:

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    2. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    3. De Cubber, Lola & Trenkel, Verena M. & Diez, Guzman & Gil-Herrera, Juan & Novoa Pabon, Ana Maria & Eme, David & Lorance, Pascal, 2023. "Robust identification of potential habitats of a rare demersal species (blackspot seabream) in the Northeast Atlantic," Ecological Modelling, Elsevier, vol. 477(C).
    4. Tenan, S. & Maffioletti, C. & Caccianiga, M. & Compostella, C. & Seppi, R. & Gobbi, M., 2016. "Hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession," Ecological Modelling, Elsevier, vol. 329(C), pages 18-28.
    5. 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.
    6. Matt Higham & Jay Ver Hoef & Lisa Madsen & Andy Aderman, 2021. "Adjusting a finite population block kriging estimator for imperfect detection," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.

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