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Predictive accuracy of population viability analysis in conservation biology

Author

Listed:
  • Barry W. Brook

    (Key Centre for Biodiversity and Bioresources, Macquarie University
    Northern Territory University)

  • Julian J. O'Grady

    (Key Centre for Biodiversity and Bioresources, Macquarie University)

  • Andrew P. Chapman

    (Key Centre for Biodiversity and Bioresources, Macquarie University)

  • Mark A. Burgman

    (Environmental Science, School of Botany, University of Melbourne)

  • H. Resit Akçakaya

    (Applied Biomathematics)

  • Richard Frankham

    (Key Centre for Biodiversity and Bioresources, Macquarie University)

Abstract

Population viability analysis (PVA) is widely applied in conservation biology to predict extinction risks for threatened species and to compare alternative options for their mangement1,2,3,4. It can also be used as a basis for listing species as endangered under World Conservation Union criteria5. However, there is considerable scepticism regarding the predictive accuracy of PVA, mainly because of a lack of validation in real systems2,6,7,8. Here we conducted a retrospective test of PVA based on 21 long-term ecological studies—the first comprehensive and replicated evaluation of the predictive powers of PVA. Parameters were estimated from the first half of each data set and the second half was used to evaluate the performance of the model. Contrary to recent criticisms, we found that PVA predictions were surprisingly accurate. The risk of population decline closely matched observed outcomes, there was no significant bias, and population size projections did not differ significantly from reality. Furthermore, the predictions of the five PVA software packages were highly concordant. We conclude that PVA is a valid and sufficiently accurate tool for categorizing and managing endangered species.

Suggested Citation

  • Barry W. Brook & Julian J. O'Grady & Andrew P. Chapman & Mark A. Burgman & H. Resit Akçakaya & Richard Frankham, 2000. "Predictive accuracy of population viability analysis in conservation biology," Nature, Nature, vol. 404(6776), pages 385-387, March.
    • Handle: RePEc:nat:nature:v:404:y:2000:i:6776:d:10.1038_35006050
    DOI: 10.1038/35006050
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    Citations

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    Cited by:

    1. John M Halley & Kyle S Van Houtan & Nate Mantua, 2018. "How survival curves affect populations’ vulnerability to climate change," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    2. Skvortsov, Alex & Ristic, Branko & Kamenev, Alex, 2018. "Predicting population extinction from early observations of the Lotka–Volterra system," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 371-379.
    3. John M Drake, 2005. "Density-Dependent Demographic Variation Determines Extinction Rate of Experimental Populations," PLOS Biology, Public Library of Science, vol. 3(7), pages 1-1, June.
    4. Wigley, Tom M.L. & Hong, Sanghyun & Brook, Barry W., 2021. "Value-added diagnostics for the assessment and validation of integrated assessment models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    5. Stéphanie C. Schai-Braun & Christine Kowalczyk & Erich Klansek & Klaus Hackländer, 2019. "Estimating Sustainable Harvest Rates for European Hare ( Lepus Europaeus ) Populations," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    6. Banks, J.E. & Banks, H.T. & Rinnovatore, K. & Jackson, C.M., 2015. "Optimal sampling frequency and timing of threatened tropical bird populations: A modeling approach," Ecological Modelling, Elsevier, vol. 303(C), pages 70-77.
    7. LaRue, Michelle A. & Nielsen, Clayton K., 2016. "Population viability of recolonizing cougars in midwestern North America," Ecological Modelling, Elsevier, vol. 321(C), pages 121-129.
    8. D.C & Nwankwoala & H. O & Okujagu, 2021. "A Review Of Wetlands And Coastal Resources Of The Niger Delta: Potentials, Challenges And Prospects," Environment & Ecosystem Science (EES), Zibeline International Publishing, vol. 5(1), pages 37-46, March.
    9. Marco D Visser & Sean M McMahon & Cory Merow & Philip M Dixon & Sydne Record & Eelke Jongejans, 2015. "Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-11, March.
    10. Kissel, Amanda M. & Palen, Wendy J. & Govindarajulu, Purnima, 2017. "A Decision-theory Approach to Cost-effective Population Supplementation for Imperiled Species," Ecological Economics, Elsevier, vol. 142(C), pages 194-202.

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