When to stop managing or surveying cryptic threatened species
- *Unité de Biométrie et Intelligence Artificielle, Institut National de la Recherche Agronomique, Unité Mixte de Recherche 875, BP 27 F-31326 Castanet-Tolosan, France;
- ‡Centre for Applied Environmental Decision Analysis, School of Integrative Biology, and
- ‖Department of Mathematics, University of Queensland, St Lucia QLD 4072, Queensland, Australia;
- §Centre for Applied Environmental Decision Analysis, The School of Botany, University of Melbourne, Parkville VIC 3010, Victoria, Australia;
- ¶Durrell Institute of Conservation and Ecology, University of Kent, Canterbury, Kent CT2 7NS, United Kingdom
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Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved July 11, 2008 (received for review June 2, 2008)

Abstract
Threatened species become increasingly difficult to detect as their populations decline. Managers of such cryptic threatened species face several dilemmas: if they are not sure the species is present, should they continue to manage for that species or invest the limited resources in surveying? We find optimal solutions to this problem using a Partially Observable Markov Decision Process and rules of thumb derived from an analytical approximation. We discover that managing a protected area for a cryptic threatened species can be optimal even if we are not sure the species is present. The more threatened and valuable the species is, relative to the costs of management, the more likely we are to manage this species without determining its continued persistence by using surveys. If a species remains unseen, our belief in the persistence of the species declines to a point where the optimal strategy is to shift resources from saving the species to surveying for it. Finally, when surveys lead to a sufficiently low belief that the species is extant, we surrender resources to other conservation actions. We illustrate our findings with a case study using parameters based on the critically endangered Sumatran tiger (Panthera tigris sumatrae), and we generate rules of thumb on how to allocate conservation effort for any cryptic species. Using Partially Observable Markov Decision Processes in conservation science, we determine the conditions under which it is better to abandon management for that species because our belief that it continues to exist is too low.
Footnotes
- †To whom correspondence should be addressed. E-mail: chades{at}toulouse.inra.fr
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Author contributions: I.C., E.M.-M., M.A.M., B.W., and H.P.P. designed research; I.C., E.M.-M., and M.A.M. performed research; I.C., E.M.-M., and M.A.M. contributed new reagents/analytic tools; I.C., E.M.-M., and M.A.M. analyzed data; M.L. collected field data; and I.C., E.M.-M., M.A.M., and H.P.P. wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission.
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This article contains supporting information online at www.pnas.org/cgi/content/full/0805265105/DCSupplemental.
- © 2008 by The National Academy of Sciences of the USA