title: Predictive accuracy of population viability analysis in conservation biology.
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 management. It can also be used as a basis for listing species as endangered under World Conservation Union criteria. However, there is considerable scepticism regarding the predictive accuracy of PVA, mainly because of a lack of validation in real systems. 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.
authors.affiliation: Key Centre for Biodiversity and Bioresources, Department of Biological Sciences, Macquarie University, New South Wales, Australia. barry.brook@ntu.edu.au
authors.forename: B W
authors.initials: BW
authors.lastname: Brook
authors.forename: J J
authors.initials: JJ
authors.lastname: O'Grady
authors.forename: A P
authors.initials: AP
authors.lastname: Chapman
authors.forename: M A
authors.initials: MA
authors.lastname: Burgman
authors.forename: H R
authors.initials: HR
authors.lastname: Akçakaya
authors.forename: R
authors.initials: R
authors.lastname: Frankham
mesh_terms: D000818:Animals; D005075:Biological Evolution; D001695:Biology / Q000379:methods*; D003247:Conservation of Natural Resources; D005069:Evaluation Studies as Topic; D011157:Population Dynamics; D012189:Retrospective Studies; D012984:Software