title: Testing decision rules for categorizing species' extinction risk to help develop quantitative listing criteria for the U.S. Endangered Species Act.
abstract: Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency.
authors: The School of Botany, The University of Melbourne, Parkville, Victoria 3010, Australia. tregan@unimelb.edu.au
authors: Tracey J
authors: TJ
authors: Regan
authors: Barbara L
authors: BL
authors: Taylor
authors: Grant G
authors: GG
authors: Thompson
authors: Jean Fitts
authors: JF
authors: Cochrane
authors: Katherine
authors: K
authors: Ralls
authors: Michael C
authors: MC
authors: Runge
authors: Richard
authors: R
authors: Merrick
mesh_terms: D001499:Bayes Theorem; D003659:Decision Making, Organizational; D003661:Decision Support Techniques; D056727:Endangered Species / Q000331:legislation & jurisprudence*; D053476:Extinction, Biological; D007407:Interviews as Topic; D011050:Policy Making; D018570:Risk Assessment / Q000379:methods; D014481:United States
keywords: Bayesian analysis; análisis bayesiano; análisis de viabilidad poblacional; loss functions; performance testing; population viability analysis; pruebas de rendimiento