Premium
Establecimiento de Clasificaciones Consistentes de IUCN bajo Incertidumbre
Author(s) -
Akçakaya H. Reşit,
Ferson Scott,
Burgman Mark A.,
Keith David A.,
Mace Georgina M.,
Todd Charles R.
Publication year - 2000
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1046/j.1523-1739.2000.99125.x
Subject(s) - iucn red list , vagueness , population , range (aeronautics) , threatened species , computer science , ecology , artificial intelligence , biology , fuzzy logic , engineering , demography , aerospace engineering , habitat , sociology
The World Conservation Union (IUCN) defined a set of categories for conservation status supported by decision rules based on thresholds of parameters such as distributional range, population size, population history, and risk of extinction. These rules have received international acceptance and have become one of the most important decision tools in conservation biology because of their wide applicability, objectivity, and simplicity of use. The input data for these rules are often estimated with considerable uncertainty due to measurement error, natural variation, and vagueness in definitions of parameters used in the rules. Currently, no specific guidelines exist for dealing with uncertainty. Interpretation of uncertain data by different assessors may lead to inconsistent classifications because attitudes toward uncertainty and risk may have an important influence on the classification of threatened species. We propose a method of dealing with uncertainty that can be applied to the current IUCN criteria without altering the rules, thresholds, or intent of these criteria. Our method propagates the uncertainty in the input parameters and assigns the evaluated species either to a single category (as the current criteria do) or to a range of plausible categories, depending on the nature and extent of uncertainties.