Making Consistent IUCN Classifications under Uncertainty

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. Establecimiento de Clasificaciones Consistentes de IUCN bajo Incertidumbre Resumen: La Unión Mundial para la Conservación (IUCN) definió un grupo de categorías referentes a estados de conservación sustentadas en reglas de decisión basadas en umbrales de parámetros como son el rango de distribución, el tamaño poblacional, la historia poblacional y el riesgo de extinción. Estas reglas han recibido aceptación internacional y se han convertido en una de las herramientas más importantes para la toma de decisiones en biología de la conservación debido a su amplia aplicabilidad, objetividad y simplicidad de uso. Los datos requeridos para estas reglas son frecuentemente estimados con una incertidumbre considerable debido a errores de medición, variación natural y vaguedad en la definición de los parámetros usados en las reglas. Actualmente no existen lineamientos específicos para enfrentar la incertidumbre. La interpretación de datos inciertos por diferentes estimadores puede conducir a clasificaciones inconsistentes debido a que ciertas actitudes hacia la incertidumbre y el riesgo pueden tener una influencia importante en la clasificación de especies amenazadas. Proponemos un método para enfrentar a la incertidumbre que puede ser aplicado a los criterios actuales de IUCN sin alterar las reglas, los umbrales, o la intención de estos criterios. Nuestro método propaga la incertidumbre en los parámetros usados y asigna a la especie evaluada a una sola categoría (a como lo hace el criterio actual) o a un rango de categorías plausibles, dependiendo de la naturaleza y la extensión de las incertidumbres. ŞI· †† email: [email protected] Paper submitted December 1, 1999; revised manuscript accepted March 3, 2000. 1002 IUCN Classifications under Uncertainty Akçakaya et al. Conservation Biology Volume 14, No. 4, August 2000 Introduction When resources for conservation are limited, there is an imperative to rank species according to the risks they face. These ranks are used to set priorities for management action at local and national scales (e.g., Czech & Krausman 1997; Breininger et al. 1998), and they are an important part of national and international reporting on the state of the environment. Through legislative and administrative mechanisms, many countries have developed approaches to setting priorities within a context of political and social constraints. Some threat assessment schemes make use of thresholds to assign scores and sum these scores over a number of attributes to indicate overall conservation status or priority (e.g., Millsap et al. 1990; Lunney et al. 1996). Other schemes use qualitative criteria (e.g., U.S. Fish and Wildlife Service 1983) or a mixture of qualitative and quantitative criteria (e.g., Master 1991; The Nature Conservancy 1994). In the early 1970s, The World Conservation Union (IUCN) adopted a set of qualitative criteria for the classification of conservation status (Fitter & Fitter 1987). Mace and Lande (1991) suggested that status should be assessed quantitatively, and they defined critically endangered species as those facing a 50% probability of extinction within 5 years. The classification of risk involves three parameters: time, probability of decline, and amount of decline (Akçakaya 1992). Threat may then be seen as a combination of the magnitude of the impending decline within some time frame and the probability that a decline of that magnitude will occur. Assessing the threat level is the main goal of the IUCN criteria, which are necessary but probably not sufficient for setting conservation priorities. The IUCN (1994) defined a set of categories for conservation status supported by decision rules based on thresholds of parameters such as distributional range, population size, and population history, as well as risk of extinction. The IUCN (1994) rules have received international acceptance and have become one of the most important decision tools in conservation biology. Decision rules are attractive because of their wide applicability, objectivity, and simplicity of use (Mace & Lande 1991). By necessity, the choices of thresholds that delimit categories of risk are somewhat arbitrary (Regan et al. 2000). Existing methods do not explicitly consider the amount and quality of the data, despite the fact the data for different species vary markedly. Like the choice of a method, the choice of the way in which uncertainty is handled can change the resulting classification of threat (Burgman et al. 1999). Moreover, despite differences in the kinds and quality of information from which inferences may be drawn, there is no guidance on how to interpret such variation, although the IUCN (1994) expresses the intent of precaution in the face of uncertainty. This is especially important because our attitude to this uncertainty may have an important influence on the ranks assigned to different species. In particular, there is no consensus regarding the problem of how to rank species when data are missing (Mace 1995). Some methods ignore the issue, effectively relegating the species to a “safe” category whenever data are absent (e.g., Millsap et al. 1990), whereas others let missing data induce a conservation status toward the middle of the range of threat (e.g., Lunney et al. 1996). Our goal is to describe how a new method of dealing with uncertainty can be applied to the current IUCN criteria without altering the rules, thresholds, or intent of the criteria. Sources of Uncertainty Any method for classification of conservation status involves several kinds of uncertainty, which may be categorized as semantic uncertainty, measurement error, and natural variability. For example, the IUCN (1994) decision rules require the user to specify the number of adult individuals, the area of occupancy, and the level of fluctuations in these parameters. Each of these parameters is affected to an extent by at least one of these sources of uncertainty. Semantic uncertainty arises from the use of inexact definitions. For example, the IUCN (1994) asks whether there have been extreme fluctuations in the number of individuals of a species. Extreme is defined as the situation in which “population size or distribution area varies widely, rapidly and frequently, typically with variation greater than one order of magnitude . . ..” The definition is such that variation in reporting among different people will not be eliminated, even if they are provided with exact information about past fluctuations. The terms widely , rapidly , and frequently may mean different things to different people, and the time horizon over which to evaluate changes of an order of magnitude is not specified. It might be possible to reduce or eliminate variation in responses to this question by making the definition more exact, but only with some loss of generality. This means replacing a vague but inclusive definition by a somewhat arbitrary numerical threshold (Regan et al. 2000). Some definitions may defy any arbitrary attempt to make them precise. For example, the IUCN (1994) recommends that extent of occurrence be measured by a minimum convex polygon that encloses all known, inferred, or projected occurrences. Different definitions of the minimum convex polygon are possible, however, if different attributes are used to define occurrences. For example, people may choose to include or exclude old records, records that are not substantiated by a mu-