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Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81

Population viability analysis is widely used for assessing population-level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers to prioritize efforts for further data collection so that model improvements are efficient, and helps managers prioritize conservation and management actions. Usually sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then regression techniques allow measuring the importance of input parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions; effectively separating the two main questions that sensitivity analysis asks. We applied this method to Snowy Plover, assessing the effects of predicted sea-level rise. A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions. This article is protected by copyright. All rights reserved

Stopping declines in biodiversity is critically important, but it is only a first step toward achieving more ambitious conservation goals. The absence of an objective and practical definition of species recovery that is applicable across taxonomic groups leads to inconsistent targets in recovery plans and frustrates reporting and maximization of conservation impact. We devised a framework for comprehensively assessing species recovery and conservation success. We propose a definition of a fully recovered species that emphasizes viability, ecological functionality, and representation; and use counterfactual approaches to quantify degree of recovery. This allowed us to calculate a set of 4 conservation metrics that demonstrate impacts of conservation efforts to date (conservation legacy); identify dependence of a species on conservation actions (conservation dependence); quantify expected gains resulting from conservation action in the medium term (conservation gain); and specify requirements to achieve maximum plausible recovery over the long term (recovery potential). These metrics can incentivize the establishment and achievement of ambitious conservation targets. We illustrate their use by applying the framework to a vertebrate, an invertebrate, and a woody and an herbaceous plant. Our approach is a preliminary framework for an International Union for Conservation of Nature (IUCN) Green List of Species, which was mandated by a resolution of IUCN members in 2012. Although there are several challenges in applying our proposed framework to a wide range of species, we believe its further development, implementation, and integration with the IUCN Red List of Threatened Species will help catalyze a positive and ambitious vision for conservation that will drive sustained conservation action. ISSN:0888-8892 ISSN:1523-1739

[Excerpt] We thank Lehtomaki et al. (2019) for widening the discussion of the Plan-S open-access initiative (https://www.coalition-s.org) in their response to Burgman et al. (2018). They provide useful links to Plan-S documentation. They are disappointed by the focus of our position, which we took to clarify a point central to the discussion of open access that we believe has received too little attention,namely, equity of access to publication.

In International Union for Conservation of Nature (IUCN) Red List assessments, extent of occurrence (EOO) is a key measure of extinction risk. However, the way assessors estimate EOO from maps of species’ distributions is inconsistent among assessments of different species and among major taxonomic groups. Assessors often estimate EOO from the area of mapped distribution, but these maps often exclude areas that are not habitat in idiosyncratic ways and are not created at the same spatial resolutions. We assessed the impact on extinction risk categories of applying different methods (minimum convex polygon, alpha hull) for estimating EOO for 21,763 species of mammals, birds, and amphibians. Overall, the percentage of threatened species requiring down listing to a lower category of threat (taking into account other Red List criteria under which they qualified) spanned 11–13% for all species combined (14–15% for mammals, 7–8% for birds, and 12–15% for amphibians). These down listings resulted from larger estimates of EOO and depended on the EOO calculation method. Using birds as an example, we found that 14% of threatened and near threatened species could require down listing based on the minimum convex polygon (MCP) approach, an approach that is now recommended by IUCN. Other metrics (such as alpha hull) had marginally smaller impacts. Our results suggest that uniformly applying the MCP approach may lead to a one-time down listing of hundreds of species but ultimately ensure consistency across assessments and realign the calculation of EOO with the theoretical basis on which the metric was founded.

Diagnosing the processes that threaten species persistence is critical for recovery planning and risk forecasting. Dominant threats are typically inferred by experts on the basis of a patchwork of informal methods. Transparent, quantitative diagnostic tools would contribute much-needed consistency, objectivity, and rigor to the process of diagnosing anthropogenic threats. Long-term census records, available for an increasingly large and diverse set of taxa, may exhibit characteristic signatures of specific threatening processes and thereby provide information for threat diagnosis. We developed a flexible Bayesian framework for diagnosing threats on the basis of long-term census records and diverse ancillary sources of information. We tested this framework with simulated data from artificial populations subjected to varying degrees of exploitation and habitat loss and several real-world abundance time series for which threatening processes are relatively well understood: bluefin tuna (Thunnus maccoyii) and Atlantic cod (Gadus morhua) (exploitation) and Red Grouse (Lagopus lagopus scotica) and Eurasian Skylark (Alauda arvensis) (habitat loss). Our method correctly identified the process driving population decline for over 90% of time series simulated under moderate to severe threat scenarios. Successful identification of threats approached 100% for severe exploitation and habitat loss scenarios. Our method identified threats less successfully when threatening processes were weak and when populations were simultaneously affected by multiple threats. Our method selected the presumed true threat model for all real-world case studies, although results were somewhat ambiguous in the case of the Eurasian Skylark. In the latter case, incorporation of an ancillary source of information (records of land-use change) increased the weight assigned to the presumed true model from 70% to 92%, illustrating the value of the proposed framework in bringing diverse sources of information into a common rigorous framework. Ultimately, our framework may greatly assist conservation organizations in documenting threatening processes and planning species recovery. Inferencia la Naturaleza de las Amenazas Antropogenicas para los Registros de Abundancia a Largo Plazo Resumen Diagnosticar los procesos que amenazan la permanencia de las especies es critico para la planeacion de la recuperacion y la prediccion de riesgos. Las amenazas dominantes se infieren comunmente por expertos con base en un collage de metodos informales. Las herramientas de diagnostico transparentes y cuantitativas podrian contribuir con la tan necesitada consistencia, objetividad y rigor para el proceso de diagnosticar amenazas antropogenicas. Los registros de censos a largo plazo, disponibles para un creciente y diverso conjunto de taxa, pueden exhibir rasgos caracteristicos de procesos especificos de amenaza y asi proporcionar informacion para la diagnosis de amenazas. Desarrollamos un marco de trabajo Bayesiano y flexible para diagnosticar amenazas con base en los registros de censos a largo plazo y diversas fuentes subsidiarias de informacion. Probamos este marco de trabajo con datos simulados de poblaciones artificiales sujetas a diferentes grados de explotacion y perdida de habitat y varias series de tiempos de abundancia reales para los cuales estan bien entendidos los procesos de amenaza: Thunnus maccoyii y Gadus morhua para la explotacion; Lagopus lagopus scotica y Alauda arvensis para la perdida de habitat. Nuestro metodo identifico correctamente el proceso conductor de la declinacion poblacional para mas del 90% de las series de tiempo simuladas bajo escenarios moderados y severos de amenaza. Nuestro metodo identifico las amenazas con menos exito cuando los procesos de amenaza eran debiles o cuando las poblaciones estaban afectadas simultaneamente por amenazas multiples. Nuestro metodo selecciono el modelo de la presunta verdadera amenaza para todos los estudios de caso reales, aunque los resultados fueron algo ambiguos en el caso de Alauda arvensis. En el ultimo caso, la incorporacion de una fuente subsidiaria de informacion (registros de cambio en el uso de suelo) incrementaron la fuerza asignada al supuesto modelo verdadero del 70% al 92%, ilustrando el valor del marco de trabajo propuesto en la contribucion de diversas fuentes de informacion para un marco de trabajo comun y riguroso. Finalmente, nuestro marco de trabajo puede asistir enormemente a las organizaciones de la conservacion en la documentacion de procesos de amenaza y en la planeacion de la recuperacion de especies.

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long-lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire-prone ecosystems, including the biodiversity hotspots of Mediterranean-type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long-lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land-use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land-use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored.

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species was increasingly used during the 1980s to assess the conservation status of species for policy and planning purposes. This use stimulated the development of a new set of quantitative criteria for listing species in the categories of threat: critically endangered, endangered, and vulnerable. These criteria, which were intended to be applicable to all species except microorganisms, were part of a broader system for classifying threatened species and were fully implemented by IUCN in 2000. The system and the criteria have been widely used by conservation practitioners and scientists and now underpin one indicator being used to assess the Convention on Biological Diversity 2010 biodiversity target. We describe the process and the technical background to the IUCN Red List system. The criteria refer to fundamental biological processes underlying population decline and extinction. But given major differences between species, the threatening processes affecting them, and the paucity of knowledge relating to most species, the IUCN system had to be both broad and flexible to be applicable to the majority of described species. The system was designed to measure the symptoms of extinction risk, and uses 5 independent criteria relating to aspects of population loss and decline of range size. A species is assigned to a threat category if it meets the quantitative threshold for at least one criterion. The criteria and the accompanying rules and guidelines used by IUCN are intended to increase the consistency, transparency, and validity of its categorization system, but it necessitates some compromises that affect the applicability of the system and the species lists that result. In particular, choices were made over the assessment of uncertainty, poorly known species, depleted species, population decline, restricted ranges, and rarity; all of these affect the way red lists should be viewed and used. Processes related to priority setting and the development of national red lists need to take account of some assumptions in the formulation of the criteria.La Lista Roja de Especies Amenazadas de la UICN (Union Internacional para la Conservacion de la Naturaleza) fue muy utilizada durante la decada de l980 para evaluar el estatus de conservacion de especies para fines politicos y de planificacion. Este uso estimulo el desarrollo de un conjunto nuevo de criterios cuantitativos para enlistar especies en las categorias de amenaza: en peligro critico, en peligro y vulnerable. Estos criterios, que se pretendia fueran aplicables a todas las especies excepto microorganismos, eran parte de un sistema general para clasificar especies amenazadas y fueron implementadas completamente por la UICN en 2000. El sistema y los criterios han sido ampliamente utilizados por practicantes y cientificos de la conservacion y actualmente apuntalan un indicador utilizado para evaluar el objetivo al 2010 de la Convencion de Diversidad Biologica. Describimos el proceso y el respaldo tecnico del sistema de la Lista Roja de la IUCN. Los criterios se refieren a los procesos biologicos fundamentales que subyacen en la declinacion y extincion de una poblacion. Pero, debido a diferencias mayores entre especies, los procesos de amenaza que los afectan y la escasez de conocimiento sobre la mayoria de las especies, el sistema de la UICN tenia que ser amplio y flexible para ser aplicable a la mayoria de las especies descritas. El sistema fue disenado para medir los sintomas del riesgo de extincion, y utiiza cinco criterios independientes que relacionan aspectos de la perdida poblacional y la declinacion del rango de distribucion. Una especie es asignada a una categoria de amenaza si cumple el umbral cuantitativo por lo menos para un criterio. Los criterios, las reglas acompanantes y las directrices utilizadas por la UICN tienen la intencion de incrementar la consistencia, transparencia y validez de su sistema de clasificacion, pero requiere algunos compromisos que afectan la aplicabilidad del sistema y las listas de especies que resultan. En particular, se hicieron selecciones por encima de la evaluacion de incertidumbre, especies poco conocidas, especies disminuidas, declinacion poblacional, rangos restringidos y rareza; todas estas afectan la forma en que las listas rojas deberian ser vistas y usadas. Los procesos relacionados con la definicion de prioridades y el desarrollo de las listas rojas nacionales necesitan considerar algunos de los supuestos en la formulacion de los criterios.

Scalar population models, commonly referred to as count-based models, are based on time-series data of population sizes and may be useful for screening-level ecological risk assessments when data for more complex models are not available. Appropriate use of such models for management purposes, however, requires understanding inherent biases that may exist in these models. Through a series of simulations, which compared predictions of risk of decline of scalar and matrix-based models, we examined whether discrepancies may arise from different dynamics displayed due to age structure and generation time. We also examined scalar and matrix-based population models of 18 real populations for potential patterns of bias in population viability estimates. In the simulation study, precautionary bias (i.e., overestimating risks of decline) of scalar models increased as a function of generation time. Models of real populations showed poor fit between scalar and matrix-based models, with scalar models predicting significantly higher risks of decline on average. The strength of this bias was not correlated with generation time, suggesting that additional sources of bias may be masking this relationship. Scalar models can be useful for screening-level assessments, which should in general be precautionary, but the potential shortfalls of these models should be considered before using them as a basis for management decisions.

The lack of management experience at the landscape scale and the limited feasibility of experiments at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse ( Tympanuchus phasianellus) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.

The conservation of ecosystems focuses on evaluating individual sites or landscapes based on their component species. To produce a map of conservation values, we developed a method to weight habitat-suitability maps for individual species by species-specific extinction risks. The value of a particular site reflects the importance and magnitude of the threats facing the component species of the ecological community. We applied this approach to a set of species from the California Gap Analysis Project. The resulting map of multispecies conservation values identified the areas with the best habitat for the species most vulnerable to extinction. These methods are flexible and can accommodate the quantity and quality of data available for each individual species in both the development of the habitat-suitability maps and the estimation of the extinction risks. Additionally, the multispecies conservation value can accommodate specific conservation goals, such as preservation of local endemics, making it useful for prioritizing conservation and management actions. This approach provides an estimate of the ecological worth of a site based on habitat characteristics and quantitative models in terms of all the ecological components of a site, rather than a single threatened or endangered species. Resumen: La conservacion de los ecosistemas se enfoca en la evaluacion de sitios individuales o paisajes en base a las especies que lo componen. Para producir un mapa de valores para la conservacion, desarrollamos un metodo que valora mapas de aptitud del habitat para especies a nivel individual en base a los riesgos de extincion especie-especificos. El valor de un sitio en particular refleja la importancia y la magnitud de las amenazas que enfrentan las especies que componen la comunidad ecologica. Aplicamos esta metodologia a un grupo de especies del Proyecto de Analisis de Aberturas de California. El mapa de valores de conservacion para multiples especies resultante identifico las areas con el mejor habitat para las especies mas vulnerables a la extincion. Estos metodos son flexibles y pueden abarcar la cantidad y calidad de los datos disponibles para cada especie individual tanto para el desarrollo de mapas de aptitud del habitat, como para la eliminacion de los riesgos de extincion. Ademas, los valores de conservacion multi-especie pueden abarcar metas especificas de conservacion, como lo es la preservacion de endemias locales, haciendolos utiles para priorizar las acciones de conservacion y manejo. Esta metodologia provee una estimacion del merito ecologico de un sitio en base a las caracteristicas y modelos cuantitativos en terminos de todos los componentes ecologicos de un sitio, y no en una sola especie amenazada o en peligro.

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. Resumen: La Union Mundial para la Conservacion (IUCN) definio un grupo de categorias referentes a estados de conservacion sustentadas en reglas de decision basadas en umbrales de parametros como son el rango de distribucion, el tamano poblacional, la historia poblacional y el riesgo de extincion. Estas reglas han recibido aceptacion internacional y se han convertido en una de las herramientas mas importantes para la toma de decisiones en biologia de la conservacion 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 medicion, variacion natural y vaguedad en la definicion de los parametros usados en las reglas. Actualmente no existen lineamientos especificos para enfrentar la incertidumbre. La interpretacion 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 clasificacion de especies amenazadas. Proponemos un metodo para enfrentar a la incertidumbre que puede ser aplicado a los criterios actuales de IUCN sin alterar las reglas, los umbrales, o la intencion de estos criterios. Nuestro metodo propaga la incertidumbre en los parametros usados y asigna a la especie evaluada a una sola categoria (a como lo hace el criterio actual) o a un rango de categorias plausibles, dependiendo de la naturaleza y la extension de las incertidumbres.

STUART H. M. BUTCHART,∗ H. RESIT AKCAKAYA,‡ ELIZABETH KENNEDY,§ AND CRAIG HILTON-TAYLOR∗∗ ∗BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA, United Kingdom, email stuart.butchart@birdlife.org ‡Applied Biomathematics, 100 North Country Road, Setauket, New York 11733, U.S.A. §Conservation International, 1919 M Street, NW Street 600, Washington, D.C. 20036, U.S.A. ∗∗IUCN Species Programme Office, 219c Huntingdon Road, Cambridge, CB3 0DL, United Kingdom

We present an analysis of the metapopulation dynamics of the federally threatened coastal Califor- nia Gnatcatcher ( Polioptila c. californica ) for an approximately 850 km 2 region of Orange County, California. We developed and validated a habitat suitability model for this species using data on topography, vegetation, and locations of gnatcatcher pair observations. Using this habitat model, we calculated the spatial structure of the metapopulation, including size and location of habitat patches and the distances among them. We used data based on field studies to estimate parameters such as survival, fecundity, dispersal, and catastrophes, and combined these parameters with the spatial structure to build a stage-structured, stochastic, spatially- explicit metapopulation model. The model predicted a fast decline and high risk of population extinction with most combinations of parameters. Results were most sensitive to density-dependent effects, the probability of weather-related catastrophes, adult survival, and adult fecundity. Based on data used in the model, the great- est difference in results was given when the simulation's time horizon was only a few decades, suggesting that modeling based on longer or shorter time horizons may underestimate the effects of alternative management actions.

The probability of extinction is sensitive to the presence and character of density dependence controlling the dynamics of a population. This means that our capacity to estimate a population's risks of extinction under varying environmental conditions or competing management re- gimes is linked to our ability to reconstruct from data the density-dependence relationships governing the natural dy- namics, especially when data do not reveal a trend of pop- ulation growth or decline. In an example using Gadus morhua, we show that even 10- or 20-year data sets are too short to make precise estimates of these risks. We also ob- serve, however, that under moderate or weak density depen- dence, the computed risks are lower than when density de- pendence is not included in the model. We propose, therefore, that when available data sets are insufficient for recon- structing reliable measurements of density dependence, con- servative estimates of extinction probabilities can be made from models that simply omit density dependence. Resumen: La probabilidad de extinci6n es sensible a la presencia y al cardcter de la dependencia de densidad que controla la dinamica de una poblaci6n. Esto significa que nuestra capacidad para estimar los riesgos de extinci6n de una poblacion bajo condiciones ambientales cambiantes, o diferentes regimenes de manejo, esta relacionada a nuestra capacidad de reconstruir, en base a datos, las relaciones de dependencia de densidad que gobiernan la dinamica natu- ral de poblaciones, especialmente cuando no se dispone de informaci6n que revele tendencias de crecimiento o decli- naci6n de poblaciones. En un ejemplo, utilizando Gadus morhua, demostramos que aun los datos sobre un periodo de 10 6 20 anios son insuficientes para hacer estimaciones pre- cisas sobre estos riesgos. Sin embargo, observamos que in- cluso en una situaci6n de dependencia de densidad mo- derada o debil, los riesgos calculados son mas bajos que cuando la dependencia de densidad no es incluida en el modelo. Por ello proponemos, que, cuando no se dispone de datos suficientes para reconstruir estimados confiables de dependencia de densidad, se pueden hacer estimaciones con- servadoras de probabilidades de extinci6n a partir de mo- delos que simplemente omiten los aspectos de dependencia de densidad.

*Key Centre for Tropical Wildlife Management, Northern Territory University, Darwin, Northern Territory 0909, Australia†School of Botany, University of Melbourne, Parkville, Victoria 3052, Australia‡Applied Biomathematics, 100 North Country Road, Setauket, NY 11733, U.S.A.§Key Centre for Biodiversity and Bioresources, Macquarie University, New South Wales 2109, Australia