Department of Environmental Science, Institute for Wetland and Water Research, Faculty of Science, Radboud University, P.O. Box 9010, NL-6500 GL, Nijmegen, The Netherlands.
BirdLife international, David Attenborough Building, Pembroke Street, Cambridge, CB23QZ, U.K.
Conserv Biol. 2019 Oct;33(5):1084-1093. doi: 10.1111/cobi.13279. Epub 2019 Feb 25.
The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species' distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5-2.3% and 6.4-14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.
IUCN（国际自然保护联盟）红色名录类别和标准是评估物种相对灭绝风险最广泛使用的框架。这些标准基于与物种分布和种群大小、趋势和结构相关的定量阈值。然而，对于许多物种来说，这些参数的数据稀疏且不确定，对于其他物种来说则不可用，这可能导致它们被错误分类或归类为数据不足。我们设计了一种方法，该方法结合了土地覆盖变化、物种特定的栖息地偏好、种群丰度和扩散距离的数据，以估计关键参数（出现范围、最大占据面积、种群规模和趋势以及破碎化程度），从而预测物种的 IUCN 红色名录类别。我们将我们的方法应用于全球非浮游鸟类和陆生哺乳动物（约 15000 种）。预测的类别与已发表的 IUCN 红色名录评估相当一致，但总体更为乐观。我们预测有 4.2%的物种（467 种鸟类和 143 种哺乳动物）比目前评估的更受威胁，有 20.2%的数据不足物种（10 种鸟类和 114 种哺乳动物）有灭绝的危险。纳入栖息地破碎化亚标准分别减少了濒危物种和数据不足物种的这些预测值 1.5-2.3%和 6.4-14.9%（取决于对破碎化的定量定义），这突显了需要为 IUCN 红色名录评估员提供关于应用 IUCN 红色名录标准这一方面的改进指导。我们的方法补充了传统的估计 IUCN 红色名录评估参数的方法。此外，它还可以根据土地覆盖信息的定期更新，为识别可能需要改变其灭绝风险类别的物种提供早期预警系统。鉴于我们的方法依赖于对物种分布和丰度的乐观假设，所有预测比目前评估更危险的物种都应优先重新评估。
