Davidson Ana D, Shoemaker Kevin T, Weinstein Ben, Costa Gabriel C, Brooks Thomas M, Ceballos Gerardo, Radeloff Volker C, Rondinini Carlo, Graham Catherine H
Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, United States of America.
NatureServe, Arlington, Virginia, United States of America.
PLoS One. 2017 Nov 16;12(11):e0186934. doi: 10.1371/journal.pone.0186934. eCollection 2017.
Identifying which species are at greatest risk, what makes them vulnerable, and where they are distributed are central goals for conservation science. While knowledge of which factors influence extinction risk is increasingly available for some taxonomic groups, a deeper understanding of extinction correlates and the geography of risk remains lacking. Here, we develop a predictive random forest model using both geospatial and mammalian species' trait data to uncover the statistical and geographic distributions of extinction correlates. We also explore how this geography of risk may change under a rapidly warming climate. We found distinctive macroecological relationships between species-level risk and extinction correlates, including the intrinsic biological traits of geographic range size, body size and taxonomy, and extrinsic geographic settings such as seasonality, habitat type, land use and human population density. Each extinction correlate exhibited ranges of values that were especially associated with risk, and the importance of different risk factors was not geographically uniform across the globe. We also found that about 10% of mammals not currently recognized as at-risk have biological traits and occur in environments that predispose them towards extinction. Southeast Asia had the most actually and potentially threatened species, underscoring the urgent need for conservation in this region. Additionally, nearly 40% of currently threatened species were predicted to experience rapid climate change at 0.5 km/year or more. Biological and environmental correlates of mammalian extinction risk exhibit distinct statistical and geographic distributions. These results provide insight into species-level patterns and processes underlying geographic variation in extinction risk. They also offer guidance for future conservation research focused on specific geographic regions, or evaluating the degree to which species-level patterns mirror spatial variation in the pressures faced by populations within the ranges of individual species. The added impacts from climate change may increase the susceptibility of at-risk species to extinction and expand the regions where mammals are most vulnerable globally.
确定哪些物种面临的风险最大、使其易受伤害的因素以及它们的分布地点是保护科学的核心目标。虽然对于一些分类群,越来越多的信息表明哪些因素会影响灭绝风险,但对灭绝相关因素及其风险地理分布仍缺乏更深入的了解。在此,我们利用地理空间数据和哺乳动物物种特征数据开发了一个预测性随机森林模型,以揭示灭绝相关因素的统计和地理分布。我们还探讨了在气候迅速变暖的情况下,这种风险地理分布可能如何变化。我们发现物种水平的风险与灭绝相关因素之间存在独特的宏观生态关系,包括地理分布范围大小、体型和分类学等内在生物学特征,以及季节性、栖息地类型、土地利用和人口密度等外在地理环境。每个灭绝相关因素都表现出与风险特别相关的值范围,并且不同风险因素的重要性在全球范围内并非地理上均匀分布。我们还发现,约10%目前未被认定为有风险的哺乳动物具有使其易灭绝的生物学特征并生活在相关环境中。东南亚实际和潜在受威胁的物种最多,凸显了该地区保护工作的迫切需求。此外,预计近40%目前受威胁的物种将以每年0.5公里或更快速度经历快速气候变化。哺乳动物灭绝风险的生物学和环境相关因素呈现出独特的统计和地理分布。这些结果为灭绝风险地理变异背后的物种水平模式和过程提供了见解。它们还为未来针对特定地理区域的保护研究提供指导,或评估物种水平模式反映单个物种分布范围内种群所面临压力的空间变异程度。气候变化带来的额外影响可能会增加濒危物种灭绝的易感性,并扩大全球哺乳动物最脆弱的区域。