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Drivers and hotspots of extinction risk in marine mammals

机译：海洋哺乳动物灭绝风险的驱动因素和热点

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The world's oceans are undergoing profound changes as a result of human activities. However, the consequences of escalating human impacts on marine mammal biodiversity remain poorly understood. The International Union for the Conservation of Nature (IUCN) identifies 25% of marine mammals as at risk of extinction, but the conservation status of nearly 40% of marine mammals remains unknown due to insufficient data. Predictive models of extinction risk are crucial to informing present and future conservation needs, yet such models have not been developed for marine mammals. In this paper, we: (ⅰ) used powerful machine-learning and spatial-modeling approaches to understand the intrinsic and extrinsic drivers of marine mammal extinction risk; (ⅱ) used this information to predict risk across all marine mammals, including IUCN "Data Deficient" species; and (ⅲ) conducted a spatially explicit assessment of these results to understand how risk is distributed across the world's oceans. Rate of offspring production was the most important predictor of risk. Additional predictors included taxonomic group, small geographic range area, and small social group size. Although the interaction of both intrinsic and extrinsic variables was important in predicting risk, overall, intrinsic traits were more important than extrinsic variables. In addition to the 32 species already on the IUCN Red List, our model identified 15 more species, suggesting that 37% of all marine mammals are at risk of extinction. Most at-risk species occur in coastal areas and in productive regions of the high seas. We identify 13 global hotspots of risk and show how they overlap with human impacts and Marine Protected Areas.
机译：人类活动使世界海洋发生了深刻变化。然而，人们对海洋哺乳动物生物多样性的影响日益加剧的后果仍然知之甚少。国际自然保护联盟（IUCN）确定有25％的海洋哺乳动物处于灭绝的危险中，但由于数据不足，尚不清楚近40％的海洋哺乳动物的保护状况。灭绝风险的预测模型对于告知当前和未来的保护需求至关重要，但是尚未为海洋哺乳动物开发这种模型。在本文中，我们：（ⅰ）使用强大的机器学习和空间建模方法来了解海洋哺乳动物灭绝风险的内在和外在驱动因素； （ⅱ）使用此信息来预测所有海洋哺乳动物（包括IUCN“数据不足”物种）的风险； （ⅲ）对这些结果进行了空间明确的评估，以了解风险如何在全球海洋中分布。子代生产率是最重要的风险预测指标。其他预测因素包括分类组，较小的地理区域和较小的社交组规模。尽管内在变量和外在变量的相互作用在预测风险中很重要，但总的来说，内在特性比外在变量更重要。除了已经在世界自然保护联盟红色名录中的32个物种之外，我们的模型还确定了15个物种，这表明所有海洋哺乳动物中有37％处于灭绝的风险。大多数高危物种发生在沿海地区和公海的生产地区。我们确定了13个全球风险热点，并说明它们如何与人类影响和海洋保护区重叠。

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