Jacob Blaustein Center for Scientific Cooperation, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel.
Mitrani Department of Desert Ecology, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel.
PLoS Biol. 2022 May 26;20(5):e3001544. doi: 10.1371/journal.pbio.3001544. eCollection 2022 May.
The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning-based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles-the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.
《濒危物种红色名录》由国际自然保护联盟 (IUCN) 发布，是保护决策的重要工具。然而，尽管付出了巨大努力，仍有许多物种尚未得到评估，或缺乏足够的数据来确定其红色名录灭绝风险类别。此外，红色名录编制过程存在各种不确定性和偏见。开发强大的自动化评估方法可以作为一种高效且非常有用的工具，以加速评估过程并提供临时评估。在这里，我们旨在：(1) 提出一种基于机器学习的自动化灭绝风险评估方法，可用于不太知名的物种；(2) 对所有爬行动物进行临时评估——这是唯一没有全面红色名录评估的主要四足动物群；(3) 评估人类决策偏见对评估结果的潜在影响。我们使用这里提出的方法对 4369 种目前未被评估或被 IUCN 归类为数据不足的爬行动物进行评估。我们预测中使用的模型在将物种分类为受威胁/非受威胁物种方面的准确率为 90%，在预测特定灭绝风险类别方面的准确率为 84%。未被评估和数据不足的爬行动物被认为受到威胁的可能性远远高于已被评估的物种，这进一步证明了这些物种需要更多的保护关注。当我们纳入临时评估时，受威胁物种的总体比例大大增加。评估员身份强烈影响预测结果，表明在灭绝风险评估中需要仔细考虑评估员的影响。我们确定的可能受到更大威胁的区域和分类群应在新的评估和保护规划中给予更多关注。最后，我们在这里提出的方法可以很容易地实施，以帮助缩小其他不太知名分类群的评估差距。
