Caution Needed When Predicting Species Threat Status for Conservation Prioritization on a Global Scale
Caution Needed When Predicting Species Threat Status for Conservation Prioritization on a Global Scale
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预测全球范围内的物种威胁状态以进行保护优先级时需要谨慎
DOI：
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发表时间：
2020
影响因子：
5.6
通讯作者：
E. N. Nic Lughadha
中科院分区：
文献类型：
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作者：
B. Walker;Tarciso C. C. Leão;S. Bachman;Friederike C. Bolam;E. N. Nic Lughadha
最近的生物多样性和生态系统服务政府间科学政策平台（IPBES）报告强调了生物多样性面临的大规模灭绝风险（Díaz等人，2019年）。评估物种的灭绝风险对于确定保护优先事项至关重要，也是保护特定地区或群体的第一步。评估灭绝风险的一个被广泛接受的方法，也是支持政府间科学政策平台报告的一个关键数据来源，是世界自然保护联盟濒危物种红色名录（以下简称红色名录）。然而，在最新更新的评估中，只有9%的植物被代表（IUCN，2019），在增加植物等巨型多样性群体的红色名录覆盖范围方面进展缓慢，限制了它们被纳入全球保护优先事项的分析（Venter等人，2014; Betts等人，2017; Di Marco等人，2018年）。针对这一问题，人们对加快评估进程的兴趣越来越大。自动化，特别是通过机器学习，提供了一个有吸引力的解决方案。然而，我们主张在采用它之前要谨慎，以帮助确定全球保护优先事项。我们从文献中借鉴了两个最近的例子（Pelletier等人，2018; Stévart等人，2019年），值得关注的是迄今为止使用机器学习或自动化来预测植物保护状况的最大研究。每项研究都建议了一项快速生成初步保护评估的协议，并且都有一个共同的目标，即利用其初步评估来突出全球或整个大陆的保护优先事项。这些研究的潜在影响值得仔细研究。在这里，我们强调了他们的设计和报告方面可以改进，使未来的研究，这种可以有最大的影响。
The recent Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report highlighted the large scale of extinction risks to biodiversity (Díaz et al., 2019). Assessing species’ extinction risk is vital for setting conservation priorities and the first step toward protecting particular areas or groups. A widely accepted approach to assess extinction risk, and a key source of data underpinning the IPBES report, is the IUCN Red List of Threatened Species (hereafter Red List). However, with only 9% of plants represented by assessments at the latest update (IUCN, 2019), slow progress in increasing Red List coverage of mega-diverse groups like plants has limited their inclusion in analyses of global conservation priorities (Venter et al., 2014; Betts et al., 2017; Di Marco et al., 2018). Responding to this problem, there is growing interest in speeding up the assessment process. Automation, particularly through machine learning, offers an attractive solution. However, we advocate caution before adopting it to help set global conservation priorities. We draw on two recent examples from the literature (Pelletier et al., 2018; Stévart et al., 2019) that deserve attention as the largest studies to date that use machine learning or automation to predict the conservation status of plants. Each study recommends a protocol for rapidly generating preliminary conservation assessments, and both share the goal of using their preliminary assessments to highlight global or continent-wide conservation priorities. The potential impact of these studies merits careful scrutiny. Herein we highlight aspects of their design and reporting that can be improved so that future studies of this kind can have maximum impact.
影响因子：
13.6
作者：
Enquist, Brian J.;Feng, Xiao;McGill, Brian J.
通讯作者：
McGill, Brian J.