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| Content Provider | frontiers |
|---|---|
| Author | Walker, Barnaby E. Leão, Tarciso C. C. Bachman, Steven P. Bolam, Friederike C. Nic Lughadha, Eimear |
| Abstract | The recent 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 towards 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), the 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.To illustrate the necessity for caution, we draw on two recent examples from the literature (Pelletier et al., 2018;Stévart et al., 2019) that make what we believe are mistakes in the design and reporting of their methods. Pelletier et al. (2018) and Stévart et al. (2019) deserve attention because they are, to date, the largest studies that use machine learning or automation to predict the conservation status of plants. Both have the goal of highlighting global or continent-wide ... |
| ISSN | 1664462X |
| DOI | 10.3389/fpls.2020.00520 |
| Volume Number | 11 |
| Journal | Frontiers in Plant Science |
| Language | English |
| Publisher Date | 2020-04-28 |
| Access Restriction | Open |
| Subject Keyword | Machine learning Transparency (Min5-Max 8) Conservation Automation IUCN Red list Extinction risk |
| Content Type | Text |
| Resource Type | Article |
| Subject | Plant Science |
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