In:
PLOS Biology, Public Library of Science (PLoS), Vol. 20, No. 5 ( 2022-5-26), p. e3001544-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1545-7885
DOI:
10.1371/journal.pbio.3001544
DOI:
10.1371/journal.pbio.3001544.g001
DOI:
10.1371/journal.pbio.3001544.g002
DOI:
10.1371/journal.pbio.3001544.g003
DOI:
10.1371/journal.pbio.3001544.g004
DOI:
10.1371/journal.pbio.3001544.g005
DOI:
10.1371/journal.pbio.3001544.t001
DOI:
10.1371/journal.pbio.3001544.t002
DOI:
10.1371/journal.pbio.3001544.s001
DOI:
10.1371/journal.pbio.3001544.s002
DOI:
10.1371/journal.pbio.3001544.s003
DOI:
10.1371/journal.pbio.3001544.s004
DOI:
10.1371/journal.pbio.3001544.s005
DOI:
10.1371/journal.pbio.3001544.s006
DOI:
10.1371/journal.pbio.3001544.s007
DOI:
10.1371/journal.pbio.3001544.s008
DOI:
10.1371/journal.pbio.3001544.s009
DOI:
10.1371/journal.pbio.3001544.s010
DOI:
10.1371/journal.pbio.3001544.s011
DOI:
10.1371/journal.pbio.3001544.s012
DOI:
10.1371/journal.pbio.3001544.s013
DOI:
10.1371/journal.pbio.3001544.s014
DOI:
10.1371/journal.pbio.3001544.s015
DOI:
10.1371/journal.pbio.3001544.s016
DOI:
10.1371/journal.pbio.3001544.s017
DOI:
10.1371/journal.pbio.3001544.s018
DOI:
10.1371/journal.pbio.3001544.s019
DOI:
10.1371/journal.pbio.3001544.s020
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2126773-X
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