Machine learning tool estimates extinction risk of previously non-priority species for conservation
The iconic Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), identifies species threatened with extinction. A study in PLOS Biology posting May 26 by Gabriel Henrique de Oliveira Caetano of Ben-Gurion University of the Negev, Israel, and colleagues present a new machine-learning tool to assess extinction risk, then use the tool to show that species of reptiles that are not listed due to lack of assessment or data are more likely to be threatened than assessed species.
The IUCN Red List of Threatened Species is the most comprehensive assessment of species extinction risk and informs conservation policy and practice globally. However, the process of species categorization is laborious and subject to bias, relying heavily on manual curation by human experts; many animal species have therefore not been assessed, or lack sufficient data, creating gaps in protection measures.
To assess 4,369 reptile species that previously could not be prioritized for conservation and develop accurate methods to assess the risk of extinction of obscure species, these researchers created a machine-learning computer model. The model assigned IUCN extinction risk categories to the 40% of the world’s reptiles that lacked published assessments or were classified as “DD” (“Data Deficient”) at the time of the study. The researchers validated the accuracy of the model, comparing it to Red List risk categorizations.
The researchers found that the number of threatened species is much higher than indicated in the IUCN Red List and that non-assessed (“not assessed” or “NE”) reptiles and those with insufficient data were more likely to be threatened than assessed species. Future studies are needed to better understand the specific factors underlying the extinction risk of threatened reptile taxa, to obtain better data on obscure reptile taxa, and to create conservation plans that include newly identified threatened species. .
According to the authors, “Overall, our models predict that the conservation status of reptiles is much worse than currently estimated, and that immediate action is needed to avoid loss of reptile biodiversity. regions and taxa that we have identified as likely to be at greater risk should receive increased attention in further assessments and conservation planning.Finally, the method we present here can be easily implemented to help fill the assessment gap on other lesser-known taxa.
Co-author Shai Meiri adds, “It is important to note that the additional reptile species identified as threatened by our models are not randomly distributed around the world or in the reptilian evolutionary tree. , and the Amazon Basin – all of which have a high diversity of reptiles and should be targeted for further conservation effort. Additionally, species-rich groups, such as geckos and elapids (cobras, mambas, coral snakes, and others), are likely to be at greater risk than the Global Reptile Assessment currently highlights, these groups should also be considered. object of greater conservation attention”
Co-author Uri Roll adds: “Our work could be very important in helping global efforts to prioritize the conservation of species at risk – for example using the IUCN Red List mechanism. Our world is facing a biodiversity crisis and severe changes in ecosystems and species, but the funds allocated to conservation are very limited, therefore it is essential that we use these limited funds where they could bring the most benefit. Advanced tools – such as those we used here, along with the accumulation of data, could significantly reduce the time and cost needed to assess extinction risk, and thus pave the way for decision-making. more knowledgeable about conservation.”
Source of the story:
Material provided by OLP. Note: Content may be edited for style and length.