A recent study introducing a unique machine learning approach for estimating extinction risk shows more unlisted reptile species threatened than previously thought.
(Photo : Vitya Lapatey / Unsplash)
Unique Machine Learning Approach
Reports from SciTech Daily show that Scientists developed a machine learning model to examine 4,369 reptile species that had previously been unable to be recognized for conservation. This is to build more effective techniques for measuring the extinction danger of cryptic species.
The algorithm assigned IUCN extinction risk categories to the 40% of the world's reptiles that did not have published assessments or were classed as 'Data Deficient.' The accuracy of the model was tested by cross-checking it with the Red List.
In the study, the authors who invented the machine learning for identifying vulnerable reptile species discovered that they accumulated larger data than that reported on the IUCN Red List. Plus, NE or Not Evaluated and Data Deficient reptiles were both more likely to be threatened than those that are already evaluated in the Red List.
"Altogether, our models predict that the state of reptile conservation is far worse than currently estimated and that immediate action is necessary to avoid the disappearance of reptile biodiversity," the researchers noted.
Additionally, coauthor Shai Meiri deduced that the machine learning's newly identified threatened reptile species are not spread randomly over the world or the reptilian evolutionary tree.
In fact, the model suggest that more reptile species are in danger, particularly in Australia, Madagascar, and the Amazon basin. All of which contain a diverse reptile population and should be prioritized for additional conservation efforts.
Furthermore, species-rich groups such as geckos and elapids (cobras, mambas, coral snakes, and others) are likely to be more threatened than the Global Reptile Assessment presently identifies. Likewise, these groups should get additional conservation attention.
However, the authors suggest that future research is needed to better understand the precise reasons behind extinction risk in vulnerable reptile taxa, collect more data on obscure reptile taxa, and develop conservation strategies that incorporate newly recognized threatened species.
Fortunately, owing to the novel machine learning technique, these future studies will already have a dependable instrument for more accurate evaluations.
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Will Reptiles Go Extinct?
The International Union for Conservation of Nature (IUCN) reveals that 'more' reptiles are at risk of extinction after creating a machine learning model that estimates the potential rate of reptiles set for possible annihilation in the future.
This is according to the iconic Red List of Threatened Species, a comprehensive evaluation of a species' extinction risk. This list impacts conservation policy and practices worldwide. However, developing the list is time-consuming, arduous, and skewed, relying mainly on manual curation by human specialists.
Prior to machine learning models, many animal species have not been reviewed or have inadequate data, leaving gaps in protection measures.
But as machine learning produces new discoveries, global initiatives will have the opportunity to collect efficient data to prioritize the conservation of vulnerable species.
The researchers urge that the globe utilise its scarce resources as efficiently as possible. Sophisticated tools, such as this one, or even a more powerful machine learning update, can assess extinction danger, paving the way for more informed conservation strategic planning.
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Written by Thea Felicity
