In a new study just published in Nature Ecology and Evolution, researchers used artificial intelligence to analyze the conservation status of over 1,900 palm species. They discovered that more than 1,000 might be in danger of going extinct.
Water Lake Trees Palms Travel
The IUCN Red List of Threatened Species
The international study team merged existing data from the International Union for Conservation of Nature (IUCN) Red List with cutting-edge machine learning algorithms to paint a more accurate picture of how palms may be in danger. Despite being well-liked and well-represented on the Red List, it has been unknown to this point what threat exists for almost 70% of these plants.
It is commonly accepted that the IUCN Red List of Threatened Species is the gold standard for assessing the conservation status of animal, plant, and microbial species. But since not all species have been included and many evaluations need to be updated, gaps in the Red List need to be filled.
Updating IUCN Red List Using Machine Learning
Even with the Red List gap, scientists are optimistic that AI would significantly speed up the first assessments of a species' conservation status. To support efforts to update and expand the IUCN Red List, researchers from RBG Kew and collaborators are developing unique approaches to quantify the extinction risk for thousands of plant species.
Royal Botanic Gardens, Kew, is a non-departmental public body in the United Kingdom. According to Wikipedia, it is a significant center for botanical study and instruction. The Department of Environment, Food, and Rural Affairs serve as its sponsor.
According to Dr. Steven Bachman, research team head for Kew's Conservation Assessment and Analysis team, we must act quickly to halt biodiversity loss. He stated that to produce quick and reliable assessments, we must use all the resources at our disposal, including automation and prediction. He emphasized that one of the crucial actions conservationists may take to increase awareness of threatened species is to add plants to the Red List.
Machine Learning is Employed to Determine the Palm Species Extinction Rate
The RBG Kew researchers used machine learning to calculate the extinction risk of more than a thousand palm species. They analyzed how extinction risks connect to palm distribution and ecology using AI and current Red List data, forecasting the extinction risk for 1,381 species.
According to Phys.org, the extinction danger of 1,889 species, or 75% of the palm family, was calculated using the newly discovered data in conjunction with existing Red List evaluations. Concerningly, 56% of these species may be threatened. If this trend were applied to the entire family, more than 1,000 species might be in danger of extinction.
According to Dr. Sidonie Bellot, research head in character evolution at Kew, the research is a little less extensive than extrapolations based only on Red List evaluation. She stated that it is still highly alarming, given the numerous interconnections between palms and other living things. These relationships include those with the animals and birds that eat their fruits, the fungi and insects that live on them, and the numerous people that rely on palm products.
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Machine Learning Employed to Determine the Palm Species Extinction Rate
Just under half of the species that are evolutionarily or functionally distinct and just under a third of the species that are exploited were found to be threatened.
Their findings identified the following countries and regions as being top priorities for palm conservation: Madagascar, New Guinea, the Philippines, Vietnam, Vanuatu, Hawaii, Borneo, Jamaica, New Caledonia, and Sulawesi.
Over 40% of the genetically unique, functionally distinct, and/or used species may be threatened in the zones containing 12 to 291 palm species. Another 15 areas with fewer than ten palm species each were also found, but they face an equally serious threat of extinction.
The study found that at least 185 helpful palm species may be in danger in 92 areas, further emphasizing the necessity to save these trees.
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