How machine learning could help save threatened species from extinction

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We know that there are thousands of species on Earth that we don't know much about, but they are on the verge of extinction. The results of the machine learning study were grim.

Some species of animals and plants are labeled data deficient because they don't have enough information to understand how they live or how many of them are left. The data deficient species are more at risk than other species that are well known. The data from this study came from the International Union for the Preservation of Nature.

Half of the data deficient species face the risk of extinction. Only a small percentage of species on the Red List are at risk of extinction.

Jan Borgelt, an ecologist at the Norwegian University of Science and Technology and the lead author of the study, said that things could be worse than they know. More species are likely to be in danger.

Understanding how human activity affects the environment is one of Borgelt's main areas of work. The Red List is a great source of information. More than 20,000 species are deficient in data. Research that relies on the Red List may be less accurate due to the blind spot.

Borgelt and his colleagues used machine learning to try to find a solution. The extinction risk of data deficient species was predicted with the help of a training program. Information on 28,363 different types of animals was used to do that. Climate change is one of the factors that can determine how threatened a species is.

Researchers looked at 7,699 data deficient species. Borgelt and his colleagues only worked with species that they knew the geographic distribution of. Almost half of the species are likely to be at risk of extinction. Eighty five percent of the data deficient salamanders are at risk of extinction. The spotted narrow-mouthed frog is one of the species of robber frog. The IUCN doesn't have photos of these creatures, but with names like that, don't you want to see them?

The Red List was updated last year. More than one hundred twenty-three of the species in the update were predicted by the program. A majority of the predictions were correct.

Borgelt was reassured by that. He knows the limitations of machine learning. He says that the expert assessments are more accurate than the algorithmic ones.

They are really quick. Borgelt says that they are not time intensive or labor intensive.

Researchers might not have been able to find the creatures' numbers in the wild. The killer whale is labeled deficient. Even though the orca starred in my favorite ‘90s movie and lived on all my childhood notebooks in the form of Lisa Frank stickers, scientists aren't sure if there's just one species of killer whale or several Some animals are only found in remote areas. It is possible that the same characteristics that make them hard to study could make them more vulnerable.

It's more important than ever to give these species the attention they deserve. Borgelt says machine learning isn't a substitute for tracing the animals on the ground. It could be used to figure out which species needs some extra care.