Studies have shown that half of the species that are not included in the IUCN red list are facing extinction risk

Chinese biological diversity protection and Green Development Foundation (referred to as China Green Fair, Green Association), as a member of the World Nature Protection Alliance (IUCN), continues to pay attention to the latest developments in the world's natural protection. Recently, the International Department of China Green Fair learned from the world -renowned journal that a study showed that half of the species that were not included in the red list of endangered species faced the risk of extinction. The International Department of the Green Club now compiles this article for reference and understanding of interesting readers.

A machine learning analysis released on August 4, 2022 shows that in species that cannot evaluate endangered state due to lack of data, more than half of the risk of extinction is expected to face extinction.

The World Nature Conservation Alliance currently has nearly 10,000 endangered species red lists, including about 41,000 endangered species.

These include 41%of amphibians, 38%of sharks and catfish, 33%of reef corals, 27%mammals and 13%of birds.

However, the World Nature Protection Alliance cannot classify thousands of species because they are "insufficient data" and are not on the red list. Although they live in the same area and face similar threats facing species that have been evaluated so far.

Researchers from the Norwegian University of Science and Technology use machine learning technology to predict the possibility of 7699 data insufficient species facing the risk of extinction.

They trained algorithms on the list of more than 26,000 species that can be classified by the World Nature Protection Alliance, combined with the data of the species living area and other factors that affect biodiversity to determine whether the algorithm predicts their extinction risk.

"These may include climate conditions, land use conditions or changes in land use, pesticide use, threats to invading species, or a series of different pressure sources of pressure." Agencence France -Presse.

After comparing the results of the algorithm with the list of the World Nature Protection Alliance, the research team subsequently applied it to the risk of extinction of the species of insufficient prediction data.

In 2019, the UN global biological diversity evaluation warned that as many as 1 million species were facing extinction threats due to various factors such as habitat loss, invasion of species and climate change.

Bogte said that analysis reveals some hot spots of data inadequate species, including Madagascar and southern India. He said that he hoped that the study would help the World Nature Conservation Alliance to formulate its strategy for species that lack reports, and added that the team has extended assistance to the World Nature Protection Alliance.

He said: "With these predictions from machine learning, we can get a real pre -evaluation, or we can use these predictions to determine which species must be studied by the World Nature Protection Alliance."

Craig Hilton-Taylor, the person in charge of the World Nature Protection Alliance Red List, said the organization is constantly using new technologies to reduce the number of data insufficient species.

He told AFP: "We also know that some species that lack data are risk of extinction. When we estimate the proportion of threatening species in a group, we will include this into our calculations."

They published an article in the "Communication Biology" magazine and found that 4,336 species (56%of the sample species) may face the threat of extinction, including 85%of amphibians and 61%mammals.

In contrast, 28%of the species evaluated in the Red List of the World Nature Protection Alliance.

"We find that if we include the lack of data, including the average extinction risk of most land and coastal areas worldwide, we will be higher."

For the original text: https://phys.org/news/2022-08-species-endangered-ExtInction.html

Compilation: daisy

Review: YJ

Edit: Pierre

Source: China Green Fair

Reprinted: Science Popularization China Net

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