The impact of having eight billion humans is undeniable — from climate change to omnipresent microplastics, Earth is becoming a very different place. Some scientists even believe we are witnessing a sixth mass extinction as a result of human activity. In light of this, a team from the Norwegian University of Science and Technology used computer models to study the risks to the species we know the least about, and it looks like they may be in more danger than anyone expected.
The study focused on so-called “data deficient” species like the Sequoia slender salamander above. These are creatures that are small in number, difficult to observe, or otherwise poorly understood by science. In the world of conservation, you need data to determine the risk posed to a species. The research, led by Ph.D. student Jan Borgelt, concludes that conservation priorities, like the International Union for Conservation of Nature Red List, are wildly wrong.
The Norwegian study attempted to assess the true risk to data deficient species using a machine learning algorithm that takes into account various factors like geographical distribution, exposure to climate change effects, and contact with human expansion. The model predicted the likelihood of extinction for 26,363 species on the Red List (data sufficient species) with a high degree of accuracy. When the researchers plugged in a subset of 7,699 data deficient organisms, they found that most have shockingly poor prospects, reports Vice.
The Omura’s whale, first identified in 2003, is another data deficient species.
The model predicts that 56 percent of data deficient species are at risk of going extinct. That’s twice as high as the Red List estimate of 28 percent. Amphibians may have the worst luck going forward, with about 85 percent likely to be threatened by extinction, but other types of life are in trouble, too. More than half of the mammals, insects, marine invertebrates, and reptiles analyzed in the study are also at high risk. Animals with the dimmest prospects are those with small ranges in isolated areas of central Africa, southern Asia, and Madagascar.
Unfortunately, the lack of data on these animals has caused many studies to exclude them, and that biases conservation recommendations. The Norwegian team says that we should try to include data deficient species in planning and decision-making whenever possible. They also encourage other researchers to use machine learning to fill in the gaps in our knowledge.