Asia dinosaur fossil at Sichuan Discovery center, west of China.

AI unveils patterns in Earth’s biological mass extinctions

Image credit: Feng Hui/Dreamstime

A study led by Japanese scientists has applied machine learning to the fossil record to visualise the history of extinct biological species, highlighting the long-term evolutionary and ecological impacts of major events of extinction and speciation.

Most species encountered throughout history that have ever existed are extinct, according to scientists. This extinction of species has, on the whole, been roughly balanced by the origination of new ones over Earth’s history, with a few major temporary imbalances scientists call mass extinction events. 

Scientists have long believed that mass extinctions create productive periods of species evolution, or 'radiations', a model called 'creative destruction'. But a new study led by scientists affiliated with the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology used machine learning to examine the co-occurrence of fossil species and found that radiations and extinctions are rarely connected, and thus mass extinctions likely rarely cause radiations of a comparable scale.

Creative destruction is central to classic concepts of evolution, according to scientists, and there are periods in which suddenly many species suddenly disappear, and many new species suddenly appear. But radiations of a comparable scale to the mass extinctions, which this study, therefore, calls the mass radiations, have received far less analysis than extinction events.

The study compared the impacts of both extinction and radiation across the period for which fossils are available, the so-called Phanerozoic Eon, which represents the most recent – 550-million-year period of Earth's total – 4.5-billion-year history, and is significant to paleontologists. Before this period, most of the organisms that existed were microbes that didn’t easily form fossils, so the prior evolutionary record is hard to observe.

The new study suggests creative destruction is not a good description of how species originated or went extinct during the Phanerozoic, and suggests that many of the times that evolutionary radiation occurred when life entered new evolutionary and ecological arenas, such as during the Cambrian explosion of animal diversity and the Carboniferous expansion of forest biomes.

Evolutionary biologist Jennifer Hoyal Cuthill and physicist/machine-learning expert Nicholas Guttenberg and ELSI scientists were kicking around the question of whether machine learning could be used to visualise and understand the fossil record. They also aimed to extend their analysis to examine the correlation between extinction and radiation events. These discussions allowed them to relate their new data to the breadth of existing ideas on mass extinctions and radiations. And as a result, they found that the evolutionary patterns identified with the help of machine learning differed in key ways from traditional interpretations.

Machine learning method

A new study applies machine learning to the fossil record to visualise life's history, showing the impacts of major evolutionary events. This shows the long-term evolutionary and ecological impacts of major events of extinction and speciation. Colours represent the geological periods from the Tonian, starting 1 billion years ago, in yellow, to the current Quaternary Period, shown in green. The red to blue colour transition marks the end-Permian mass extinction, one of the most disruptive events in the fossil record.

Image credit: J Hoyal Cuthill and N Guttenberg

The team used a novel application of machine learning to examine the temporal co-occurrence of species in the Phanerozoic fossil record, examining over a million entries in a massive curated, public database including almost 200,000 species.

“Some of the most challenging aspects of understanding the history of life are the enormous timescales and numbers of species involved,” said Dr Hoyal Cuthill, lead author of the study. “New applications of machine learning can help by allowing us to visualise this information in a human-readable form. This means we can, so to speak, hold half a billion years of evolution in the palms of our hands, and gain new insights from what we see.”

Using their objective methods, the researchers found that the 'big five' mass extinction events previously identified by paleontologists were picked up by the machine-learning methods as being among the top 5 per cent of significant disruptions in which extinction outpaced radiation or vice versa, as were seven additional mass extinctions, two combined mass extinction-radiation events, and 15 mass radiations. 

In contrast to previous narratives emphasising the importance of post-extinction radiations, the study also found that the most comparable mass radiations and extinctions were only rarely coupled in time, refuting the idea of a causal relationship between them. The team further found that radiations may in fact cause major changes to existing ecosystems, an idea the authors named 'destructive creation'. They found that, during the Phanerozoic Eon, on average, the species that made up an ecosystem at any one time are almost all gone by 19 million years later. But when mass extinctions or radiations occur, this rate of turnover is much higher.

This gives a new perspective on how the modern 'Sixth Extinction' is occurring. The Quaternary period, which began 2.5 million years ago, had witnessed repeated climate upheavals, including dramatic alternations of glaciation, times when high-latitude locations on Earth, were ice-covered. This means that the present 'Sixth Extinction' is eroding biodiversity that was already disrupted, and the authors suggest it will take at least eight million years for it to revert to the long-term average of 19 million years.

Dr Cuthill said: “Each extinction that happens on our watch erases a species, which may have existed for millions of years up to now, making it harder for the normal process of ‘new species origination’ to replace what is being lost.”

Sign up to the E&T News e-mail to get great stories like this delivered to your inbox every day.

Recent articles