AI detects a secret lion roar no one knew existed

Researchers at the University of Exeter uncovered a previously unrecognized "intermediary roar" that appears alongside the well-known full-throated version. The study, published in Ecology and Evolution, is the first to apply artificial intelligence to automatically sort lion roars into different types. The system reached a 95.4 per cent accuracy rate and greatly reduced the influence of human interpretation, allowing for more consistent identification of individual lions.

Lead author Jonathan Growcott of the University of Exeter said: "Lion roars are not just iconic -- they are unique signatures that can be used to estimate population sizes and monitor individual animals. Until now, identifying these roars relied heavily on expert judgment, introducing potential human bias. Our new approach using AI promises more accurate and less subjective monitoring, which is crucial for conservationists working to protect dwindling lion populations."

Lion Numbers Continue to Fall

The International Union for Conservation of Nature red list categorizes lions as vulnerable to extinction. Current estimates suggest Africa holds only 20,000 to 25,000 wild lions, and this population has dropped by about half over the last quarter century.

The new study concludes that a lion's roaring sequence includes both the established full-throated roar and the intermediary version, overturning the long-standing assumption that only one roar type existed. Similar developments have been reported in research on other large carnivores, including spotted hyaenas, and reinforce the expanding value of bioacoustics in ecological science.

AI Improves Monitoring Accuracy

By applying machine learning to classify full-throated roars, the research team advanced the ability to distinguish individual lions. The automated, data-focused method also streamlines passive acoustic monitoring, offering a more dependable and accessible option than common techniques such as spoor surveys or camera trapping.

Jonathan Growcott added: "We believe there needs to be a paradigm shift in wildlife monitoring and a large-scale change to using passive acoustic techniques. As bioacoustics improve, they'll be vital for the effective conservation of lions and other threatened species."

Broad Collaboration Supports New Findings

The project was carried out by the University of Exeter in partnership with the Wildlife Conservation Unit at the University of Oxford, Lion Landscapes, Frankfurt Zoological Society, TAWIRI (Tanzania Wildlife Institute for Research) and TANAPA (Tanzania National Parks Authority). Computer scientists from Exeter and Oxford also contributed to the work.

Funding came from the Lion Recovery Fund, WWF Germany, the Darwin Initiative, and the UKRI AI Centre for Doctoral Training in Environmental Intelligence.