Scientists discover new type of lion roar, which could help protect the iconic big cats

A new study has found African lions produce not one, but two distinct types of roars - a discovery set to transform wildlife monitoring and conservation efforts.

Researchers at the University of Exeter have identified a previously unclassified “intermediary roar” alongside the famous full-throated roar. The study, published in Ecology and Evolution, used artificial intelligence to automatically differentiate between lion roars for the first time. This new approach had a 95.4 per cent accuracy and significantly reduced human bias to improve the identification of individual lions.

Lead author Jonathan Growcott from 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.”

According to the International Union for Conservation of Nature red list, lions are listed as vulnerable to extinction. The total population of wild lions in Africa is estimated to be between 20,000 and 25,000, but this number has decreased by half in the last 25 years.

The study establishes that a lion’s roaring bout contains both a full-throated roar and a newly named intermediary roar, challenging the long-held belief that only one roar type existed. These findings echo similar advances in the study of other large carnivores, such as spotted hyaenas, and highlight the growing potential of bioacoustics in ecological research.

Researchers used advanced machine learning techniques and by implementing this automated, data-driven approach to classify full-throated roars, the team improved the ability to distinguish individual lions. The new process simplifies passive acoustic monitoring, making it more accessible and reliable compared to traditional methods like camera traps or spoor surveys.

Jonathan Growcott continued: “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.”

The research was a collaborative effort between the University of Exeter, 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), as well as computer scientists from Exeter and Oxford.

The work was supported by the Lion Recovery Fund, WWF Germany, the Darwin Initiative, and the UKRI AI Centre for Doctoral Training in Environmental Intelligence.

The paper titled ‘Roar Data: Redefining a lion’s roar using machine learning’ is published in Ecology and Evolution.

ENDS