AI has revealed a hidden lion roar that adds a new dimension to how scientists identify and monitor big cats.
The breakthrough could strengthen conservation efforts as wild lion populations continue to decline.
Researchers Identify Two Types of Lion Roars
A recent scientific investigation has revealed that African lions produce two separate kinds of roars, not just one. This finding is expected to influence the future of wildlife monitoring and conservation work.
Researchers at the University of Exeter documented a previously unknown “intermediary roar” that occurs along with the well-known full-throated roar. Their study, published in Ecology and Evolution, is the first to apply artificial intelligence to automatically separate lion vocalizations into distinct types. The automated system achieved a 95.4% accuracy rate and reduced the influence of human interpretation, allowing for more reliable 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 has 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 Populations Continue to Decline
The International Union for Conservation of Nature red list currently classifies lions as vulnerable to extinction. Scientists estimate that only 20,000 to 25,000 wild lions remain in Africa, and this figure has dropped by about half over the past 25 years.
The study shows that each roaring sequence contains both the traditional full-throated roar and the intermediary version, revising the long-standing assumption that lions used only one roar type. Similar progress has occurred in research on other large carnivores, including spotted hyaenas, and demonstrates how bioacoustics is becoming increasingly important in ecological studies.
AI Improves Identification and Monitoring
By applying advanced machine learning tools to classify full-throated roars, the research team strengthened the ability to distinguish individual lions. This automated and data-driven process also streamlines passive acoustic monitoring, offering a more accessible and dependable alternative to common methods such as spoor tracking or camera traps.
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.”
Reference: “Roar Data: Redefining a Lion’s Roar Using Machine Learning” by Jonathan Growcott, Alex Lobora, Andrew Markham, Charlotte E. Searle, Johan Wahlström, Matthew Wijers and Benno I. Simmons, 20 November 2025, Ecology and Evolution.
DOI: 10.1002/ece3.72474
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.
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