AI Reveals Hidden Lion Roar: Scientists Discover Second Type of Vocalization in African Lions

Lions have a second roar that no one noticed until now

A closer listen to lion calls may help map where the big cat is under threat The thunderous roar of the MGM lion that has opened Hollywood films for nearly a century has conditioned us to hear the big cat's call as a blunt declaration: a booming blast announcing power and presence. But the real soundscape of a lion pride is far more intricate than that cinematic caricature, researchers report November 20 in the journal Ecology and Evolution. Using field recordings from Africa and machine learning techniques to analyze the acoustics, scientists found that African lions (Panthera leo) produce two distinct types of roars: the familiar, guttural one that anchors a roaring bout -- and carries vocal signatures unique to each animal -- plus an overlooked "intermediary" roar that is shorter and lower-pitched than the classic full-throated version. The findings challenge decades-old assumptions. Biologists have long known that a lion's roar helps advertise territory, attract mates and locate pride members -- and that a complete roaring bout begins with moans and ends with grunts. But everything in the middle was treated as a single, undifferentiated roar. Now, by decoding that roar into its component parts, and with artificial intelligence trained to tell one lion's voice from the next, conservation groups may be able to count and track lions by sound alone. "If you can identify a lion by its roar, this could potentially be a tool to count the number of individuals within a landscape," says Jonathan Growcott, a conservation technologist and large carnivore biologist at the University of Exeter in England. Such insights could prove especially valuable at a time of shrinking habitat and poaching pressures, when lions have vanished from more than 90 percent of their historic range, he adds. Still, what the newly identified intermediary roar actually communicates remains unclear. "We don't know yet," Growcott says. "Unfortunately, we don't speak lion. There is no option of 'lion' on Duolingo." The discovery emerged from tens of thousands of hours of audio captured by remote recorders in Tanzania's Nyerere National Park and by acoustic collars fitted to lions in Zimbabwe. When Growcott's team ran more than 3,000 calls through pattern-recognition algorithms, subtle differences jumped out. Full-throated roars traced a clear arc, rising in pitch before ending in a trailing fall, while intermediary roars were flatter and far less elaborate. Focusing on how long an utterance lasted and how high in pitch it climbed, the researchers could then build an algorithm capable of classifying each type of roar, moan and grunt with high precision. In at least one lion population, accuracy topped 91 percent. By parsing roar types and pulling out the more informative full-throated call, the tool even identified which individual lion was roaring, outperforming human experts. According to Tanya Berger-Wolf, a computational ecologist at Ohio State University in Columbus, this is one of the first clear demonstrations that machine learning can reliably interpret the vocalizations of a mammal. "It is a good example of bioacoustic monitoring beyond birds, amphibians and insects," she says. But because the recordings lacked behavioral context, scientists still can't say why lions choose one roar type over the other -- an open question that intrigues lion experts like Craig Packer of the University of Minnesota in St. Paul. "It would be interesting to have enough recordings in known contexts to know if lions roar more loudly in certain situations," he says. As for the MGM lion, his iconic roar contains no hidden intermediary for one simple reason: It doesn't belong to a lion at all. In a bit of Hollywood movie magic, sound designers opted for something even more ferocious, Growcott says. "The MGM lion is actually a tiger."

AI detects a secret lion roar no one knew existed

A recent investigation has revealed that African lions use two separate kinds of roars, not just one. This finding is expected to play an important role in improving how conservation groups track and study these big cats. 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.

Lions have a secret roar that was just uncovered by AI

Across the savanna, a lion's roar breaks the night silence. The sound is unmistakable, yet the deeper structure behind it has gone unnoticed for decades. A new study now reveals clear layers within each roaring sequence. The research arrives at an important moment, as conservation teams search for better ways to track a species under pressure. The researchers identified four sound types within a roaring bout. A lion begins with soft moans, moves into full throated roars, shifts to a shorter intermediary roar, and ends with grunts. Earlier thinking focused on one roar only. The new investigation shows a second roar type with a shorter rise, a quicker fall, and a lower maximum frequency. Its repeated position within each sequence signals a defined role rather than a weakened roar. "Lion roars are not just iconic - they are unique signatures that can be used to estimate population sizes and monitor individual animals," said Jonathan Growcott from the University of Exeter. "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." Roaring behavior changes with age, social position, and daily rhythms. Younger individuals call less. Males without a pride avoid calling because loud announcements may draw rivals. Calling peaks before dawn when sound travels far. Water sources attract more vocal activity. Females do call, though new mothers often stay quiet to avoid drawing attention to cubs. Every choice reflects risk, territory, and pride connections. Spectrograms in the study show clear contrasts between each sound type. Full throated roars stretch longer and reach higher frequencies. Intermediary roars rise and fall quickly. Moans have softer, uneven contours at the start of the bout. Grunts finish the sequence with brief, low notes. Consistent placement of the intermediary roar confirms its identity as a distinct call. The pattern repeats across many samples. Hidden Markov Models helped classify sound types by following the movement of the fundamental frequency over time. This approach handled the structure well. A simpler method also worked: K means clustering based on duration and maximum frequency. Once moans were removed, classification accuracy rose sharply. The simpler workflow avoids heavy computational needs and allows wider use by field teams. Recordings from collared lions in Zimbabwe helped test the system further. AI based classification improved identification of individuals. Automatic selection captured more usable full throated roars and matched those calls to specific lions with higher accuracy. Manual spotting still handles moans because moans always appear at the start and have an unmistakable shape. Research on spotted hyaenas revealed multiple whoop types inside a single calling sequence. The lion study follows the same pattern of expanded understanding. Many large carnivores rely on layered sequences rather than isolated calls. Hidden acoustic structure appears more common than earlier work suggested. Passive acoustic monitoring offers strong advantages for wide landscapes. Camera traps miss animals in dense or rugged areas. Sound sensors detect calls over long distances. With reliable classification, acoustic surveys can produce steadier population estimates and reduce human bias in data processing. "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," said Growcott. Frequency and duration varied among lions from Tanzania, Zimbabwe, and Botswana. One male from Botswana produced unusually short roars. Geographic origins may influence vocal traits. Nomadic males often travel huge distances, so understanding regional variation will support accurate classification across borders. The researchers designed a straightforward system. It avoids deep learning models that require massive datasets. Lions do not vocalize often enough to support such systems. The chosen workflow uses accessible techniques, making adoption easier for conservation workers. The roar no longer stands as a single towering sound. It exists as a sequence with distinct stages and valuable information. With stronger tools, researchers can track individuals, estimate populations, and understand behavior more clearly. Improved listening may help protect lions as pressures on wild populations continue to rise. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.