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.

How lions roar

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.”

Changes in roaring behavior

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.

Acoustic features of the lion roar

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.

How AI sorts calls

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.

Identifying lion roars

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.

Monitoring lion numbers

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.

Protecting lions under pressure

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.

The study is published in the journal Ecology and Evolution.

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