Newly Discovered Lion Roar Shows the Species Is More Complex Than We Thought
For decades, a lion’s roar was thought to be a single, familiar sound. But hidden inside that iconic call is a second, long-overlooked “intermediary roar” that reveals a more complex vocal landscape than realized.
The findings, published in Ecology and Evolution, mark the first time artificial intelligence has been used to automatically classify these roar types — a task that previously depended on expert listeners.
“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,” said lead author, Jonathan Growcott, in a press release.
Why Lion Roars Matter More Now
Lion numbers in Africa have fallen sharply, with only an estimated 20,000 to 25,000 remaining — about half the population that existed a quarter-century ago. The International Union for Conservation of Nature now lists the species as vulnerable to extinction, reflecting ongoing habitat loss, conflict with humans, and shrinking ranges across the continent.
Lions don’t roar just to be heard — their calls help defend territory and keep pride members connected across the landscape.
With passive acoustic monitoring expanding across large conservation areas, those far-carrying roars are becoming an increasingly valuable source of information. Because each call can travel long distances and contains acoustic features unique to individual lions, researchers see vocalizations as a promising, low-impact way to detect and count animals — provided the calls can be accurately classified.
Read More: Lion Breaks Swimming Record Across Hippo and Crocodile-Infested Waters
Revealing a Second Lion Roar
To limit the bias that can come from human interpretation, the team created a statistical model to tease apart full-throated roars from the other sounds in a lion’s roaring bout. The model confirmed that lions produce two different roar types and sorted them correctly about 85 percent of the time, supporting the idea that the intermediary roar is a separate call rather than just a variation of the familiar one.
A second approach simplified the process even further. By focusing on just two basic features of a lion’s call — how long it lasts and how high it reaches — the system reached a 95.4 percent accuracy rate in sorting the two roar types. This streamlined method also made it easier to tell individual lions apart, outperforming the accuracy of expert listeners.
The results show that automated tools can outperform expert judgment while dramatically reducing labor and bias. And because the workflow is intentionally simple, the authors say it can be easily implemented in conservation settings where resources are limited. The approach also echoes advances in bioacoustics for other large carnivores, including spotted hyenas.
A New Path Forward for Monitoring Lions
Together, the findings point toward a major shift in how lions can be monitored in the wild. By pairing simple acoustic measurements with automated classification, the approach could ultimately reshape how conservation teams track individual lions and estimate population sizes across vast landscapes.
“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, in the press release.
Read More: Neanderthals Killed a Cave Lion 48,000 Years Ago, and Scientists Know How
Article Sources
Our writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article: