Researchers decode the humming of elephants
US experts have used AI to discover what elephants are saying to each other. Through their deep rumbling choruses, the animals prompt each other to move on or greet one another after a long separation.
African elephants (Loxodonta africana) are highly social animals that often communicate with each other via low-frequency rumbles in the infrasound range – either as individual sounds or in a chorus. The cacophony when several of these pachyderms rumble at once has previously been difficult to analyze. Now, a US team has used AI to discover that these rumbling choruses serve as a social group signal that helps the animals coordinate joint actions. The elephants also align their voices with one another, as the experts write in Scientific Reports.
Sara Keen of the Earth Species Project in Berkeley, California, and her colleagues analyzed a total of 902 rumbles from 84 wild elephants across 39 families. The recordings were taken from four decades of field research in Amboseli National Park in Kenya. Machine learning helped to categorize the rumbling choruses into six different social contexts – including greetings, reunions after separations, comforting and calming calves, and "let's go" situations in which movements are initiated.
During some social interactions, the pachyderms increasingly aligned their sounds with one another – a pattern known as "vocal convergence." The team suspects that this helps the families coordinate their actions among themselves. However, the authors emphasize that this has not been explicitly tested via playback experiments.
"Our results provide a new insight into how elephants maintain bonds, negotiate movements, and coordinate their social lives," says co-author Joyce Poole of Elephant Voices in a press release. While the polyphonic group sounds of sperm whales and orcas have already been linked to behavioral contexts, this has now been achieved for elephants for the first time. "For years, elephant researchers have suspected that the choruses are meaningfully structured. Machine learning has now enabled us to formally test this idea," says Sara Keen.
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