Machine learning reveals adaptive maternal responses to infant distress calls in wild chimpanzees

Distress calls are an acoustically variable group of vocalizations ubiquitous in mammals and other animals. Their presumed function is to recruit help, but it is uncertain whether this is mediated by listeners extracting the nature of the disturbance from calls. To address this, we used machine learning to analyse distress calls produced by wild infant chimpanzees. It enabled us to classify calls and examine them in relation to the external event triggering them and the distance to the intended receiver, the mother. In further steps, we tested whether the acoustic variants produced by infants predicted maternal responses.