Animal vocal signals are increasingly used to monitor wildlife populations and to obtain estimates of species occurrence and abundance. In future, acoustic monitoring should function not only to detect animals, but also to extract detailed information about populations by discriminating sexes, age groups, social or kin groups, and potentially individuals. Here we show that it is possible to estimate age groups of African elephants (Loxodonta africana) based on acoustic parameters extracted from rumbles recorded under field conditions in a National Park in South Africa. Statistical models reached up to 70% correct classification to four age groups (infants, calves, juveniles and adults) and 95% correct classification when categorizing into two groups (infants/calves lumped into one group vs. adults). The models revealed that parameters representing absolute frequency values have the most discriminative power. Comparable classification results were obtained by fully automated classification of rumbles by high-dimensional features that represent the entire spectral envelope, such as Mel-frequency cepstral coefficient (75% correct classification) and Greenwood function cepstral coefficient (74% correct classification). The reported results and methods provide the scientific foundation for a future system that could potentially automatically estimate the demography of an acoustically monitored elephant group or population.
Loxodonta africana, acoustic cues, age groups, acoustic monitoring