Until recently, the prevailing theory about male African elephants (Loxodonta africana) was that, once adult and sexually mature, males are solitary and targeted only at finding estrous females. While this is true during the state of ‘musth’ (a condition characterized by aggressive behavior and elevated androgen levels), ‘non-musth’ males exhibit a social system seemingly based on companionship, dominance and established hierarchies. Research on elephant vocal communication has so far focused on females, and very little is known about the acoustic structure and the information content of male vocalizations. Using the source and filter theory approach, we analyzed social rumbles of 10 male African elephants. Our results reveal that male rumbles encode information about individuality and maturity (age and size), with formant frequencies and absolute fundamental frequency values having the most informative power. This first comprehensive study on male elephant vocalizations gives important indications on their potential functional relevance for male-male and male-female communication. Our results suggest that, similar to the highly social females, future research on male elephant vocal behavior will reveal a complex communication system in which social knowledge, companionship, hierarchy, reproductive competition and the need to communicate over long distances play key roles.

Infant giant pandas are highly vocal during the first few weeks of life, producing vocalisations that are characterised by noisy, aperiodic segments. The aperiodic character of many animal vocalisations results from irregular vibratory regimes of the vocal folds, and one proposed function of this so‐called nonlinear phenomena (NLP) in animal vocalisations is to convey information about the caller's arousal state. This hypothesis was tested in the vocalisations of six hand‐reared giant panda cubs recorded during handling and feeding procedures that had been categorised into low‐ and high‐arousal contexts based on quantified motor activity. Ninety‐three per cent of the vocalisations contained NLP, including deterministic chaos and subharmonics. Vocalisations produced in the high‐arousal contexts, however, were characterised by an increase in chaos, as well as increased call duration and x¯ fundamental frequency (pitch). These results suggest that infant giant panda vocal signals have the potential to express different arousal states. Furthermore, because giant panda cubs are the smallest placental mammal offspring at birth compared with adult size, acoustically conveying arousal state to the mother might be crucial for infant survival under natural rearing conditions.

This study used the source and filter theory approach to analyse sex differences in the acoustic features of African elephant (Loxodonta africana) low-frequency rumbles produced in social contexts (‘social rumbles’). Permuted discriminant function analysis revealed that rumbles contain sufficient acoustic information to predict the sex of a vocalizing individual. Features primarily related to the vocalizer’s size, i.e. fundamental frequency variables and vocal tract resonant frequencies, differed significantly between the sexes. Yet, controlling for age and size effects, our results indicate that the pronounced sexual size dimorphism in African elephants is partly, but not exclusively, responsible for sexual differences in social rumbles. This provides a scientific foundation for future work investigating the perceptual and functional relevance of specific acoustic characteristics in African elephant vocal sexual communication.

Animal vocal signals are increasingly used to monitor wildlife populations and to obtain estimates of species occurrence and abundance. In the 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, adults) and 95 % correct classification when categorising into two groups (infants/calves lumped into one group versus 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 highdimensional features that represent the entire spectral envelope, such as MFCC (75 % correct classification) and GFCC (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.

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