Age-group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles

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.

动物发声信号正愈发广泛地应用于野生动物种群监测，以获取物种出现频次与种群丰度的评估结果。未来，声学监测不仅需实现动物检测功能，更应通过区分性别、年龄组、社会群体或亲缘群体，乃至潜在个体，来提取种群的详细信息。本研究证实，基于南非某国家公园野外环境中录制的非洲象（Loxodonta africana）次声鸣叫声（rumbles）提取的声学参数，可实现其年龄组的估算。统计模型对四个年龄组（新生幼崽、幼象、亚成体象与成年象）的分类准确率可达70%；若将其划分为两组（将新生幼崽与幼象合并为一组，与成年象相对照），分类准确率可达95%。模型结果显示，代表绝对频率值的参数具备最强的区分能力。通过表征完整频谱包络的高维特征对次声鸣叫声（rumbles）开展全自动分类，可获得相近的分类结果：梅尔频率倒谱系数（Mel-frequency cepstral coefficient）分类准确率达75%，格林伍德函数倒谱系数（Greenwood function cepstral coefficient）分类准确率达74%。本研究的结果与方法，可为未来通过声学监测自动估算象群或种群统计学特征的系统奠定科学基础。