African elephant rumbles differ between populations and sympatric social groups: possible consequences of vocal learning?

Vocal production learning, the ability to modify vocalizations in response to sounds made by others, was a critical prerequisite for the evolution of human speech but is rare among mammals. Elephants have exhibited this ability in captivity, yet its function in wild elephants remains unknown. Female African savannah elephants (Loxodonta africana) live in large societies with nested tiers of association in which vocal signatures of group identity could facilitate recognition of distant social affiliates. Vocal production learning allows the formation of such group signatures in many species and can also cause vocal differentiation between populations. However, the existence of vocal signatures of social group or population in elephants was unexplored. We recorded multiple social groups of wild elephants in two Kenyan populations (Samburu and Amboseli) and used random forest models to determine if calls could be assigned to individual callers, family groups, bond groups (collections of family groups), or populations based on acoustic structure. Calls were assigned by a random forest model to individual callers and populations with better-than-chance accuracy, demonstrating population-level divergence in vocalization structure. While random forest models failed to accurately assign calls to family or bond group, calls from the same family or bond group were significantly more similar (higher proximity scores) than calls from different groups, suggesting the existence of group signatures as well. We discuss possible drivers of this differentiation and argue that vocal learning is the most likely explanation for population- and group-level variation in elephants. The existence of group signatures suggests recognition of large numbers of individuals as a possible adaptive function for vocal production learning in elephants.发声学习（vocal production learning）是指个体根据他人发出的声音调整自身发声的能力，这是人类语言演化的关键前提之一，但在哺乳动物中极为罕见。圈养环境中的大象已被证实具备该能力，但野生大象的该项功能仍未明确。非洲草原象（Loxodonta africana）的雌性个体栖息于具有多层嵌套社交关联的大型社群中，群体身份的发声特征可辅助识别远距离的社交同伴。发声学习可帮助诸多物种形成此类群体发声特征，同时也会引发不同种群间的发声分化。然而，此前尚未针对大象是否存在社交群体或种群的发声特征开展相关研究。本研究在肯尼亚的两个种群（桑布鲁与安博塞利）中记录了多群野生大象的叫声，并采用随机森林（random forest）模型，基于叫声的声学结构，判断能否将叫声归类至单个发声个体、家族群、联结群（多个家族群组成的集合）或种群。实验结果显示，随机森林模型可将叫声准确归属至个体与种群，准确率显著高于随机猜测水平，证实了大象发声结构存在种群层面的分化。尽管该模型无法将叫声精准归属至家族群或联结群，但同一家族或联结群的叫声相似度（邻近得分更高）显著高于不同群体的叫声，这同样提示了群体发声特征的存在。我们探讨了该发声分化的潜在驱动因素，并认为发声学习是解释大象发声在种群与群体层面产生差异的最合理解释。群体发声特征的存在，意味着大象能够识别大量个体，这或许是发声学习在大象身上演化出适应性功能的潜在动因。