Individual or group signatures in spotted hyena whoops

In animal societies, identity signals mediate interactions within groups,
and allow individuals to discriminate group-mates from out-group
competitors. However, individual recognition becomes increasingly
challenging as group size increases and as signals must be transmitted
over greater distances. Group vocal signatures appear to evolve when
successful in-group/out-group distinctions are at the crux of
fitness-relevant decisions, but individual-based recognition systems may
be favored when differentiated within-group relationships are important
for decision-making. Spotted hyenas are social carnivores that live in
stable clans of <125 individuals composed of multiple unrelated
matrilines. Clan members cooperate to defend resources and communal
territories from neighboring clans and other mega carnivores; this
collective defense is mediated by long-range (up to 5 km range)
recruitment vocalizations, called whoops. Here, we use machine learning to
determine that spotted hyena whoops contain individual but not group
signatures, and that fundamental frequency features that propagate well
are critical for individual discrimination. For effective clan-level
cooperation, hyenas face the cognitive challenge of remembering and
recognizing individual voices at long range. We show that serial
redundancy in whoop bouts increases individual classification accuracy and
thus extended call bouts used by hyenas likely evolved to overcome the
challenges of communicating individual identity at long distance.

在动物社群中，身份信号介导群体内的互动，并使个体能够区分群内成员与群外竞争者。然而，随着群体规模扩大以及信号需在更远距离传递，个体识别的难度日益增加。当成功区分群内/群外个体是与适合度相关决策的关键时，群体声音特征似乎会发生演化；但当差异化的群内关系对决策至关重要时，基于个体的识别系统可能更受青睐。斑点鬣狗（spotted hyenas）是群居食肉动物，生活在由多个无亲缘关系的母系谱系组成的稳定氏族中，氏族规模通常少于125只个体。氏族成员通过合作抵御邻近氏族及其他大型食肉动物对资源和公共领地的侵占；这种集体防御由远距离（最远可达5公里）的招募叫声介导，这类叫声被称为“呼嚎”（whoops）。本研究利用机器学习技术证实，斑点鬣狗的呼嚎包含个体特征而非群体特征，且传播性良好的基频特征对个体识别至关重要。为实现有效的氏族层面合作，鬣狗需应对在远距离记忆并识别个体声音的认知挑战。我们发现，呼嚎序列中的串行冗余可提高个体分类准确率，因此鬣狗使用的长时呼嚎序列可能是为克服远距离传递个体身份信息的挑战而演化形成的。