Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany.
CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Nat Commun. 2022 Mar 28;13(1):1630. doi: 10.1038/s41467-022-28881-w.
Culturally transmitted communication signals - such as human language or bird song - can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that 'cryptic song dialects' predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females' adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers.
文化传递的通讯信号——如人类语言或鸟鸣——可以通过文化漂移随时间而改变，由此产生的方言可能会因此增强种群的隔离。然而，当歌曲高度个性化时，如在斑胸草雀（Taeniopygia guttata）中，歌曲方言的出现被认为是不太可能的。在这里，我们展示了机器学习仍然可以非常准确地区分来自多个圈养斑胸草雀种群的歌曲，并且“隐蔽的歌曲方言”预测了该物种中强烈的选择性交配。我们使用已经独立进化了大约 100 代的圈养种群，在三个连续的世代中检查了交配模式。我们在这些种群内和种群之间进行了卵交叉养育，并使用自动条码跟踪系统来量化社会互动。我们发现，雌性更倾向于与那些与雌性青春期同伴的歌曲相似的雄性配对。我们的研究表明，在斑胸草雀（song learning 的模式物种）中，个体对迄今为止研究人员尚未注意到的歌曲差异敏感。
