Machine Learning Reveals Cryptic Dialects That Explain Mate Choice in a Songbird

Culturally transmitted communication signals – such as human language or bird song – can change over time through a process of cultural drift, and may consequently enhance the separation of populations, potentially leading to reproductive isolation1–4. Local song dialects have been identified in bird species with relatively simple songs where individuals show high cultural conformity5–10. In contrast, the emergence of cultural dialects has been regarded as unlikely11–13for species with more variable song, such as the zebra finch (Taeniopygia guttata). Instead, it has been proposed that selection for individual recognition and distinctiveness may lead to a complete spread across the space of acoustic and syntactical possibilities11–15. However, another possibility is that analytical limitations have meant that subtle but possibly salient group differences have not yet been discovered in such species. Here we show that machine learning can distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that these ‘cryptic song dialects’ drive strong assortative mating in this species. We studied mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. Cross-fostering eggs within and between these populations and quantifying social interactions of the resulting offspring later in life revealed that mate choice primarily targets cultural traits that are transmitted during a short developmental time window. Detailed social networks showed that females preferentially approached males whose song resembled that of their adolescent peers. Our study shows that birds can be surprisingly sensitive to cultural traits for mating that have hitherto remained cryptic, even in this well-studied species that is used as a model for song-learning13,14,16–28.