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Oscine songbirds learn vocalizations that function in mate attraction and territory defense. Sexual selection pressures on these learned songs could accelerate speciation. The Eastern and Spotted towhees are sister species that diverged recently (0.28 Ma) but now have partially overlapping ranges with evidence of some hybridization; widespread community-science recordings of these species, including songs within their zone of overlap and from potential hybrids, enable us to investigate whether song differentiation might facilitate their reproductive isolation. Here, we quantify 16 song features to analyze geographic variation in Spotted and Eastern towhee songs and test for species-level differences. We then use random-forest models to measure how accurately their songs can be classified by species, both within and outside the zone of overlap. While no single song feature reliably distinguishes the two species, a random-forest model trained on 16 features accurately classified 89.5% of songs; interestingly, species classification was less accurate in the zone of overlap. Finally, our analysis of the limited publicly available genetic data from each species supports the hypothesis that they are reproductively isolated. Together, our results suggest that, in combination, small variations in song features may contribute to these sister species’ ability to recognize their species-specific songs. 

The dark-eyed junco (Junco hyemalis) has experienced rapid phenotypic diversification within the last 18,000 years, resulting in several subspecies that reside in partially overlapping regions across North America. These subspecies have distinct plumage and morphology. If members of a subspecies disproportionately mate with one another, we would expect genetic differences to accumulate between the subspecies. In parallel, their learned songs could also accumulate changes. If song is used by individuals to recognize members of their own subspecies during mate selection, which would prevent the production of less fit hybrid offspring between subspecies, then song differences might co-localize with subspecies boundaries. Here, we quantify 10 song features to explore subspecies-level song variation using song recordings from community-science databases. We build a machine learning classifier to measure how accurately the subspecies’ songs can be distinguished from one another. Here, we show that songs of dark-eyed junco subspecies exhibit significant song-feature differences. However, these differences do not necessarily lead to distinguishability between subspecies. Notably, we find that subspecies pairs with adjacent ranges that do not hybridize have much more distinguishable songs, and also more evidence for genetic differentiation, than pairs that are known to hybridize. Thus, song distinguishability appears to have predictive power about which subspecies will hybridize, suggesting that song might play a role in reinforcing certain subspecies boundaries more than others. Finally, we analyze subspecies-level song differences alongside available genetic data and geographic coordinates to characterize the current evolutionary landscape of the dark-eyed junco subspecies complex. We observe geographic signal in the song and genetic data, indicating that individuals who share a range are more likely to share song characteristics and be genetically similar. This study illuminates the existence of subspecies-level song differences in the dark-eyed junco and provides further clarity on the role learned song plays in reinforcing reproductive boundaries between dark-eyed junco subspecies.