Dialect differences correlate with environment in migratory coastal White-crowned Sparrows
Yang, J.; Carstens, B. C.; Provost, K. L.
Show abstract
Vocalization behavior in birds, especially songs, strongly affects reproduction, but it is also highly impacted by geographic distance, climate, and time. For this reason, phenotypic differences in vocalizations between different bird populations are often interpreted as evidence of lineage divergence. Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. However, it is unknown whether the divergence in their songs correlates to environmental or geographical factors. Previous work has been hindered by time-consuming traditional methods to study bird songs that rely on the manual annotation of song spectrograms into individual syllables. Here we explore the performance of automated machine learning methods of song annotation, which can process large datasets more efficiently, paying attention to the question of subspecies differences. We utilize a recently published artificial neural network to automatically annotate hundreds of White-crowned Sparrow vocalizations across two subspecies. By analyzing differences in syllable usage and composition, we find that Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in syllables in the White-crowned Sparrow repertoire. However, the large sample size enabled by the AI approach allows us to demonstrate that divergence in song is correlated with environmental difference and migratory status, but not with geographical distance. Our findings support the hypothesis that the evolution of vocalization behavior is affected by environment, in addition to population structure. LAY SUMMARYO_LIBirdsong is an important behavior because it is important in bird communication and reproduction. C_LIO_LIWhite-crowned Sparrows in western North America are known to use different songs along their range, but it is unknown if those songs vary due to the environment. C_LIO_LIWe used machine learning to analyze these songs and found that populations of White-crowned Sparrows can be differentiated based on their songs. C_LIO_LIEnvironmental factors during the breeding season exert a greater influence on song evolution in migratory subspecies. C_LI
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Integrating population genetics to define conservation units from the core to the edge of Rhinolophus ferrumequinum western range 93%
- Island syndrome in the critically endangered Lord Howe Island cockroach Panesthia lata 93%
- Does breeding season variation affect evolution of a sexual signaling trait in a tropical lizard clade? 93%
Similar papers in this journal
- Revisiting a classic hybrid zone: rapid movement of the northern flicker hybrid zone in contemporary times 93%
- Partitioning variance in a signaling trade-off under sexual selection reveals among-individual covariance in trait allocation 92%
- Testing the predictability of morphological evolution in contrasting thermal environments 92%
Similar papers in this journal
- Allopatric montane wren-babblers exhibit similar song notes but divergent vocal sequences 95%
- Testing the maintenance of natural responses to survival-relevant calls in the conservation breeding population of a critically endangered corvid (Corvus hawaiiensis) 95%
- How do ecological and social environments reflect parental roles in birds? A comparative analysis 93%
Similar papers in this journal
- Environmental and morphological constraints interact to drive the evolution of communication signals in frogs 93%
- A path analysis disentangling determinants of natal dispersal in a cooperatively breeding bird 93%
- Rare morph Lake Malawi mbuna cichlids benefit from reduced aggression from con- and hetero-specifics 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.