Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys

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. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific. Automated machine learning methods of bird song annotation can process large datasets more efficiently, allowing us to examine 1,913 recordings across ~60 years. We utilize a recently published artificial neural network to automatically annotate White-crowned Sparrow vocalizations. By analyzing differences in syllable usage and composition, we recapitulate the known pattern where Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are co..., , , # Data for: Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys.

Authors:

Jiaying Yang, Kaiya Provost, Bryan Carstens
Last updated: 15 December 2023

This README file describes the files associated with the above publication. For any
questions or comments, please contact Kaiya L. Provost at .

This dataset is used to replicate our analyses for understanding the relative role
of climate, geography, and cultural drift on song in White-crowned Sparrows. We used a
previously-published Machine Learning framework to automatically annotate syllables of
\~1,900 recordings from the 1960s to the 2010s. We find that in the two subspecies we
examined, climate, geographic distance, and cultural drift significantly predict song
traits.

Below we describe each dataset used, its contents, and its file types.

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# SCRIPTS

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