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 consistent with the interpretation that these differences are
caused by the changes in characteristics of syllables in the White-crowned
Sparrow repertoire. This supports the hypothesis that the evolution of
vocalization behavior is affected by the environment, in addition to
population structure.