Lark Sparrows ( Chondestes grammacus ) have Ecogeographic Song Variation across North America
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Machine learning models can be used to analyze large bioacoustics datasets and explore variation due to geography or habitat. We find that in the monotypic Lark Sparrow ( Chondestes grammacus ), both environmental variables and geographic distance influence song variation in this species. Bird song is an important method of communication within avian species. The variation in bird song within a species can be due to a variety of factors, including genetic drift and isolation by distance. However, it remains unclear in species with wide ranges how environmental factors in particular can cause changes to the song. In this study, the song C. grammacus was analyzed via machine learning to determine if it had significant variation based on multiple geographical metrics. We trained a convolutional neural network to segment individual syllables of 91 C. grammacus recordings, then extracted song characteristics. We found that ecoregion and state explain variation in C. grammacus songs. Our results demonstrate the efficacy of using machine learning models to analyze large datasets, as well as the impact that ecogeographic variation has on song variance.
Lay Summary
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Birds often have a lot of variation in how they sound depending on where they live, but it is difficult to know what causes this variation without a lot of recordings
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The number of recordings that are needed to understand sound variation at continent-level scales means that analysis by hand is too time consuming
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We used machine learning to process the sound variation which let us understand all Lark Sparrow song in North America
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Lark Sparrows sing differently in different habitat types and they also sing differently when they are far from one another
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This variation is important to understand because it can help these species communicate across wide ranges of environments
