SG: Leveraging massive song databases and deep learning to examine the mechanisms causing diversification of bird vocalizations.

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Bird songs provide a fascinating example of animal communication that help birds find mates, hold territories, and more. Like human language, bird songs can change over time and across the range of a given species. The evolution of song differences is thought to be an important component in the formation of new bird species. However, biologists' understanding how bird songs evolve over space and time, and what causes their evolution, is incompletely known. This project will analyze decades of field recordings made by ornithologists and birding enthusiasts, combined with existing genetic data, to test hypotheses related to the evolution of bird song in multiple species across the continental United States. It will develop software to evaluate differences in bird calls across the range of many species and integrate these data with data on genetic, geographic, and environmental variation to test hypotheses about the causes and consequences of bird song evolution. This project will involve collaboration with computer scientists and software engineers to advance the field of computational biology and "big data" analysis for biological questions. Undergraduate students will have the opportunity to participate in the research, learn important skills in computational biology, and have novel experiences inside and outside of the classroom. Birds are well known for possessing a diverse array of songs and calls that mediate social behavior, reproduction, and communication. Within a single species, bird vocalizations change through space and time due to the influence of drift, selection and mate choice, but there is a relative lack of knowledge about how biogeographic history and community dynamics across larger regions influence the evolution of bird vocalizations and speciation. This project will use large datasets available both for bird vocalizations and phylogeography to analyze relationships between vocalizations, geography, history, and community composition, for multiple species, on a continental scale. The project will assess the impact that Plio-Pleistocene glacial cycles had on phylogeography and vocalizations within species and across communities and test hypotheses regarding the evolutionary drivers of song divergence. The proposed work will develop pipelines to collect massive amounts of song data, process it algorithmically, integrate it into existing pipelines for the analysis of phylogeographic data, contrast these data to environmental predictor variables, and ultimately test hypotheses of divergence. This work will provide the groundwork for the integration of phenotypic and genetic databases to investigate diversification of bird vocalization on a global scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.