Studying the macroevolution of the songs of Passeriformes (perching birds) has proved challenging. The complexity of the task stems not just from the macroevolutionary and macroecological challenge of modeling so many species, but also from the difficulty in collecting and quantifying birdsong itself. Using machine learning techniques, we extracted songs from a large citizen science dataset, and then analyzed the evolution, and biotic and abiotic predictors of variation in birdsong across 578 passerine species. Contrary to expectations, we found few links between life-history traits (monogamy and sexual dimorphism) and the evolution of song pitch (peak frequency) or song complexity (standard deviation of frequency). However, we found significant support for morphological constraints on birdsong, as reflected in a negative correlation between bird size and song pitch. We also found that broad-scale biogeographical and climate factors such as net primary productivity, temperature, and regional species richness were significantly associated with both the evolution and present-day distribution of bird song features. Our analysis integrates comparative and spatial modeling with newly developed data cleaning and curation tools, and suggests that evolutionary history, morphology, and present-day ecological processes shape the distribution of song diversity in these charismatic and important birds.
Animal Communication Animal Distribution