Abstract

Recently, Pearse et al. explored the macroecology of passerine song using a large citizen science database of bird songs and machine learning techniques. They used standard deviation of frequency (SDF) as a surrogate for song complexity, finding only weak support for correlation between SDF and life-history traits such as monogamy and sexual dimorphism. Their finding that song complexity increases toward more productive environments and warmer areas seemingly contradicts several previous multitaxonomic studies. By comparing SDF scores with traditionally used song complexity metrics (syllable repertoire size and the number of syllable types per song), we found no evidence of any correlation. This may help to explain the discrepancy between their findings and findings of previous studies. While we agree that simple metrics that can be quantified and compared between multiple, highly variable species are crucial for progress in large-scale analysis of birdsong complexity, the biological relevance of SDF remains unclear and more research is needed to clarify its relevance for further studies of birdsong complexity.

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