LAUSR

AIM: Anthropogenic noise pollution (ANP) is a globally invasive phenomenon impacting natural systems, but most research has occurred at local scales with few species. We investigated continental‐scale breeding season associations with ANP for 322 bird species to test whether small‐scale predictions related to breeding habitat, migratory behaviour, body mass and vocal traits are consistent at broad spatial extents for an extensive group of species. LOCATION: Conterminous USA. TIME PERIOD: 2004–2011. MAJOR TAXA STUDIED: North American breeding birds. METHODS: We calculated, for each species, the association between the breeding season and ANP, using spatially explicit estimates of ANP from the National Park Service and weekly estimates of probabilities of occurrence based on observations from the eBird citizen‐science database. We evaluated how the association of the breeding season for each species with ANP was related to expectations based on size, migratory behaviour and breeding habitat. For a subset of species, we used vocal trait data for song duration, pitch and complexity to evaluate hypotheses from the birdsong literature related to habitat complexity and sensitivity to ANP. RESULTS: Species that breed predominantly in anthropogenic environments were associated with twice the level of ANP (~7.4 dB) as species breeding in forested habitats (~3.2 dB). However, we did not find evidence to suggest that birds with higher‐pitched songs are more likely to be found in areas with higher levels of ANP. Residents and migratory species did not differ in associations with ANP, but songs were less complex among forest‐breeding species than non‐forest‐breeding species and increased in complexity with increasing ANP. MAIN CONCLUSIONS: Anthropogenic noise pollution is an important factor associated with breeding distributions of bird species in North America. Vocal traits could be useful to understand factors that affect sensitivity to ANP and to predict the potential impact of ANP, although future studies should aim to understand how and why patterns differ across spatial scales.