OL D C LE E G Birdsong in Urban Environments with Varying Structures E E R

F 1 2 2 2 O 1 1 Ananke Garani Krishnan , Drew Myers , Halee Long , Sarah Foltz U 9 N 1 1Reed College Biology Department, 2Radford University Biology Department DED IN Urban Soundscapes Birdsong in Urban Environments Strong positive correlation Animals that use primarily acoustic communication face higher levels of low-frequency noise that can mask vocalizations and anthropogenic changes to vegetation and building structure that No correlation can affect how travels. These features also vary across different urban land use types. species such as the Strong negative white-crowned sparrow have been shown to adjust their songs NDVI* correlation 1

to the changing environment , prompting researchers to ask Canopy Cover what specific variables have the most impact on this response. Vegetation Cover Number of Buildings Buildings of Number Vegetation Aggregation Background Amplitude Background Background Frequency Background

Expected interactions between physical Observed mean background RMS (Root Observed interactions between physical properties and background ambient noise. Mean Square) amplitudes across different properties and background ambient noise. *Normalized Difference Vegetation Index urban land uses (p < 0.001). Error bars = 1SD. Bird Vocalization Responses Future steps How do components of urban environments, such A • Consider more variables: B A. Expected relationships between • as building and vegetation structure, vary across Species identity vocalizations and background • Proximity to highways or different urban land use types and in turn impact noise. B. Observed mean RMS wildlife preserves. vocalization? amplitudes across different coast • More land use categories, locations (Background: p = 0.119, such as industrial. Vocalization: p = 0.796) and urban • Further explore relationships land uses (Background: p < 0.001, between building density Experimental Design Vocalization: p = 0.205). Error bars Background Amplitude Background Background Frequency Background Vocalization Amplitude Vocalization Vocalization Frequency Frequency Vocalization and material, vegetation 2 urban locations on each coast, 4 types of urban land use = 1SD. structure and SNR. categories per coast • Explore connections to C D 0 E homeowner and socioeconomic status. Audio processing with RavenPro. Acknowledgements Acoustic Uniform Aggregated Random Many thanks to Drew Meyers, Halee Long, the 0 1 2.5 Cornell Lab of Raven support desk, Physical Aggregation Index Sam Fey, Suzy Renn, and Sarah Foltz for their support and guidance for this project. This study Vegetation Aggregation SNR Vocalization was supported by the Paul K. Richter & Evalyn Number of Buildings of Number Elizabeth Cook Richter Memorial Fund Aerial view of the bi-costal Vegetation Aggregation research site locations. Vocalization SNR Literature Cited Aerial images from Google Maps 1. Derryberry, E. P., Phillips, J. N., Derryberry, G. C. Expected effect of vegetation aggregation on vocalization SNR (signal-to-noise ratio). D. Expected effect of E., Blum, M. J., & Luther, D. (2020). Singing in a used for analysis of physical number of buildings on vocalization SNR and vegetation aggregation. E. Observed vegetation aggregation vs. silent spring: respond to a half-century variables. Vegetation analysis soundscape reversion during the COVID-19 vocalization SNR (R2 = 0.146, p = 0.03), with number of buildings by color. Aggregation index values calculated shutdown. Science, 370(6516), 575–579. with ImageJ. within a 20m radius of observer using the Clark-Evans spatial test with the Donnelly modification. https://doi.org/10.1126/science.abd5777