Data for "Passive acoustic data as phenological distributions: uncovering signals of temporal ecology"

Passive Acoustic Monitoring (PAM) is an increasingly common method for monitoring birds and other sound-producing organisms at scale, but methods that digest these data streams into ecological insight remain underdeveloped. Specifically, using PAM and classification algorithms powered by artificial intelligence (AI) to uncover the phenology of vocal animals is an emerging use of these data but currently lacks standardized, repeatable methods with verified connections to biological phenomena. In the paper accompanying this dataset, we articulate specific hypotheses regarding the relationship between avian vocal activity and phenological events, and present a flexible, reproducible methodological workflow for quantifying avian vocal phenology from PAM data. We applied our pipeline to 18,568 hours of audio from 185 recording sites across Olympic National Park, USA.

The data hosted here can be used in concert with the code in the linked Github repository to reproduce the analyses in the manuscript "Passive acoustic data as phenological distributions: uncovering signals of temporal ecology." They should be saved into the "input" folder initialized by the Github repository.