Abstract
Billions of birds migrate annually, triggered by endogenous behaviors but also by ecoclimatic drivers which are shifting with climate change. These dynamics play out over huge spatiotemporal scales, making monitoring of phenology challenging with traditional biodiversity survey approaches. In this study, over a complete spring migration season (April through June), we collected 37,429 hours of audio from 28 networked sensors in forests across Norway using a nationwide passive acoustic monitoring (PAM) system. We applied an open-source detection algorithm to automatically classify bird vocalizations; through expert validation we found the algorithm classified 57 species (14 full migrants) with at least 80% precision. Using these automated detections, we developed regional arrival curves for three common migratory passerines: Willow Warbler, Common Chiffchaff, and Spotted Flycatcher. We then demonstrate that PAM detections can be used to train audio species distribution models that map how species vocalization probability changes across Norway during spring migration. Lastly, we discuss how PAM can complement existing manual surveys to support the design and implementation of effective policy and conservation measures.
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Acknowledgements
We thank Tom Roger Østerås of Norsk hekkefuglovervåking for his expert opinion in validating BirdNET species detections, NINA field staff for assistance with audio recorder setup, and the Norwegian Environment Agency (Miljødirektoratet) for funding of data collection. We also thank the homeowners who allowed us to install acoustic recorders on their property for this project.
Funding
I.A.B. was funded by a doctoral research grant 323294 from the Research Council of Norway.
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Bick, I.A., Bakkestuen, V., Cretois, B. et al. National-scale acoustic monitoring of avian biodiversity and migration. Commun Biol (2026). https://doi.org/10.1038/s42003-026-10389-4
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DOI: https://doi.org/10.1038/s42003-026-10389-4
