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Peer-reviewedCC BYCommunications Biology

National-scale acoustic monitoring of avian biodiversity and migration

  • Ian Avery Bick1,2,3
  • Vegar Bakkestuen4
  • Benjamin Cretois4
  • Ben V. Hillier5
  • John Atle Kålås4
  • Ingar Jostein Øien6
  • Marius Pedersen7
  • Kiran Bylappa Raja7
  • Carolyn M. Rosten4
  • Marius Somveille5,8
  • Bård Gunnar Stokke4
  • Julia Wiel4
1 more authors
  • Sarab S. Sethi9
9 affiliations
  1. 1Norwegian Institute for Nature Research, Trondheim, Norway. [email protected]
  2. 2Norwegian University of Science and Technology, Gjøvik, Norway. [email protected]
  3. 3Stanford Doerr School of Sustainability, Stanford, CA, USA. [email protected]
  4. 4Norwegian Institute for Nature Research, Trondheim, Norway
  5. 5Centre for Biodiversity and Environment Research, University College London, London, UK
  6. 6BirdLife Norway, Trondheim, Norway
  7. 7Norwegian University of Science and Technology, Gjøvik, Norway
  8. 8School of Environmental Sciences, University of East Anglia, Norwich, UK
  9. 9Department of Life Sciences, Imperial College London, London, UK. [email protected]
Open at sourceDOICite

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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Cite

BibTeXRIS
BibTeX
@article{bick2026nationalscale,
  title = {{National-scale acoustic monitoring of avian biodiversity and migration}},
  author = {Ian Avery Bick and Vegar Bakkestuen and Benjamin Cretois and Ben V. Hillier and John Atle Kålås and Ingar Jostein Øien and Marius Pedersen and Kiran Bylappa Raja and Carolyn M. Rosten and Marius Somveille and Bård Gunnar Stokke and Julia Wiel and Sarab S. Sethi},
  journal = {Communications Biology},
  year = {2026},
  month = may,
  doi = {10.1038/s42003-026-10389-4},
  url = {https://doi.org/10.1038/s42003-026-10389-4},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - National-scale acoustic monitoring of avian biodiversity and migration
AU  - Ian Avery Bick
AU  - Vegar Bakkestuen
AU  - Benjamin Cretois
AU  - Ben V. Hillier
AU  - John Atle Kålås
AU  - Ingar Jostein Øien
AU  - Marius Pedersen
AU  - Kiran Bylappa Raja
AU  - Carolyn M. Rosten
AU  - Marius Somveille
AU  - Bård Gunnar Stokke
AU  - Julia Wiel
AU  - Sarab S. Sethi
PY  - 2026
DA  - 2026/05/30
JO  - Communications Biology
DO  - 10.1038/s42003-026-10389-4
UR  - https://doi.org/10.1038/s42003-026-10389-4
AB  - 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.
SN  - 2399-3642
ER  -