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Sci Adv. 2018 Jun 20;4(6):eaaq1084. doi: 10.1126/sciadv.aaq1084. eCollection 2018 Jun.

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology.

Author information

1
Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA.
2
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
3
Google, New York, NY 10011, USA.
4
Department of Neurobiology, Physiology, and Behavior, University of California, Davis, Davis, CA 95616, USA.
5
Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
6
Department of Biological Sciences, Towson University, Towson, MD 21252, USA.

Abstract

Bioacoustic networks could vastly expand the coverage of wildlife monitoring to complement satellite observations of climate and vegetation. This approach would enable global-scale understanding of how climate change influences phenomena such as migratory timing of avian species. The enormous data sets that autonomous recorders typically generate demand automated analyses that remain largely undeveloped. We devised automated signal processing and machine learning approaches to estimate dates on which songbird communities arrived at arctic breeding grounds. Acoustically estimated dates agreed well with those determined via traditional surveys and were strongly related to the landscape's snow-free dates. We found that environmental conditions heavily influenced daily variation in songbird vocal activity, especially before egg laying. Our novel approaches demonstrate that variation in avian migratory arrival can be detected autonomously. Large-scale deployment of this innovation in wildlife monitoring would enable the coverage necessary to assess and forecast changes in bird migration in the face of climate change.

PMID:
29938220
PMCID:
PMC6010323
DOI:
10.1126/sciadv.aaq1084
[Indexed for MEDLINE]
Free PMC Article

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