期刊
SCIENCE ADVANCES
卷 4, 期 6, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aaq1084
关键词
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资金
- NSF grant from the Office of Polar Programs [ARC 0908444, ARC 0908602, ARC 0909133]
- NSF Graduate Research Fellowship Program [DGE 16-44869, 1148897]
- Columbia University's Data Science Institute through the Research Opportunities and Approaches to Data Science program
- NASA's Arctic-Boreal Vulnerability Experiment [NNX15AV92A]
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.
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