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.
journal_name
Sci Advjournal_title
Science advancesauthors
Oliver RY,Ellis DPW,Chmura HE,Krause JS,Pérez JH,Sweet SK,Gough L,Wingfield JC,Boelman NTdoi
10.1126/sciadv.aaq1084subject
Has Abstractpub_date
2018-06-20 00:00:00pages
eaaq1084issue
6issn
2375-2548pii
aaq1084journal_volume
4pub_type
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