Machine learning approach could help
scientists monitor wild dolphin populations
Date: December
7, 2017
Source:
PLOS
Summary:
Scientists have developed a new algorithm
that can identify distinct dolphin click patterns among millions of clicks in
recordings of wild dolphins. This approach, presented in PLOS
Computational Biology by Kaitlin Frasier of Scripps Institution of
Oceanography, California, and colleagues, could potentially help distinguish
between dolphin species in the wild.
Frasier and her colleagues build autonomous
underwater acoustic sensors that can record dolphins' echolocation clicks in
the wild for over a year at a time. These instruments serve as non-invasive
tools for studying many aspects of dolphin populations, including how they are
affected by the Deepwater Horizon oil spill, natural resource development, and
climate change.
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