image: Three-dimensional rendering of Risso's dolphin echolocation click spectra recorded in the Gulf of Mexico, aggregated by an unsupervised learning algorithm. view more
Credit: Kaitlin Frasier
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.
Because the sensors record millions of clicks, it is difficult for a human to recognize any species-specific patterns in the recordings. So, the researchers used advances in machine learning to develop an algorithm that can uncover consistent click patterns in very large datasets. The algorithm is "unsupervised," meaning it seeks patterns and defines different click types on its own, instead of being "taught" to recognize patterns that are already known.
The new algorithm was able to identify consistent patterns in a dataset of over 50 million echolocation clicks recorded in the Gulf of Mexico over a two-year period. These click types were consistent across monitoring sites in different regions of the Gulf, and one of the click types that emerged is associated with a known dolphin species.
The research team hypothesizes that some of the consistent click types revealed by the algorithm could be matched to other dolphin species and therefore may be useful for remote monitoring of wild dolphins. This would improve on most current monitoring methods, which rely on people making visual observations from large ships or aircraft and are only possible in daylight and good weather conditions.
Next, the team plans to integrate this work with deep learning methods to improve their ability to identify click types in new datasets recorded different regions. They will also perform fieldwork to verify which species match with some of the new click types revealed by the algorithm.
"It's fun to think about how the machine learning algorithms used to suggest music or social media friends to people could be re-interpreted to help with ecological research challenges," Frasier says. "Innovations in sensor technologies have opened the floodgates in terms of data about the natural world, and there is a lot of room for creativity right now in ecological data analysis."
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In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005823
Citation: Frasier KE, Roch MA, Soldevilla MS, Wiggins SM, Garrison LP, Hildebrand JA (2017) Automated classification of dolphin echolocation click types from the Gulf of Mexico. PLoS Comput Biol 13(12): e1005823. https://doi.org/10.1371/journal.pcbi.1005823
Funding: This research was made possible by a grant from The Gulf of Mexico Research Initiative. The analyses and opinions expressed are those of the authors and not necessarily those of the funding entities. Funding for HARP data collection and analysis was provided by the Natural Resource Damage Assessment partners (20105138), the US Marine Mammal Commission (20104755/E4061753; http://www.mmc.gov), the Southeast Fisheries Science Center under the Cooperative Institute for Marine Ecosystems and Climate (NA10OAR4320156) with support through Interagency Agreement #M11PG00041 between the Bureau of Offshore Energy Management (BOEM; http://www.boem.gov) Environmental Studies Program and the National Marine Fisheries Service (NMFS), Southeast Fisheries Science Center (SEFSC; http://www.sefsc.noaa.gov), the CIMAGE Consortium of the BP/Gulf of Mexico Research Initiative (SA 12-10/GoMRI-007; http://www.marine.usf.edu/c-image; gulfresearchinitiative.org), and Mike Weise with the Office of Naval Research (N00014-15-1-2299; http://www.onr.navy.mil). The AMAPPS 2011 cruise was funded by BOEM, NOAA Fisheries Service, US Fish and Wildlife Service (USFWS), and the US Navy. SEFSC was authorized to conduct the research activities during this cruise under Permit No. 779-1633-00 issued to the SEFSC by the NMFS Office of Protected Resources. The GU12-02 cruise in 2012 was funded by the Interagency Agreement # GM-11-03 between NMFS SEFSC and the BOEM. The SEFSC was authorized to conduct marine mammal research activities during the cruise under MMPA Research Permit No. 779-1633, issued to the SEFSC by the NMFS Office of Protected Resources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Journal
PLoS Computational Biology