Researchers develop automated method to identify fish calls underwater


Date:
January 6, 2022
Source:
Oregon State University
Summary:
Researchers have developed an automated method that can accurately
identify calls from a family of fishes.



FULL STORY ==========================================================================
An Oregon State University research team and collaborators have developed
an automated method that can accurately identify calls from a family
of fishes.


==========================================================================
The method takes advantage of data collected by underwater microphones
known as hydrophones and provides an efficient and inexpensive way to understand changes in the marine environment due to climate change and
other human-caused influences, said researchers from Oregon State's
Cooperative Institute for Marine Ecosystem and Resource Studies.

The findings were published in the journal Marine Ecology Progress Series.

Hydrophones are increasingly being deployed in the world's oceans. They
offer advantages over other types of monitoring because they work at
night, in low- visibility conditions and over long periods of time. But techniques to efficiently analyze data from hydrophones are not well
developed.

This new research led by Jill Munger when she was an undergraduate
student, begins to change that. Munger came to Oregon State having worked
more than 20 years in the corporate world.

An avid scuba diver, she wanted to study the ocean. She received a
fellowship from CIMERS to research underwater acoustics with Joe Haxel,
who at the time was at the Hatfield Marine Science Center in Newport
working with National Oceanic and Atmospheric Administration's Pacific
Marine Environmental Lab's acoustic program.



========================================================================== Haxel handed her a hard drive with 18,000 hours of acoustic data collected
over 39 months in a tropical reef region within the National Park of
American Samoa.

American Samoa is a U.S. territory in the western Pacific Ocean.

The data was collected via a 12-station hydrophone area maintained by NOAA
and the National Park Service that is distributed throughout the world in
water controlled by the United States. The hydrophones were designed and
built by NOAA and CIMERS researchers at Hatfield Marine Science Center.

Munger decided to focus on calls from damselfish, in part, because
they are distinctive. They grind their teeth to create pops, clicks
and chirps associated with aggressive behavior and nest defense. She
compared the sound to purring kittens. Quickly, it became apparent to
her that manually listening to the recordings was not going to work.

"This is such a slow and tedious process," she remembered thinking. "I
have all this data, and I am just looking at a tiny, tiny portion
of it. What's happening in all the other parts that I haven't had a
chance to listen to?" A conversation with her brother, Daniel Herrera,
a machine learning engineer, sparked an idea. Could they use machine
learning to automate the analysis of the data?


========================================================================== Machine learning algorithms build a model based on sample data, known as training data, to make predictions or decisions without being explicitly programmed to do so.

Machine learning techniques have been used to automate processing of large amounts of data from passive acoustic monitoring devices that collected
sound data from birds, bats and marine mammals. The techniques have
been used for fish calls, but it is an underdeveloped area of science,
Munger said.

In this case, the machine learning sample or training data was 400 to 500 damselfish calls Munger identified by manually listening to the hydrophone recordings. With that start, Herrera, a co-author of the paper, built a
machine learning model that accurately identified 94% of damselfish calls.

"We built a machine learning model on a relatively small set of training
data and then applied it to an enormous set of data," Munger said. "The implications for monitoring the environment are huge." Munger, who
now works in the lab of Scott Heppell, an associate professor in Oregon
State's Department of Fisheries, Wildlife, and Conservation Sciences in
the College of Agricultural Sciences, believes machine learning will increasingly be used by scientists to monitor many species of fish in
the ocean because it requires relatively little effort.

"The benefit to observing fish calls over a long period of time is that
we can start to understand how it's related to changing ocean conditions,
which influence our nation's living marine resources," Munger said. "For example, damselfish call abundance can be an indicator of coral reef
health." Munger received input from National Park Service staff on the
biology of the damselfish and the reef habitats in close proximity to
the hydrophone.

Other co-authors of the paper are Haxel, Heppell, and Samara Haver, all
of Oregon State; Lynn Waterhouse, formerly of John G. Shedd Aquarium
in Chicago; Megan McKenna, formerly of Natural Sounds and Night Skies
Division, National Park Service, Fort Collins, Colorado; and Jason
Gedamke and Robert Dziak of NOAA's Office of Science and Technology,
Silver Spring, Maryland, and Pacific Marine Environmental Laboratory,
Newport, respectively.

========================================================================== Story Source: Materials provided by Oregon_State_University. Original
written by Sean Nealon.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. JE Munger, DP Herrera, SM Haver, L Waterhouse, MF McKenna, RP
Dziak, J
Gedamke, SA Heppell, JH Haxel. Machine learning analysis reveals
relationship between pomacentrid calls and environmental
cues. Marine Ecology Progress Series, 2022; 681: 197 DOI:
10.3354/meps13912 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220106122317.htm

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