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Remote acoustic detection and characterization of fish schooling behavior
Kelly J Benoit-Bird1, Chad M Waluk1
1Monterey Bay Aquarium Research Institute, 7700 Sandholdt Road, Moss Landing, California 95039, USA.
The Journal of the Acoustical Society of America
|January 1, 2022
Summary
Researchers developed a new method to automatically identify fish schools from acoustic data. This technique distinguishes schooling fish from other aggregations, aiding in ecological and management studies.
Area of Science:
- Aquatic ecology
- Bioacoustics
- Fisheries science
Background:
- Fish species often form social aggregations or shoals.
- Understanding schooling behavior is crucial for ecology, ecosystem impact, and stock management.
Purpose of the Study:
- To introduce an automated method for isolating acoustic aggregations in echosounder data.
- To identify and characterize fish schools using this automated approach.
Main Methods:
- An automated approach was developed to isolate acoustic aggregations relative to local background scattering.
- A large dataset from autonomous platforms and research vessels was analyzed.
- Statistical differences in geometry, frequency response, scattering intensity, and distribution were examined.
Main Results:
- Fish schools were statistically distinct from other fish aggregations.
- Acoustic scattering from fish shoals followed a Rayleigh distribution.
- Acoustic scattering within fish schools showed a distinct pattern, differing from the Rayleigh distribution.
Conclusions:
- The distinct acoustic scattering distribution within fish schools allows for remote observation of organized behavior.
- This method has significant implications for understanding and managing schooling fish populations.
