Development of a method for classifying and transmitting high-resolution feeding behavior of fish using an acceleration pinger Article Swipe
Jun Horie
,
Hiromichi Mitamura
,
Yusuke Ina
,
Yuichiro Mashino
,
Takuji Noda
,
K. Moriya
,
Nobuaki Arai
,
Toyoki Sasakura
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1186/s40317-017-0127-x
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1186/s40317-017-0127-x
Background Monitoring the feeding behavior of animals in the wild is key to understanding their energetics and the influence of the environment on their survival. Recently, a novel acceleration transmitter that processes acceleration data onboard and outputs identification results has been developed by AquaSound Inc. (Kobe, Japan) to investigate feeding biology in fish. To date, few attempts have been made to identify the feeding behavior of fish using transmitters, and none of these attempts accomplished classification of alternative feeding behaviors according to prey items. The objective of this study was to develop an algorithm that can be incorporated in the acceleration transmitter and can identify alternative feeding behaviors in fish, using red-spotted grouper (Epinephelus akaara) as a model species. Results Most of the identification algorithms describing feeding behavior in fish developed in previous studies used a combination of acceleration and angular velocity. In this study, we constructed an algorithm based on three-axis accelerometry data alone, since a gyroscope consumes much more electricity and would shorten the battery life of the transmitter. Acceleration data were obtained in tank experiments. Feeding behaviors, induced by feeding three types of live prey (Trachurus japonicus, Metapenaeus ensis and Hemigrapsus sanguineus), as well as other behaviors (routine and escape movements), were simultaneously recorded at 200 Hz by acceleration data loggers, implanted in the abdominal cavities of fish, and by a video camera. A decision tree, including a three-dimensional lookup table, was constructed to classify the behaviors into four behavior classes: shrimp-eating, fish-eating, crab-eating and other behaviors. The classification accuracy was estimated to be 0.77 (F-measure) for shrimp-eating, 0.73 for fish-eating, 0.71 for crab-eating and 0.78 for other movements, using fivefold cross-validation. Conclusions The algorithm developed in this study could be incorporated into the transmitter, which would record acceleration data at high frequency (200 Hz), process the data onboard and output classification results of behaviors. This method would reveal more aspects of fish biology, such as individual feeding strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s40317-017-0127-x
- https://animalbiotelemetry.biomedcentral.com/track/pdf/10.1186/s40317-017-0127-x
- OA Status
- gold
- Cited By
- 21
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2623626287
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2623626287Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s40317-017-0127-xDigital Object Identifier
- Title
-
Development of a method for classifying and transmitting high-resolution feeding behavior of fish using an acceleration pingerWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-06-08Full publication date if available
- Authors
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Jun Horie, Hiromichi Mitamura, Yusuke Ina, Yuichiro Mashino, Takuji Noda, K. Moriya, Nobuaki Arai, Toyoki SasakuraList of authors in order
- Landing page
-
https://doi.org/10.1186/s40317-017-0127-xPublisher landing page
- PDF URL
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https://animalbiotelemetry.biomedcentral.com/track/pdf/10.1186/s40317-017-0127-xDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://animalbiotelemetry.biomedcentral.com/track/pdf/10.1186/s40317-017-0127-xDirect OA link when available
- Concepts
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Acceleration, Grouper, Biology, Accelerometer, Predation, Fish
, Fishery, Ecology, Zoology, Computer science, Physics, Classical mechanics, Operating system Top concepts (fields/topics) attached by OpenAlex - Cited by
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21Total citation count in OpenAlex
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2025: 1, 2024: 4, 2023: 3, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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