Development of a method for classifying and transmitting high-resolution feeding behavior of fish using an acceleration pinger

Abstract

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.