Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish.

ABSTRACT:

Background

Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many "burst" behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different "burst" behaviours occurring naturally, where direct observations are not possible.

Methods

We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during

SUBMITTER: Clarke TM 

PROVIDER: S-EPMC8145823 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature