Using Tri-axial Accelerometer Loggers to Identify Spawning Behaviours of Large Pelagic Fish
A new paper is published in Movement Ecology, focusing on the accurate automated techniques to identify different “burst” behaviours fishes. The paper is entitled :"Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish".
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.
They trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. They identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables.
Their findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, their findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.
This publication was led by Thomas M. Clarke (Flinders University, Adelaide, Australia), and our Marine Mapping Group Member Sasha K. Whitmarsh (as a researcher at Flinders University, Adelaide, Australia) contributed to the publication.
Congratulation to Sasha!
To read the full article, click here.
Last edited on the November 19th, 2021.