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

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 spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables. Results Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) – 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration. Conclusion Our 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, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur

A Field and Video-annotation Guide for Baited Remote Underwater stereo-video Surveys of Demersal Fish Assemblages

1. Baited remote underwater stereo‐video systems (stereo‐BRUVs) are a popular tool to sample demersal fish assemblages and gather data on their relative abundance and body size structure in a robust, cost‐effective and non‐invasive manner. Given the rapid uptake of the method, subtle differences have emerged in the way stereo‐BRUVs are deployed and how the resulting imagery is annotated. These disparities limit the interoperability of datasets obtained across studies, preventing broadscale insights into the dynamics of ecological systems. 2. We provide the first globally accepted guide for using stereo‐BRUVs to survey demersal fish assemblages and associated benthic habitats. 3. Information on stereo‐BRUVs design, camera settings, field operations and image annotation are outlined. Additionally, we provide links to protocols for data validation, archiving and sharing. 4. Globally, the use of stereo‐BRUVs is spreading rapidly. We provide a standardized protocol that will reduce methodological variation among researchers and encourage the use of Findable, Accessible, Interoperable and Reusable workflows to increase the ability to synthesize global datasets and answer a broad suite of ecological questions