Machine learning based assessment of small-bodied fish tracking to evaluate spoiler baffle fish passage design

Fish passage research is important to mitigate the adverse effects of fragmented river habitats caused by waterway structures. The scale at which this research is undertaken varies from small-scale laboratory prototype studies to in-situ observations at various fish passage structures and bottlenecks. Using DeepLabCut, we introduce and evaluate a machine learning based workflow to track small-bodied fish in order to facilitate improved fish passage management. We specifically studied the behaviour and kinematics of Galaxias maculatus, a widespread diadromous Southern Hemisphere fish species. Upstream fish passage was studied in the presence of three different patches of spoiler baffles at an average water velocity of 0.4 m/s. In semi-supervised mode, the fish locations were extracted, and fish behaviour, such as swimming pathways and resting locations, was analysed based on extracted positions and recorded kinematic parameters. Individual fish behaviour and kinematic parameters were then used to assess the suitability of the three different spoiler baffle designs for enhancing fish passage. Using this technique, we were able to demonstrate where different spoiler baffle configurations resulted in significant differences in fish passage success and behaviour. For example, medium-spaced smaller baffles provided more accessible and uniform resting locations, which were required for efficient upstream passage. Results are discussed in relation to fish passage management at small instream structures.