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.

Macroplastics in rivers: present knowledge, issues and challenges.

Macroplastics are the primary contributor to riverine plastic pollution by mass, posing a wide range of serious threats for riverine systems, from adversely affecting various life forms within the riverine system, to potentially increasing flood risk, and generally resulting in adverse effects on any livelihoods. Compared to other river-related research disciplines, research into riverine macroplastics and their effects has not yet featured prominently. Various quantification methods are presently used to assess the presence of macroplastics at different locations within river systems; however, overcoming limitations and unifying methods remain an essential need. Macroplastic dynamics in rivers are subject to various factors, including both material and river characteristics. We review the diverse factors that potentially influence macroplastic dynamics in rivers, and highlight our knowledge limits. We advocate for future research that enables synergies between improved field quantification techniques, use of global protocols and data sharing, and laboratory experiments. This is needed to obtain a riverine macroplastic budget model, required for the implementation of targeted management practices. Finally, a multilayer potential management strategy is presented: (i) reducing the macroplastic supply into rivers; (ii) removing effectively and safely macroplastics from within rivers; and (iii) treating macroplastics once removed from the riverine system.