Application, development and opportunities of Remote Underwater Video for freshwater fisheries management
Remote Underwater Video (RUV) is a promising tool for progressing the future of freshwater fisheries monitoring and management. While uses have previously been focused on marine systems there has been a rise in application for freshwaters. Given the potential for coordinated geographical research using RUVs it is essential that standardised methodologies are described and promoted. We therefore conducted a systematic literature review which returned 185 publications that discussed using RUVs in freshwater environments. These publications used RUVs to measure: abundance, species richness, length-frequency, spawning/mating, behaviour, migration, foraging, size, habitat use, species presence and nesting. There were taxonomic and geographic biases in the results, with commercial salmonid fisheries the primary focus and 49% of published research was performed in North and Central America. While some research has investigated best practices, there are numerous gaps including: determining optimal deployment time in different systems/species compositions, determining suitable acclimation time for behavioural analysis and ascertaining the costs and benefits of using bait as an attractant and stereo-camera for photogrammetry. Until these gaps are addressed, we recommend a cautious set of standards for freshwater RUVs deployment which includes using a standard action camera, recording at ≥30 fps with a resolution of 1080p for 60 minutes. This will ensure that data are broadly comparable between studies. Current bottlenecks in methodology uptake relate to data storage, processing time and cost but this may be overcome with the optimisation of computer vision and machine learning. There are broad opportunities to develop RUV application into a powerful tool for freshwater fisheries management, invasive species detection, and ethological observations if standardised and findability, accessibility, interoperability, and reusability (FAIR) workflows are followed.
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