ArticleScientific reports2025
Applying deep learning and the ecological home range concept to document the spatial distribution of Atlantic salmon parr (Salmo salar L.) in experimental tanks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Fish welfare in a changing world: New developments and current challenges.Journal of fish biology · 2026Review
- An integrated renewable energy and machine learning framework for techno economic analysis of water and energy nexus management in arid climates.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measuring and monitoring fish welfare in aquaculture research relies on the use of outcome- (biotic) and input-based (e.g., abiotic) welfare indicators (WIs). Incorporating behavioural auditing into this toolbox can sometimes be challenging because sourcing quantitative data is often labour intensive and it can be a time-consuming process. Digitalization of this process via the use of computer vision and artificial intelligence can help automate and streamline the procedure, help gather continuous quantitative data and help process optimisation and assist in decision-making. The tool introduced in this study (1) adapts the DeepLabCut framework, based on computer vision and machine learning, to obtain pose estimation of Atlantic salmon parr under replicated experimental conditions, (2) quantifies the spatial distribution of the fish through a toolbox of metrics inspired by the ecological concepts home range and core area, and (3) applies it to inspect behavioural variability in and around feeding. This proof of concept study demonstrates the potential of our methodology for automating the analysis of fish behaviour in relation to home range and core area, including fish detection, spatial distribution and the variations within and between tanks. The impact of feeding on these patterns is also briefly outlined, using 5 days of experimental data as a demonstrative case study. This approach can provide stakeholders with valuable information on how the fish use their rearing environment in small-scale experimental settings and can be used for the further development of technologies for measuring and monitoring the behaviour of fish in research settings in future studies.
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Registered trials
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