Automated predictive welfare assessment in groups of fish

Fish are increasingly popular as a research model and, when combined with research revealing putative pain experiences and aversive behaviour in fish, this highlights a clear and urgent need to identify ways in which we can refine the procedures used to assess fish welfare. Existing measures are of limited value, either due to invasiveness, ambiguity or late onset; we therefore propose a novel way of assessing welfare in fish - the use of social network analysis to reveal consistent changes in social interactions within groups of fish, predictive of subsequent welfare status. Social network analysis can be used to quantify the overall structure of an animal group based on the group-wide association patterns of its constituent members, by considering the frequency, strength, type and direction of associations. Temporal analysis of these group-level metrics allows the rapid detection of deviations from group norms (e.g. relating disruption to group structure caused by the abnormal behaviour of one or more group members). Computing these metrics automatically in real-time will let us detect early changes in intra-group social interactions that can reliably predict subsequent welfare status, and potentially identify the animal(s) responsible for this change. A social network analysis approach to assessing group behaviour can detect more detail and subtlety in the mechanisms that underpin changes in social behaviour in comparison to the conventional analysis of social behaviour, and the flexibility of this approach makes it suitable not only for the assessment of fish welfare in experimental studies (i.e. for assessing the impact of particular procedures on welfare) but also for use in general housing/breeding environments (i.e. for refining and monitoring husbandry to optimise welfare), extending its potential impact to all areas of aquaculture.

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Project grant



Principal investigator

Dr Oliver Burman


University of Lincoln


Dr Thomas Pike

Grant reference number


Award date:

Dec 2016 - Nov 2019

Grant amount