Automated predictive welfare assessment in groups of fish

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Fish are widely used as research models in many different areas of science, from helping to increase our understanding of traumatic brain injury, to assessing chemical toxicity. Given that, just like mammals, fish appear capable of experiencing pain, show avoidance of unpleasant events and actively seek out pleasurable rewards, it is our responsibility to identify better ways to measure, and therefore to improve, fish welfare. However, as well as being relatively understudied, work in this area presents a considerable challenge, and some of the currently used measures have limitations that restrict their practical use. For example, some behavioural measures of poor welfare are observable at such a late stage that considerable suffering is already likely to have taken place by the time that the behaviours are noticed. What is therefore needed is a far more sensitive method that gives a much earlier indication of welfare status, giving us the chance to act sooner and therefore maximise fish welfare. In order to achieve this, we propose to develop a new way of measuring the welfare of fish: the detailed, real-time quantification of the social interactions that take place between fish living in groups. The rationale underpinning this is that social behaviours are highly sensitive to any changes that the fish perceive in their environment, both negative and positive. Therefore, by recording any unexpected changes in who interacts with who, and how often, we can be alerted to any potential threats to welfare, allowing us the chance to intervene before significant suffering has taken place. Such detailed and extensive observations of fish are likely to be time consuming, and so we will fully automate the data capturing process using the latest imaging technologies, ensuring that we produce a system that is effective, reliable, easy to use, and practical in a range of industrial and academic research environments, with the potential to improve the welfare of large numbers of fish. Although our proposal will focus on two of the most widely used species of fish, zebrafish and rainbow trout, our approach, once developed, is likely to be applicable to assessing welfare in any group-housed species (both fish and non-fish).