Automated predictive welfare assessment in groups of fish

鱼类被广泛用作许多不同科学领域的研究模型，从帮助提高我们对创伤性脑损伤的理解，到评估化学毒性。有鉴于此，就像哺乳动物一样，鱼类似乎有能力体验痛苦，表现出对不愉快事件的回避，并积极寻求愉快的奖励，我们有责任找到更好的方法来衡量，从而改善鱼类的福利。然而，除了研究相对不足之外，这一领域的工作也面临着相当大的挑战，目前使用的一些措施存在局限性，限制了它们的实际应用。例如，一些衡量贫穷福利的行为措施是在很晚的阶段才可以观察到的，以至于当这些行为被注意到时，可能已经发生了相当大的痛苦。因此，我们需要的是一种更灵敏的方法，能够更早地显示鱼类的福利状况，使我们有机会尽早采取行动，从而最大限度地提高鱼类的福利。为了实现这一目标，我们建议开发一种衡量鱼类福利的新方法：对群居鱼类之间发生的社会互动进行详细、实时的量化。支撑这一理论的基本原理是，社会行为对鱼类感知到的环境中的任何变化都高度敏感，无论是消极的还是积极的。因此，通过记录人与人之间的任何意想不到的变化，以及发生的频率，我们可以警惕任何潜在的福利威胁，使我们有机会在重大痛苦发生之前进行干预。对鱼类进行如此详细和广泛的观察可能非常耗时，因此我们将使用最新的成像技术完全自动化数据捕获过程，确保我们生产的系统在一系列工业和学术研究环境中有效、可靠、易于使用和实用，并有可能改善大量鱼类的福利。虽然我们的建议将集中于两种最广泛使用的鱼类，斑马鱼和虹鳟鱼，但我们的方法一旦开发出来，很可能适用于评估任何群体养殖物种（包括鱼类和非鱼类）的福利。

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).