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，从帮助增加我们对创伤性脑损伤的理解到评估化学毒性。正如哺乳动物一样，鱼类似乎能够体验痛苦，避免不愉快的事件并积极寻求令人愉快的奖励，因此我们有责任找到更好的方法来衡量，从而改善鱼类福利。然而，这一领域的工作不仅没有得到充分研究，而且也是一个相当大的挑战，目前使用的一些措施有其局限性，限制了它们的实际使用。例如，一些福利差的行为措施是在很晚的阶段才观察到的，以至于在注意到这些行为时，很可能已经发生了相当大的痛苦。因此，我们需要的是一种更敏感的方法，可以更早地显示福利状况，让我们有机会更快地采取行动，从而最大限度地提高鱼类福利。为了实现这一目标，我们建议开发一种新的方法来衡量鱼的福利：详细的，实时量化的社会互动，发生在鱼类生活在群体之间。其基本原理是，社会行为对鱼类在其环境中感知到的任何变化都非常敏感，无论是消极的还是积极的。因此，通过记录谁与谁互动以及互动频率的任何意外变化，我们可以警惕任何对福利的潜在威胁，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很可能是耗时的，因此我们将使用最新的成像技术完全自动化数据采集过程，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在两个最广泛使用的鱼类，斑马鱼和虹鳟鱼，我们的方法，一旦开发，很可能适用于评估福利在任何群体圈养的物种（鱼类和非鱼类）。

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).