Development of validated cognitive and behavioural indicators of welfare in pigs towards a predictive early warning system for poor welfare.

统计学，物理学，工程学和心理学利用了广泛的方法，只要它们可以在模型动物系统中得到验证，就可以适用于基于动物的福利措施。经验证的指标可用于开发预测性预警系统，以便提前发现和缓解潜在的福利问题。以前的研究表明，猪是这样一个项目的理想模型系统，显示出显着的福利改善时，提供丰富，皮肤损伤的程度持续可靠地表明福利状态。在这项研究中，将使用物理和环境因素来创建不同福利状态的猪的类别，以开发和验证新的福利指标，基于统计学，物理学，工程学和心理学中使用的方法。 将大型白色×长白猪以20头为一组饲养在以下两种环境条件之一中：（1）深稻草围栏和（2）部分板条地板。将对共计800头猪（10批，4组，每组20头）进行为期6周的观察。闭路电视摄像机将在整个实验过程中每周一次每天记录12小时的笔。每头猪都将被单独标记，以便从相机中识别。在前4周的间隔时间内，将对每头猪进行评估，并根据损伤（分别对身体和尾部）的测量值给予“福利”评分。来自每个围栏的具有最高平均%身体损伤评分的4头猪将是“低福利”组，并且具有最低平均%评分的4头猪将是“高福利”组。这将产生四个组：（a）深稻草层的高福利;（B）部分板条地板的高福利;（c）深稻草层的低福利;（d）部分板条地板的低福利。每次试验开始前，从每个畜栏中随机选择5头猪，并在整个试验期间使用3项认知行为测试进行训练和测试：认知偏差（以确定个体的乐观或悲观水平）、功能记忆（以评估个体跟踪模式的能力）和间隔计时（以确定不同个体感知的时间推移）。认知偏差已被证明是受动物的福利状态。功能性记忆和间隔计时尚未在动物福利研究中使用;本项目旨在验证这些方法作为评估福利对认知处理影响的新有用工具。此外，在试验结束时，将使用三项认知行为测试对每个围栏中具有最高和最低%损伤的4头猪进行测试。我们还将记录笔内和笔内选定个体的声学通信。根据记录的视频片段，将回顾性跟踪每头焦点猪，并记录其行为，以确定试验过程中个体活动预算的差异。此外，还将使用一些工具来调查每个围栏内的社会群体动态。运动的分形分析和半隐藏马尔可夫链分析将识别重复的行为序列，社交网络分析将确定每头猪在其围栏网络中的相对位置以及群体内的整体连通性水平，将评估聚类水平以确定围栏中的个体是否比预期的更拥挤，考虑到它们可用的空间，最后，我们将评估每个围栏内的同步行为水平。我们将分析每一个指标，以寻找4个所述群体类型之间的差异。 最后，将根据整个数据集开发一个预测性统计工具，可用作福利问题的预警系统。预测模型的目的是预测哪些围栏和其中的哪些个人可能在稍后阶段出现福利问题，从而允许采取缓解策略。

Statistics, physics, engineering and psychology utilise a wide-range of methods that can be adapted for use as animal-based welfare measures if only they could be validated in a model animal system. Validated indicators could then be used to develop a predictive early warning system so that potential welfare problems can be detected and mitigated in advance. Previous research has shown pigs to be an ideal model system for such a project, showing significant welfare improvements when provided with enrichment, and extent of skin injuries sustained reliably indicating welfare state. In this study, categories of pigs of different welfare status will be created using both physical and environmental factors to develop and validate novel welfare indicators, based methods used in statistics, physics, engineering and psychology. Large White x Landrace pigs will be kept in groups of 20 in one of two environmental conditions: (1) deep straw pen, and (2) part-slatted floor. A total of 800 pigs (10 batches of 4 groups of 20) will be observed for 6 week periods. CCTV video cameras will record the pens for 12 hours per day once a week throughout the experiment. Every pig will be individually marked so that they are identifiable from the camera. At intervals over the first 4 weeks, each pig will be assessed and given a 'welfare' score based on measures of injury (to body and tail separately). The 4 pigs from each pen with the highest average % body injury score will be the 'Low welfare' group and the 4 with the lowest average % score will be the 'High welfare' group. This will give four groups: (a) High welfare on deep straw; (b) High welfare on part-slatted floor; (c) Low welfare on deep straw; (d) Low welfare on part-slatted floor. Before each trial starts, 5 pigs will be selected at random from each pen and will be trained and tested throughout the trial using 3 cognitive behaviour tests: cognitive bias (to determine the individual's level of optimism or pessimism), functional memory (to assess how well individuals can keep track of a pattern) and interval timing (to determine the perceived passage of time by different individuals). Cognitive bias has been shown to be affected by the welfare state of the animal. Functional memory and interval timing have not been utilised in animal welfare studies; this project will aim to validate these methods as new useful tools to assessing the impact of welfare on cognitive processing. In addition, the 4 pigs with the highest and lowest % injury from each pen will be tested using the three cognitive behaviour tests at the end of the trial. We will also record acoustic communication both within a pen and of selected individuals within a pen. From the recorded video footage, each of the focal pigs will be retrospectively tracked and their behaviours recorded for differences in individual activity budgets over the course of the trial. In addition, a number of tools will be used to investigate the social group dynamics within each pen. Fractal analysis of movement and semi-hidden Markov-chain analysis will identify repeating sequences of behaviour, social network analysis will determine the relative positions of each pig within its pen network and the overall level of connectedness within the group, levels of clustering will be assessed to determine whether individuals in a pen are crowding together more than expected given the space available to them, and finally we will assess the levels of synchronous behaviour within each pen. We will analyse each of indicator to look for differences between the 4 stated group types. Finally, a predictive statistical tool will be developed based on the entire data set, which can be used as an early warning system for welfare problems. The aim of the predictive model will be to predict which pens and which individuals within them, are likely to develop welfare problems at a later stage and hence permit mitigation strategies to be put in place.