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，只要它们能在模型动物系统中得到验证，就可以适用于作为基于动物的福利措施。然后，可以使用经过验证的指标来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。先前的研究表明，猪是这种项目的理想模型系统，当提供丰富时，表现出显着的福利改善，皮肤损伤的持续程度可靠地表明福利状态。在本研究中，基于统计学、物理学、工程学和心理学的方法，将使用物理和环境因素创建不同福利状态的猪类别，以开发和验证新的福利指标。大型白猪和长白猪将以20头为一组，饲养在以下两种环境条件中的一种：(1)深稻草围栏，(2)部分板条地板。试验选取800头猪（10批次，4组，每组20头），进行为期6周的试验。在整个实验过程中，闭路电视摄像机将每周一次，每天12小时对笔进行记录。每头猪都将被单独标记，以便从摄像机中识别出来。在头4周的每隔一段时间，将对每头猪进行评估，并根据伤害程度（身体和尾巴分别）给予“福利”评分。每个猪圈中身体伤害平均百分比得分最高的4头猪为“低福利”组，平均百分比得分最低的4头猪为“高福利”组。这将给四个群体：(a)深草高福利；(b)部分板条地板的高福利；(c)深秸秆的福利低；(d)部分板条地板的福利较低。在每次试验开始之前，将从每个猪圈中随机选择5头猪，并在整个试验过程中使用3种认知行为测试进行训练和测试：认知偏差（确定个体的乐观或悲观程度），功能记忆（评估个体跟踪模式的程度）和间隔时间（确定不同个体感知的时间流逝）。认知偏差已被证明会受到动物福利状况的影响。功能性记忆和间隔时间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加工影响的新有用工具。此外，在试验结束时，将对每个猪圈受伤率最高和最低的4头猪进行三种认知行为测试。我们还将记录笔内和笔内选定个体的声音交流。从录制的视频片段中，将对每头焦点猪进行回顾性跟踪，并记录其行为，以了解试验过程中个体活动预算的差异。此外，将使用一些工具来调查每个笔内的社会群体动态。运动的分形分析和半隐马尔可夫链分析将识别重复的行为序列，社会网络分析将确定每头猪在其围栏网络中的相对位置和群体内的整体连接水平，聚类水平将被评估，以确定在给定可用空间的情况下，围栏中的个体是否比预期更拥挤。最后，我们将评估每个笔内同步行为的级别。我们将分析每一个指标，以寻找四种所述群体类型之间的差异。最后，将基于整个数据集开发预测统计工具，作为福利问题的早期预警系统。预测模型的目的是预测哪些围栏和围栏内的哪些个人可能在后期出现福利问题，从而允许实施缓解战略。

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.