The defence cascade as an indicator of animal welfare in the lab and field

假设非人类动物可以主观地经历负面的情绪状态，因此受苦，这是许多人对动物福利的关注的基础。虽然我们不能直接测量其他动物的主观状态，但重要的是我们要制定准确的间接措施。同样重要的是，这些措施可以在实验室和田间使用，特别是在农场。这是因为福利评估正在成为农场质量保证计划的重要组成部分，这些计划旨在为消费者提供有关食品生产方式的可靠信息。这些计划往往通过衡量动物可利用的资源（例如槽空间）（一种非常间接的衡量标准）或动物的身体损害（可能只揭示相对严重的问题）来评估福利。我们的目标是开发一种新的福利措施，更密切地反映了动物福利关注的核心情感状态。这一措施在实验室研究中也很有价值，不需要隔离动物进行试验。当动物受到可能发出危险信号的警告刺激（例如大声噪音）的干扰时，它们会表现出一系列防御反应，包括惊吓和定向，冻结和评估情况，以及逃跑或恢复持续行为的最终反应。理论预测以及对人类和啮齿动物的研究表明，这种“防御级联”（DC）的组成部分受到个体情绪状态的调节。例如，与处于积极状态的个体相比，处于消极状态的个体被预测会表现出更强和更快的初始惊吓反应，更有可能表现出最终的逃跑反应，更快地做出这个决定，并且更慢地做出留在原地的最终决定。我们将在一个重要的养殖物种--家猪身上检验这些预测。猪对突然的噪音（例如摔门）表现出明显的直流反应，我们将开发诱导这种反应的标准化方法。DC的部分已经在人类和啮齿动物中在“金标准”实验室条件下使用测力板技术进行了研究。在农场使用这种设备是不切实际的，但可以很容易地收集和分析防御级联的视频图像，以量化反应。为了开发这些基于视频的方法，我们将在可控条件下研究个体猪的DC响应，在可控条件下我们可以同时获得视频和力板数据。我们将使用计算图像分析从视频片段中获得数值输出，并将其与传统的“黄金标准”措施相关联，以确定图像分析是否准确测量DC响应。我们还将开发新的图像分析措施的组DC响应（猪通常是群体圈养）-例如，如何迅速的响应在整个组传播-并调查是否操纵福利/情绪状态（例如，通过住房组在不同的条件下）影响DC响应如上所述。然后，我们将尝试最能反映农场情绪状态/福利的DC响应的图像分析措施。由于我们使用的图像分析方法不需要识别单个动物，我们预计它将科普“现实生活”农场环境的视觉挑战。我们将陪同农场保证评估员到农场，测量DC响应，收集农场和围栏条件的数据，并使用统计技术评估这些不同措施之间的关系。这将向我们展示我们的DC措施如何反映对农场和围栏水平福利的独立评估。因此，我们希望产生一个有效的，非侵入性的，快速和实用的方法来测量动物福利，可以适应其他物种，可以在现场以及实验室（包括作为农场保证审计的一部分），并得到更接近反映福利的重要情感成分比任何现有的基于现场的措施。

The assumption that non-human animals can subjectively experience negative emotional states, and hence suffer, underlies many people's concerns about animal welfare. Whilst we cannot measure the subjective states of other animals directly, it is important that we develop accurate indirect measures. It is also important that these measures can be used in both lab and field, especially on farms. This is because assessment of welfare is becoming a significant part of on-farm quality assurance schemes which aim to provide reliable information for the consumer about how food is produced. These schemes tend to assess welfare by measuring the resources available to animals (e.g. trough space) - a very indirect measure - or physical damage to the animal which may only reveal relatively severe problems. We aim to develop a new measure of welfare that more closely reflects the emotional states which lie at the heart of animal welfare concerns. This measure will also be of value in lab studies, precluding the need to isolate animals for testing. When animals are disturbed by an alerting stimulus that may signal danger (e.g. loud noise), they show a suite of defensive responses including startle and orientation, freezing and evaluation of the situation, and a final response of fleeing or resuming ongoing behaviour. Theoretical predictions, and studies of humans and rodents, suggest that the components of this 'defence cascade' (DC) are modulated by the individual's emotional state. For example, individuals in a negative state are predicted to show a stronger and faster initial startle response, to be more likely to show a final fleeing response, to make this decision faster, and to be slower to make a final decision to stay put, than individuals in a positive state. We will test these predictions in an important farmed species, the domestic pig. The pig shows a clear DC response to sudden noises (e.g. door slam), and we will develop standardised methods for inducing this response. Parts of the DC have been studied in humans and rodents under 'gold standard' laboratory conditions using force plate technology. It would be impractical to use such equipment on farms, but video images of the defence cascade could easily be collected and analysed to quantify the response. To develop these video-based methods, we will study individual pigs' DC responses under controlled conditions where we can obtain video and force plate data simultaneously. We will use computational image analysis to derive numerical output from the video footage and correlate this with the conventional 'gold standard' measures to determine whether image analysis accurately measures the DC response. We will also develop novel image analysis measures of group DC responses (pigs are usually group housed) - e.g. how rapidly a response spreads across a group - and investigate whether manipulation of welfare / emotional state (e.g. by housing groups in different conditions) affects DC responses as predicted above. We will then trial the image analysis measures of DC responses that best reflect emotional state / welfare on farms. Because the image analysis approach we use does not need to identify individual animals, we anticipate that it will cope with the visual challenges of a 'real-life' farm environment. We will accompany farm assurance assessors to farms, measure DC responses, collect data on the conditions on farms and in pens, and evaluate the relationships between these different measures using statistical techniques. This will show us how our DC measures reflect independent assessments of welfare at farm and pen level. We thus hope to produce a validated, non-invasive, quick and practical method for measuring animal welfare that can be adapted to other species, can be used in the field as well as the lab (including as part of farm assurance audits), and gets closer to reflecting the important emotional component of welfare than any existing field-based measure.