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

防御级联作为实验室和现场动物福利的指标

• 批准号: BB/I005641/1
• 负责人: Michael Mendl
• 金额: $ 76.19万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI005641%2F1
• 关键词: defence; cascade; indicator; animal; welfare

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.

非人类动物可以主观地经历负面情绪状态并因此遭受痛苦的假设是许多人对动物福利的担忧的基础。虽然我们无法直接测量其他动物的主观状态，但制定准确的间接测量方法非常重要。同样重要的是，这些措施可以在实验室和现场使​​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，该计划旨在为消费者提供有关食品生产方式的可靠信息。这些计划倾向于通过测量动物可用的资源（例如槽空间）（一种非常间接的措施）或对动物的物理损害来评估福利，这可能只会揭示相对严重的问题。我们的目标是开发一种新的福利衡量标准，更准确地反映动物福利问题核心的情绪状态。这项措施在实验室研究中也很有价值，无需隔离动物进行测试。当动物受到可能预示危险的警报刺激（例如大声喧哗）打扰时，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，以及逃跑或恢复正在进行的行为的最终反应。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，处于消极状态的个体预计会表现出更强更快的初始惊吓反应，更有可能表现出最终的逃跑反应，更快地做出决定，并且更慢地做出留在原地的最终决定，比处于积极状态的个体。我们将在重要的养殖物种——家猪中测试这些预测。猪对突然的噪音（例如关门声）表现出明显的直流反应，我们将开发标准化方法来诱导这种反应。部分 DC 已在“黄金标准”实验室条件下使用测力台技术在人类和啮齿动物身上进行了研究。在农场使用此类设备是不切实际的，但可以轻松收集和分析防御级联的视频图像以量化响应。为了开发这些基于视频的方法，我们将在受控条件下研究个体猪的直流反应，我们可以同时获得视频和测力板数据。我们将使用计算图像分析从视频片段中获取数值输出，并将其与传统的“黄金标准”测量相关联，以确定图像分析是否准确地测量直流响应。我们还将开发群体 DC 反应的新颖图像分析方法（猪通常是群体饲养的） - 例如反应在群体中传播的速度有多快 - 并调查福利/情绪状态的操纵（例如，通过不同条件下的住房群体）是否会影响 DC 反应，如上面预测的那样。然后，我们将尝试最能反映农场情绪状态/福利的 DC 反应的图像分析措施。由于我们使用的图像分析方法不需要识别个体动物，因此我们预计它将应对“现实生活”农场环境的视觉挑战。我们将陪同农场保障评估人员前往农场，测量 DC 响应，收集有关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层面福利的独立评估。因此，我们希望制定一种经过验证的、非侵入性的、快速且实用的方法来测量动物福利，该方法可以适用于其他物种，可以在现场和实验室中使用（包括作为农场保证审计的一部分），并且比任何现有的基于现场的措施更能反映福利的重要情感成分。

项目成果

Affect and decision making: a conceptual overview of affect-decision-making links and how they provide a grounding for the development of new measures of animal welfare

情感与决策：情感与决策联系的概念概述以及它们如何为动物福利新措施的制定提供基础

• 发表时间: 2017
• 作者: Mendl M

An integrative and functional framework for the study of animal emotion and mood.

用于研究动物情感和情绪的综合功能框架。

• 发表时间: 2011
• 期刊: Proceedings of the 2nd Latin American Congress on Applied Ethology
• 作者: Mendl M

50 years on: are animals still machines?

50 年后：动物还是机器吗？

• 发表时间: 2014
• 期刊: Proceedings of the 48th International Congress of the International Society for Applied Ethology
• 作者: Mendl M

Getting to the heart of animal welfare: the study of animal emotion

深入动物福利的核心：动物情感研究

• 发表时间: 2017
• 作者: Mendl M

Animal affect and decision-making

动物的情感和决策

• DOI: 10.1016/j.neubiorev.2020.01.025
• 发表时间: 2020-01-25
• 期刊: Neuroscience & Biobehavioral Reviews
• 影响因子: 8.2
• 作者: M. Mendl;E. Paul
• 通讯作者: E. Paul