Feeding interaction network analyses enhance management of NHP breeding groups

喂养相互作用网络分析增强 NHP 育种群体的管理

• 批准号: 10090122
• 负责人: Kelly F Ethun
• 金额: $ 80.27万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10090122
• 关键词: Adult; Affect; Aggressive behavior; Animal Feed; Animals; Behavior; Behavioral; Birth; Breeding; Case Study; Communities; Complex; Consumption; Contracts; Data; Detection; Disease Outbreaks; Eating; Ensure; Estradiol; Exhibits; Family; Female; Frequencies; Funding; Generations; Goals; Health; Injury; Intake; Lactation; Lead; Macaca mulatta; Machine Learning; Methodology; Mission; Modeling; Monitor; Monkeys; Ovulation; Pathway Analysis; Pattern; Phase; Practice Management; Primates; Progesterone; Publishing; Reporting; Research; Research Subjects; Risk; Signal Transduction; Social Dominance; Social Network; Social support; Structure; Testing; Time; Trauma; United States National Institutes of Health; affiliative behavior; animal resource; behavior observation; cohesion; feeding; fighting; improved; machine learning algorithm; member; network models; pregnant; reproductive; season of birth; social; social attachment; social relationships; social unrest; wound

Abstract A primary mission of Animal Resources Divisions at National Primate Research Centers (NPRC) is to maintain the overall social health of Indian-origin rhesus macaques (Macaca mulatta) living in large multi-generational breeding groups in order to ensure the availability of high-quality research subjects for NIH-funded research. The social dynamics of these breeding groups, however, are complex and disturbances in their dominance hierarchy and kinship structures can lead to increased aggression and fighting that may result in significant wounding. Thus, a common challenge in their management is the utilization of effective social heath surveillance methodology to identify groups at risk for social instability before the onset of significant fighting. These captive groups are routinely monitored for changes in dominance ranks and rates of trauma to detect social instability at the family- and group-level; however, the precise timing of severe outbreaks of aggression is largely considered unpredictable. Further, the frequency of interactions that define dominance and affiliative relationships in these groups are sparse; thus, the gathering of enough behavioral data to unequivocally detect social instability is time- consuming. We recently published a report that indicated automated feeding data can provide critical real-time data and assist management with monitoring social stability in rhesus macaque breeding groups, by possibly predicting the timing of intense aggression. The purpose of this proposal is to expand upon these observations by establishing data-driven models that help management identify groups at risk for social instability. Because automated feeding data include precise timestamps and the order in which animals feed, network analyses can be used to construct Feeding Interaction Networks (FINs), allowing us to examine patterns of temporal proximity feeding among kin and non-kin. Because reproductive status also influences food intake in female monkeys, we will study six outdoor breeding groups at the Yerkes NPRC across the breeding and birthing seasons, and integrate social and reproductive data, with the overarching aim to develop comprehensive data-driven FIN models that will enhance colony management practices of NIH-supported rhesus macaque breeding colonies. Aim 1 will characterize the community structures within static FINs of rhesus macaque breeding groups, varying in size and composition. Aim 2 will determine whether the directionality and strength of connections between adult females within a static FIN can be used to predict patterns of affiliative and dominance relationships among kin and non-kin. Aim 3 will quantify and model the temporal changes of community structures within dynamic FINs of rhesus macaque groups across the breeding and birthing season with respect to female reproductive phase. Finally, Aim 4 will detect changes in the community structures of dynamic FINs, not associated with reproductive seasonality, that are precursor to increased aggression and other signs of social instability. These FIN models will be used to supplement behavioral and trauma data, allowing management to identify groups at risk for social instability and develop strategies to intervene prior to the escalation of additional aggression.

摘要 国家灵长类研究中心(NPRC)动物资源部的主要任务是维持 大型多世代印度恒河猴(Macaca Mulatta)的整体社会健康状况 为NIH资助的研究提供高质量的研究对象。 然而，这些繁育群体的社会动力是复杂的，它们的统治地位受到干扰。 等级和亲属关系结构会导致更多的攻击性和战斗，这可能会导致显著的 伤人。因此，他们管理中的一个共同挑战是利用有效的社会卫生监测。 在重大战斗开始之前确定有社会不稳定风险的群体的方法。这些俘虏 组织经常被监测支配地位的变化和伤害率，以发现社会不稳定 家庭和团体层面；然而，严重攻击行为爆发的确切时间在很大程度上被考虑 变幻莫测。此外，定义这些领域中主导地位和从属关系的互动频率 群体是稀疏的；因此，收集足够的行为数据来明确地检测社会不稳定是时间- 在消费。我们最近发布了一份报告，指出自动喂养数据可以提供关键的实时 数据和协助管理层监测恒河猴繁育群体的社会稳定，可能 预测发生激烈攻击的时间。这项建议的目的是对这些观察结果进行扩展。 通过建立数据驱动的模型，帮助管理层识别面临社会不稳定风险的群体。因为 自动喂食数据包括精确的时间戳和动物喂食的顺序，网络分析可以 用于构建摄食相互作用网络(FINS)，使我们能够检查时间接近的模式 在亲属和非亲属之间进食。由于生殖状况也会影响雌性猴子的食物摄入量，我们 将在耶克斯NPRC研究六个户外繁殖小组，跨越繁殖和分娩季节，以及 整合社会和生殖数据，总体目标是开发全面的数据驱动型FIN 将加强美国国立卫生研究院支持的猕猴繁殖群体的群体管理实践的模型。 目标1将描述恒河猴繁殖群体静态鳍内的群落结构，不同的 在大小和组成上。目标2将决定两者之间的联系的方向性和强度 静态鳍中的成年雌性可以用来预测它们之间的附属关系和支配关系的模式 亲属和非亲属。目标3将对动态范围内群落结构的时间变化进行量化和建模 恒河猴种群在整个繁殖和分娩季节对雌性生殖的鳍 相位。最后，目标4将检测动态鳍群落结构的变化，这些变化与 生殖季节性，这是攻击性增加和社会不稳定的其他迹象的前兆。这些 FIN模型将用于补充行为和创伤数据，使管理层能够在 面临社会不稳定的风险，并制定在更多攻击升级之前进行干预的战略。