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 国家灵长类动物研究中心（NPRC）动物资源部的主要使命是维护 印度恒河猴（Macaca mulatta）的整体社会健康生活在大型多代 育种小组，以确保高质量的研究课题的NIH资助的研究。 然而，这些繁殖群体的社会动态是复杂的， 等级制度和亲属关系结构可能导致侵略和战斗的增加， 伤人因此，在其管理方面的一个共同挑战是利用有效的社会卫生监督 在重大战斗开始之前，确定有可能造成社会不稳定的群体的方法。这些俘虏 定期监测群体的支配地位和创伤率的变化，以检测社会不稳定性， 家庭和群体一级;然而，严重的攻击性爆发的确切时间在很大程度上被考虑 不可预测此外，在这些领域中定义支配和从属关系的相互作用的频率 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- 消耗。我们最近发表了一份报告，指出自动喂养数据可以提供关键的实时 数据，并协助管理层监测猕猴繁殖群体的社会稳定性， 预测强烈攻击的时间本提案的目的是进一步阐述这些意见 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 可以用来构建喂养互动网络（FIN），使我们能够检查时间接近的模式 在亲戚和非亲戚之间觅食由于生殖状况也影响雌性猴子的食物摄入， 将在耶基斯NPRC研究六个户外繁殖群体的繁殖和分娩季节， 整合社会和生殖数据，总体目标是开发全面的数据驱动型财务信息系统 模型，这将提高NIH支持的恒河猴繁殖群的群体管理实践。 目的1将描述恒河猴繁殖群体静态FIN内的群落结构， 在大小和组成上。目标2将决定是否有方向性和强度的连接之间 静态FIN内的成年雌性可用于预测 亲属和非亲属目标3将量化和模拟动态范围内群落结构的时间变化 恒河猴群体在繁殖和分娩季节雌性生殖相关的FIN 相位最后，目标4将检测动态FIN社区结构的变化， 生殖季节性，这是增加侵略和其他社会不稳定迹象的前兆。这些 FIN模型将用于补充行为和创伤数据，使管理层能够识别 社会不稳定的风险，并制定战略，在进一步的侵略升级之前进行干预。