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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

基本信息

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
项目类别：
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

项目摘要

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