Preservation of an Automated Feeding System to Enhance Nonhuman Primate Social Management

摘要 在过去的8年里，YNPRC现场站成功地保持了大规模的自动化投放 系统，以加强对猕猴社会群体的照顾和管理。目前，有8种化合物是 配备自动喂料器，住房约。其SPF猕猴种群的37%。这种自动喂食 与标准的垃圾箱喂入实践相比，该系统具有几个优势，包括减少食物浪费、改进 动物健康的临床监督，超重动物的体重管理，以及一种自动方法 进行集体普查。鉴于我们之前的成功，最近的研究努力集中在增加 在非人灵长类(NHP)社会管理中提供数据。事实上，恒河猴社会中的一个共同挑战 管理是利用有效的社会健康监测方法来识别处于危险中的群体 在发生重大战斗和伤人之前，社会不稳定。定义主导地位的互动 这些群体中的从属关系是稀疏的；因此，收集足够的行为数据来 明确地发现社会不稳定需要相当多的时间和人力资源。家长赠款(R24 OD030035)寻求为NHP建立更有效的数据采集策略 通过开发基于馈送交互网络(FIN)的机器学习(ML)模型进行社会管理 可以用来补充行为数据，帮助管理者识别处于社会不稳定风险中的群体。 尽管YNPRC外勤站的自动供料系统已经成功运行了很多年 多年来，我们最近遇到了意想不到的设备挑战。首先，我们最古老的电路板 加料器已被制造商停产。目前，有两种化合物配备了这些 较老的馈线(4个单元/复合体)和一个单元的电路板无法运行。第二，虚拟机器 我们当前数据服务器的操作系统不再受支持，无法接受升级。因此，这一点 补充申请请求支持1)8台新的自动加料器，以取代8台停产设备和2台) 升级后的服务器配备了更快的处理器和更大的存储容量，并配备了新的运行的虚拟机 系统，以保存YNPRC野战站的8-化合物自动供料系统。 鉴于自动喂养数据是家长赠款目标的核心，购买升级的 服务器和自动饲喂装置对该项目的成功及其改进动物的潜力至关重要 福利和管理效率，这反过来又促进了使用国家卫生政策的高质量科学的开展。 此外，保留8种化合物的自动进料系统将允许必要的灵活性 选择最合适的社会群体进行研究(每年2个群体，共6个群体)，考虑到繁殖者的变化 雄性引种时间表，群体规模和稳定性，以及家庭结构。使用最合适的社交方式 基于母公司赠款中概述的标准的小组将对于构建稳健的FIN驱动的ML模型至关重要，该模型 将对YNPRC和其他圈养的NHP殖民地的NHP社会管理做法产生持续的影响。

ABSTRACT For the past 8 years, the YNPRC Field Station has successfully maintained a large-scale automated feeding system to enhance the care and management of its rhesus macaque social groups. Currently, 8 compounds are equipped with automated feeders, housing approx. 37% of its SPF rhesus population. This automated feeding system offers several advantages over standard bin feeding practices, including reduced food waste, improved clinical oversight of animal health, weight management of overweight animals, and an automated method to conduct group census. Given our prior success, recent research efforts have focused on increasing the utility of feeding data in nonhuman primate (NHP) social management. Indeed, a common challenge in rhesus social management is the utilization of efficient social health surveillance methodology to identify groups at risk for social instability before the onset of significant fighting and wounding. The interactions that define dominance and affiliative relationships in these groups are sparce; thus, the gathering of sufficient behavioral data to unequivocally detect social instability requires considerable time and staff resources. The parent grant (R24 OD030035) of this supplement application seeks to establish a more efficient data acquisition strategy for NHP social management by developing feeding interaction network (FIN)-based machine learning (ML) models that can be used to supplement behavioral data and help managers identify groups at risk for social instability. Although the automated feeding system at the YNPRC Field Station has been operated successfully for many years, we have recently encountered unanticipated equipment challenges. First, the circuit boards of our oldest feeder units have been discontinued by the manufacturer. Currently, two compounds are equipped with these older feeders (4 units/compound) and one unit’s circuit board is non-operational. Secondly, the virtual machine operating system of our current data server is no longer supported and cannot receive upgrades. Thus, this supplement application requests support for 1) 8 new automated feeders to replace 8 discontinued units and 2) an upgraded server with a faster processor and large storage capacity along with a new virtual machine operating system, to preserve the 8-compound automated feeding system at the YNPRC Field Station. Given that automated feeding data is central to the aims of the parent grant, the purchase of an upgraded server and automated feeding units is critical to the success of this project and its potential to improve animal wellbeing and management efficiency, which in turn, fosters the conduct of high-quality science using NHPs. Additionally, the preservation of an 8-compound automated feeding system will allow necessary flexibility to select the most appropriate social groups to study (2 groups/yr, 6 groups total), considering changes in breeder male introduction schedules, group size and stability, and family structures. Use of the most appropriate social groups based on criteria outlined in the parent grant will be critical for building robust FIN-driven ML models that will exert a sustained impact on NHP social management practices at YNPRC and other captive NHP colonies.