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.