A computational model to support the welfare-based management of a laboratory colony of common marmosets

Abstract

Here, a computational model to forecast the population dynamics of a laboratory colony of common marmosets (Callithrix jacchus) is presented. This tool supports decision-making that seeks to maximize welfare and maintain a healthy and genetically diverse colony. The model considers the population in terms of three compartments: breeding adults, their offspring and nonbreeding adults. Natural events are explicitly represented, including births, deaths and the transfer of mature offspring from family housing to adult housing. These events are simulated using rates based on historical data extracted from the colony record-keeping system. Multi-year forecasts of population dynamics are generated, taking full account of interventions such as the implementation of breeding controls, the usage of animals by a portfolio of research projects and relocation to external primate facilities. Model forecasts are validated against real data. Uncertainties in animal usage are propagated through the model. The resulting forecasts provide a realistic range of future stock levels to support colony management and decision-making. The model outputs provide evidence to help assess the impacts of making interventions in the system, for example, breeding control strategies. This evidence-based approach to colony management serves to enhance animal welfare and accountability to regulatory bodies and stakeholders. The model can be adapted to simulate the dynamics of other nonhuman primate colonies.