Investigating Stress and Coping Behaviors in African Green Monkeys (Chlorocebus aethiops sabaeus) Through Machine Learning and Multivariate Generalized Linear Mixed Models

Abstract

Understanding both behavior and physical health is important for measuring how well animals are coping in captivity. In this study, we collected hair, blood, and saliva samples from 40 male African green monkeys (AGMs) (Chlorocebus aethiops sabaeus) to measure their stress responses. We used principal component analysis (PCA) with a Bayesian mixed model analysis to find patterns in their behaviors related to cortisol, lysozyme, and β-endorphin. While the animals were divided into two groups to see if an enrichment activity would help their welfare, there was no difference in their hair cortisol levels. The statistical analysis, however, shows certain behaviors were connected to stress, suggesting that we need more research to understand how factors like environmental and social interactions are connected to animal welfare. This study shows that looking closely at animal behaviors with advanced statistical techniques can provide better objective assessments of behavior, which can lead to better veterinary management practices.