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

Integrating behavioral and physiological assessment is critical to improve our ability to assess animal welfare in biomedical settings. Hair, blood, and saliva samples were collected from 40 recently acquired male African green monkeys (AGMs) to analyze concentrations of hair cortisol, plasma β-endorphin, and lysozyme alongside focal behavioral observations. The statistical methodology utilized machine learning and multivariate generalized linear mixed models to find associations between behaviors and fluctuations of cortisol, lysozyme, and β-endorphin concentrations. The study population was divided into two groups to assess the effectiveness of an enrichment intervention, though the hair cortisol results revealed no difference between the groups. The principal component analysis (PCA) with a Bayesian mixed model analysis reveals several significant patterns in specific behaviors and physiological responses, highlighting the need for further research to deepen our understanding of how behaviors correlate with animal welfare. This study's methodology demonstrates a more refined approach to interpreting these behaviors that can help improve animal welfare and inform the development of better management practices.