Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) 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-endo...

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Main Authors: Brittany Roman, Christa Gallagher, Amy Beierschmitt, Sarah Hooper
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Veterinary Sciences
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Online Access:https://www.mdpi.com/2306-7381/12/3/209
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author Brittany Roman
Christa Gallagher
Amy Beierschmitt
Sarah Hooper
author_facet Brittany Roman
Christa Gallagher
Amy Beierschmitt
Sarah Hooper
author_sort Brittany Roman
collection DOAJ
description 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.
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spelling doaj-art-edc26edb059a499bae4a451d9a2164f22025-08-20T03:44:01ZengMDPI AGVeterinary Sciences2306-73812025-03-0112320910.3390/vetsci12030209Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed ModelsBrittany Roman0Christa Gallagher1Amy Beierschmitt2Sarah Hooper3Center for Conservation Medicine and Ecosystem Health, Ross University School of Veterinary Medicine, Basseterre KN 0101, Saint Kitts and NevisDepartment of Biomedical Sciences, Ross University School of Veterinary Medicine, Basseterre KN 0101, Saint Kitts and NevisDepartment of Clinical Sciences, Ross University School of Veterinary Medicine, Basseterre KN 0101, Saint Kitts and NevisDepartment of Biomedical Sciences, Ross University School of Veterinary Medicine, Basseterre KN 0101, Saint Kitts and NevisIntegrating 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.https://www.mdpi.com/2306-7381/12/3/209animal welfare3Rsstress responsebehaviorAfrican green monkeyrefinement
spellingShingle Brittany Roman
Christa Gallagher
Amy Beierschmitt
Sarah Hooper
Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
Veterinary Sciences
animal welfare
3Rs
stress response
behavior
African green monkey
refinement
title Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
title_full Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
title_fullStr Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
title_full_unstemmed Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
title_short Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models
title_sort investigating stress and coping behaviors in african green monkeys i chlorocebus aethiops sabaeus i through machine learning and multivariate generalized linear mixed models
topic animal welfare
3Rs
stress response
behavior
African green monkey
refinement
url https://www.mdpi.com/2306-7381/12/3/209
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