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Olfactory Enrichment of Captive Pygmy Hippopotamuses with Applied Machine Learning
Jonas Nielsen1,2, Frej Gammelgård1,2, Silje Marquardsen Lund1,2
1Department of Chemistry and Bioscience, Aalborg University, Frederik Bajers Vej 7H, 9220 Aalborg, Denmark.
Animals : an Open Access Journal From MDPI
|February 13, 2026
Summary
Olfactory enrichment reduced inactivity and increased environmental engagement in endangered pygmy hippopotamuses. Machine learning accurately complemented manual behavioral analysis for welfare monitoring.
Area of Science:
- Zoology
- Animal Welfare Science
- Conservation Biology
Background:
- The endangered pygmy hippopotamus (Choeropsis liberiensis) is understudied, with potential welfare concerns in zoological institutions.
- Environmental enrichment is a key strategy for improving captive animal welfare.
- Olfactory enrichment offers a promising avenue for enhancing pygmy hippopotamus welfare.
Purpose of the Study:
- To investigate the impact of olfactory enrichment on pygmy hippopotamus behavior.
- To evaluate the efficacy of machine learning (SLEAP) for automated behavioral analysis in this species.
- To compare manual and automated behavioral classification methods for welfare monitoring.
Main Methods:
- Behavioral analysis of three pygmy hippopotamuses using continuous focal sampling.
- Application of olfactory enrichment stimuli.
- Heat-map visualization of behaviors.
- Machine learning-based pose estimation (SLEAP) for automated behavior classification.
- Comparison of manual and automated annotations using confusion matrices and Kendall's W.
Main Results:
- Olfactory enrichment led to decreased inactivity and increased environmental engagement, particularly scenting behavior.
- Machine learning (SLEAP) showed strong and moderate agreement with manual annotations for different individuals.
- Automated analysis demonstrates potential for efficient and complementary welfare monitoring.
Conclusions:
- Olfactory enrichment positively influences pygmy hippopotamus behavior, suggesting its utility in improving captive welfare.
- Automated behavioral analysis using machine learning is a viable tool to support traditional observation methods.
- Further research integrating these methods can enhance welfare monitoring for endangered species.
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