Assessing Welfare in Ex Situ Lowland Tapirs Through Activity Patterns and Machine Learning

This study evaluates activity patterns and determines optimal observation periods for assessing the welfare of lowland tapirs (Tapirus terrestris L.) housed in the following two Danish zoological institutions: Aalborg Zoo and Randers Regnskov. The objectives were to identify the most efficient time window for welfare assessments, determine whether machine learning (ML) could support behavioral evaluations by providing automated estimates of activity, and examine whether automated pose-based tracking could serve as a proxy for manual ethogram observations. Behavioral data were collected using standardized ethograms from wildlife camera footage recorded over 72 h. Lowland tapirs were generally more active during daytime, with individuals at Aalborg Zoo showing peak activity between 07:00 and 14:00, while those at Randers Regnskov were most active between 12:00 and 18:00. Activity patterns differed between institutions, with Aalborg individuals displaying concentrated activity peaks and Randers individuals showing more evenly distributed activity. A preliminary ML analysis using the pose-estimation tool SLEAP demonstrated that movement-based activity estimates closely matched manually coded data, suggesting that automated tracking may offer an efficient and non-invasive tool for welfare monitoring. The findings highlight the potential for integrating automated analysis into routine welfare assessments of zoo-housed animals.