A Standardized Methodological Framework for Model Development in LabGym: A Case Study in Lions

Behavioral observation is central to animal welfare assessment in zoological institutions, but manual observation is time-consuming and difficult to apply continuously. We developed and applied a LabGym-based workflow for automated behavioral monitoring of captive lions (Panthera leo). Development footage from three Danish zoological institutions was used to train a lion detector and a seven-class behavior categorizer; video collected on 22–28 December 2025 was temporally held out from model development and used for applied validation. Detector performance was moderate (bounding-box AP 47.1%; segmentation AP 41.6%), while the internal categorizer validation reached 0.62 accuracy and weighted F1. In the held-out validation, 52.6% of manually annotated lion-time received a retained pipeline prediction. Fine-scale end-to-end accuracy was 0.231 and increased to 0.440 when calculated only on covered lion-time. Grouping behaviors as active, inactive/maintenance, and pacing-like locomotion increased these values to 0.317 and 0.602, respectively. Coverage varied substantially among institutions (33.8–59.8%) and was particularly low for cubs and nighttime footage. Although model macro-F1 exceeded simple majority-class baselines, end-to-end accuracy did not. The workflow demonstrates the potential of computer-vision approaches for scalable behavioral quantification and future welfare assessment in zoological settings, while highlighting the importance of accounting for prediction coverage and classification performance when interpreting automated behavioral data.