Drone Monitoring and Behavioral Analysis of White-Beaked Dolphins (Lagenorhynchus albirostris)

Marine mammals serve as indicator species for environmental and human health. However, they are increasingly exposed to pressure from human activities and climate change. The white-beaked dolphin (Lagenorhynchus albirostris) (WBD) is among the species negatively affected by these conditions. To support conservation and management efforts, a deeper understanding of their behavior and movement patterns is essential. One approach is drone-based monitoring combined with artificial intelligence (AI), allowing efficient data collection and large-scale analysis. This study aims to: (1) investigate the use of drone imagery and AI to monitor and analyze marine mammal behavior, and (2) test the application of machine learning (ML) to identify behavioral patterns. Data were collected in Skjálfandi Bay, Iceland, between 2021 and 2023. Three behavioral types were identified: Traveling, Milling, and Respiration. The AI_RGB model showed high performance on Traveling behavior (precision 92.3%, recall 96.9%), while the AI_gray model achieved higher precision (97.3%) but much lower recall (9.5%). The model struggled to classify Respiration accurately (recall 1%, F1-score 2%). A key challenge was misidentification of WBDs due to visual overlap with birds, waves, and reflections, resulting in high false positive rates. Multimodal AI systems may help reduce such errors in future research.