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Global warming is predicted to profoundly impact ocean ecosystems. Fishbehavior is an important indicator of changes in such marine environments.Thus, the automatic identification of key fish behavior in videos represents amuch needed tool for marine researchers, enabling them to study climatechange-related phenomena. We offer a dataset of sablefish (Anoplopoma fimbria)startle behaviors in underwater videos, and investigate the use of deeplearning (DL) methods for behavior detection on it. Our proposed detectionsystem identifies fish instances using DL-based frameworks, determinestrajectory tracks, derives novel behavior-specific features, and employs LongShort-Term Memory (LSTM) networks to identify startle behavior in sablefish.Its performance is studied by comparing it with a state-of-the-art DL-basedvideo event detector.

Movement Tracks for the Automatic Detection of Fish Behavior in Videos