Real-Time Behaviour Recognition on Bio-Loggers Enables Autonomous Audio Playback Experiments in Free-Ranging Seabirds.

Audio playback experiments in the natural environment have been a powerful tool in animal behaviour and ecology, revealing causal relationships between animal movements/behaviours and audio stimuli. However, traditional audio playback experiments could only be performed in limited locations and/or situations where direct observation and/or video recording by human observers or installation of automated devices, such as camera traps, were possible. To overcome the limitation, we designed an autonomous audio playback system on bio-loggers in the natural environment. In this system, an on-board machine learning model estimates animals' behavioural state (e.g., flying or not) in real time using data from a low-power accelerometer. If the target behaviour (e.g., flying) is detected and other predefined criteria are met, the logger activates high-cost sensors, including a video camera, and plays audio from a built-in speaker. The logger can record fine-scale behavioural data before, during, and after the playback using multiple modalities (e.g., acceleration, GPS, and video). To examine the validity of the system, we performed field experiments targeting freely ranging black-tailed gulls (