Measuring activity-rest rhythms under different acclimation periods in a marine fish using automatic deep learning-based video tracking.

Most organisms synchronize to an approximately 24-hour (circadian) rhythm. This study introduces a novel deep learning-powered video tracking method to assess the stability, fragmentation, robustness and synchronization of activity rhythms in . Experimental were distributed into three groups and monitored for synchronization to a 14/10 hours of light/dark to assess acclimation to laboratory conditions. Group GP7 acclimated for 1 week and was tested from days 7 to 14, GP14 acclimated for 14 days and was tested from days 14 to 21 and GP21 acclimated for 21 days and was tested from days 21 to 28. Telemetry data from individuals in the wild depicted their natural behavior. Wild fish displayed a robust and minimally fragmented rhythm, entrained to the natural photoperiod. Under laboratory conditions, differences in activity levels were observed between light and dark phases. However, no differences were observed in activity rhythm metrics among laboratory groups related to acclimation period. Notably, longer acclimation (GP14 and GP21) led to a larger proportion of individuals displaying rhythm synchronization with the imposed photoperiod. Our work introduces a novel approach for monitoring biological rhythms in laboratory conditions, employing a specifically engineered video tracking system based on deep learning, adaptable for other species.