Circadian Activity Predicts Breeding Phenology in the Asian Burying Beetle Nicrophorus nepalensis

Climate change continues to alter breeding phenology in a range of plant and animal species across the globe. Traditional methods for assessing when organisms reproduce often rely on time-intensive field observations or destructive sampling, creating an urgent need for efficient, non-invasive approaches to assess reproductive timing. Here, we examined three populations of the Asian burying beetle Nicrophorus nepalensis from subtropical Okinawa (500 m) and Taiwan mountains (1100-3200 m) that were reared under contrasting photoperiods in order to develop a predictive framework linking circadian activity to breeding phenology. Using automated activity monitors, we quantified adult circadian rhythms and employed machine learning to predict breeding phenology (seasonal versus year-round breeders) from behavior alone. Our model achieved 95% accuracy under long-day conditions using just three behavioural features, and notably, maintained 76% accuracy under short-day conditions when both types are reproductively active, revealing persistent behavioural differences between breeding strategies. These results demonstrate how integrating behavioural monitoring with machine learning can provide both a rapid, scalable method for tracking population responses to climate change and novel insights into species' adaptive responses to shifting seasonal cues across different elevational gradients in their native range.