# Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis

## TL;DR

This study shows that circadian activity patterns in Asian burying beetles can predict their breeding behavior, offering a non-invasive way to track how climate change affects reproduction.

## Contribution

A novel machine learning framework that links circadian behavior to breeding phenology in beetles using non-invasive methods.

## Key findings

- A model predicted breeding phenology with 95% accuracy using three behavioral features under long-day conditions.
- The model retained 76% accuracy under short-day conditions, showing distinct behavioral differences between breeding strategies.
- The approach provides a scalable method for tracking population responses to climate change across elevational gradients.

## Abstract

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, Japan (500 m) and Taiwan (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 used machine learning to predict breeding phenology (seasonal versus year-round breeding) from behaviour alone. Our model achieved 95% accuracy under long-day conditions using just three behavioural features. Notably, it 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 a rapid, scalable method for tracking population responses to climate change. This approach also offers novel insights into species’ adaptive responses to shifting seasonal cues across different elevational gradients in the beetles’ native range.