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During seasons with little or no food, hibernators enter inactive states to survive in harsh environments. Due to its complexity, the mechanism of hibernation remains largely unknown. Recent advances in experimental technology have enabled monitoring of body temperature in hibernators for more than 100 days. In this study, we aim to reveal the mechanisms of hibernation by analyzing body temperature time-series data.
Syrian hamsters start hibernation in response to winter-like short photoperiods and low temperatures. During hibernation, their body temperature shows fluctuation between euthermia and hypothermia, but the rules governing these cycles have remained unclear. By applying statistical analysis, we showed that the frequency modulation (FM) model reproduces the experimental data. This analysis also revealed a circannual period in a hibernator thought not to have circannual rhythms.
It is also still unclear when they hibernate, as the duration of the pre-hibernation period varies among individuals. According to the bifurcation theory, systems recover more slowly from small perturbations near a bifurcation point, implying that the onset of hibernation can be predicted by analyzing fluctuations in body temperature. Using a machine learning approach, we successfully predict the onset of hibernation for most individuals.