|Seasonal variation in rest–activity patterns in barnacle geese: are measurements of activity a good indicator of sleep–wake patterns?|van Hasselt, S.J.; Piersma, T.; Meerlo, P. (2022). Seasonal variation in rest–activity patterns in barnacle geese: are measurements of activity a good indicator of sleep–wake patterns? J. Exp. Biol. 225(20). https://dx.doi.org/10.1242/jeb.244177
In: The Journal of Experimental Biology. Cambridge University Press: London. ISSN 0022-0949; e-ISSN 1477-9145, meer
Activity; Sleep; EEG; Bird; Geese; Seasonality
|Auteurs|| || Top |
- van Hasselt, S.J.
- Piersma, T., meer
- Meerlo, P.
Sleep is a widely spread phenomenon in the animal kingdom and is thought to serve important functions. Yet, the function of sleep remains an enigma. Studies in non-model animal species in their natural habitat might provide more insight into the evolution and function of sleep. However, polysomnography in the wild may not always be an option or first choice and some studies may need to rely on rest–activity recordings as a proxy for sleep and wakefulness. In the current paper, we analyzed how accelerometry-based activity data correlate with electroencephalogram (EEG)-based sleep–wake patterns in barnacle geese under seminatural conditions across different seasons. In winter, the geese had pronounced daily rhythms in rest and activity, with most activity occurring during the daytime. In summer, activity was more spread out over the 24 h cycle. Hourly activity scores strongly correlated with EEG-determined time awake, but the strength of the correlation varied with phase of the day and season. In winter, the correlations between activity and waking time were weaker for daytime than for night-time. Furthermore, the correlations between activity and waking during daytime were weaker in winter than in summer. During daytime in winter, there were many instances where the birds were awake but not moving. Experimental sleep deprivation had no effect on the strength of the correlation between activity scores and EEG-based wake time. Overall, hourly activity scores also showed significant inverse correlation with the time spent in non-rapid eye movement (NREM) sleep. However, correlation between activity scores and time spent in REM sleep was weak. In conclusion, accelerometry-based activity scores can serve as a good estimate for time awake or even the specific time spent in NREM sleep. However, activity scores cannot reliably predict REM sleep and sleep architecture.