Using skin temperature and activity profiles to assign chronotype in birds

Abstract

Chronotypes describe consistent differences between individuals in biological time-keeping. They have been linked both with underlying variation in the circadian system and fitness. Quantification of chronotypes is usually by time of onset, midpoint, or offset of a rhythmic behaviour or physiological process. However, diel activity patterns respond flexibly to many short-term environmental influences, which can make chronotypes hard to identify. In contrast, rhythmic patterns in physiological processes, such as body temperature, may provide more robust insights into the circadian basis of chronotypes. These can be telemetrically recorded from skin-mounted, temperature-sensitive transmitters, offering minimally invasive opportunities for working on free-ranging animals in the wild. Currently, computational methods for deriving chronotype from skin temperature require further development, as time series are often noisy and incomplete. Here, we investigate such methods using simultaneous radio telemetry recordings of activity and skin temperature in a wild songbird model (Great Tit Parus major) temporarily kept in outdoor aviaries. Our aims were to first develop standardised selection criteria to filter noisy time series of skin temperature and activity, to second assign chronotype based on the filtered recordings, and to third compare chronotype as assigned based on each of the two rhythms. After the selection of rhythmic data using periodicity and autocorrelation parameters, chronotype estimates (onset and offset) were extracted using four different changepoint approaches for skin temperature and one approach for activity records. The estimates based on skin temperature varied between different approaches but were correlated to each other (onset: correlation coefficient r = 0.099–0.841, offset: r = 0.131–0.906). In contrast, chronotype estimates from skin temperature were more weakly correlated to those from activity (onset: r = −0.131–0.612, offset: r = −0.040– −0.681). Overall, chronotype estimates were less variable and timed later in the day for activity than for skin temperature. The distinctions between physiological and behavioural chronotypes in this study might reflect differences in underlying mechanisms and in responsiveness to external and internal cues. Thus, studying each of these rhythms has specific strengths, while parallel studies of both could inform broadly on natural variation in biological time-keeping, and may allow assessment of how biological rhythms relate to changes in the environment.