Methodology
To identify moult in puffins, we have developed a new method combining multiple data streams from geolocator loggers to more accurately identify year-round behaviours of seabirds. While this seems to work reasonably well for dual-equipped individuals, it also highlights some shortcomings of using standard single-deployment geolocators to identify fine-scale behaviours. Halpin et al. (2021) show how species’ behaviour can unpredictably influence location estimates using light-level geolocators. Leg-tucking in alcids presents a similar problem for the interpretation of behaviours from saltwater immersion loggers on the same devices (Fayet et al. 2017, Linnebjerg et al. 2014). We provide a method to partially correct for this behavioural classification issue using concurrent light and immersion data. The limitations of this partial correction are reflected in the low success rate of moult inference in single-logger birds. Because puffins and some other alcids spend very little time in flight even when not undergoing moult (see results, Dunn et al. 2020), flightless moult is impossible to identify without relatively accurate behavioural data, and the few individuals for which moult periods were detected using a single logger are likely biased towards times of the year when leg tucking behaviour is less prevalent. Despite these constraints, our methods provide insights into the behaviour and life-history traits of a threatened species, and progresses our knowledge surrounding the timing and location of a highly vulnerable period in their annual cycle.
Over the last 10 – 15 years, hundreds of alcids have been tagged with a single geolocator throughout their biogeographic range (Fayet et al. 2017, Reiertsen et al. 2021), but our method does not have the power to identify moult in a sufficient proportion of individuals to robustly investigate population-wide patterns. More involved methods, for instance using machine learning to identify flightless stopovers (Guilford et al. 2009), usually require large amounts of pre-assigned training data to confidently infer behaviour, but may even then be liable to misclassification due to individual- or colony-level differences in behaviour (Bennison et al. 2018). Finer resolution data, such as from accelerometers, would allow us to identify flight with much more confidence (e.g. Patterson et al. 2019). GPS loggers would record far more accurate locations, potentially allowing us to identify imposed residency due to flightless moult. To date, none of these alternative devices are small or efficient enough for year-round deployment on puffins. Geolocators that record temperature can also be used to help correct for leg-tucking (Elliot & Gaston 2014, Dunn et al. 2020), though like the light-based corrections used in this study, temperature-based corrections do not fully capture all instances of leg-tucking. A ventrally mounted immersion switch would provide a truer representation of flight/non-flight behaviour, but despite being light enough for long-term deployment, techniques to mount devices long term on the body, as opposed to on a leg-ring, that do not affect the bird’s performance, have not been developed (Lameris et al. 2018). For now, dual-equipped geolocators are probably the most viable method to investigate the flightless moult of puffins and other alcids. As technology improves and devices become smaller, the combined weight of two loggers will have less impact on an animal. Stable isotope analysis of moult feathers may also be used to coarsely gauge the location of the most recent primary moult (e.g. St. John Glew et al. 2018) and to validate geolocator based findings. To complement this, a relatively accurate geolocator-informed moult timing and location tells us where and when flight feathers were formed, allowing us to analyse the trophic position of food consumed during feather formation using stable isotope analysis (St. John Glew et al. 2019). It may also provide information on the prevalence of toxic chemicals in marine food webs at the time and location of moult by looking at chemical composition of feathers (Fort et al. 2016).