Light-immersion data from geolocators can be used to estimate behavioural budgets and energetics in seabirds throughout the annual cycle. However, all methods used to categorise time spent in behaviours rely on assumptions that are difficult to validate. Additional data, such as pressure and temperature data from time-depth recorders (TDRs), can help to refine these assumptions. We explore the utility of previous methods to derive behavioural budgets from light-immersion data using a dataset from Common Guillemots Uria aalge, where individuals were equipped with both a TDR and a solar Global Location Sensor (GLS), also known as a ‘geolocator’. We compared behavioural allocations from previous methods to those derived when also using TDR data. Previous methods used light-immersion data to distinguish between time foraging, active, and resting on the water, but the addition of TDR data revealed that these activities resulted in similar light and immersion levels. It was also more difficult to differentiate between rest and flight using light-immersion data alone. However, by using insights gained from combined light-immersion and TDR data, we developed an improved method to assign behaviours using light-immersion data alone, and provide an adjusted equation to use these data to calculate energetics in Guillemots. We recommend using our approach when processing light-immersion data; however, if detailed activity budgets (particularly foraging information) are required, we recommend using higher resolution loggers, e.g. integrated light-immersion-temperature-pressure devices. Our findings are likely to be relevant for studies of other seabird species (particularly other auks) that dive and spend most of their time at sea during winter.

Solar Global Location Sensor (GLS) loggers with immersion sensors (‘geolocators’) have been widely deployed on seabirds, providing data that can be used to estimate location, activity budgets and energetics throughout the annual cycle. During the breeding season, short-lived higher-resolution loggers can be used to derive seabird locations and behaviour (e.g. Trevail et al. 2023; Tremblay et al. 2024); however, such loggers often have short battery lives (or batteries that are too large for many species) or fall off when feathers are moulted. Therefore, relatively small and long-lived geolocators have become an important tool for seabird studies conducted outside the breeding period, which have broadened our understanding of seabird ecology (e.g. Militão et al. 2022). Geolocators typically record light, saltwater immersion and temperature data, from which location and activity can be derived and subsequently used to estimate energy expenditure (e.g. Pelletier et al. 2020). The limitations of using light-immersion data from geolocators to estimate location have been widely discussed (e.g. Halpin et al. 2021) yet using these data to allocate time spent in behaviours is less common, thus less attention has been given to validating these methods (although see Dunn et al. 2020, Darby et al. 2022 and Bennett et al. 2024).

A key limitation of using light-immersion data to assign activity budgets is that data are typically stored in a summarised format, for example only the maximum light value across a period (e.g. five or ten minutes), or the proportion of time spent immersed in saltwater across a period (ranging from e.g. five minutes to four hours). It is much more difficult to assign behaviours to these summarised datasets than if the raw data were available, as different behaviours can present with similar immersion patterns. For example, loggers will be dry when birds are in flight or resting on land and wet when birds are submerged in saltwater, but activities with medium immersion levels (such as foraging for surface-feeding seabirds or preening whilst on the water) are difficult to differentiate from those that might include a change of behaviour, such as a take-off from water. In addition, there are a wide variety of geolocator models available, which all record and store data in slightly different ways, meaning that datasets can be difficult to combine or compare.

Most previous studies that use light-immersion data to derive seabird energetics during the non-breeding season have focussed on auks, with a range of methods used to estimate activity budgets (e.g. Fayet et al. 2017; Dunn et al. 2022). A recent study deployed two devices simultaneously on Common Guillemots (hereafter ‘Guillemots’) Uria aalge, equipping each individual with a time-depth recorder (TDR) on one leg and a geolocator on the other (Buckingham et al. 2023). TDRs record pressure data, therefore providing a more accurate estimation of time spent foraging for species such as auks that dive to catch prey than immersion data alone. Additionally, the higher-resolution temperature data recorded by the TDR, and the fact that both legs were equipped with loggers, meant that time spent ‘tucking’ a leg into plumage (which is typically how auks rest whilst on water; Elliott & Gaston 2014; Linnebjerg et al. 2014) could be measured more accurately. This leg-tucking behaviour results in a warm, dry leg and thus can be difficult to differ- entiate from flight or colony attendance when the tucked leg is equipped with a geolocator, or from foraging or active on the water when the untucked leg is wearing the geolocator (Darby et al. 2022). Here, we compare the activity budget derived from both a TDR and geolocator in Buckingham et al. (2023) to previous methods of allocating time to behaviours using only light-immersion data. Based on our results, we provide insights into the information that geolocators can provide, develop an improved method for estimating time-activity budgets and energy expenditure for future geolocator-only studies of Guillemots during the non-breeding season, and provide guidance for other similar studies.

Lila Buckingham was funded by a PhD studentship as part of a funding package from Vattenfall to MacArthur Green. Biologging devices were funded by Vattenfall, Scottish Government’s Marine Directorate, Equinor (as part of Hywind Scotland’s Environmental Monitoring Programme) and SEATRACK. We are very grateful to James Duckworth for help processing TDR data and to all individuals and groups who contributed to data collection: Treshnish Isles Auk Ringing Group, Chris Andrews, Phil Bloor, Calum Campbell, Martin Davison, Hayley Douglas, Raymond Duncan, Sarah Fenn, Alexander Gilliland, Robin Gray, Carrie Gunn, Chris Heward, Anne Middleton, Tim Morley, Robert Rae, Stuart Rae, Moray Souter, Caitlin Tarvet, Robin Ward, Jenny Weston and Alastair Young. We thank Hallvard Strøm and Vegard Sandøy Bråthen for their help and field support with Guillemots on the Isle of May as part of the SEATRACK project (http://www.seapop.no/en/seatrack/). We thank all landowners for access, and NatureScot and the Isle of May Bird Observatory for logistical support on the Isle of May. We thank two anonymous reviewers for providing helpful feedback on an earlier version of this manuscript.

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