Data from: Nocturnal foraging lifts time-constraints in winter for migratory geese but hardly speeds up fueling
Lameris, Thomas et al. (2020), Data from: Nocturnal foraging lifts time-constraints in winter for migratory geese but hardly speeds up fueling, Dryad, Dataset, https://doi.org/10.5061/dryad.gmsbcc2m7
Climate warming advances the optimal timing of breeding for many animals. For migrants to start breeding earlier, a concurrent advancement of migration is required, including pre-migratory fueling of energy reserves. We investigate whether barnacle geese are time-constrained during pre-migratory fueling and whether there is potential to advance or shorten the fueling period to allow an earlier migratory departure. We equipped barnacle geese with GPS-trackers and accelerometers to remotely record birds’ behavior, from which we calculated time budgets. We examined how time spent foraging was affected by the available time (during daylight and moonlit nights) and thermoregulation costs. We used an energetic model to assess onset and rates of fueling, and whether geese can further advance fueling by extending foraging time. We show that during winter, when facing higher thermoregulation costs, geese consistently foraged at night, especially during moonlit nights, in order to balance their energy budgets. In spring, birds made use of the increasing day length and gained body stores by foraging longer during the day, but birds stopped foraging extensively during the night. Our model indicates that by continuing night-time foraging throughout spring, geese may have some leeway to advance and increase fueling rate, potentially reaching departure body mass 4 days earlier. In light of rapid climatic changes on the breeding grounds, whether this advancement can be realized and whether it will be sufficient to prevent phenological mismatches remains to be determined.
GPS and tri-axial acceleration data was collected from wild Barnacle Geese using UvA-BiTS GPS loggers.
GPS data was resampled to 30 minute intervals.
From tri-axial accelerometer data we classified behaviors using a random forest classifier, see methods in the paper.
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