Aim  An understanding of the non‐breeding distribution and ecology of migratory species is necessary for successful conservation. Many seabirds spend the non‐breeding season far from land, and information on their distribution during this time is very limited. The black‐legged kittiwake, Rissa tridactyla, is a widespread and numerous seabird in the North Atlantic and Pacific, but breeding populations throughout the Atlantic range have declined recently. To help understand the reasons for the declines, we tracked adults from colonies throughout the Atlantic range over the non‐breeding season using light‐based geolocation. Location  North Atlantic. Methods  Geolocation data loggers were deployed on breeding kittiwakes from 19 colonies in 2008 and 2009 and retrieved in 2009 and 2010. Data from 236 loggers were processed and plotted using GIS. Size and composition of wintering populations were estimated using information on breeding population size. Results  Most tracked birds spent the winter in the West Atlantic, between Newfoundland and the Mid‐Atlantic Ridge, including in offshore, deep‐water areas. Some birds (mainly local breeders) wintered in the North Sea and west of the British Isles. There was a large overlap in winter distributions of birds from different colonies, and colonies closer to each other showed larger overlap. We estimated that 80% of the 4.5 million adult kittiwakes in the Atlantic wintered west of the Mid‐Atlantic Ridge, with only birds from Ireland and western Britain staying mainly on the European side. Main conclusions  The high degree of mixing in winter of kittiwakes breeding in various parts of the Atlantic range implies that the overall population could be sensitive to potentially deteriorating environmental conditions in the West Atlantic, e.g. owing to lack of food or pollution. Our approach to estimating the size and composition of wintering populations should contribute to improved management of birds faced with such challenges.

BackgroundWindscapes affect energy costs for flying animals, but animals can adjust their behavior to accommodate wind-induced energy costs. Theory predicts that flying animals should decrease air speed to compensate for increased tailwind speed and increase air speed to compensate for increased crosswind speed. In addition, animals are expected to vary their foraging effort in time and space to maximize energy efficiency across variable windscapes.ResultsWe examined the influence of wind on seabird (thick-billed murre Uria lomvia and black-legged kittiwake Rissa tridactyla) foraging behavior. Airspeed and mechanical flight costs (dynamic body acceleration and wing beat frequency) increased with headwind speed during commuting flights. As predicted, birds adjusted their airspeed to compensate for crosswinds and to reduce the effect of a headwind, but they could not completely compensate for the latter. As we were able to account for the effect of sampling frequency and wind speed, we accurately estimated commuting flight speed with no wind as 16.6 ms?1 (murres) and 10.6 ms?1 (kittiwakes). High winds decreased delivery rates of schooling fish (murres), energy (murres) and food (kittiwakes) but did not impact daily energy expenditure or chick growth rates. During high winds, murres switched from feeding their offspring with schooling fish, which required substantial above-water searching, to amphipods, which required less above-water searching.ConclusionsAdults buffered the adverse effect of high winds on chick growth rates by switching to other food sources during windy days or increasing food delivery rates when weather improved.

The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick‐billed murres (Uria lomvia) and black‐legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri‐axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird behavior and ecology.

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