Quantifying the behavior of motile, free‐ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study‐specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse‐scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.

How animals allocate their time to different behaviours has important consequences for their overall energy budget and reflects how they function in their environment. This potentially affects their ability to successfully reproduce, thereby impacting their fitness. We used accelerometers to record time‐activity budgets of 21 incubating and chick‐rearing kittiwakes (Rissa tridactyla) on Puffin Island, UK. These budgets were examined on a per day and per foraging trip basis. We applied activity‐specific estimates of energy expenditure to the kittiwakes' time‐activity budgets in order to identify the costs of variation in their allocation of time to different behaviours. Estimates of daily energy expenditure for incubating kittiwakes averaged 494 ± 20 kJ d−1 while chick‐rearing birds averaged 559 ± 11 kJ d−1. Time‐activity budgets highlighted that kittiwakes did not spend a large proportion of their time flying during longer foraging trips, or during any given 24‐h period. With time spent flying highlighted as the driving factor behind elevated energy budgets, this suggests behavioural compensation resulting in a possible energetic ceiling to their activities. We also identified that kittiwakes were highly variable in the proportion of time they spent either flying or on the water during foraging trips. Such variation meant that using forage trip duration alone to predict energy expenditure gave a mean error of 19% when compared to estimates incorporating the proportion of a foraging trip spent flying. We have therefore highlighted that trip duration alone is not an accurate indicator of energy expenditure.

We understand little about the energetic costs of flight in free-ranging birds; in part since current techniques for estimating flight energetics in the wild are limited. Accelerometry is known to estimate energy expenditure through body movement in terrestrial animals, once calibrated using a treadmill with chamber respirometry. The flight equivalent, a wind tunnel with mask respirometry, is particularly difficult to instigate, and has not been applied to calibrate accelerometry. We take the first steps in exploring a novel method for calibrating accelerometers with flight energy expenditure. We collected accelerometry data for Harris's Hawks Parabuteo unicinctus flying to varying heights up to 4.1 m over a small horizontal distance; the mechanical energy expended to gain height can be estimated from physical first principles. The relationship Accepted ArticleThis article is protected by copyright. All rights reserved. between accelerometry and mechanical energy expenditure was strong, and while a simple wing flapping model confirmed that accelerometry is sensitive to both changes in wing beat amplitude and frequency, the relationship was explained predominately by changes in wing beat frequency, and less so by changes in amplitude. Our study provides initial, positive evidence that accelerometry can be calibrated with body power using climbing flights, potentially providing a basis for estimating flapping flight metabolic rate at least in situations of altitude gain.Keywords: Harris Hawk, dynamic body acceleration, energetics, wing beat frequency, wing beat amplitude Volant birds can travel further and faster than animals employing other modes of locomotion.

We investigated spatio-temporal distribution patterns of the Critically Endangered Balearic shearwater Puffinus mauretanicus in the northern part of its migratory range, using a combination of effort-corrected land-and boat-based survey data (2007−2010). The species was recorded regularly along the western English Channel (Western Channel) coasts of northwest France and the southwest UK, with peak counts occurring during the summer and autumn months. Foraging aggregations comprising hundreds to thousands of birds (~1 to 20% of the global population) were recorded in the large shallow embayments of northern Brittany in all survey years. Elsewhere, most birds were recorded on passage, with maximum birds-per-hour (BPH) of 169 off northwest France and 36 off the southwest UK. Few birds were recorded offshore, beyond sight of land. A distance-from-shore analysis revealed that the species passed closer to shore than other pelagic seabirds such as sooty shearwater Puffinus griseus. A constant-effort seasonal survey from the southwest tip of the UK mainland recorded the species on 93% of survey days, with BPH rates peaking in the morning between 08:00 and 11:00 h. These results have important monitoring and conservation implications for this Critically Endangered species. In particular, the records of large aggregations in spatially restricted areas of the Western Channel during the inter-breeding period suggests the species could be vulnerable to impacts such as oil spills, or disturbance from offshore construction projects. We also provide evidence that some birds remain in the survey area during the breeding season, suggesting it may be an important site for non-breeding birds.

Movement is a necessary yet energetically expensive process for motile animals. Yet how individuals modify their behaviour to take advantage of environmental conditions and hence optimise energetic costs during movement remains poorly understood. This is especially true for animals that move through environments where they cannot easily be observed. We examined the behaviour during commuting flights of black‐legged kittiwakes Rissa tridactyla breeding on Middleton Island, Alaska in relation to wind conditions they face. By simultaneously deploying GPS and accelerometer devices on incubating birds we were able to quantify the timing, destination, course and speed of flights during commutes to foraging patches, as well as how wing beat frequency and strength relate to flight speeds. We found that kittiwakes did not preferentially fly in certain wind conditions. However, once in the air they exhibited plasticity through modulation of effort by increasing air speed (the speed at which they fly relative to the wind) when travelling into headwinds and decreasing their air speed when flying with tailwinds. Moreover, we identified a biomechanical link behind this behaviour: that to achieve these changes in flight speeds, kittiwakes altered their wing beat strength, but not wing beat frequency. Using this information, we demonstrate that the cost of flying into a headwind outweighs the energy saving benefit of flying with a tailwind of equivalent speed; therefore, exploiting a tailwind when commuting to a foraging patch would not be beneficial if having to return in the same direction with the same conditions. Our findings suggest that extrinsic factors, such as prey availability, have a more influential role in determining when and where kittiwakes fly during foraging trips than do wind conditions. However, once flying, kittiwakes exhibit behavioural plasticity to minimise transport costs.