Data from: Flap or soar? How a flight generalist responds to its aerial environment ...

• Main Authors: Van Loon, E. Emiel, Shamoun-Baranes, Judy, Bouten, Willem, Meijer, Christiaan, Camphuysen, C. J.
• Format: Dataset
• Language: English
• Published: Dryad 2017
• Subjects: 2013 and 2014; Larus fuscus; orographic lift; accelerometer; soar; 15 May - 14 June in 2012; 15 May - 14 June 2014; foraging ecology; GPS; thermal convection; 15 May - 14 June 2013; 15 May - 14 June 2012; Lesser black-backed gull
• Online Access: https://dx.doi.org/10.5061/dryad.j6s47; https://datadryad.org/stash/dataset/doi:10.5061/dryad.j6s47

The aerial environment is heterogeneous in space and time and directly influences the costs of animal flight. Volant animals can reduce these costs by using different flight modes, each with their own benefits and constraints. However, the extent to which animals alter their flight modes in response to environmental conditions has rarely been studied in the wild. To provide insight into how a flight generalist can reduce the energetic cost of movement, we studied flight behaviour in relation to the aerial environmental and landscape using hundreds of hours of global positioning system and triaxial acceleration measurements of the lesser black-backed gull (Larus fuscus). Individuals differed largely in the time spent in flight, which increased linearly with the time spent in flight at sea. In general, flapping was used more frequently than more energetically efficient soaring flight. The probability of soaring increased with increasing boundary layer height and time closer to midday, reflecting improved ... : Lesser black-backed gull gps tracks with behaviour and weather dataThis dataset contains gps tracking and accelerometer data of 18 adult lesser black-backed gulls on Texel (the Netherlands) during their breeding season, using UvA-BiTS tags [1]. The gps data is enriched with with behaviour and weather data.The behaviour has been predicted by a classification model using accelerometer data (the approach is detailed in [2]). The weather data is derived from the ERA-Interim dataset [3] by interpolating towards the gps-points. The data fall in the time-window May 15 00:00:00 until June 15 00:00:00 of the years 2012, 2013 and 2014 (i.e. within the breeding season for this population). The data have a resolution of 5 minutes, but data coverage over the time-window is incomplete (the set does not contain a record at every 5-minute interval over the time-window) because at some instances either accelerometer or weather data were not available. The data set comprises a collection of five different file types: 1. gps ...