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Black-tailed Gull GPS foraging trip data and acceleration raw data

Cite this dataset

Ma, Rui et al. (2022). Black-tailed Gull GPS foraging trip data and acceleration raw data [Dataset]. Dryad.


Areas at which seabirds forage intensively can be discriminated by tracking the individuals’ at-sea movements. However, such tracking data may not accurately reflect the birds’ exact foraging locations. In addition to tracking data, gathering information on the dynamic body acceleration of individual birds may refine inferences on their foraging activity. Our aim was to classify the foraging behaviors of surface-feeding seabirds using data on their body acceleration and use this signal to discriminate areas where they forage intensively. Accordingly, we recorded the foraging movements and body acceleration data from seven and ten black-tailed gulls (Larus crassirostris) in 2017 and 2018, respectively, using GPS loggers and accelerometers. By referring to video footage of flying and foraging individuals, we were able to classify flying (flapping flight, gliding, and hovering), foraging (surface plunging, hop plunging, and swimming), and maintenance (drifting, preening, etc.) behaviors using the speed, body angle, and cycle and amplitude of body acceleration of the birds. Foraging areas determined from acceleration data corresponded roughly with sections of low speed and area-restricted searching (ARS) identified from the GPS tracks. However, this study suggests that the occurrence of foraging behaviors may be overestimated based on low-speed trip sections, because birds may exhibit long periods of reduced movement devoted to maintenance. Opposite, the ARS-based approach may underestimate foraging behaviors since birds can forage without conducting an ARS. Therefore, our results show that the combined use of accelerometers and GPS tracking helps to adequately determine the important foraging areas of black-tailed gulls. Our approach may contribute to better discriminate ecologically or biologically significant areas in marine environments.


The dataset was collected using GPS data logger (GipSy5, TechnoSmart, Italy) and accelerometer (Axy, TechnoSmart Italy). Only the trip period of  GPS data were included.

Usage notes

The files need to be opened using R or Igor pro.


Environmental Restoration and Conservation Agency