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Individual variation in feeding performance and kinematics in the canary

Cite this dataset

Andries, Tim; Müller, Wendt; Van Wassenbergh, Sam (2023). Individual variation in feeding performance and kinematics in the canary [Dataset]. Dryad.


  1. In granivorous songbirds, feeding is a complex process as seeds need to be dehusked before they can be consumed, making the feeding act a biomechanically challenging endeavour. However, most previous research has focused on how beak morphology affects feeding performance, while the influences of beak kinematics remain largely unknown.
  2. In this study, we hence investigated at the individual level how feeding performance (i.e. seed processing time and success rate) relates to both beak kinematics (i.e. beak tip speed, acceleration, frequency) and skill (i.e. seed handling tactics and cracking techniques) in the Canary (Serinus canaria). To do so, high-speed videos during feeding were recorded and subjected to automated tracking of beak tip movements.
  3. Better skills, i.e. accurate positioning of the seed for being split in half, reduced total seed handling time compared to more random positioning and crushing the husk into multiple, scattering fragments. Surprisingly, individual variation in beak speed, acceleration, or frequency generally did not relate to differences in performance.
  4. Thus, our data suggests that seed positioning precision, and hence the control of coordinated beak and tongue movement, is critical to minimize feeding durations in songbirds. Further studies are needed to explore whether this develops via a positive feedback between behaviour, learning and increased efficiency or if it relates to intrinsic differences.


  • Feeding performance (phase durations and success rate) and skill (cracking mode and head position) data were manually extracted from video data. Raw video data can be accessed upon request by contacting the corresponding author (Tim Andries)
  • Kinematic data were calculated in Microsoft Excel from 3D-coordinate data obtained through use of DeepLabCut automated pose estimation software on the video data.
  • All data were further analysed using R statistical software (version 4.2.1).

Usage notes

According to the guidelines of the respective journal, all data files are submitted in .csv format. We still advise opening the data in Microsoft Excel or similar programs.


University of Antwerp, Award: DOCPRO4-TTZAPBOF FFB210025