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Estimating fine-scale changes in turbulence using the movements of a flapping flier

Citation

Lempidakis, Emmanouil et al. (2022), Estimating fine-scale changes in turbulence using the movements of a flapping flier, Dryad, Dataset, https://doi.org/10.5061/dryad.w9ghx3fsb

Abstract

All animals that operate within the atmospheric boundary layer need to respond to aerial turbulence. Yet little is known about how flying animals do this because evaluating turbulence at fine scales (tens to ~ 300 m) is exceedingly difficult. Recently, data from animal-borne sensors have been used to assess wind and updraft strength, providing a new possibility for sensing the physical environment. We tested whether highly-resolved changes in altitude and body acceleration measured onboard solo-flying pigeons (as model flapping fliers) can be used as qualitative proxies for turbulence. A range of pressure and acceleration proxies performed well when tested against independent turbulence measurements from a tri-axial anemometer mounted onboard an ultralight flying the same route, with stronger turbulence causing increasing vertical displacement. The best proxy for turbulence also varied with estimates of both convective velocity and wind shear. The approximately- linear relationship between most proxies and turbulence levels suggests this approach should be widely applicable, providing insight into how turbulence changes in space and time. Furthermore, pigeons were able to fly in levels of turbulence that were unsafe for the ultralight, paving the way for the study of how freestream turbulence affects the costs and kinematics of animal flight.

Methods

For information on data collection and processing please see the methods section and the supplementary information of the manuscript:

"Estimating fine-scale changes in turbulence using the movements of a flapping flier"

Because of the large number and diversity of attibutes used across datasets, a specific summary of attributes and description ("Attribute Summary.txt")

is included in each folder and subfolder for each dataset. For dataset and code organisation, please see the main "README.docx" file first.

Funding

H2020 European Research Council, Award: 715874