Data supporting "Airflows redefine the cost of aerial transport in the wild"

These two datasets support the findings of the study "Airflows redefine the cost of aerial transport in the wild".

The aim of this study was to update existing equations defining the cost of transport for flying animals, mainly developed using data extracted from wind tunnels under laboratory settings, with detailed information about flight behaviour in the wild obtained from biologging technology and radar. Estimating the proportion of time flying animals spent in flapping flight, allowed us to redefine how energetic costs vary with body mass and with airflows, and the selective benefits of different forms of aerial locomotion.

These two summary datasets gather flight segments from 50 bird and bat species collected using biologgers and radar. The biologging dataset includes information about >194000 flight segments from 850 individuals and 38 species of birds and bats (sized 250 g to 11 kg) collected using GPS and acceleration (ACC) sensors over 17 years (2007 - 2024). The radar dataset contains information about > 5000 segments from passerines species sized 12.5 - 21.2 g, collected during 4 years (2018-2021).
All segments are associated with:
- morphological information (at the species level, obtained from published dataset),
- flight metrics and behavioural information obtained from the GPS trajectories and radar echoes,
- eCOT effective cost of transport, defined specifically for this study and expressed in joules per kg of body mass and metre travelled,
- environmental information (wind and uplift potentials) obtained from ERA5 hourly reanalyses, interpolated in space and time at the location and later summarised per segment.

These are summary values per segment, but as an additional pdf file in this folder contains a table listing all the raw datasets (available on Movebank or elsewhere) used for this study as well as the ethical permits and the contact information of the researchers to be contacted for data requests.
Upon interest in this dataset as well as in the raw data listed in the table, we strongly encourage contacting the researchers who collected them (who are also listed as authors of this study).
For extensive details about the aim and results of the study and the methodology that brought us to produce the datasets shared in this folder, please refer to the full text and material and methods of the paper.
All the scripts used to produce and analyse these datasets are deposited on Github (https://github.com/mscacco/COT_publ/tree/main).