PT  - JOURNAL ARTICLE
AU  - Guillaume Péron
AU  - Justin M. Calabrese
AU  - Olivier Duriez
AU  - Christen H. Fleming
AU  - Ruth García-Jiménez
AU  - Alison Johnston
AU  - Sergio Lambertucci
AU  - Kamran Safi
AU  - Emily L.C. Shepard
TI  - The challenges of estimating the distribution of flight heights from telemetry or altimetry data
AID  - 10.1101/751271
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 751271
4099  - http://biorxiv.org/content/early/2019/09/01/751271.short
4100  - http://biorxiv.org/content/early/2019/09/01/751271.full
AB  - Background Global positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their niche that we need to know to understand their ecology and conservation needs, and to manage our own use of the airspace. However, there are various sources of error in flight height data (“height” above ground, as opposed to “altitude” above a reference like the sea level): vertical error from the devices themselves, error in the ground elevation below the tracked animals, and error in the horizontal position of the animals and thus the predicted ground elevation below them.Methods We used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management.Results Errors of a magnitude of 20 meters appear in benign conditions (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative recorded flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences.Conclusion The state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to raw flight height records, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.hflight height above ground;zflight altitude (relative to the same reference as the DEM, e.g., the ellipsoid);DEMdigital elevation model;UEREuser equivalent range error;DOPdilution of precision;SDstandard deviation.