The challenges of estimating the distribution of flight heights from telemetry or altimetry data

Abstract

Background Global positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their ecological niche, that we need to know to manage our own use of the airspace, and to protect those species. 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). First the altitude is measured with a vertical error from the devices themselves. Then there is error in the ground elevation below the tracked animals, which translates into error in flight height computed as the difference between altitude and ground elevation. Finally, there is error in the horizontal position of the animals, which translates into error in the predicted ground elevation below the animals. 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 m appear in benign conditions for barometric altimeters and GPS vertical positioning (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 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 the output of GPS devices, 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.

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

“…129] and variable latency [cf. 130]) can vary by a few metres or be appreciably more depending upon the propagation of signal quality and/or receiver reception capability[35,131,132]. As such, VP error becomes more relevant at smaller scales of assessed movement and this is the reason why VP distance-moved estimates can go from being typically underestimated at low frequencies (due to linear interpolation of tortuous movements)[23,133,134] to overestimated at high frequencies[91,129] and result in highly variable correction factors within the VPC dead-reckoning process [cf.…”

Influence of individual biological traits on GPS fix-loss errors in wild bird tracking

“…Battery power limits the time for the transmitter to search satellites to obtain a location and www.nature.com/scientificreports/ so influence in the number of satellites acquired for the process 1 , however it is a parameter only available in the newer GPS models. In this respect, the logger generation, transmitters' manufacturers, and data receiving system-all of the three uniform parameters for our study case given that all the 17 devices were Microwave solar-powered Argos-GPS bought between 2005 and 2008-are also important technical variables that need to be considered when studying GPS accuracy and location errors 25,29,30 . On the other hand, our results show that individual flight activity could be one of the most influential factors determining the fix performance of a device.…”

“…All of these changing elements can influence the data collected and lead to errors in interpreting patterns of movement. Fortunately, these kinds of tracking error, together with accuracy biases in the horizontal plane (x and y coordinates) are being addressed and overcome as transmitter technology improves, thus reducing the potential influence of tracking device shortcomings on the recording and interpretation of basic parameters regarding the spatial ecology of a species 26 , 29 , 30 , 37 , 38 .…”

Physical limits of flight performance in the heaviest soaring bird

“…The GPS logger was programmed to sample once per minute. We analyzed ASL, rather than altitude above ground level (AGL), due to the higher recording frequency of barometric pressure compared with GPS data and increased error in calculating AGL (35). Tags were housed together in a unit representing ∼1% of bird body mass.…”