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Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks
Animal Biotelemetry, Volume: 9, Issue: 1
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BackgroundFine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolu...
|Published in:||Animal Biotelemetry|
Springer Science and Business Media LLC
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BackgroundFine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub).ResultsThe Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed.ConclusionsThe function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.
Animal behaviour; Animal movement; Global Positioning System; R (programming language); Track integration; Tri-axial accelerometers; Tri-axial magnetometers
College of Science
This research contributes to the CAASE project funded by King Abdullah University of Science and Technology (KAUST) under the KAUST Sensor Initiative. Fieldwork in the Kgalagadi Transfrontier Park was supported in part by a Department for Economy Global Challenges Research Fund grant to MS.
Fieldwork within the Chubut Province was supported in part by the National Agency for Scientifc and Technological Promotion of Argentina (PICT 20171996 and PICT 2018-1480), and the Grants-in-Aid for Scientifc Research from the Japan Society for the Promotion of Science (16K18617).