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

Abstract

Background Fine-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). Yet relatively few bio-logging studies have adopted this approach due to an apparent inaccessibility of the complex analytical processes involved. 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 supplementary information as well as online (GitHub). Results The 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, respectively. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The 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.

How Often Should Dead-Reckoned Animal Movement Paths be Corrected for Drift?

“…Species' speci c speed allocation details according to movement-speci c behaviour and/or topological whereabouts, are given in Table . 1. Any required tag orientation offsets (e.g., due to imperfect tag placement along the longitudinal axis of the animal) as well as baseline pressure drift were accounted for by rotation correction of magnetic and acceleration vectors [50,58] and trend estimation with asymmetric least squares [cf. 71], respectively.…”

“…Whilst the accuracy of current ow vectors may be imprecise, their integration (see Gunner et al [50] for method) can improve dead-reckoning estimates substantially (both pre-and post-VP correction; Fig. 8), which is especially important when VPs are scarce.…”

“…The usual method to derive Euler angles is to determine a set vectoral orientation with each orthogonal channel representing a particular body plane (anterior-posterior, medio-lateral and dorsal-ventral) with respect to the earth's frame of reference [41,60,116], and the order of these channels is pivotal for deriving correct estimates of body rotation about the three axes [58] (for equations see; Gunner et al [50]). However, this assumption breaks down for animals (or attached tags) that change orientations frequently at angles greater than perpendicular from their longitudinal and lateral axes of 'normal' posture due to the singularity issues (Gimbal lock) that arise when using the Euler sequence of 3-D vector rotation [117].…”

“…Indeed, studies are increasingly demonstrating the value of motion sensors for resolving continuous and nescale movements in 2-or 3-D space, on/in terrestrial-[e.g., 42,46], marine [e.g., 47, 48] and aerial [e.g., 49] environments -far beyond what would have been obtained using VP systems alone [f. 14,24,46]. Crucially, inertial measurements, being unaffected by factors that modulate VP accuracy, provide an independent and higher resolution comparator for assessing the extent (and type) of movement elicited [50], which can be localised in environmental space when paired with a VP system. Uncorrected deadreckoned paths have been referred to as 'pseudo-tracks' [41,51], because extrapolated travel vectors always incur some error, and being additive [42], even small errors accumulate to have more substantial in uences on path shape (conventionally termed 'drift' [33,46,52]).…”

Decision rules for determining terrestrial movement and the consequences for filtering high-resolution GPS tracks – A case study using the African Lion (Panthera leo)

“…It is notable here that we have focussed on terrestrial movement, and this is primarily because the relationship between DBA and speed can break down substantially for many aquatic and aerial species. This occurs because, for example, birds can glide at a variety of grounds speeds (depending on e.g., wind vectors and glide angle) without changing DBA, and air compression with depth affects the buoyancy of many marine animals, which complicates the DBA~speed relationship depending on swim angle [92][93][94]. Furthermore, GPS is restricted to (potentially infrequent) resurfacing events for diving animals and so scaling DBA with GPS-derived speed is problematic for extended periods of time during underwater movements.…”