aniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Abstract

Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. State–space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error‐prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time‐consuming to fit to diverse animal tracking data sets. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: (1) a simple but extensible workflow that accommodates both novice and experienced users; (2) automated processes that alleviate complexity from data processing and model specification/fitting steps; (3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. We highlight aniMotum's capabilities through three applications to real animal tracking data. Full R code for these and additional applications is included as Supporting Information, so users can gain a deeper understanding of how to use aniMotum for their own analyses.

The dynamic interaction between predator and prey drives mesopredator movement and foraging ecology

“…Further, ARGOS data are prone to error (54). We applied a state-space model (55), a class of model that has been shown to improve the location quality (56). However, newer tags (i.e., Fastloc-GPS, ( 56)) can provide finer-resolution and more accurate temporal and spatial data that could investigate smaller-scale patterns in movement and diving.…”

“…ARGOS locations are prone to error (54,55), thus we used a state-space model to predict locations that account for this error and used these predicted locations in all subsequent analyses. Specifically, we used the aniMotum R package (formerly foieGras; (55)) to fit a continuous-time correlated random walk state-space model that used irregularly sampled locations with error to produce predicted location data at a 2-hr interval. Before applying the model, we split tracks into multiple smaller segments when transmission halted for >12 hrs.…”

Uncovering loggerhead ( Caretta caretta ) navigation strategy in the open ocean through the consideration of their diving behaviour

“…Since PSY4 provides daily averages of the current velocity fields centred at t mid = 12.00 UTC, hereinafter referred to as Vc^, the ground displacement from which V g is derived, had to be estimated over daily time steps. To do this, Argos locations are interpolated at 00.00 and 12.00 UTC using the aniMotum R package [35] . From there, the daily ground velocity Vg^ was estimated at x ( t mid ) from the distance travelled between positions x ( t mid − 12 h) and x ( t mid + 12 h).…”

Comparing acoustic and satellite telemetry: an analysis quantifying the space use of Chelonia mydas in Bimini, Bahamas

“…COA positions were used in the dBBMMs for the acoustic telemetry ODs. For the satellite data, a continuous-time correlated random walk was fit within a state-space model (SSM) using the aniMotum package in R [79] to account for location error of the raw data at the observed time interval. This model handles irregular sampling frequencies well and uses semi-major and -minor axis lengths, as well as ellipse orientation of Kalman filtered errors, to estimate 'true' locations quickly and reliably [80].…”