Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state-space modelling) were developed with lower-resolution data in mind. To make such methods usable with high-resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning-points in high-resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight-line segments interspersed with turns. We also demonstrate our algorithm on data of free-ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break-point inference. The algorithm scales linearly and can analyse several hundred-thousand data points in a few seconds on a mid-range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of ±8∘, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high-resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step-and-turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. As such, it provides an important breakthrough required as a starting point for analysing subsecond resolution data.
Bibliographical noteFunding Information:
National Plan for Science, Technology and Innovation, Grant/Award Number: 11-ENV1918-02; Deanship of Scientific Research at the King Saud University; National Environmental Research Council (NERC), Grant/Award Number: NE/
The data-gathering part of this project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (11-ENV1918-02) and the Deanship of Scientific Research at the King Saud University through Vice Deanship of Research Chairs. Ethical clearance for the data-gathering was obtained from the University of the Witwatersrand Animal Ethics Committee (clearance certificate number 2014/53/D). Permission to work in the field was granted by the President of the Saudi Wildlife Authority. J.R.P. acknowledges support from the National Environmental Research Council (NERC) grant NE/ R001669/1. The authors thank two anonymous reviewers and an associate editor for comments that have helped improve the manuscript.
© 2018 The Authors. Methods in Ecology and Evolution © 2018 British Ecological Society
Copyright 2018 Elsevier B.V., All rights reserved.
- animal movement
- change point
- high-resolution data
- movement ecology
- turning point
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Ecological Modelling