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Finding turning-points in ultra-high-resolution animal movement data / Jonathan R. Potts; Luca Börger; D. Michael Scantlebury; Nigel C. Bennett; Abdulaziz Alagaili; Rory P. Wilson
Methods in Ecology and Evolution, Volume: 9, Issue: 10, Pages: 2091 - 2101
Swansea University Author: Borger, Luca
Accepted Manuscript under embargo until: 1st October 2019
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...
|Published in:||Methods in Ecology and Evolution|
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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.
animal movement, biologging, turn angle, big data
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