Making sense of ultrahigh‐resolution movement data: A new algorithm for inferring sites of interest

Abstract

Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data’s resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm’s speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of “high confidence” paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point analysis techniques such as optimal foraging theory. It is encoded into an package called , so should serve as a valuable tool for making sense of these increasingly large data streams.

Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data

“…The point x cp is defined to be the centre of the single site of interest found using the method of Munden et al. (2019) for each path, where a site of interest is an area where an animal spends a disproportionately long time. Here, the single site of interest encompasses part of a goat pen and the area outside this goat pen where salt‐licks are placed.…”

Big-data approaches lead to an increased understanding of the ecology of animal movement

“…In this pattern-driven process, model specification, calibration, and validation steps are all implemented dynamically and iteratively during the model runtime, thus enabling "learning on the go" (89). Overall, the increased availability of highthroughput data will continue to motivate the uptake, refinement, and development of novel methods for both data processing and analysis (3,84,86,87,92).…”

Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data

“…The point x cp is defined to be the centre of the single site of interest found using the method of Munden et al (2019) for each path, where a site of interest is an area where an animal spends a disproportionately long time. Here, the single site of interest encompasses part of a goat pen and the area outside this goat pen where salt-licks are placed.…”