Tortuosity entropy: A measure of spatial complexity of behavioral changes in animal movement

Abstract

The goal of animal movement analysis is to understand how organisms explore and exploit complex and varying environments. Animals usually exhibit varied and complicated movements, from apparently deterministic behaviours to highly random behaviours. It has been a common method to assess movement efficiency and foraging strategies by means of quantifying and analyzing movement trajectories. Here we introduce a tortuosity entropy (TorEn), a simple measure for quantifying the behavioral change in animal movement data. In our approach, the differences between pairwise successive track points are transformed into symbolic sequences, then we map these symbols into a group of pattern vectors and calculate the information entropy of pattern vectors. We test the algorithm on both simulated trajectories and real trajectories to show that it can accurately identify not only the mixed segments in simulated data, but also the different phases in real movement data. Tortuosity entropy can be easily applied to arbitrary real-world data, whether deterministic or stochastic, stationary or non-stationary. It could be a promising tool to reveal behavioral mechanism in movement data.