An Information Theory Framework for Movement Path Segmentation and Analysis

Abstract

Improved animal tracking technologies provide opportunities for novel segmentation of movement tracks/paths into behavioral activity modes (BAMs) critical to understanding the ecology of individuals and the functioning of ecosystems. Current BAM segmentation includes biological change point analyses and hidden Markov models. Here we use an elemental approach to segmenting tracks into {micro}-step-long "base segments" and m-base-segment-long "words." These are respectively clustered into n statistical movement elements (StaMEs) and k "raw" canonical activity modes (CAMs). Once the words are coded using m extracted StaME symbols, those encoded by the same string of symbols, after a rectification processes has been implemented to minimize misassigned words, are identified with particular "rectified" CAM types. The percent of reassignment errors, along with information theory measures, are used to compare the efficiencies of coding both simulated and empirical barn owl data for a selection of parameter values and approaches to clustering.