DOI: 10.5061/dryad.jm63xsjkv
title: An information theory framework for movement path segmentation and analysis

description: 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 µ‐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.
description: The methods developed in this manuscript have been demonstrated
 on both empirical and simulated relocation data. The former corresponds to
 an adult female barn owl (<em>Tyto alba</em>) individual,
 which is part of a population tagged at our study site in the Harod Valley
 in northeast Israel. The simulated data has been generated using a
 two‐mode step‐selection kernel simulator called Numerus ANIMOVER_1 (Getz et al.
 (2023)). Data processing has been carried out
 using a series of several machine learning and other algorithms presented
 in Varun Sethi's GitHub repository Hierarchical-path-segmentation-II.
description: # Computational and dataset information
 [https://doi.org/10.5061/dryad.jm63xsjkv](https://doi.org/10.5061/dryad.jm63xsjkv) ## Description of the data and file structure Relocation data from an adult female barn owl (Tyto alba) is obtained using an ATLAS reverse GPS technology system at a relocation frequency of 0.25 Hz. It is available in the file list-DARsAsDataframes_owl.Rds. The simulation data (multi-DAR_sim.csv) has been generated using a two-mode movement simulator Numerus ANIMOVER_1 ([Getz et al. (2023)](https://doi.org/10.1101/2023.12.27.573450)). ### Files and variables #### File: list-DARsAsDataframes\_owl.Rds **Description:** This file corresponds to the adult female barn owl. Data is in the form of a list of dataframes, one for each diel activity routine (DAR; a 24 h period). The dataframes have several variables, the most relevant being the x and y position coordinates (in meters) and 'dateTime' (which represents time) in the 'POSIXct' format of R programming language. #### File: multi-DAR\_sim.csv **Description:** This file has the two-mode relocation data simulated using Numerus ANIMOVER_1 (more details in [Getz et al. (2023)](https://doi.org/10.1101/2023.12.27.573450)). ##### Variables * Day: Represents a "nominal day"; integer. * Delta: Represents time, resets at the turn of each 'Day'; integer. * X: Denotes x coordinate (initially X=0 with left-right continuity identification); numeric. * Y: Denotes y coordinate (initially Y=0 with top-bottom continuity identification); numeric. * Distance: Distance travelled (or speed) between previous and present relocation; numeric. * Theta (Deg.): Heading angle in degrees; numeric. * Resources: Quantifies how rich a resource patch is; numeric. * Kernel: Represents movement modes; character. ## Code/software The codes can be found in Varun Sethi's GitHub repository [Hierarchical-path-segmentation-II](https://github.com/Observarun/Hierarchical-path-segmentation-II). ## Access information Other publicly accessible locations of the data: * The barn owl individual GG41259 used for demonstrating the methods developed here is part of the population sample referenced [here](https://github.com/LudovicaLV/DAR_project). Data was derived from the following sources: * Simulation data is generated from the software described in [Getz et al. (2023)](https://doi.org/10.1101/2023.12.27.573450)