Quantifying Animal Movement and Space-Use Patterns with Statistical Physics

Provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodríguez et al. (2017) and Sequeira et al. (2018) .

PhysMove

Authors: Hannah J. Calich, Jorge P. Rodríguez, Víctor M. Eguíluz & Ana M. M. Sequeira

Maintained by: Hannah Calich ([email protected])

Overview

PhysMove contains a comprehensive collection of methods for documenting species' movement and space-use patterns from satellite telemetry data. The accompanying vignettes demonstrate how to calculate each of the methods and review all relevant functions and parameters. We demonstrate each function with a simulated telemetry dataset, called tracks, which is automatically loaded with PhysMove (see the Introduction vignette). Please see our corresponding manuscript for further details on our methods and interpreting results. PhysMove focuses on three major categories of movement data analyses, and each category is accompanied by method-specific functions:

1. Characterization of movement patterns, including:

• Scale of movement: rms()
• Movement patterns across temporal scales: calcDisp() and plotDispPDF()
• Search patterns: fitDist(), compDist(), and plotDist()
• Influence of correlations on movement decisions: randomise() and plotRandomTracks()
• Turning angles: turningAngles() and plotAngles()

1. Identification of space-use patterns, including:

• Occupancy patterns: occupancy() and plotPDF()
• Community-wide movements: infomapCommunities() and communityMap()

1. Detection of variability in intraspecific movements, including:

• Track dispersion: gyrationRad() and plotPDF()
• Track entropy: entropy() and plotPDF()
• Track predictability: predictability() and plotPDF()

Installation

# The official version from CRAN:
install.packages("PhysMove")

# Download the development version from GitHub:
install.packages("devtools")
devtools::install_github("HannahCalich/PhysMove", build_vignettes = TRUE, force = TRUE)

Data formatting

PhysMove was designed to be user-friendly and most functions only require you to input a data frame containing standard telemetry data. The input data frame must only contain these four columns in the following order: ref, lon, lat, and day.

Columns must be formatted as follows:

• ref: the unique telemetry tag ID number for each animal in numeric format (note that characters are not accepted because they can be slower to process than integers, so please convert all reference IDs to integers before proceeding)
• lon and lat: the longitude (-180 to + 180) and latitude (-90 to +90) in decimal degrees of each position estimate, respectively, in numeric format, and
• day: the datetime stamp for each location estimate in POSIXct format following '%Y-%m-%d %H:%M:%S'.

You can compare your data frame to our sample dataset tracks to ensure your data are formatted correctly.

Usage

All of the information you need to apply the PhysMove methods can be found in our accompanying manuscript and vignettes, which are available here:

library(PhysMove)

browseVignettes("PhysMove")