General Information

This website contains materials from a workshop on Movebank, Env-DATA, Resource and Step-Selection Models held at the NC Museum of Natural Sciences, Raleigh, North Carolina, USA on May 21-23, 2018. The purpose of the workshop is to provide hands on training and methods for analyzing data describing the movements of individual animals for movement ecology research and wildlife management. The workshop focuses on tools available through Movebank (, a free, online resource for managing, sharing, analyzing and archiving animal tracking data. The EnvDATA System ( is a tool on Movebank to allow anyone to link animal movements to environmental covariates provided by global remote sensing and weather model data products. The workshop was funded in part by US National Science Foundation Biological Infrastructure award 1564380.

Repository prepared by:

Additional Instructors:

Teaching Assistant/Lab tech: Matthew Snider, North Carolina State University

The scripts make heavy use of the amt package. You can download a pre-print describing the package here:

Preparation Steps

Steps to prepare for the workshop (or to run files on your own):

The scripts provided are based on Movebank’s data format and access and read data directly from Movebank. Data owners can upload and manage data on Movebank in private or public studies and retain ownership of and control over access to their data. See for instructions to set up your own study or contact for help.

  1. Download latest versions of RStudio and R from here:
  2. Download all the R Scripts contained in the zip file here: R Scripts for Workshop.

  3. Unzip these files and make note of where you have saved them on your computer.

  4. Open Rstudio and create a project associated with the directory where you stored all the R scripts. Choose File -> New Project, then select “Existing directory”, and find the directory on your computer where you stored all of the R scripts.

  5. Install the following packages (with dependencies): amt, ggmap, knitr, htmltools, leaflet, lubridate, maptools, move, raster, rglwidget, sp, tictoc, tidyverse (ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, and forcats). Note: you can install packages along with dependencies by typing: install.packages(“packagename”, dependencies=TRUE)

  6. Create a subdirectory called “data” within your project folder (the folder where you stored the scripts). Download the annotated data sets, below, and place them in your data subdirectory.

These data sets contain environmental covariates that will be merged onto the location data sets (using the mergdat.R script).

  1. Work through the Videos and Rscripts and contact us if you have questions!


Please cite the use of material presented here as:

Fieberg J, Bohrer G, Davidson SC, Kays R (2018) Short course on analyzing animal tracking data. Presented at the North Carolina Museum of Natural Sciences, Raleigh, NC, USA. May 21–23, 2018.