ExMove: An open‐source toolkit for processing and exploring animal‐tracking data in R

Abstract

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal‐tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre‐processing steps are often time‐consuming and involve a steep learning curve but are crucial for the creation of high‐quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital. Here we present an open‐access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well‐documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning. The toolkit is generalisable to different data formats and device types, uses modern ‘tidy coding’ practices, and relies on a few well‐maintained packages. Among these, we perform spatial manipulations using the package sf. Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology.