This blog post is provided by Liam Patrick Langley, Stephen Lang, Luke Ozsanlav-Harris, Alice Trevail and tells the #StoryBehindthePaper for the paper “ExMove: An open source toolkit for processing and exploring animal tracking data in R”, which was recently published in the Journal of Animal Ecology. In their paper, they present an R toolkit for cleaning and processing raw data files from animal tracking devices.

We are in an era where the use of animal tracking devices is skyrocketing. With technological advancements soaring to new heights, we find ourselves amidst a treasure trove of information that could help push forward our understanding of animal ecology. But harnessing this data for meaningful scientific analysis can pose a significant challenge, especially for the growing number of students across the globe whose research is underpinned by animal tracking data.

The challenge:

Picture this: a myriad of tracking devices, deployed from a multitude of locations, and a mountain of data. As researchers, we’re inundated with raw files that demand meticulous cleaning and processing before their secrets can be revealed. Yet, the tools to streamline this crucial data processing phase have been notably scarce, leaving many stumbling through a maze of data with little guidance. That’s where our toolkit comes into play.

Our solution: https://exmove.github.io/

We have written an open-access, reproducible toolkit, written in the programming language R. The aim? To transform raw data files into cleaned datasets primed for analysis and online archiving, whilst helping users to understand the code along the way. (For those who prefer to steer clear of code, fret not! User-friendly platforms, such as MoveApps https://www.moveapps.org/, might better suit your needs). Unlike existing tools that focus solely on post-cleaning analyses, our toolkit tackles the often-neglected pre-processing steps head-on. We believe that transparency starts at the source, which is why we’ve prioritized user understanding every step of the way. With well-documented code and a user-friendly website to interact with the toolkit.

At its core, this toolkit serves as a vital training ground for the next generation of scientists. By mastering the basic steps of tracking data processing, budding researchers can pave the way for future discoveries in the field of animal ecology.

How we do it:

Our toolkit is a flexible pipeline capable of handling data from an array of tracking devices, from GPS to Argos tags. By embracing modern coding practices and relying on well-maintained R packages like tidyverse and sf, we’re ensuring stability and accessibility for our users.

So, what exactly can you expect from our toolkit? Here’s a look at the steps in a simplified workflow:

• Step 1: Streamlined data import. Easily merge data from multiple devices and animals into a cohesive dataset.
• Step 2: Metadata management. Advice on how to curate metadata for each device and individual.
• Steps 3/4: Quality control. Utilise tag deployment information to ensure only data from when an animal is exhibiting normal behaviour post-tagging is retained for analysis. Then weed out erroneous data points, duplicates, and no-data values from the tag.
• Step 5: Derive metrics. Calculate essential metrics like speed, displacement, and turning angles, with guidance on the use of coordinate reference systems.
• Step 6: Visualization and summary tools: Visualize and summarize your data through tables, maps and interactive apps, allowing for easy exploration and validation. We focus on checking the quality of data and that no erroneous data has slipped through the cracks.
• Further steps: post-processing. Prepare your data for in-depth analysis, including subsampling and segmentation techniques and defining foraging trips for central place foragers like breeding seabirds.

Looking to the future:

Our goal is to foster a community of confident, code-savvy researchers equipped to tackle the challenges of animal movement analysis. Through these open-access resources and a commitment to learning, we hope to contribute to a more open and reproducible future for the field of movement ecology.

We also believe that there’s always room for improvement. Whether you have suggestions for new features or spot areas for refinement, we welcome your input with open arms. Get in touch with us via our GitHub page https://github.com/ExMove/ExMove or reach out to the corresponding author—we’d love to hear from you!

In conclusion, our toolkit isn’t just about processing data—it’s about empowering researchers to unlock the full potential of animal tracking data and drive forward our understanding of the natural world.

Read the paper

Read the full paper here: Langley, L. P., Lang, S. D. J., Ozsanlav-Harris, L., & Trevail, A. M. (2024). ExMove: An
open-source toolkit for processing and exploring animal-tracking data in R. Journal of Animal Ecology, 00, 1–12. https://doi.org/10.1111/1365-2656.14111