Understanding the movement and behavior of extinct tetrapods is a fundamental aspect of palaeobiology, offering a glimpse into how these organisms interacted with their environment and each other. Fossil trackways provide a dynamic record of locomotor patterns, ecological interactions, and even potential social behavior. However, extracting meaningful information from these ancient tracks requires robust analytical tools capable of processing complex datasets.
Introducing QuAnTeTrack (Quantitative Analysis of Tetrapod Trackways), an integrated R package specifically designed to facilitate the semi-automated extraction of palaeobiological insights from fossil trackways. This versatile tool allows researchers to seamlessly convert digitized footprint data into analytical objects and apply a range of statistical and graphical methods to explore locomotor and ecological hypotheses.
The QuAnTeTrack workflow begins with data digitization, where footprint coordinates are recorded and saved in .TPS files using tools like tpsUtil and tpsDig. These files are then converted into structured R objects using the tps_to_track() function, transforming raw coordinates into well-organized datasets that can be easily manipulated and analyzed.
Once the data is properly structured, exploratory analyses can be conducted to assess fundamental movement parameters. Functions like track_param() provide detailed information on turning angles, track distances, step lengths, sinuosity, and straightness. Simultaneously, the velocity_track() function allows users to estimate locomotor speed and relative stride length, providing crucial insights into gait and locomotor performance. Visualizing these results is made simple through functions like plot_track() and plot_velocity(), which generate high-quality, publication-ready graphs.
Beyond exploratory analysis, QuAnTeTrack offers powerful tools for statistical testing and hypothesis evaluation. Functions like test_direction() and test_velocity() allow users to test for directional consistency and velocity differences among tracks, while mode_velocity() assesses whether trackmakers were accelerating, decelerating, or maintaining steady speed along their paths.
A central aspect of the package is its ability to simulate tracks under different movement models (simulate_track()). These models are informed by geological and environmental constraints, allowing researchers to evaluate how landscape features or resource availability may have influenced ancient trackmakers’ paths. The plot_sim() function provides an intuitive way to compare simulated tracks against the original dataset.
Once simulated tracks are generated, QuAnTeTrack provides robust tools to test ecological and ethological hypotheses. Trajectory similarity can be assessed through Dynamic Time Warping (DTW) and Fréchet distance metrics (simil_DTW_metric() and simil_Frechet_metric()), while potential interactions between individuals can be quantified using the track_intersection() function. By comparing these metrics against null models generated from simulations, researchers can assess whether trackways display patterns suggestive of coordinated behavior, pursuit, or other ecologically significant interactions.
Additionally, QuAnTeTrack supports combining multiple metrics into comprehensive tests of hypothesis robustness using the combined_prob() function. This allows researchers to aggregate the results of similarity metrics, intersection counts, and other statistics into a single overall measure of similarity or interaction significance.
The package also includes functionality to cluster tracks based on movement parameters (cluster_track()). This tool is particularly useful for detecting distinct behavioral modes within a dataset or for grouping tracks that share similar movement characteristics prior to further analysis.
Throughout the workflow, QuAnTeTrack offers flexibility in visualizing, testing, and comparing tracks. The use of R’s powerful visualization tools ensures that all results can be effectively communicated and further refined as necessary.
By integrating data processing, statistical testing, simulation modeling, and visualization into a single, user-friendly package, QuAnTeTrack provides a comprehensive framework for analyzing tetrapod trackways and testing complex ecological and behavioral hypotheses.
Installation
To install the QuAnTeTrack package, you can choose between installing the stable version from CRAN (recommended) or the development version from GitHub.
From CRAN (recommended)
To install the stable version from CRAN, use:
install.packages("QuAnTeTrack")From GitHub (development version)
If you want the latest development version, you will need to use the devtools package. If you haven’t installed devtools yet, you can do so with the following command:
install.packages("devtools")Once devtools is installed, you can install QuAnTeTrack using:
devtools::install_github("MacroFunUV/QuAnTeTrack")If you have already installed QuAnTeTrack and want to ensure you have the latest version, you can update it with:
devtools::install_github("MacroFunUV/QuAnTeTrack", force = TRUE)Usage Details & Functionality
For a detailed description of the package functionalities, including usage examples and explanations of key functions, a detailed vignette is available online.