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.
To install the QuAnTeTrack package, you can choose between installing the stable version from CRAN (recommended) or the development version from GitHub.
To install the stable version from CRAN, use:
install.packages("QuAnTeTrack")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)For a detailed description of the package functionalities, including usage examples and explanations of key functions, a detailed vignette is available online.