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Understanding the movement and behavior of extinct tetrapods is afundamental aspect of palaeobiology, offering a glimpse into how theseorganisms interacted with their environment and each other. Fossiltrackways provide a dynamic record of locomotor patterns, ecologicalinteractions, and even potential social behavior. However, extractingmeaningful information from these ancient tracks requires robustanalytical tools capable of processing complex datasets.

IntroducingQuAnTeTrack (QuantitativeAnalysis ofTetrapodTrackways), an integrated R package specificallydesigned to facilitate the semi-automated extraction of palaeobiologicalinsights from fossil trackways. This versatile tool allows researchersto seamlessly convert digitized footprint data into analytical objectsand apply a range of statistical and graphical methods to explorelocomotor and ecological hypotheses.

The QuAnTeTrack workflow begins withdata digitization, wherefootprint coordinates are recorded and saved in.TPS files using toolsliketpsUtil andtpsDig. These files are then converted intostructured R objects using thetps_to_track() function, transformingraw coordinates into well-organized datasets that can be easilymanipulated and analyzed.

Once the data is properly structured,exploratory analyses can beconducted to assess fundamental movement parameters. Functions liketrack_param() provide detailed information on turning angles, trackdistances, step lengths, sinuosity, and straightness. Simultaneously,thevelocity_track() function allows users to estimate locomotor speedand relative stride length, providing crucial insights into gait andlocomotor performance. Visualizing these results is made simple throughfunctions likeplot_track() andplot_velocity(), which generatehigh-quality, publication-ready graphs.

Beyond exploratory analysis,QuAnTeTrack offers powerful tools forstatistical testing and hypothesis evaluation. Functions liketest_direction() andtest_velocity() allow users to test fordirectional consistency and velocity differences among tracks, whilemode_velocity() assesses whether trackmakers were accelerating,decelerating, or maintaining steady speed along their paths.

A central aspect of the package is its ability tosimulate tracksunder different movement models (simulate_track()). These models areinformed by geological and environmental constraints, allowingresearchers to evaluate how landscape features or resource availabilitymay have influenced ancient trackmakers’ paths. Theplot_sim()function provides an intuitive way to compare simulated tracks againstthe original dataset.

Once simulated tracks are generated,QuAnTeTrack provides robusttools totest ecological and ethological hypotheses. Trajectorysimilarity can be assessed throughDynamic Time Warping (DTW) andFréchet distance metrics (simil_DTW_metric() andsimil_Frechet_metric()), while potential interactions betweenindividuals can be quantified using thetrack_intersection() function.By comparing these metrics against null models generated fromsimulations, researchers can assess whether trackways display patternssuggestive of coordinated behavior, pursuit, or other ecologicallysignificant interactions.

Additionally,QuAnTeTrack supports combining multiple metrics intocomprehensive tests of hypothesis robustness using thecombined_prob()function. This allows researchers to aggregate the results of similaritymetrics, intersection counts, and other statistics into a single overallmeasure of similarity or interaction significance.

The package also includes functionality tocluster tracks based onmovement parameters (cluster_track()). This tool is particularlyuseful for detecting distinct behavioral modes within a dataset or forgrouping tracks that share similar movement characteristics prior tofurther analysis.

Throughout the workflow,QuAnTeTrack offers flexibility invisualizing, testing, and comparing tracks. The use of R’s powerfulvisualization tools ensures that all results can be effectivelycommunicated and further refined as necessary.

By integrating data processing, statistical testing, simulationmodeling, and visualization into a single, user-friendly package,QuAnTeTrack provides a comprehensive framework for analyzingtetrapod trackways and testing complex ecological and behavioralhypotheses.

To install theQuAnTeTrack package, you can choose betweeninstalling thestable version from CRAN (recommended) or thedevelopment 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 thedevtools package. If you haven’t installeddevtools yet, you can doso with the following command:

install.packages("devtools")

Oncedevtools is installed, you can installQuAnTeTrack using:

devtools::install_github("MacroFunUV/QuAnTeTrack")

If you have already installedQuAnTeTrack and want to ensure youhave the latest version, you can update it with:

devtools::install_github("MacroFunUV/QuAnTeTrack",force=TRUE)

For a detailed description of the package functionalities, includingusage examples and explanations of key functions, a detailed vignette isavailableonline.