Abstract
The Campanian–Maastrichtian Konzentrat-Lagerstätte at Lo Hueco (Spain) has yielded a large sample of appendicular elements referable to sauropod titanosaurs that exhibit considerable morphological variability. The taxonomic assessment of these elements is difficult, they display various degrees of preservation (including fragmentary preservation and taphonomic deformation, each of which were addressed in the methods applied in this study), and also many of the appendicular elements are found as isolated remains. Accurate taxonomic determinations are generally difficult to achieve from the morphological information available on appendicular remains of sauropods, and particularly in groups such as titanosaurirformes. In these cases, the geometric morphometrics (GMM) tool kit is a suitable methodology to explore morphological variability for taxonomic assessment. In this study, several femora, tibiae and fibulae from Lo Hueco were analyzed. To this purpose, the remains were digitized, and the resultant mesh representations were used for definition of 3D landmarks and curves that were analyzed through GMM methods. The quantification of shape variables allowed the use of clustering and K-means techniques as well as a proposed statistical workflow. Several hypotheses of a priori anatomically-defined morphotypes and the results from our machine learning algorithms revealed the presence of two main morphotypes as the most plausible explanation for the morphological variability in the sample of titanosaurian hind limb elements from Lo Hueco.
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