Three-dimensional geometric morphometrics of thorax-pelvis covariation and its potential for predicting the thorax morphology: a case study on Kebara 2 Neandertal

Abstract

The skeletal torso is a complex structure of outstanding importance in understanding human body shape evolution, but reconstruction usually entails an element of subjectivity as researchers apply their own anatomical expertise to the process. Among different fossil reconstruction methods, 3D geometric morphometric techniques have been increasingly used in the last decades. Two-block partial least squares analysis has shown great potential for predicting missing elements by exploiting the covariation between two structures (blocks) in a reference sample: one block can be predicted from the other one based on the strength of covariation between blocks. The first aim of this study is to test whether this predictive approach can be used for predicting thorax morphologies from pelvis morphologies within adult Homo sapiens reference samples with known covariation between the thorax and the pelvis. The second aim is to apply this method to Kebara 2 Neandertal (Israel, ∼60 ka) to predict its thorax morphology using two different pelvis reconstructions as predictors. We measured 134 true landmarks, 720 curve semilandmarks, and 160 surface semilandmarks on 60 3D virtual torso models segmented from CT scans. We conducted three two-block partial least squares analyses between the thorax (block 1) and the pelvis (block 2) based on the H. sapiens reference samples after performing generalized Procrustes superimposition on each block separately. Comparisons of these predictions in full shape space by means of Procrustes distances show that the male-only predictive model yields the most reliable predictions within modern humans. In addition, Kebara 2 thorax predictions based on this model concur with the thorax morphology proposed for Neandertals. The method presented here does not aim to replace other techniques, but to rather complement them through quantitative prediction of a virtual ‘scaffold’ to articulate the thoracic fossil elements, thus extending the potential of missing data estimation beyond the methods proposed in previous works.