Fossil body mass estimation is a well established practice within the field of physical anthropology. Previous studies have relied upon traditional allometric approaches, in which the relationship between one/several skeletal dimensions and body mass in a range of modern taxa is used in a predictive capacity. The lack of relatively complete skeletons has thus far limited the potential application of alternative mass estimation techniques, such as volumetric reconstruction, to fossil hominins. Yet across vertebrate paleontology more broadly, novel volumetric approaches are resulting in predicted values for fossil body mass very different to those estimated by traditional allometry. Here we present a new digital reconstruction of Australopithecus afarensis (A.L. 288-1; ‘Lucy’) and a convex hull-based volumetric estimate of body mass. The technique relies upon identifying a predictable relationship between the ‘shrink-wrapped’ volume of the skeleton and known body mass in a range of modern taxa, and subsequent application to an articulated model of the fossil taxa of interest. Our calibration dataset comprises whole body computed tomography (CT) scans of 15 species of modern primate. The resulting predictive model is characterized by a high correlation coefficient (r2 = 0.988) and a percentage standard error of 20%, and performs well when applied to modern individuals of known body mass. Application of the convex hull technique to A. afarensis results in a relatively low body mass estimate of 20.4 kg (95% prediction interval 13.5–30.9 kg). A sensitivity analysis on the articulation of the chest region highlights the sensitivity of our approach to the reconstruction of the trunk, and the incomplete nature of the preserved ribcage may explain the low values for predicted body mass here. We suggest that the heaviest of previous estimates would require the thorax to be expanded to an unlikely extent, yet this can only be properly tested when more complete fossils are available.
Journal of Human Evolution – Elsevier
Published: Feb 1, 2018
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