Morphological variation of skeletal elements, and the potential use of such variation
in distinguishing among demographic groups, is often investigated using traditional
metric or non-metric assessments. Traditional approaches, however, often fail to
sufficiently capture the ―true‖ shape of features, thus also failing to identify
potentially important feature characteristics. The development of geometric
morphometrics has allowed more comprehensive and accurate three-dimensional data
capture which maintains the geometric properties of an object while isolating the
effect of size from the data.
The aim of this study was to employ the geometric morphometric approach to a 3D
digitized sample of 1132 South African individuals from the skeletal collections of
the Universities of Cape Town, Stellenbosch, Witwatersrand and Pretoria.
Morphological variation among demographic groups was assessed using Generalized
Procrustes Analyses applied to the individual bones of the neurocranium and the long
bones of the limbs. The ability to distinguish groups based on the detected variation
was assessed using Discriminant Function Analysis.