Analysis of Sexual Dimorphism of Craniofacial Traits Using Geometric Morphometric Techniques

GONZALEZ P; BERNAL V.; PEREZ S. I.

International Journal of Osteoarchaeology.

Willey

Año: 2011 vol. 21 p. 82 - 91

<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman";} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> This work deals with the assessment of cranial sexual dimorphism in human skeletal samples applying geometric morphometric techniques. The purpose of this research is to apply such techniques to quantitatively describe in craniofacial traits the degree and pattern of shape and size sexual dimorphism. Likewise, we evaluate the precision and accuracy of semilandmark-based techniques for sex estimation. We employ a sample of 125 adult skulls of known sex from the Coimbra collection. A set of coordinate points was selected to describe glabella, mastoid, frontal and zygomatic processes. The results of intra-class correlation coefficient (ICC) show excellent intra- and inter-observer agreement (ICC>0.96) in the location of the coordinates of points employed. The principal component analysis (PCA) performed on shape variables shows a large superposition of both sexes, suggesting a relatively low degree of dimorphism in shape. As a consequence, the average percentages of correct sex estimations based on these variables were of 60.12 and 68.90%,obtained by discriminant analysis with leave-one-out cross validation and k-means clustering respectively. Conversely, when centroid size is included in PCA, females and males exhibit large separation along the first component. The highest values of correct assignment (77.86 and 72.15%) were found using shape?size variables with discriminant and k-means clustering analysis, indicating that the traits analysed display marked sex differences related to the larger size and more robust features of males. Finally, the advantages of geometric morphometric techniques are discussed.