USING THE FULL SET OF LANDMARKS

3 .2 .1 Ma t e r ia l s a n d m e t h o d s

A sample of 59 individuals from the Arikara Plains Indian population was used to carry out this study. It was chosen for this analysis because it was the largest sample of individuals with a full set of facial landmarks, representing a distinct population and geographical area. The individuals ranged in age from estimated 2.5 years to adulthood. All the 27 facial landmarks described in section 2.2.2i were used in this analysis.

The specimens were superimposed using G PA, and the Procrustes fitted data were subsequently subjected to a PCA.

3.2.2 Re s u l t s

A Procrustes distance matrix was calculated between all the individuals in the analysis (the matrix of 1711 distances is not shown, but used in later comparative analyses). The relationships between the individuals can be summarised using PCA. The proportion of variance described by each of the PCs was as follows: PCI =44.82%; PC2=7.02%; PC3=6.43%. The first 10 PCs explained 80% of the total variance (see table 3.1).

Figure 3.1 show a plot of PCI against PC2 and figures 3.2a and 3.2b representations of the morphological transformations along PCI calculated by warping the mean along this PC, as described in section 2.2.2.4iii. The most notable changes are general proportional changes, rather than discrete localised changes, within the facial skeleton. Thus the face increases in size

relative to the braincase (as represented by the frontal bone; figure 3.2a-1), and the orbits get proportionally smaller (figure 3.2b-2), as the alveolus gets relatively larger. In addition there is a relative widening of the zygomatic region (figure 3.2b-3), a relative expansion in the temporal muscle attachment area (figure 3.2a-4), a relative increased prognathism in the nasal region (figure 3.2a-5), and a relative posterior expansion of the palate and alveolus (figure 3.2a-6). These are changes normally associated with growth of the facial skeleton (e.g. Enlow 1990; Enlow and Hans, 1996). When the scores on PCI are plotted against ages estimated on the basis of dental maturation (figure 3.3a), the relationship between the two is curvilinear. Despite this there is a correlation of 0.894 (p=3.3*10'^\ between the two (table 3.2). No other PC shows any significant linear, or curvilinear, relationship with biological age. Plotting the scores on PCI against facial centroid size (partitioned from the original landmark co-ordinates), as shown in figure 3.3b, reveals a linear relationship with greater correlation of 0.902 (p=4.9*10‘^^), (table 3.2). No other PC shows any significant linear or curvilinear relationship with facial centroid size. PCI can therefore be said to represent allometric growth in that it represents changes in facial proportion related to changes in overall size during growth. The correlation between biological age and facial centroid size is 0.905 (p=2.0*10'^^), (table 3.2), and the relationship is curvilinear (not plotted), indicating that there are changes in growth rate (i.e. rate of increase in size with time), during the course of development. In keeping with the definition of growth at the start of this chapter, and the large and significant correlations between the variables of PCI scores (and no other PC), facial centroid size, and biological age, it can be concluded that PCI represents growth changes in

shape in this population in that it appears to account for the majority if not all the allometric changes with increasing age (no other PC shows any significant correlation with centroid size or age).

Eig e n v a l u e Pr o p o r t io n Cu m u l a t iv e PCI 0.002123938 0.44825647 0.448256474 PC2 0.000332447 0.07016273 0.518419208 PC3 0.000304688 0.06430437 0.582723582 PC4 0.000197668 0 04171768 0.624441265 PCS 0.000186814 0.03942706 0.663868325 PC6 0.000167194 0.03528616 0.699154487 PC7 0.00014758 0.03114676 0.730301251 PCS 0.000129299 0.02728854 0.757589795 PC9 0.000113924 0.02404367 0.781633465 PC1Ü 0.000091588 0.01932969 0.800963158

Table 3.1 A rikara Indians, full set of landm arks.

The table shows the eigenvalues, proportion of variance, and accum ulated proportion of variance fo r PC1-10, which together m ake up 80% o f the variance within the entire data set.

Ce n t r o id SIZE r=0.902 p=4.9*10^^ PCI r=0.894 p=3.3*10'^^ r=0.905 p=2.0*10^^ De n t a l AGE Ce n t r o id SIZE

Table 3.2 Arikara Indians: all facial landm arks

The table shows the pairwise correlations between the variables of PC1 scores, dentally determ ined biological age, and facial centroid size

PC2 explains 7.02% of the total variance within the data set (figure 3.1). The morphological variations (figures 3.2c and 3.2d), represented by this PC (from the negative to the positive extreme in figure 3.1), are an increase in prognathism of the lower face, relative to the frontal and nasal bones (not easily detected in figure 3.2c -1, but noted on the computer monitor when morphing

the mean form between the extremes of PC2), a displacement of the face in an inferior direction relative to the frontal (figure 3.2c-2), and a decreased angle between the palate and the anterior surface of the maxillary alveolus (figure 3.2C-3). Although no attempt has been made at sexing the sub-adult data (with the exception of a few older adolescents), the disposition of the adults along PC2 suggests that this PC may be related to sexual dimorphism, the morphological differences represented by this PC representing the differences observed between Arikara males at the negative extreme and females at the positive extreme. A Student’s t-test of the PC2 scores of the sexed individuals, assuming equal variance between the two groups, revealed a highly statistically significant difference between the adult male and female individuals (t=3.226; p=0.008). No other PCs show a biologically interpretable patterning.

3.2.3 Dis c u s s io n o ft h e s in g l e p o p u l a t io n g r o w t h a n a l y s is

The foregoing analysis shows clearly that PC1 represents allometric changes in facial proportions that take place with increasing size and age. The difference between the plots of PC1 against age and size is purely a function of differences in growth rate at different ages. When PC1 scores are plotted against centroid size their relationship is linear. Furthermore, the scatter of specimens is tight enough to suggest that there seems to be no different growth allometry represented by PC1, between male and female adults. This can not be established with certainty for sub-adults given the fact that the majority of the sub-adult material is unsexed. There is evidence that the males achieve their adult morphology, at least in those aspects described by PC1, by extending the growth allometry (a pattern akin to the intra-specific heterochronic process of

e ■

peratypy, Reilley^ 1997; see also chapter 7), although the limited number of sexed individuals in this study makes it impossible to establish this with any great statistical significance.

In this sample, at least, PC1 represents allometric growth of the human face. If these findings are repeated with other populations, such analyses might be used to establish similarities or differences in allometric processes between populations of modern humans. Excluding these allometric PCs, it would be possible to compare facial morphologies between populations, independent of the growth related shape changes. This possibility will be examined further in chapter 6.

PC2 explains a comparatively small portion of the total variance within the data set (7.02%). However, the morphological differences evident between the extremes of the two components are quite pronounced. There is an indication that these differences are related to sexual dimorphism in this particular population. Although the known male and female individuals have a considerable overlap on PC2, there is a continuous grouping of male individuals from the negative extreme of PC2 towards the middle (PC1 scores -0.03 to 0), and of female individuals from the positive extreme of PC2 to the middle (PC2 scores 0.019 to -0.023). There is also a considerable increase in the spread of scores along PC2 with increasing age, in particular during and after the onset of adolescence. This further supports a hypothesis of a link between PC2 and sexual dimorphism. However, the sexed individuals are too few, and the amount of variance explained by PC2 too low, to draw any concrete conclusions about the biological explanations underlying variation along this PC in this population.

Having established the pattern of variation amongst individuals in this population, as revealed by PCA and a Procrustes distance matrix of the whole 27 landmark data set, the study now examines the stability of this pattern of variation in light of selective exclusion of particularly commonly missing landmarks.

3 .3 Co m m o n l y e x c l u d e d l a n d m a r k s: t h e ir in f l u e n c e o n t h e a n a l y s is o f

GROWTH.

3 .3 .1 In t r o d u c t io n

Given that this study focuses on facial variation amongst modern and fossil Homo, it would seem desirable that as complete a set of landmarks as possible be used for any given analysis. However, the fragmentary nature of much of the available sub-adult skeletal material often makes it necessary to exclude some commonly missing landmarks in order to get as complete an age sampling as possible for each growth series. Examples of such commonly missing landmarks are rhinion, bregma, staphylion, and landmarks on the zygomatic arch.

Rhinion is one of the most commonly missing landmarks in both adult and sub­ adult material. This is due to the ease with which the nasal bones can become detached from the rest of the skull and be lost due to excavating techniques or taphonomic processes. Whereas nasion and maxillofrontale can be relatively easily estimated in the absence of the nasal bones, this is impossible in the case of rhinion, and the considerable intra- and inter-population variation in nasal bone form means that any attempt at doing so would most likely be subject to considerable error. It is therefore of great interest to assess how