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Non-metric Cranial and Pelvic Traits as a Measure of Sexual Dimorphism in a Modern South African Population

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Non-metric Cranial and Pelvic Traits as a Measure of Sexual
Dimorphism in a Modern South African Population
Kyra E. Stull1, Michael W. Kenyhercz2, and Ericka L’Abbe1
1 Department of Anatomy, University of Pretoria, Private Bag x323, 0007, Arcadia, South Africa
2 Department of Anthropology, University of Alaska, Fairbanks
INTRODUCTION
Even though the overall size and shape of
the South African crania is less dimorphic than
other populations, morphological cranial traits
have moderate to high classication accuracies
when population specic formulae are applied
(Kruger et al, 2013; Messer, 2013; L’Abbé et al,
2013). Researchers expect the pelvis to exhibit
the largest levels of sexual dimorphism in the
human body and similarly, previous studies have
demonstrated that evaluation of morphological
pelvic traits result in high classication accuracies
in South African populations (Kenyhercz, 2012).
However, none of the previous studies have
looked at corresponding pelves and crania to
evaluate variable importance in terms of the
degree of sexual dimorphism. The current study
investigated the variable trends and classication
accuracies when morphological variables of the
cranium and pelvis were evaluated from the
same individuals.
MATERIALS AND METHODS
The sample consists of 112 individuals from the
University of Pretoria Bone Collection. Individuals
represent modern South Africans from black,
coloured, and white population groups (B=98,
C=1, W=13). The Walker (2008) morphological
traits were used to score the crania (Figure 1)
and the Klales et al. (2012) method, based on
the Phenice (1969) traits, was used to score the
pelves (Figure 2). The mental eminence showed
no signicant relationship to sex; therefore, the
trait was removed from further analyses.
Statistical analyses included discriminant
function analysis (DFA), exible discriminant
analysis (FDA), logistic regression (LG),
classication trees (CT), and random forest
models (RFM). Each analysis was completed
with the innominate variables, the cranial
variables, and then a combination of both
innominate and cranial. All statistical analyses
were completed in R Statistical Software (R
version 2.15.2 (2012-10-26).
Table 1. Frequency distribution of cranial trait scores for females and males.
Glabella 1 2 3 4 5
F72.4 17.2 6.9 0.0 3.5
M38.9 30.6 8.3 11.1 11.1
Mastoid
F69.0 17.2 10.3 3.5 0.0
M22.2 11.1 33.3 22.2 11.1
Mental
F35.0 50.0 15.0 0.0 0.0
M34.6 26.9 15.4 19.2 3.6
Nuchal
F37.9 48.3 6.9 3.5 3.5
M22.2 19.4 16.7 25 16.7
Orbit
F44.8 31.0 20.7 3.5 0.0
M8.3 13.9 16.7 30.6 30.6
Figure 1. Cranial traits and their associated scores (taken from Buikstra and
Ubelaker 1994 and used by Walker 2008)
Figure 2. Pubic bone traits and their associated scores (taken from Klales et al.
2012)
Table 2. Frequency distribution of pelvic trait scores for females and males.
SPC 1 2 3 4 5
F9.1 70.5 20.5 0.0 0.0
M0.0 4.9 29.5 50.8 13.1
VA
F29.6 43.2 22.7 4.6 0.0
M0.0 1.6 9.8 44.3 44.3
MIPR
F38.6 40.9 20.5 0.0 0.0
M0.0 8.2 45.9 37.7 8.2
Table 3. Percent correct (%) classications by statistical method for cranial traits
Method Male Female Combined Sex Bias
DFA 77 84 80.5 7
FDA 86 79 82.5 7
LG 87.7 86.1 86.9 1.6
CT 75 92.9 84 17.9
RFM 79 77.2 78.1 1.8
Table 4. Percent correct (%) classications by statistical method for pelvic traits.
Method Male Female Combined Sex Bias
DFA 97 96 96.5 1
FDA 97 96 96.5 1
LG 98.4 100 99.2 1.6
CT 97.5 86.8 92.2 10.8
RFM 95 97 96 2
Table 5. Percent correct (%) classications by statistical method for all traits.
Method Male Female Combined Sex Bias
DFA 100 94 97 6
FDA 98 100 99 2
LG 100 100 100 0
CT 97.5 86.8 92.2 10.7
RFM 95 97 96 2
Results
Males demonstrated low cranial scores (3 and
below) in all traits except orbit (Table 1)
Males demonstrated high pelvic scores (3 and
above) and females demonstrated low pelvic
scores (< 3) (Table 2)
Total correct classications range from 80.5-
100% (Tables 3, 4, and 5)
Greatest sex bias favored females in cranial
traits (17.9)
Logistic regression consistently performed best
Combined trait accuracies showed the highest
classications
Discussion & Conclusion
Pelvic traits demonstrated greater sexual
dimorphism than cranial traits in the modern South
African sample under study, as demonstrated
by the trait frequency distributions and overall
correct classications
Use of pelvic traits only resulted in greatest
correct classications and least sex bias
Sexually dimorphic expressions are reduced
in the cranial traits of South Africans. Similarly,
craniometric analyses also show lower rates of
sexual dimorphism when compared to worldwide
populations (L’Abbé et al. 2013; Messer and
Ousley, 2013). Differential genetic backgrounds
or assortative mating patterns may inuence
the reduced pattern expression
*For complete list of references email: mwkenyhercz@alaska.edu
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