QUANTIFYING SEXUAL DIMOPRHISM in the ADULT HUMAN CRANIUM Dissertation Presented in Partial Fulfillment of the Requirements for T

Home , Brow ridge, Sexual dimorphism

QUANTIFYING SEXUAL DIMOPRHISM IN THE ADULT HUMAN CRANIUM

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

in the Graduate School of The Ohio State University

Ana M. Casado

Graduate Program in Anthropology

The Ohio State University

2017

Dissertation Committee:

Dr. Paul W. Sciulli, Advisor

Dr. Samuel D. Stout, Co-Advisor

Dr. Douglas Crews

Ana M. Casado

2017

ABSTRACT

Biological anthropologists estimate sex from skeletal remains for forensic identification purposes or to reconstruct demographic profiles of past populations. Several features of the human cranium exhibit observable differences between males and females, known as sexual dimorphism. These sex-based differences are due to size disparities in muscle attachment sites as well as hormonal variations

(Russell, 1985; Bass, 2005). A problem facing biological anthropologists is that of secular trends, or changes in physical traits over time; in the human skeleton. Most skeletal secular changes have been noted within the last 50 years (Jantz and Jantz, 1999,

2000). The most apparent secular trend is the increase in long bone length, and the consequent increase in height (Jantz and Jantz, 1999). Additionally, the human cranium has gotten taller and narrower, which can affect accurate sex estimation (Jantz et al.,

2012). This research examines areas of the human skull that differ between the sexes and explores whether secular changes have affected sexually dimorphic areas of the cranium.

The sample consists of 716 adult human crania of European European and

African American ancestries from three skeletal collections: the Hamann-Todd

Collection at the Cleveland Museum of Natural History, the Documented Skeletal

Collection from the University of New Mexico’s Maxwell Museum, and the William M.

Bass Donated Skeletal Collection housed at the University of Tennessee. The crania come from four defined generations, spanning birth years from 1864 to 1992. The

ii individuals in the present sample were specifically chosen to fall between the ages of 20 and 60 to eliminate those who would not reflect pubertal changes and those who might show the most exaggeratedly robust cranial features in older age. To increase measurement accuracy, crania exhibiting fragmentation, trauma, or pathological conditions were not included.

This dissertation includes skeletal samples from European American and African

American individuals, and excludes crania of Hispanic, Asian, or any other ancestral affinity. The sample is limited to two groups due to underrepresentation of other populations in American skeletal collections.

The six areas of the cranium analyzed here were specifically selected for their contribution to overall cranial robustness. These areas include: the external occipital protuberance, the nuchal area (or protuberances), the glabella, the mastoid processes, the supraorbital ridge(s) and frontal bossing. The novel measurement method proposed here quantitatively measures each trait (in millimeters), and considers the outward projection of each trait instead of the traditional method, which is to visually assess each feature and score its size and appearance on a scale of 1-5. To measure the cranial features, coordinate calipers were used, which provide an inexpensive and highly accessible alternative for three-dimensional skeletal data collection.

Measurements were analyzed using a variety of statistical tests, including the measure of sexual dimorphism in a population, known as the calculation of D (after

Bennett, 1981, and Chakraborty and Majumder, 1982). In addition, summary statistics, t- tests, analyses of variance, and discriminant function analyses were undertaken to test the hypotheses proposed in this dissertation. Results show that 12 features across the

iii generations and ancestral groups exhibit sexual dimorphism (defined here as a D value of

> 0.5). The sexual dimorphism associated with certain features, such as the supraorbital ridge, increased over the four generations, while the dimorphism associated with others stayed the same. No features experienced a decrease in sexual dimorphism over the four generations.

From the discriminant function on the premodern dataset (Generations 1 and 2 combined), 77.8% of crania were correctly classified as males or females, a number which is roughly equivalent to the classification accuracy reported in most studies using traditional sex estimation methods. Only 51.1% of crania were correctly classified from the modern dataset (Generations 3 and 4 combined), and this may be because of secular trends affecting these areas of the cranium. Additionally, other discussions concerning changes in specific traits over time is included. Another two discriminant analyses were performed using the three traits exhibiting the highest levels of sexual dimorphism; classification accuracies were much higher for both premodern and modern datasets, and higher for premodern than the modern group of crania.

This research is crucial for biological anthropologists and anatomists interested in skeletal change and human identification. Clearly, crania have changed over time, and the extent of those changes is somewhat quantified here, though more research is needed. As the modern human population changes, anthropologists must continue to improve their standards. This study will improve our understanding of cranial secular trends and will allow for more accurate skeletal identification.

ACKNOWLEDGMENTS

I wish to thank those who allowed me to access the skeletal collections used for this dissertation: Lyman Jellema, of the Cleveland Museum of Natural History, Dawnie

Wolfe Steadman of the University of Tennessee’s Bass Collection, and Heather Edgar, of the Maxwell Museum at the University of New Mexico. I would also like to thank the members of my committee for advice and comments along the way, especially statistical advice. Special thanks to Meng Li and Stacy Porter for statistical guidance.

Samantha Blatt, thank you for the beautiful drawings to supplement my method descriptions. Thanks to James Roberts, for supporting me and being willing to put up with the odd and hectic schedule of a graduate student. Y para Ben: Mi vida ha sido por tí.

VITA

May 2003 ...... Farragut High School

2007...... B.A. Anthropology, University of Tennessee

2010...... M.A. Anthropology, The Ohio State

University

Publications

Casado AM. 2017. Quantifying Sexual Dimorphism in the Human Cranium: A Novel

Method. Journal of Forensic Sciences

Fields of Study

Major Field: Anthropology

Table of Contents ABSTRACT ...... ii ACKNOWLEDGMENTS ...... v VITA...... vi List of Tables ...... ix List of Figures ...... xi CHAPTER 1 – INTRODUCTION ...... 1 Background: Literature Review and Previous Research ...... 1 Sexual Dimorphism: An Overview ...... 1 Causes for sexual dimorphism ...... 2 Proximate Causes, or Extrinsic Factors for Sexual Dimorphism ...... 3 Ultimate Causes, or Intrinsic Factors for Sexual Dimorphism ...... 5 Measuring Sexual Dimorphism ...... 7 Sexual Dimorphism in the Human Skeleton ...... 8 Sexual dimorphism in the human pelvis...... 9 Sexual dimorphism in the long bones ...... 13 Sexual dimorphism in the human cranium ...... 13 Sexual Dimorphism Across Populations...... 15 Biological Ancestry and the Human Cranium ...... 17 Sexual Dimorphism and Age-At-Death ...... 19 Environmental Effects on the Human Cranium ...... 21 Climate ...... 21 Nutrition ...... 23 Mastication ...... 24 Growth and Development of the Human Cranium ...... 25 Cranial growth ...... 26 Cranial development ...... 27 The cranial base ...... 29 The cranial vault ...... 31 The face ...... 32 The role of growth and development in determining shape and size ...... 33 The role of growth and development on sexually dimorphic features of the cranium ...... 35 Secular Trends ...... 38 Secular trends in the human cranium ...... 39 Motivation for the present study ...... 41 Research Questions and Hypotheses ...... 44

CHAPTER 2: MATERIALS ...... 48 vii Skeletal Collections ...... 48 The Hamann-Todd Collection ...... 48 The Maxwell Documented Collection ...... 50 The Bass Collection ...... 50 Representativeness of the collections ...... 51 Ancestral representation ...... 52 Generations and sample sizes ...... 52 Age at death of skeletons used in this study ...... 53 Measurement tools ...... 54 CHAPTER 3: METHODS ...... 55 Cranial Measurements ...... 55 Instruments for Data Collection ...... 61 Measurement Instructions ...... 62 Statistical Analyses and Rationale ...... 70 Summary statistics and the calculation of D values ...... 70 T-tests ...... 74 Analysis of Variance (ANOVA)...... 75 Discriminant Function Analysis (DFA) ...... 76 CHAPTER 4: RESULTS ...... 78 D values ...... 78 T-test Results ...... 79 DISCUSSION AND CONCLUSION ...... 119 Hypotheses ...... 119 Future Research ...... 127 Conclusions ...... 128 BIBLIOGRAPHY ...... 130 APPENDIX A………………………………………………………………………….142

APPENDIX B…………………………………………………………...……………..156

viii

List of Tables

Table 1. The Phenice criteria for sex estimation. Adapted from Phenice, 1969…………10

Table 2. Sex differences in pelvic morphology. Adapted from Krogman and İşcan, 1986………………………………………………………………………………………12

Table 3. Cranial features traditionally used in sex estimation, along with their typical male and female presentation on the human skull. (Buikstra and Ubelaker, 1994; Bass, 2005; Komar and Buikstra, 2008)………………………………………………………..15

Table 4. Summary table of males, females, European Americans, and African Americans included in this study………………………………………………………………….....52

Table 5. Cranial features often used in sex estimation and the proposed methods for their measurement……………………………………………………………………………..56

Table 6. Traits expressing the highest levels of sexual dimorphism (> 0.5)………….....79

Table 7. Results from paired t-tests including p-values and means of the differences.....80

Table 8. Color key for g-plots. Colors correspond to traits in g-plots. Color names generated by R…………………………………………………………………………...83

Table 9. Summary of interactions for Trait 4 (Supraorbital ridge -- right)……………..111

Table 10. Summary of interactions for Trait 5 (Supraorbital ridge -- left)……………..111

Table 11. Summary of interactions for Trait 6 (Glabella)……………………………...112

Table 12. Summary of interactions for right mastoid width……………………………113

Table 13. Summary of interactions for the right mastoid projection…………………...113

Table 14. Summary of interactions for the left mastoid length………………………...114

Table 15. Summary of interactions for Trait 15 (Mastoid width - left)………………...114

Table 16. Summary of interactions for Trait 18 (Frontal bossing – left)……………….115

Table 17. Summary of interactions for Trait 19 (Frontal bossing -- center)……….…..115

Table 18. Classification accuracy from the Discriminant Function Analysis for Generations 1 and 2 combined…………………………………………………………116

Table 19. Classification accuracy from the DFA using only external occipital protuberance, central nuchal protuberance, and the left mastoid length………………..117

Table 20. Classification accuracy from the DFA using only external occipital protuberance, central nuchal protuberance, and the left mastoid length………………..117

Table 21. Classification accuracy from the DFA using only external occipital protuberance, central nuchal protuberance, and the left mastoid length………………..118

List of Figures

Figure 1. Hypothetical distributions of male and female traits, where D represents the non-shaded areas under the curves, or area of non-overlap. From Chakraborty and Majumder, 1982…………………………………………………………………………..8

Figure 2. Anterior view of the human skull. Boxes indicate cranial features measured (sor = supraorbital ridge; fb = frontal bossing; g = glabella). Black dots indicate cranial landmarks used in measuring (ft = frontotemporale; fmt = frontomalare temporale)…..58

Figure 3. Lateral view of human skull. Box indicates the cranial feature measured (mastoid process). Dots indicate the landmarks used for measuring……………....……59

Figure 4. Posterior view of human skull. Box indicates the cranial feature(s) measured (external occipital protuberance). Dots indicate cranial landmarks used in measuring (l = lambda; o = opisthion)………………………………………………………………...... 60

Figure 5. Box indicates the cranial feature(s) measured (nuchal protuberances/region). Dots indicate cranial landmarks used in measuring (i = inion; o = opisthion)……...... …61

Figure 6. A researcher uses coordinate calipers to measure the outward projection of the left mastoid process………………………………………………………………………62

Figure 7. A researcher measures halfway between ft and fmt to find the newly devised cranial landmark, Point A………………………………………………………………..63

Figure 8. A researcher uses coordinate calipers to measure the outward projection of the frontal boss……………………………………………………………………………….64

Figure 9. A researcher uses coordinate calipers to measure the outward projection of the exernal occipital protuberance…………………………………………………………...65 Figure 10. A researcher uses coordinate calipers to measure the outward projection of the right side of the nuchal region…………………………………………………………...66

Figure 11. A researcher uses coordinate calipers to measure the outward projection of the center of the nuchal region……………………...... ……………………………………..67

Figure 12. A researcher uses coordinate calipers to measure the outward projection of the left mastoid process…………………………………………………………………...….68

Figure 13. A researcher uses coordinate calipers to measure the outward projection of the frontal boss……………………………………………………………………………….69

Figure 14. Function for D1………………………………………………………………73

Figure 15. Function for D2. ……………………………………………………………..74

Figure 16. Change in sexual dimorphism in European Americans over Generations 1-4. Color key in Table 9……………………………………………………………………..82

Figure 17. Change in D values for African Americans over Generations 1-4. Color key in Table 9……………………………………………………………………………….…..84

Figure 18. Change in D value for Trait 1 (right supraorbital ridge) in Europeans over Generations 1-4……………………………………………………………………….....85

Figure 19. Change in D value for Trait 2 (left supraorbital ridge) in Europeans over Generations 1-4………………………………………………………………………….86

Figure 20. Change in D value for Trait 3 (glabella) in Europeans over Generations 1- 4...... 87

Figure 21. Change in D value for Trait 4 in European-Americacns from Generations 1- 4…………………………………………………………………………….…………....88

Figure 22. Change in sexual dimorphism for Trait 7 in European-Americans over Generations 1-4………………………………………………………………………….89

Figure 23. Change in sexual dimorphism for Trait 8 in European-Americans over Generations 1-4………………………………………………………………………….90

Figure 24. Change in sexual dimorphism for Trait 9 in European-Americans over Generations 1-4………………………………………………………………………….91

Figure 25. Change in sexual dimorphism for Trait 11 in European-Americans over Generations 1-4………………………………………………………………………….92

Figure 26. Change in sexual dimorphism for Trait 12 in European-Americans over Generations 1-4………………………………………………………………………….93

Figure 27. Change in sexual dimorphism for central frontal bossing in European- Americans over Generations 1-4…………………………………………………….….97

xii Figure 28. Change in sexual dimorphism for the right supraorbital ridge in African- Americans over Generations 1-4………………………………………………………..95

Figure 29. Change in sexual dimorphism for the left supraorbital ridge in African- Americans over Generations 1-4…………………………………………………….….96

Figure 30. Change in sexual dimorphism for the glabella in African-Americans over Generations 1-4………………………………………………………………...…….….97

Figure 31. Change in sexual dimorphism for the external occipital protuberance in African-Americans over Generations 1-4……………………….……...... ….98

Figure 32. Change in sexual dimorphism for the central nuchal protuberance in African- Americans over Generations 1-4………………………………………………….....….99

Figure 33. Change in sexual dimorphism for the right mastoid length in African- Americans over Generations 1-4………………………………………………..…...…100

Figure 34. Change in sexual dimorphism for the left mastoid length in African-Americans over Generations 1-4…………………………………………...... …101

Figure 35. Change in sexual dimorphism for the left mastoid width in African-Americans over Generations 1-4………………………………………………..…...... …102

Figure 36. Change in sexual dimorphism for the left mastoid projection in African- Americans over Generations 1-4…………………………………………………….…103

Figure 37. Change in sexual dimorphism for the right frontal boss in African-Americans over Generations 1-4………………………………………………..………………..…104

Figure 38. Change in sexual dimorphism for the left frontal boss in African-Americans over Generations 1-4………………………………………………………………....…105

Figure 39. Change in sexual dimorphism for the central frontal boss in African- Americans over Generations 1-4………………………………………………….....…106

Figure 40. Interaction plot showing relationship between male and female means from both ancestral groups for the right supraorbital ridge…………………………………..107

Figure 41. Interaction plot showing relationship between male and female means from both ancestral groups for the central nuchal protuberance/region……………………...108

Figure 42. Interaction plot showing relationship between male and female means from both ancestral groups for the left supraorbital ridge…………………………….……..109

xiii Figure 43. Interaction plot showing relationship between male and female means from both ancestral groups for Trait 12 (mastoid width -- right)………………….………..110

Figure 44. Interaction plot showing relationship between male and female means from both ancestral groups for Trait 12 (mastoid width -- right)……………………………112

xiv

CHAPTER 1 – INTRODUCTION

Background: Literature Review and Previous Research

Sexual Dimorphism: An Overview

Sexual dimorphism refers to observable differences in size, shape, color, behavior, or other factors between males and females of a species (Frayer and Wolpoff,

1985). Common examples of sexual dimorphism in non-human animals include the peacock’s colorful tail (in comparison to the peahen’s less remarkable tail) and the size disparity between male and female gorillas (Larsen, 2014). In nonhuman primates, there exists much variation in degrees of sexual dimorphism; species that are monogamous exhibit less sexual dimorphism than species that are polygynous, presumably because of male-male competition for access to females and greater maternal investment in offspring, which leads to the “choosy female” phenomenon (Trivers, 1972; Leutenegger,

1982). Variation in the sexual dimorphism of non-human primates includes differences in body size and weight, canine size, color, and muscular development (Leutenegger,

1982).

In modern humans, sexual dimorphism is less pronounced at around 12-15%

(Bogin, 1999). Primary sexual characteristics in humans are the genitalia, and are first observable in utero, as hormone signals cause differentiation early in fetal development.

Secondary sexual characteristics become evident during puberty, which varies among

1 mammals, but is relatively predictable among humans (though males and females experience puberty at different times and phases) (Moore, 2013).

Other physical differences exhibited between males and females include stature, muscle size, fat deposits, growth rates, and hormone levels (Moore, 2013). As it pertains to bone, humans express some sexual dimorphism in certain bony elements, most notably in the pelvis because of its direct relation to parturition; however, sexual dimorphism may also be noted in the skull, long bones, canines, and other areas (discussed further below)

(European, et al., 2012; Moore, 2013). Skeletal sexual dimorphism is not especially marked in humans, however, and there exists considerable overlap between males and females (Frayer and Wolpoff, 1985). These disparities are often visible and measurable, and certain populations exhibit a greater degree of sexual dimorphism than others. Some groups express a high level of sexual dimorphism, where mean male stature is much higher than mean female stature (Frayer and Wolpoff, 1985) or male crania are significantly larger than female crania, as is seen in historic African American population

(Kimmerle, et al., 2008).

Causes for sexual dimorphism

There are several schools of thought surrounding sexual dimorphism and its varying levels across animal species. Some cite male-male competition for females (Van

Gerven and Armelagos, 1980; Hamilton, 1982), while others suggest males’ overall larger size as an indicator that they serve as “protectors” of other individuals in the group, or as primary hunters and thus rely on large body sizes to hunt effectively (Hamilton,

1982; Brace 1973). Other researchers state that sexual dimorphism may be influenced by

2 a variety of factors, including hormone regulation, genetic and epigenetic factors, and other various aspects of the social and environmental context (Stinson, 1985; Stinson,

2012), though the primary cause is debated (Stinson, 1985). Environmental factors that can affect sexual dimorphism in a population might be climate, temperature, malnutrition, and socioeconomic status, among others (Stinson, 1985). These will be discussed in greater detail below.

Proximate Causes, or Extrinsic Factors for Sexual Dimorphism In an attempt to explain human sexual dimorphism, Frayer and Wolpoff (1985) propose two models, the proximate and the ultimate causation models. The proximate causation mode, more recently referred to in terms of extrinsic factors that affect sexual dimorphism (Stinson, 2012; Moore, 2013), attributes sex-based morphological differences to diet and nutrition. Examples of environmental, or extrinsic, factors may include nutrition, activity levels (because of biomechanical forces acting upon the skeleton), and body mass (Moore, 2013). For example, human populations that enjoy good nutrition, with foods high in protein and fats, can experience earlier appearances of puberty, which includes sexually dimorphic skeletal features and an earlier age of menarche (Moore, 2013). This extrinsic influence on the body and skeleton can cause changes to the level of sexual dimorphism on the population-level. Frayer and Wolpoff assert culture can override genetics and refer to various studies in which males and females became more sexually dimorphic as their level of nutrition improves. This is probably because when nutritionally deprived, males are more affected than females, as

“females…prove to be more stable under the same food deficits, presumably because of

3 reproductive demands, storage of more subcutaneous fat, and overall smaller body size”

(Frayer and Wolpoff, 1985:431). Gray and Wolfe (1980) substantiate this, stating males will achieve their growth potential in situations of high nutrition. Nutritional stress may have a disproportionate effect on males and females, causing male adult mean statures to decline, which would subsequently cause sexual dimorphism to decrease in a population.

Some scholars point to the sexual division of labor and sexual selection as extrinsic reasons for sex-based morphological differences. In many human societies, duties are often divided, most commonly into food acquisition and childcare roles.

According to this notion, males would need to be larger and more robust to be successful hunters, and sexual dimorphism would thus increase as males and females exploit differing food procurement strategies (Holden and Mace, 1999). Males may also be larger and more robust if females are regularly selecting these traits in mating partners

(Hamilton, 1982; Blanckenhorn, 2005).Sexual selection, as this phenomenon is termed, was first described by Charles Darwin (1871), and relies on several tenets. First, there should be some parameters in place socially, such as an unequal access to females by males (Frayer and Wolpoff, 1985). This is often called the “choosy female” phenomenon.

Human females spend more energy and time gestating, nursing, and feeding their young than do males; this prevents them from contributing as much genetic material to subsequent generations as males are theoretically able to. Females are then considered the

“choosy” sex, as increased maternal responsibilities cause females to be more particular about mating partners (Trivers, 1972; Hamilton, 1982). The choosy female phenomenon can lead to male-male competition, dominance hierarchies, and polygynous mating systems. Male-male competition for females can also lead to an increase in male

4 aggression, larger body size, and larger canine size. In culturally complex human and non-human primate societies, male-male competition can often be psychological or involve cultural adaptations, such as the formation of alliances (Hamilton, 1982). The lack of physical competition in these situations may explain why Homo sapiens display relatively little sexual size dimorphism.

Mating systems can also be the cause of differing sizes between males and females (Blanckenhorn, 2005). In polygynous non-human primate species, for example, where the social strategy is typically a group with one alpha male and many females, one can expect much larger male body size than in females (Leutenegger and Cheverud,

1982). In contrast, monogamous nonhuman primate species such as gibbons and marmosets, display little to no sexual dimorphism (Leutenegger and Cheverud, 1982), though monogamy is a rare mating strategy for mammals in general (Frayer and Wolpoff,

1985).

Ultimate Causes, or Intrinsic Factors for Sexual Dimorphism In contrast to the proximate causation model, the ultimate causation model for sexual dimorphism relies on genetics to account for a society’s level of sexual dimorphism. This model states that selection forces acting on underlying genetic adaptations control sexual dimorphism, and nutrition has very little impact (Frayer and

Wolpoff, 1985). Examples of intrinsic factors affecting sexual dimorphism include internal body processes, such as hormone activity.

Hormonal differences between the sexes, initiated in utero, can greatly affect sexual dimorphism in human groups. All fetuses begin developing as females until the Y

5 chromosome activates the SRY (sex-determining region on the Y chromosome, also called testis-determining factor [TDF]) protein to assist in the development of testes in a male fetus (Sadler, 2006; Crespi, 2008; Ammerpohl, et al., 2013). Later skeletal differences between the sexes are formed by androgen production that is stimulated by fetal development, the fetal environment, and genetic factors (Sadler, 2006).

Most sexual dimorphism does not appear in the skeleton until puberty, as high hormone levels are initiated during puberty. Puberty also signals the final growth spurt in humans (Moore, 2013). With the onset of puberty, increased secretion of gonadotropin- releasing hormone (GnRH) and follicle stimulating hormone (FSH) begin to affect growth and development (Bogin, 1999). The cause of the rising levels of the above hormones is not known, though some have attributed it to the involvement of the hormone melatonin (Bogin, 1999). Growth hormone (GH) can also affect the expression of skeletal robustness, as a 1993 study confirmed that more GH administered to mice was positively correlated with larger muscle attachment sites on the mandible, occipital, and zygomatic bones (Vogl et al., 1993).

In general, sexual dimorphism in the skeleton is due to the later onset of puberty in males. Females reach puberty earlier than males, and finish the pubertal spurt more quickly. Males take longer to finish their development, and therefore end up larger than their female counterparts (Iuliano-Burns, et al., 2009). It is important to remember that sexual dimorphism is a complicated phenomenon, with multiple interacting mechanisms.

Its cause is therefore likely due to a combination of genetic and environmental factors, or intrinsic and extrinsic factors, respectively Stinson (2012).

6 Measuring Sexual Dimorphism

Sexual dimorphism can be measured in a variety of ways. Researchers interested in reconstructions of Hominin lifeways will typically have fewer elements available to them and are thus more likely to rely on dental evidence; anthropologists who study living humans and non-human primates often consider body weight in sexual dimorphism comparisons; and bioarchaeologists and forensic researchers are more likely to use skeletal remains (Hall, 1982). Within the human skeleton, there are several regions considered to be highly sexually dimorphic; these include the cranium, the pelvis, and some long bone dimensions. Other anthropologists have even analyzed the robustness of muscle attachment sites on long bones (Hamilton, 1975).

Statistically, researchers have taken several avenues in their attempts to quantify sexual dimorphism. Many have calculated sexual dimorphism as a percent score which could include defining the female mean as a percentage of the male mean or publishing the mean of males and females combined (Hamilton, 1982; Godde, 2015). In 1981,

Bennett published a method for quantifying sexual dimorphism in a population by calculating the area under two normally distributed curves, representative of male and female phenotypic variation in a given trait. A year later, Chakraborty and Majumder

(1982) critiqued Bennett’s method for failing to acknowledge the considerable overlap between the sexes that is prevalent in nearly every population, as well as failing to acknowledge the fact that males and female distributions often express differing variances, both of which render Bennett’s measure ill-advised for use on most human skeletal series.

In their revision to Bennett’s method, Chakraborty and Majumder instead propose

7 the area of non-overlap under the two curves (for male and female) to be representative of the sexual dimorphism of a population (Figure 1). This revision to Bennett’s measure relies on finding the mean and standard deviation of both male and female groups to then calculate the D statistic, or the area of non-overlap, represented in Figure 1 with the non- shaded area (Chakraborty and Majumder, 1982).

Figure 1. Hypothetical distributions of male and female traits, where D represents the non-shaded areas under the curves, or area of non-overlap. From Chakraborty and Majumder, 1982.

This dissertation uses the D statistic to calculate the level of sexual dimorphism in male and female European Europeans and African Americans from United States skeletal collections. For more information on statistical analyses undertaken in this study, see

Chapter 3.

Sexual Dimorphism in the Human Skeleton

Many areas of the skeleton have been assessed for sexual dimorphism, including studies of the talus (Abd-elaleem et al., 2012; Barrett et al., 2001; Harris and Case, 2012), 8 calcaneus (Steele, 1976; Introna et al., 1997; Bidmos and Asala, 2004; Gualdi-Russo,

2007), nasal aperture (Schlager and Rudell, 2015), cranial air passages (Bastir, et al.,

2011), foramen magnum (Gapert et al., 2009), clavicle (Alcina et al., 2015; Shirley,

2009), scapula (Scholtz et al., 2010; Papaioannou et al., 2012; Frutos, 2002), radius (Mall et al., 2001) ulna (Barrier and L’Abbé, 2008; Purkait, 2001), ribs (İşcan and Loth, 1986), vertebrae (Marino, 1995), metacarpals (Falsetti, 1995), metatarsals (Robling and

Ubelaker, 1997), long bone circumference (African, 1978; İşcan and Miller-Shaivitz,

1984; Safont et al., 2000), humeral head diameter (Frutos, 2005), distal humerus (Rogers,

1999), proximal femur (Albanese, 2008), distal femur (Mahfouz et al., 2007a), and patella (Kemkes-Grottenthaler, 2005; Mahfouz et al., 2007b). Though authors may sometimes report acceptable accuracies using these methods (DiGangi and Moore, 2013), the best indicators of sex continue to be the pelvis, cranium, and long bones (Bass, 2005;

DiGangi and Moore, 2013). It is, however, crucial to develop and test new methods for human identification from skeletal material, as forensic and archaeological remains can be quite fragmentary (Komar and Buikstra, 2008), and the combination of traits is sometimes more useful in estimating sex than is the use of just one bone or feature

(Bigoni et al., 2010). Therefore, anthropology can benefit from multiple identification methods for greater accuracy and precision.

Sexual dimorphism in the human pelvis

Sex estimation of the pelvis is based on either metric or morphological methods.

Metric methods include vertical measurement of the auricular surface, width of the sciatic notch (Novak, et al., 2012) and measurement of the ischio-pubic proportion, wherein the

9 length of the iliopubic bone is longer than the ischiopubic bone in females and the opposite in males (İşcan and Steyn, 2013). Nonmetric, or morphological, methods for estimating sex rely on visually assessed traits, such as relative size or shape of a feature.

These include the widely used and trusted Phenice (1969) method and traits outlined in

Krogman and İşcan’s (1986) volume.

Since its 1969 publication, the Phenice method has become a well-known and widely used method for visual estimation of sex. It has been tested many times and produces low error rates, especially among remains of European European ancestry

(Kelley, 1978; Lovell, 1989; Sutherland and Suchey, 1991; Ubelaker and Volk, 2002).

These sex indicators are found only in the pubic region of the pelvis and include the ventral arc, sub-pubic concavity, and the medial aspect of the ischio-pubic ramus (Table

1; Phenice, 1969). Phenice (1969) reported correct sex assessment of 96% of the Terry

Collection across many levels of experience. McLaughlin and Bruce (1990) tested this method using historical European samples and found that the subpubic concavity was the most reliable of trait for estimation of sex, even more reliable than the three traits combined. These authors also found a 12% difference in the accuracy of estimations among observers of differing experience for the subpubic concavity alone.

Trait Male Female Ventral arc Ridge is small or Ridge is present nonexistent Subpubic concavity V-shaped, angle is <90 U-shaped, angle is >90 degrees degrees Medial aspect of Ischiopubic ramus is Ischiopubic ramus is ischiopubic ramus straight or slightly convex concave Table 1. The Phenice criteria for sex estimation. Adapted from Phenice, 1969.

Three fundamental flaws of the Phenice method were discussed by Klales et al.

(2012); 1) the equal weighting of each trait used, 2) the scoring system, which does not cover the full range of variation of trait expression possible, and 3) traits are assigned as male, female, or indeterminate, which does not allow the observer any flexibility when assessing ambiguous morphology. Further, and most important for subscribing to the

Daubert criteria (1993), the method does not allow for accounting of probabilities when faced with ambiguous morphology. Kales et al. (2012) evaluated each trait using a 5- point scale and tested for reliability and validity of each trait as well as inter- and intraobserver agreement. The authors found that classification results were most accurate when all three traits were used, but that the ventral arc provided the most accurate results

(88.5%), followed closely by the subpubic concavity (86.6%). Further, several authors have discussed the developmental (Budinoff and Tague, 1990) and gross anatomical

(Todd, 1921; Anderson, 1990) reasons behind the morphological difference of the ventral arc and subpubic angle (Washburn, 1949; Anderson, 1990) confirming these traits as variable between the sexes on multiple levels after sexual maturity.

Trait Male Female Pelvis overall Massive, rugged, marked Less massive, gracile, muscle sites smoother Symphysis Higher Lower Subpubic angle V-shaped U-shaped, rounded, broader divergent obtuse angle Obturator foramen Large, often ovoid Small, triangular Acetabulum Large, tends to be directed Small, tends to be directed laterally antero-laterally Greater sciatic notch Smaller, close, deep Larger, wider, shallower Ischiopubic rami Slightly everted Strongly everted Sacroiliac joint Large Small, oblique Postauricular space Narrow Wide Preauricular sulcus Not frequent More frequent, better developed Postauricular sulcus Not frequent More frequent, sharper auricular surface edge Ilium High, tends to be vertical Lower, laterally divergent Iliac tuberosity Large, not pointed Small or absent, pointed or varied Sacrum Longer, narrower, with Shorter, broader, with more evenly distributed tendency to marked curvature, often 5 or more curvature at S1-2 and S2-5; segments 5 segments the rule Pelvic brim, or inlet Heart-shaped Circular, elliptical True pelvis, or cavity Relatively smaller Oblique, shallow, spacious Table 2. Sex differences in pelvic morphology. Adapted from Krogman and İşcan, 1986

In addition, many anthropologists refer to Krogman and İşcan’s (1986) volume

which describes 16 sexually dimorphic traits of the pelvis (Table 2). In general, traits

used to estimate sex from the pelvis follow the dimorphic trend of males having greater

body size than females. However, due to the obstetric function of the pelvis in females,

some pelvic dimensions are inverse and unrelated to body size difference between males

and females. Specifically, females have greater dimensions related to the pelvic canal

than males (Arsuaga and Carretero, 1994; Kurki, 2007).

12 Sexual dimorphism in the long bones Though the pelvis may be most directly connected to sex differences because of its role in human parturition, there are other areas of the skeleton that display a high degree of sexual dimorphism. Many authors have looked to the humeral head diameter and femoral head diameter as indicative of sex, especially when the pelvis and skull are unavailable (Frutos, 2005; DiGangi and Moore, 2013). These skeletal elements are thought to exhibit sexual dimorphism because of general body size differences between males and females, as well as perhaps occupational differences in some populations

(DiGangi and Moore, 2013).

Sexual dimorphism in the human cranium While some argue that metric measures of the long bones are accurate sex indicators and should be used second to the pelvis (Spradley and Jantz, 2011), many more anthropologists instead look to the skull as the second best sex indicator (Stewart, 1979;

Krogman and İşcan, 1986; Bass, 2005; Komar and Buikstra, 2008; Walker, 2008;

European, et al., 2012; DiGangi and Moore, 2013; Garvin et al., 2014). There are many areas of the skull that are considered sexually dimorphic (Table 3) and studies corroborate this (Konigsberg and Hens, 1998; Rogers, 2005; Williams and Rogers, 2006;

Walker, 2008; DiGangi and Moore, 2013; Garvin et al., 2014; Lewis and Garvin, 2016).

These features are sexually dimorphic either because they serve as muscle attachment sites (Bass, 2005) or because of hormonal differences (Godde, 2015). The six cranial features chosen for this study include three that serve as sites for muscle attachment: the external occipital protuberance, the nuchal area (or protuberances), and the mastoid

13 processes. The other three are thought to be sexually dimorphic because of hormonal differences between the sexes (Russell, 1985), and include the glabella, the supraorbital ridge(s), and frontal bossing. More detail on each cranial feature used in this study can be found below.

Cranial sex estimation can include both metric and non-metric methods.

Examples of non-metric, or visually assessed, methods are the relative robustness of the external occipital protuberance, or the relative size and shape of bossing on the frontal bone. These are usually assessed using an ordinal scoring system (usually 1-5) (Buikstra and Ubelaker, 1994). Though non-metric methods have been shown to be accurate and easy to use (Buikstra and Ubelaker, 1994), metric measurements are considered by many to be more objective, with lower inter- and intraobserver error rates. This allows for greater courtroom admissibility for metric methods used in forensic cases. Examples of metric measurements used in sexing human skulls are the length and width of the mastoid processes (Howells, 1973) and the cranial base (Holland, 1986). Most reported accuracies for cranial sex estimation range in the eighties; Williams and Rogers (2006) report 80% classification accuracy, Konigsberg and Hens (1998) report 83% accuracy, Giles (1964) reports between 82-89% accuracy, and Walker’s (2008) study reports 88% accuracy using discriminant function analysis, though a drawback is that this method is population specific.

Feature Male Presentation Female Presentation

Overall size and appearance Large/rugged/robust Small/smooth/gracile

Frontal bossing Small or nonexistent Large /present

Parietal bossing Small or nonexistent Large/present

Supraorbital ridges Medium to large Small to medium

Orbital margin Rounded margins Sharp margins

Glabella Small or nonexistent; little to Present; may appear large; no projection rounded projection

Zygomatic arches Extends past the external Does not extend past the auditory meatus (EAM) EAM

Mastoid processes Medium to large Small to medium

Nuchal Large/rugged/robust Small/smooth/gracile ridges/eminences/crests

External occipital Large/rugged/robust; can Small/gracile protuberances even form a hook (inion hook)

Mandible Gonial angle everted Gonial angle inverted

Mandible Large, wide ascending ramus Small, narrow ascending ramus

Chin Square; little to no eminence Rounded; large, portruding projection eminence

Palate Larger; U-shaped Smaller; parabolic Table 3. Cranial features traditionally used in sex estimation, along with their typical male and female presentation on the human skull. (Buikstra and Ubelaker, 1994; Bass, 2005; Komar and Buikstra, 2008).

Sexual Dimorphism Across Populations Variation in sexual dimorphism across populations has been extensively studied and most studies have found significant differences between and among groups in terms 15 of body size (Frayer, 1980; Cunha and van Vark, 1991; Holden and Mace, 1999; Kemkes and Gobel, 2006; Kimmerle, et al., 2008; Wells, 2012; Garvin, et al., 2014, just to name a few). Garvin and colleagues (2014) found that sexual dimorphism in cranial trait scores

(after Walker’s [2008] method) vary across populations. This dissertation is motivated in part by this idea, and tests where the lines can be drawn between United States groups

(see Chapter 3, Methods).

Sexual dimorphism in populations through time is another question that has been studied, but not as extensively. Frayer (1980) compared European hunting and gathering groups across time and found a significant decrease in levels of sexual dimorphism from the Upper Paleolithic to the Mesolithic, and then from the Mesolithic to the Neolithic.

The reduction in sexual dimorphism over time in this case is probably due to changes in subsistence strategies, and subsequently, nutrition, through time. As people began farming and moved away from hunting and gathering, Frayer hypothesizes, the division in labor coupled with male stunting from nutritional deficiencies brought on by the onset of agriculture, decreased sexual dimorphism (Frayer, 1980; Sciulli, et al., 1991).

Godde (2015) also studied sexual dimorphism through time, but in a much more focused way. Similar to this dissertation, Godde analyzed modern and historic crania and discovered an overall gracilization in sexually dimorphic cranial features, save for the supraorbital margin. Though the margin is different anatomically from the supraorbital ridge analyzed here, it will be interesting to note similarities and differences in our results.

The level of dimorphism in a human population may be mainly influenced by male size (Hall, 1978) because males are more likely to be negatively affected by

16 environmental stressors such as malnutrition. That is, male growth is more sensitive to malnutrition and males are thus more likely to be stunted during lean times, resulting in a lower populational level of sexual dimorphism (Stini, 1969; France, 1998). Frayer and

Wolpoff (1985) refer to this as the proximate causation model that explains how sexual dimorphism can be affected nutrition.

This phenomenon is only related to size dimorphism, however, as males are generally larger than females in many areas. In contrast, when comparing traits that are typically larger in females (for example, frontal bossing on the frontal bone of the skull), sexual dimorphism increases during times of nutritional stress (Relethford and Hodges,

1985). There are many instances, however, where human males and females overlap in their levels of dimorphism; some males can be of shorter stature than some females, many females may have robust crania, and hormone levels can vary greatly from person to person. This overlap is all part of normal human variation, and generally speaking, when anthropologists consider sexual dimorphism, it is generally assumed that there will be observable, quantifiable differences between the skeletons of males and females.

Biological Ancestry and the Human Cranium It is the position of some anthropologists that biological ancestry can sometimes affect forensic and bioarchaeological estimations of sex (Sauer, 1992; Ousley, et al.,

2009). In a comparison of 26 skeletal series worldwide (from Howells, 1973), Van Vark and colleagues determined that populations show differing levels of sexual dimorphism in cranial size (1989).

17 Anthropologists eschewing the race concept have argued that more differences exist within geographic populations than between and among them (Howells, 1973;

Relethford, 1994; Andreasen, 1998; Roseman and Weaver, 2004; Albanese and Saunders,

2006). Uytterschaut (1986) attempted to create sex discriminant functions for sexually dimorphic cranial traits that are independent of populational variability by measuring crania from Amsterdam, Japan, and a Zulu series; results indicate 'less than 20% misclassifications' (Uytterschaut, 1986:249). The three populations used in Uytterschaut's study are most likely genetically distant groups, but still exhibit significant cranial similarities, insofar as sex-based differences are concerned.

In a 2008 study comparing and contrasting several ancestral groups, Walker measured 304 known English, European Americans, and American African crania, as well as 156 ancient indigenous Americans of unknown sex (but who were sexed using traditional pelvic features) (2008). In general, Walker found Native Americans to be less sexually dimorphic and more robust when compared to American Europeans and

Africans. In addition, Walker found that European and African Americans are more similar to one another than either is to English crania, and the modern American sample exhibits a greater degree of robustness in most cranial traits as compared to the English series, save for the glabella. This may be due to diet or nutrition differences between continents (2008).

Walker’s (2008) results support the fact that European American and African

Americans share a great deal of genetic information, more so than African Americans and

Africans, and therefore more similar cranial trait expression. This is logical, as a paraphrase of the first law of geography is that populations that are physically closer

18 together are more biologically related than groups that are physically farther apart from one another (Sciulli and Schneider, 1985). Walker asserts that European Americans and

African Americans could be grouped together in terms of the robustness of their cranial traits.We can extrapolate from this that African Americans and European Americans can therefore be easily grouped into a single category when devising osteological methods, especially methods that are partially intended for use in American forensic contexts. This dissertation groups African Americans and European Americans separately in statistical tests to determine if there are differences in their sexual dimorphism (see Chapter 3).

Sexual Dimorphism and Age-At-Death

The literature is divided on whether or not age affects cranial sexual dimorphism, and, in turn, sex estimation. In general, bone remodeling increases with age; it can thus be inferred that with increasing age, an individual's skeletal structure will be modified

(Maggiano, 2012). Past studies have shown that because of appositional bone remodeling, cranial shape can change with increasing age (Israel, 1973; Albert et al.,

2007). These age-related changes due to bone remodeling in the skull and face can include a lateral shifting of the eye orbits, increased robustness in the zygomatics, and more prominent nasal bones; the bones of the face in effect are remodeled and exhibit

'drift' into a more downward location (Enlow, 1966; Maggiano, 2012). In addition, sexually dimorphic features of the skull can also be altered with advancing age

(Hamilton, 1982). Bone remodeling can bring about a more bulbous frontal bone, including the glabella and supraorbital ridge (Enlow, 1966).

19 Some research has shown that females express increasingly more 'masculine' traits after menopause. According to Walker (2008), increasing age affects the robustness of the human skull, with males of 30 years old and females over 45 years old becoming gradually more robust over time. Cranial traits considered sexually dimorphic, such as the supraorbital ridge or the glabella, become more robust as a number of hormonal changes affect bone remodeling (Walker, 1995). This can be challenging for bioarchaeologists, who seek to reconstruct the demographic composition of a population.

Walker has termed this problem "sexism in sexing," maintaining that Western bioarchaeologists may be superimposing their culturally-constructed expectations about female gracility upon skeletal series (1995). Walker scored the supraorbital ridge, orbital margin, mastoid process, nuchal crest, and the chin on 300 known crania and found age- related changes in most of the traits; findings confirm females past the age of 45 exhibit cranial morphology that is more typically 'masculine,' while males under age 30 consistently show less robust features, especially in the supraorbital area (1995). Other studies have found the same apparent male bias in sexually dimorphic cranial traits

(Weiss, 1972; Walker et al., 1988; Williams and Rogers, 2006). More recent studies argue, however, that age-related bone changes in the cranium are not statistically significant (Nikita, 2014) and age at death actually does not affect sex estimation

(Garvin, et al., 2014).

The above has implications for bioarchaeology and forensic anthropology. If age does indeed affect cranial morphology, and osteologists are consistently misinterpreting morphological traits, paleodemographic reconstructions will be rendered meaningless, and forensic identifications will be inaccurate and unreliable. Weiss (1972) has discussed

20 the apparent male bias present in skeletal series. The above age-related changes to sexually dimorphic features may help explain why more 'males' are found in cemeteries and skeletal collections. Additionally, as females experience more age-related loss in bone mass, their bones may be disintegrating more easily and prior to that of male skeletal material, suggesting a sex-based differential in mortality and preservation

(Weiss, 1972). To remedy these problems, Walker suggests assessing the level of preservation in a skeletal series before attempting a paleodemogaphic reconstruction.

This can be done by determining the number of measurable long bones in a series or by comparing the population's age structure to a theoretical mortality profile (Walker, 1995).

For osteologists conducting museum or collections work, caution should be exercised when testing sex estimation methods or creating new methods on skeletal series. Perhaps it would be wise, if the collections used are large enough, to choose only individuals between the ages of 20 and 40 for sex estimation purposes, thereby possibly eliminating any chance for a male bias.

Environmental Effects on the Human Cranium

Climate

Environmental factors can affect growth and development in all areas of the human body. Climate and temperature are well-known to affect body size and shape; the theoretical underpinning of this is commonly known in biological anthropology as

Bergmann’s and Allen’s Rules (Mayr, 2005). The evolution of thermal regulation in different environments has led to global variation in cranial size and shape as well (Ruff,

1994; Mayr, 2005). These environmental effects on body and skull size can begin as early

21 as the prenatal stage and can extend into the early childhood years. These are also the years in which an individual's growth is most vulnerable to environmental stressors

(Bogin, 1999).

Some authors have suggested cranial morphology is subject to climatic conditions, and posit certain cranial shapes are adaptations to certain environments (Boas,

1912; Beals, 1972; Howells, 1973; Guglielmino-Matessi et al., 1979; Beals et al., 1984).

Beals has proposed a "special case of Allen's Rule" (1972:90) in cranial morphology, asserting that more rounded skulls will be found in colder climates, while more long, narrow skulls are adaptations to hot environments. Furthermore, Beals et al. suggest a rounder, more globular shape, in addition to a more 'pedomorphic' appearance, as adaptations to cold environments (1984). More recently, Katz and coauthors (2016) have corroborated this, writing that crania tend to be larger, including an overall increase in vault breadth, in cold climates. Katz, et al. (2016) found that the cranial areas most clearly associated with climate and temperature to be maximum cranial breadth, zygomatic height, and various breadth measures on the basicranium, which confirms earlier studies’ findings.

Climate can affect specific cranial features in addition to overall shape and size.

Some researchers have asserted that the human cranium will exhibit more robust, rugged features in cold, harsh climates (Bernal, et al., 2006; Perez, et al., 2007). Researchers have noted for decades that cold climates lead to robust post-crania (Trinkaus, 1981;

Churchill, 1998; Pearson, 2000), so it stands to reason that the same effect may take place on the cranium. Perez and colleagues (2007) support this idea, and maintain that cold conditions stimulate increased hormone production, which in turn influences the

22 deposition of cortical bone. More cortical bone on the skull will inevitably lead to a more rugged cranial appearance (Perez, et al., 2007). This is essential to population studies that are interested in human global biological variation, as population differences in sexual dimorphism would certainly be affected by these cranial adaptations to differing climates.

This would in turn affect bioarchaeological and forensic investigations, as sex estimation standards may have to be revisited and revised. At the very least, consideration of ancestral climate and the variation that may accompany differing cranial shapes should be considered.

Nutrition Nutrition, or rather, lack thereof, can affect cranial form and robustness as well. It has been proposed that males may be less 'buffered' against the effects of poor environmental conditions than are females, and may thus exhibit stunting at a higher rate

(Stinson, 1985; Stinson, 2012). This is probably not due to climate, as several studies have shown the sexes adapt in equivalent ways to climatic stresses, but nutritional deficiencies may be the cause. If male growth in malnourished populations is interrupted, male mean stature will decrease (Stinson, 1985), and other sexually dimorphic indicators may also be affected. As a result, females and male means will grow closer together, decreasing sexual dimorphism in the population. Though Stinson

(1985) discusses many studies of stunted growth, and thus decreased sexual dimorphism in mean height across populations, these findings can be extrapolated and applied to cranial dimensions and features as well. If growth in height and weight is interrupted by environmental stressors, cranial growth must certainly be affected as well. Presuming

23 that the hypothesis that males are less buffered than females in a nutritionally deficient environment is correct, male crania should also exhibit this effect. Suazo Galdames and colleagues (2008) found that severe malnutrition can cause changes in the robustness of certain cranial features, especially those related to muscle size and strength. The researchers analyzed skulls of individuals who died of malnutrition and found that the zygomatic bone, the size and thickness of the mastoid processes, and the “ridges of the occipital bone” (termed “nuchal protuberances” in this dissertation) were all less robust because of decreased muscle strength. In turn, this caused researchers to face difficulties in estimating sex from these crania, as the smaller and more gracile features decreased estimation accuracy (Suazo Galdames, et al., 2008).

Noback and Harvati (2015) found that diet drives cranial variation globally, citing pronounced population differences between groups with animal-based diets and groups with plant-based diets. Areas of the skull that show the highest correlations between global cranial shape and diet were the shape of the temporalis muscle and “general cranial shape” (Noback and Harvati, 2015). This suggests that mastication may cause evolutionary changes in cranial anatomy.

Mastication Though previous work has indicated cranial form could be subjected to climate and environmental pressures (Boas, 1912; Beals, 1972; Howells, 1973; Guglielmino-

Matessi et al., 1979; Beals et al., 1984), Baab and colleagues assert diet and, in turn, masticatory stresses, may play a larger role (2010). Baab and colleagues examined 11 cranial features for their relative robustness; these features can also all be considered

24 sexually dimorphic sex indicators. The authors found links between robustness and diet, indicating that the stress of mastication from a diet of tough foods (like the diets common in hunting and gathering groups) may have affected the morphology and relative robusticity of cranial traits used in standard sex estimation methods (Larsen, 2002; Baab et al., 2010). Earlier work by Hilloowala and Trent (1988) found that the supraorbital ridge in particular is affected by masticatory strain. The authors found a positive correlation between robustness of the supraorbital ridge and strength and size of the temporalis muscle, which is located on the temporal bone and is directly related to chewing (Marieb and Hoehn, 2015).

Some authors refer to biomechanical loading on the skull from mastication as the

‘biomechanical hypothesis’ (Bernal, et al, 2006). Bernal and colleagues analyzed

Patagonian skulls from hunting and gathering groups while considering the biomechanical loading hypothesis. They found conflicting results, noting that hunter- gatherers displayed differing levels of cranial robusticity no matter how soft or hard their diets were (Bernal, et al., 2006). The conclusions here are that biomechanical loading cannot safely predict cranial robusticity. Which factor more greatly affects cranial morphology is still not known and further research is required.

Growth and Development of the Human Cranium

The human head is an incredibly complex network of bones, organs, and other tissues that is still not completely understood. The head’s complexity can be attributed to its numerous functions. The skull protects the brain and other important organs, such as the eyes. In addition, the human head works to regulate body temperature, respiration,

25 locomotion, mastication, balance, vocalization, and the senses of vision, taste, hearing, and smell. Lieberman states, “Almost every particle entering your body, either to nourish you or to provide information about the world, enters via your head, and almost every activity involves something going on in your head” (8:2011). The skull is the part of the head of most interest in this dissertation. The skull is also quite complex; its 22 bones are modular and simultaneously integrated (more on this below), and, from embryonic beginnings to adulthood, grow and develop without sacrificing any of the bones’ functions. What follows is a brief discussion on cranial growth and development to provide a backdrop for the cranial research described in this dissertation.

Cranial growth The growth of the cranium is primarily directed by the growth of the brain

(Enlow, 1968). Endochondral and intramembranous ossification are responsible for skull growth, though each involves different biological and cellular processes. Endochondral ossification begins when chondrocytes, or cells that form cartilage, lay down a cartilage template that is later replaced by an osteoid matrix (Lieberman, 2011; Gosman, 2012).

Endochondral ossification is responsible for the growth of some facial bones, including the sphenoid, the ethmoid, the nasal conchae, the lacrimals, and the zygomatics (Shipman et al., 1985). Parts of the temporal bones and occipital are also formed endochondrally

(Enlow, 1968). Intramembranous ossification, on the other hand, arises from mesenchymal cells (which are a type of stem cell) that have condensed into an analog, or precursor, to the bone (Maggiano, 2012). This method of ossification is primarily responsible for the development of the cranium, as it initiates the majority of the cranial

26 vault, as well as some bones of the face (Gosman, 2012). Intramembranous ossification is so called because it is situated within membranes that surround structures such as organs or tissues. As each structure grows (i.e., brain or eye), the surrounding mesenchyme reacts appropriately, and bone grows in response. Lieberman (2011) discusses microcephaly and hydrocephaly as examples of this interesting developmental relationship. Microcephaly is a condition which results in a smaller than normal brain; microcephalic skulls are always small in response to the stunted growth of the brain. In contrast, hydrocephaly is an the enlargement of the brain due to an excess of cerebrospinal fluid; the corresponding skull bones also grow to fit the brain precisely

(Lieberman, 2011).

Cranial development Following a brief discussion of the growth of the skull, further consideration of development, or ontogeny, is logical. Lieberman eloquently defines ontogeny as “the series of stages over which an organism develops and grows -- an unfolding of events in time” (99:2011). Since this dissertation deals solely with Homo sapiens and no other primate species, time will be discussed in terms of absolute age. That is, when describing a phase of development, the stages will be mostly uniform because humans develop at a mostly predictable rate. The human cranium’s ontogeny can be described as modular and simultaneously integrated (Lieberman, 2011). The skull is modular in that it is composed of many separate, independent parts. For example, there are 22 bones in the cranium; one can argue each bone is visibly and, to an extent, functionally distinct.

27 The skull is also highly integrated (Halgrimsson et al., 2007; Lieberman, 2011).

Integration, which, in this context, describes how the modules of the skull work together to form a cohesive whole, can also be thought of as correlation. The modules of the skull, though separate to a degree, are also highly correlated with one another. The size of the brain is correlated with the shape of the eyes, for example. If a mutation occurs that compromises growth in one part of the skull, other parts may be affected (Lieberman,

2011). In the middle of the twentieth century, van der Klaauw proposed the Functional

Matrix Hypothesis as a way to describe the interactions among modules of the skull

(1948-1952). His modules, or matrices, include the eyes, the nasal and oral cavities, the tooth roots, and the brain. In van der Klaauw’s interpretation, the skull integrates by not only growing parts specific to each module, but also by accommodating other modules’ growth trajectories (van der Klaauw, 1948-1952). Then, throughout the course of human evolution, if one part of the skull were to change, the hypothesis suggests the organs and proximal spaces need to change first (Moss, 1997). This hypothesis, however, does not account for the multiple influences acting on a specific section of skull. In 1990, Enlow proposed the part-counterpart principle, which argues the separate structures of the skull should be proportional with their counterparts. For example, the mandible and maxilla are counterparts to one another; growth in one bone must be matched by growth in the other

(Enlow, 1990; Liberman, 2011), as evidenced in the above example describing skull growth around microcephalic and hydrocephalic brains.

The modularity and integrated nature of the skull are important to consider when discussing cranial growth, ontogeny, and morphological characteristics. As this dissertation discusses varying modules or features important to biological anthropology,

28 it is also important to recognize the biology of cranial growth and development. It is possible that Homo sapiens have been evolutionarily successful in part because of the modularity and integration of the skull. A skull that is flexible enough to react to selective pressures and differentiate its different parts for different tasks belongs to an individual that will enjoy reproductive success. As selection “tinkers” with one module, so another area of the skull must respond. Throughout human evolution, one can argue that the success of the primate order, and the species, Homo sapiens, in particular, has rested on the plasticity and generalizability of many traits. Primate bodies are generalized rather than specifically tailored to one locomotive pattern or activity, as are primate teeth

(Larsen, 2014). Similarly, the skull’s flexibility may make it amenable to “tinkering,” or small changes, that can accumulate over time and contribute to the success of a species.

The next section will briefly discuss each module of the cranium, as defined by

Lieberman (2011), and their growth and development during human ontogeny

The cranial base The cranial base is the most inferior portion of the skull and is also called the basicranium, or the inferior cranium. It differs from the neurocranium (braincase) and the splanchnocranium (facial skeleton) in that the bones of the cranial vault and face grow intramembranously while the cranial base and face grow endochondrally (Lieberman,

2000). The cranial base has several important functions: it connects the neck to the skull securely while also allowing some movement, it connects the mandible to the skull, it serves as a platform for the brain and face, and it houses the pharynx (Lieberman, 2011).

Aside from these key actions, the basicranium has also been linked to the development of

29 the neurocranium (Hallgrimmson et al., 2007) and has been found to influence overall cranial dimensions (Lieberman et al., 2000) and other modules’ growth trajectories

(Lieberman, 2011). In fact, a recent study corroborates this, as researchers were able to genetically manipulate the width of the cranial base in mice, resulting in shape changes to the neurocranium and the face (Parsons, et al., 2015). The angulation of the cranial base might arguably be the most critical feature of this module, as it has implications for estimating brain size (Lieberman, et al., 2008) and body size (Agosto, et al., 2016) in fossil Hominins; cranial base angulation has also been shown to correlate with fetal body weight in mice (Dixon, 1997). This can positively influence studies of human evolution, the core of biological anthropology.

The way the basicranium influences facial and neurocranial growth has important evolutionary implications in biological anthropology. The growth and development of the human skull is largely dependent on genetic and epigenetic interactions (Relethford,

1994; Humphrey, 1998; Sparks and Jantz, 2002; Lieberman, 2011 ), but environmental influences have also been discussed as possible factors affecting cranial dimensions and features (Boas, 1912; Beals, 1972; Howells, 1973; Guglielmino-Matessi et al., 1979;

Angel, 1982; Beals et al., 1984). Beals and coworkers, for example, discuss certain cranial forms as adaptations to climate (1984), while Angel proposes nutritional causes for an increase in cranial base height (1982). Whatever the factors that push cranial change may be, it is certain that the skull will continue to evolve in response to environmental pressures and evolutionary forces. One example of cranial evolution can be seen in the secular changes that have taken place, even very recently, in the human skull.

The cranial vault The neurocranium grows intramembranously and is directly related to brain growth, as tensile stress from the outward growth of the brain promotes osteoblastic activity; this phenomenon is also termed intracranial pressure (ICP). The neurocranium includes five bones: the frontal bone, right and left parietal bones, right and left temporal bones, and the occipital bone. Growth in the cranial vault occurs three different ways: 1) in the sutures (or fontanelles), primarily influenced by ICP; 2) displacement and rotation from osseous drift (described in greater detail below); and 3) thickening of the vault bones (Lieberman, 2011).

Osseous drift, also called modeling drift, occurs during bone modeling, which is the process by which osteoclasts lay down primary bone tissue (Maggiano, et al., 2016).

Modeling drift has been difficult to quantify or even observe, but researchers are confident of the process: as osteocytes are laid down on one surface during modeling, bone cells are removed by osteoclasts on the opposing surface, which can “...alter the curvature of the element or even its position relative to the rest of the skeleton”

(Maggiano, et al., 2016:192). For example, in the human skull, the interior surface of the occipital bone is resorptive in nature, while the outer surface is a “depository growth field” (Lieberman, 2011:120). This causes osseous drift in this region over time; the occipital bone grows outward, but also rotates horizontally (Lieberman, 2011). The nuchal plane is then said to have rotated horizontally in humans, but in nonhuman primates, it rotates vertically. This has important implications for the position of the

31 foramen magnum and the attachment site for neck muscles (on the nuchal plane), and, in turn, the rise of bipedalism in human evolution (Lieberman, 2011).

The face The face is the third and final module of the skull, as defined by Lieberman

(2011). The bones of the face are highly integrated, as each bone is a product of how it reacts to other bones in the face. Functional matrices apply to the face as well as the other modules of the skull. Lieberman (2011) identifies seven matrices that make up the face: the anterior cranial fossa and supraorbitals, orbits, the nasal cavity, paranasal sinuses and zygomatic arches, the oral cavity, and the tempromandibular joint. The purpose of this dissertation is not to describe in detail all functional matrices and their development, however, two of these matrices are of direct importance to this project. One, the supraorbital ridge, often displays sexually dimorphism, and the other area of interest involves the midface, as it has recently undergone changes that may affect skeletal and anthropological research.

The face is a unique module of the skull, as it grows much more slowly than the neurocranium and basicranium. Growth in the face is not complete until the neurocranium and basicranium are finished growing, which is usually around 6-7 years of age. The face continues to grow, inferiorly and anteriorly from the basicranium, until the end of the adolescent growth spurt (Lieberman, 2011). Given that the face is the final module to complete growth, Lieberman states, “...the brain, the eyes, and the cranial base provide a sort of template from which much of the facial skeleton grows” (2011:123).

32 The face is also unique in the most derived module of the skull in that it has undergone the most evolutionary change relative to the cranial vault and basicranium in our (geologically) recent past (Lieberman, 2011). This is probably due to the organs associated with cranial and facial growth; as our ancestors relied less on their sense of smell and more on eyesight, the need for long faces (“snouts”) decreased, while the need for larger orbits increased (Larsen, 2014), and since the bones of the skull are so highly integrated, other changes took place alongside those naturally selected. Additionally, as our ancestors’ brains grew, the surrounding bone grew accordingly, thus affecting bone growth and development in all the modules.

The role of growth and development in determining shape and size

Much of cranial growth and development occurs early in life, with 75% of growth accomplished by age three (Jantz and Jantz, 2000). This implies that the early years of development are the most crucial in terms of attaining the individual's potential for skull shape and size. The growth of the cranium, in both size and shape, is affected by a number of biological, as well as environmental factors. The growth of the brain, triggered by genetic and epigenetic signals, first affects the frontal and occipital bones

(Humphrey, 1998). These processes are closely followed by growth in the eye orbit region, and nasal and zygomatic regions (Humphrey, 1998). It is important to note, however, that initial skull growth does not necessarily follow a structured program; the skull is a highly integrated structural unit that grows and develops in response to other signals (Lieberman, 2000). For example, the bones of the face are certainly interdependent with one another, so the mid-facial region cannot grow unless it is in

33 response to another growing region (in this case, the nasal and oral areas) (Humphrey,

1998). Recent research points to epigenetic influences affecting the basicranium in mammals, which in turn affects the growth of the entire cranium (Hallgrimsson et al.,

2007), as the basicranium is known to exert the most influence on the rest of the cranium

(Lieberman, 2000; Sperber, 2001).

There exists some literature that supports the notion that most cranial growth is largely genetically determined, and is the result of extensive gene flow (Relethford,

1994). In this view, variation in human crania is highly heritable and not susceptible to most environmental pressures (Sparks and Jantz, 2002), however, there have been studies that have attempted to explain variation in skull shape and size in terms of environmental pressures and climatic conditions (Boas, 1912; Beals, 1972; Howells, 1973; Guglielmino-

Matessi et al., 1979; Beals et al., 1984). For example, Beals (1972) hypothesizes that cephalic index could be a biological adaptation to climate. According to this position, groups adapted to cold climates should have rounder skulls than groups adapted to hot environments (Beals, 1972). Alternatively, Angel (1982) proposed that an increase in skull base height (measured as porion-basion height) occurs in areas with better nutrition.

Research has also pointed to the possibility of diet affecting cranial morphology (Carlson and Van Gerven, 1977; Baab et al., 2010). Baab and colleagues analyzed modern crania from 14 differing regional groups for evidence of variation in robustness in several cranial traits. Their results show that variation in cranial form is not necessarily a result of harsh, cold climates, but may instead be due to differing masticatory stresses (Baab et al., 2010). Populations exploiting different types of food may indeed have evolved differing cranial features in response to masticatory loads. This has implications for

34 research in sex estimation based on cranial morphology, as most sex indicators from the cranium are based on the rugosity of features, with the most 'rugged' types indicating the

'male' type, while more gracile traits are associated with the 'female' form (Bass, 2005).

For the forensic purposes of estimating sex from the cranium, the above debate is not quite as pressing. Most forensic cases in the United States concern individuals, not populations, and there are many studies that support the hypothesis there exist no significant differences in sexually dimorphic cranial features between European

Americans and African Americans, regardless of social constructions of 'race' (Giles and

Elliot, 1962; Cunha and Van Vark, 1991; Jantz and Jantz, 2000; Walker, 2008; Garvin and Ruff, 2012). For sex estimation studies that rely on modern U.S. populations, anthropologists either analyze European Americans and African Americans together, as a single population, or separately, as two disparate groups. Part of the purpose of this dissertation is to better understand the American population and how to best analyze them.

The role of growth and development on sexually dimorphic features of the cranium

Sexual dimorphism does not develop the same way throughout the entire skeleton; certain parts grow and develop at different rates, thus affecting the levels of sexual dimorphism present in each skeletal element (Humphrey, 1998). For example, early growing parts of the skeleton, such as the neurocranium, emerge less sexually dimorphic in adulthood than later growing parts, such as the pelvis, which is heavily influenced during puberty (Humphrey, 1998). Later growing parts of the craniofacial skeleton include the nasal and maxillary areas (Rogers, 1991; Saini et al., 2011), which

35 can explain some researchers' assertions that the nasal aperture is sexually dimorphic

(Enlow, 1968; Rogers, 2005). Humphrey's assertions contrast with a study by Baughan and Demirjian (1978), which hypothesizes that the skull will exhibit sex-based differences even before puberty. Using data from a longitudinal growth study, Baughan and Demirjian (1978) found a difference in cranial size present at an early age, even when differences in stature were not yet apparent. Before the pubertal growth spurt, males and females exhibited a difference in head length of between 2.5% - 3%, with males displaying the larger cranium. This difference is expanded to 4% after puberty

(Baughan and Demirjian, 1978). The results of this study are controversial, as mainstream anthropology holds that sex estimation should never be attempted on subadult skeletal material (Komar and Buikstra, 2008).

In general, most research supports the notion that the male and female sexes do not begin to differentiate until the pubertal growth spurt (Baughan and Demirjian, 1978), though the onset of puberty differs across populations (Cameron and Bogin, 2012). The degree of differences between the sexes can be identified cranially post-puberty, and analyzed using a variety of statistical methods. Sexually dimorphic cranial features used in this project include: 1) the glabella, 2) the supraorbital ridge, 3) the external occipital protuberance, 4) the nuchal eminences, 5) the mastoid processes, and 6) frontal bossing.

These features have long been considered reliable morphological sex estimators, and have traditionally been assessed and scored using an ordinal scale (typically ranging from

1-5) (Buisktra and Ubelaker, 1994).

Some of these features are sites for muscle attachment, thus sexual dimorphism can be easily explained by asserting that robusticity in muscle mass is reflected in the

36 rugged nature of these areas. Specifically, the nuchal eminences, the external occipital protuberance, and the mastoid processes serve to anchor muscles (Garvin and Ruff, 2012) and will exhibit significant size and shape differences between the sexes (Bass, 2005).

The supraorbital ridge, or the brow ridge, is ordinarily more robust in males, while females exhibit a more gracile appearance (Bass, 2005). Some research has attempted to describe the purpose of the supraorbital ridge as a functional adaptation to bending stresses when biting with the incisors, or as protection for the eyes from foreign objects or sunlight (Russell, 1985), while others suggest it may be a byproduct of an overall cranial adaptation to a warm climate (Beals et al., 1984), but these assertions do not adequately explain the level of sexual dimorphism present in this feature. The explanation for the presence of the supraorbital ridge should instead be reliant on biological differences between the sexes. It is not well understood what causes the sex disparity in supraorbital ridge morphology (Garvin and Ruff, 2012), but endocrine and hormonal regulations may play a role (Russell, 1985).

The glabella is another feature whose adaptive value cannot be explained adequately. Sexual dimorphism in the glabellar region may be hormonally induced; it may also be a byproduct of the growth in the supraorbital region (Garvin and Ruff, 2012).

For now, research points to the glabella as a reliable morphological indicator of sex

(Buikstra and Ubelaker, 1994; Bass, 2005; Komar and Buisktra, 2008).

One obvious problem persists in the discussion of growth and development's influence on sexually dimorphic skeletal characteristics. As growth trajectories and development rates differ among human groups (Cameron and Bogin, 2012), attempts to identify individuals based on skeletal indicators of sex and age can be seriously

37 undermined. For example, a human osteologist faced with a skeleton of unknown sex, age, or ancestry, may estimate the sex of the individual first without knowing the average growth rates of the population from which the individual comes. This could in turn affect the entire biological profile of the individual. This is one major justification for continuing craniometric research within, between, and among geographic populations.

Secular Trends

Secular trends are changes in traits that occur over time (Cameron et al., 1990).

Observable phenotypic differences from one generation to the next are considered short- term secular trends and are probably due to environmental factors, such as diet and nutrition, while long-term secular changes involve a change in genotypic frequencies

(Jantz and Jantz, 2000). Positive secular trends are more commonly found than negative ones, and are associated with an overall increase in the size of the trait in question

(Stinson, 2012). In anthropology, positive secular trends have been noted in overall human stature from the early 19th century to the late 20th century; these secular changes probably occurred as a result of increased nutrition and improved living conditions in the

United States during that time span (Jantz and Jantz, 1999; Gustafsson, et al., 2007).

Another positive secular change has also been documented in cranial size and shape

(discussed further below) (Angel, 1982; Jantz and Jantz, 2000; Jantz et al., 2012).

Negative secular trends, on the other hand, may be due to malnutrition and poor quality of life (Cameron, et al., 1990).

38 Researchers also look to other areas of the skeleton to discover evidence of change through time. Kales (2016) analyzed the pubic region from nearly 200 pelves from the Hamann-Todd and Bass Collections, which are representative of historic (or

“premodern”) and modern skeletal remains, respectively. Klales found that secular trends have affected pelvic morphology in that females are getting more gracile over time. This gracilization of the female pelvis has led to an even greater classification accuracy on modern pelves than with historic pelves.

Secular trends in the human cranium Secular changes have also affected cranial size and shape. In a study that analyzed American Europeans and Africans from the mid-19th century to the 1970s,

Jantz and Jantz showed that cranial vaults have gotten higher and longer, while faces have gotten more narrow (2000). These findings validated Moore-Jansen’s (1989) earlier conclusion. These changes are the most drastic in European males, which may support the thesis that males are less buffered against environmental pressures than are females

(Stinson, 1985; Stinson, 2012). The fact that vault size and shape has changed significantly even in very recent years has important implications for American forensic anthropology. Since forensic anthropologists most often deal with skeletal material from the United States, understanding cranial variation in North America is crucial for proper identification. Jantz and Jantz also posit that shape differences are even more striking than overall size changes in this American sample (2000). Studies that investigate shape differences in sexually dimorphic cranial features, then, are undeniably needed.

39 Other studies have looked at the change in skull size and shape over a number of decades and conclude that, over time, anatomically modern humans have experienced a secular trend toward larger, more rounded crania (Beals et al., 1984; Jantz et al., 2012) and less robust skull traits (Jantz et al., 2012), including more gracilization of the mandible (Martin and Danforth, 2009). Sexually dimorphic features of skulls have also experienced change through time, with most features becoming less robust (Godde,

2015). Though all populations globally are undergoing cranial secular changes, the remarkable gracilization of skulls is strictly a Western phenomenon; other populations see differing trends, many of which may be affected by climate or nutrition (Godde,

2015). The less robust cranial traits seen in industrialized settings may be due to a decrease in the force and duration of mastication since the onset of agriculture, but hormones may also play a role (Godde, 2015).

A more recent study concerning secular change in the cranium shows that the crania of modern Americans are perhaps in the middle of another positive secular trend, as overall skull size continues to increase in length, while facial width continues to diminish (Jantz et al., 2012) and corroborates the earlier statement from Lee Meadows

Jantz that, “cranial morphology is strongly dependent on year of birth” (2001).

Jantz and colleagues (2012) speculate that environmental influences, such as improvements in health and nutrition and perhaps even an increase in exogamous mating and marriages, may have affected the latest series of secular trends in cranial growth.

However, this secular trend has been notoriously difficult to explain fully. Garvin and colleagues (2014) suggest better socioeconomic status, differences in diet and nutrition, and hormonal changes through the generations may be causally related to secular trends.

40 Secular trends can also affect levels of cranial sexual dimorphism between populations. A recent study comparing American European and African crania from the mid-1800s to the mid-1980s found significant changes in cranial length and breadth, with skulls exhibiting a gradual narrowing of the face and elongation of the cranial vault (Jantz et al., 2012). The authors posit this is most likely due to changes in diet/nutrition, lower infant mortality rates, and changes in mating/marriage practices (Jantz et al., 2012). A similar study of a more restricted scope found the same secular changes in craniofacial morphology (Jantz and Jantz, 2000). This phenomenon will be discussed in more detail later.

For forensic anthropologists working in the United States, many authors argue that American Europeans and African Americans can be analyzed together, as a single biological population (Giles and Elliot, 1962; Cunha and Van Vark, 1991; Jantz and

Jantz, 2000; Walker, 2008; Garvin and Ruff, 2012). This is especially useful for forensic analyses which use modern American Europeans and African Americans together to test and create novel methods for sex and age estimation. This dissertation will analyze

American Europeans and African Americans separately, as two distinct populations, to determine if secular trends are affecting European and African American cranial morphology equally.

Motivation for the present study

As has been discussed above, the human skull is widely considered to be the second-best indicator of sex (Bass, 2005; White and Folkens, 2005). Sexual dimorphism has often been assessed from the cranium by using morphological traits and scoring them

41 using an ordinal scale (generally 1-5) (Buikstra and Ubelaker, 1994). This methodology is prone to greater inter-observer error and is often difficult to quantify (Slice, 2005;

Kimmerle, et al. 2008). In contrast, many other studies have relied on metric measures based on well-documented cranial landmark data (Giles and Elliot, 1963; Howells, 1973;

Moore-Jansen, et al., 1994). Metric analysis is considered less subjective and prone to lower intraobserver error rates than visual assessment (Adams and Byrd, 2002; Spradley and Jantz, 2011), which is why this project has been undertaken. Many of the aforementioned studies of sex-based cranial differences have been largely concerned with size disparities (Giles and Elliot, 1963; Howells, 1973), as males are generally considered more robust than females (Bass, 2005; European and Folkens, 2005), however the role of cranial shape must also be taken into account in a discussion of sexual dimorphism in the human skeleton. Recent studies have attempted to document the role of shape as it pertains to differences between the sexes (Kimmerle, et al., 2008), but have relied on technology that is expensive and often difficult to acquire (Pretorius, et al., 2006;

Kimmerle, et al., 2008; Garvin and Ruff, 2012). A re-examination of standard craniometric measurements using inexpensive equipment that can be easily acquired, and methodology that can be easily replicated, is necessary. As shape is not frequently a factor in many craniometric studies, coordinate measures that incorporate the three dimensional nature of the shape of the human skull as a consequence of biological growth and development must also be included.

Additionally, anthropologists must know about skeletal variation and change over time in the populations within which their work is centered. For American forensic anthropologists, secular trends, or changes in the size and shape of a trait over time, must

42 be considered. It is well-documented in the literature that American crania have become taller and faces have narrowed between the 1840s through the 1970s (Jantz and Jantz,

2000; Jantz, et al., 2012). Clearly, there is a need for a comparison of sexually dimorphic traits in historic and modern skeletal populations to determine if the cranial traits used in sex estimation have been affected by secular trends in cranial size and shape.

Greater precision in metric cranial measurements is increasingly more critical, especially since the landmark court case, Daubert vs. Merrill Dow Pharmaceuticals

(1993). This case ended the precedent set by the Frye vs. the United States (1923) ruling, which stated that expert testimony is admissible in court if the methodology and the witness are both “generally accepted” as reliable by the scientific community (Grivas and

Komar, 2008). In contrast, the Daubert ruling requires federal judges to analyze scientific evidence and testimony much more closely before admitting it into the courtroom (Grivas and Komar, 2008). For anthropologists who wish to see forensic casework carried out in court, methodology must be viewed as having been gathered using strictly empirical, objective techniques that have been repeatedly tested and peer-reviewed (Christenson,

2004; Grivas and Komar, 2008). Further Dirkmaat and colleagues (2008) have asserted that the improvement upon existing methods, especially regarding method quantification, has significantly impacted forensic research and will continue to do so in coming decades.

Craniometrics aims to quantify cranial size and shape variation. Though many anthropologists have success in identifying and measuring crania, there still exists disagreements concerning measuring protocols. In 1973, Howells provided some measurement descriptions to standardize cranial measures, but some of the descriptions

43 remained more or less unclear (Howells, 1973). For example, Howells proposes measuring the “height” of the mastoid process, and defines it as beginning at the “...upper edge of the meatus,” (1973: 61); this direction is also described in Giles and Elliot’s paper on craniometric measures (1963). Other researchers are more specific and instead define the “length” of the mastoid as beginning at the landmark just above the external auditory meatus (known as cranial landmark “porion”) (Buikstra and Ubelaker, 1994) and still others define the length as beginning at the upper edge of the zygomatic process

(Saini et al., 2012). Inconsistency in craniometric methods can affect interobserver error and discourage method replication (Petaros, et al., 2015). There is a rising need for modifications of traditional methods for craniometric sex estimation. It is for this reason and others stated above that this dissertation project was pursued.

Research Questions and Hypotheses

The preceding literature review and motivation sections have led to the development of several research questions and hypotheses. The research questions addressed in this dissertation are listed below. Hypotheses came out of the following research questions, which are also listed below each research question.

1. Can sexually dimorphic cranial traits that are traditionally visually assessed be

quantified to reduce subjectivity and increase accuracy and precision in sex

estimation?

1a. How effective is the new method in estimating the sex of ‘modern’

human crania that would typically be seen in forensic investigations?

44 • Hypothesis 1a: The newly developed method will be as accurate

or more accurate as standard visual methods in estimating sex of

‘modern’ human crania.

• Null hypothesis 1a: The newly developed method will not be as

accurate as standard visual methods in estimating sex of ‘modern’

human crania

1b. How effective is the new method in estimating the sex of ‘premodern’

human crania that would typically be seen in bioarchaeological or historic

investigations?

• Hypothesis 1b: The newly developed method will be as accurate

or more accurate as standard visual methods in estimating sex of

‘premodern’ human crania.

• Null hypothesis 1b: The newly developed method will not be as

accurate as standard visual methods in estimating sex of

‘premodern’ human crania

2. What is the level of sexual dimorphism between African Americans and European

Americans? Are African-Americans and European-Americans cranially different

enough to be considered two disparate groups?

• Hypothesis 2: European Americans and African Americans will be

significantly different (p < 0.05) in their levels of sexual

dimorphism (D values)

45 • Null hypothesis 2: Americans and African Americans will not be

significantly different in their levels of sexual dimorphism (D

values)

3. How is sexual dimorphism in European Americans and African Americans

changing through time? Are secular changes occurring on the sexually dimorphic

cranial features chosen for this study?

• Hypothesis 3: Each generation will be significantly different (p <

0.05) in their levels of sexual dimorphism (D values) from one

another.

• Null hypothesis 3: All generations are similar in their levels of

sexual dimorphism.

4. Which factors (ancestry, generation, sex) have a significant effect on the measurements used in this study?

• Hypothesis 4a: Generation will have a significant effect on the

measurements used in this study

• Null hypothesis 4a: Generation will not have a significant effect

on the measurement used in this study

• Hypothesis 4b: Ancestry will have a significant effect on the

measurements used in this study

46 • Null hypothesis 4b: Ancestry will not have a significant effect on

the measurements used in this study

• Hypothesis 4c: Sex will have a significant effect on the

measurements used in this study

• Null hypothesis 4c: Sex will not have a significant effect on the

measurements used in this study

CHAPTER 2: MATERIALS

The sample consists of 710 adult human crania of European European and

African American ancestries from three skeletal collections: the Hamann-Todd

Collection at the Cleveland Museum of Natural History, the Documented Skeletal

Collection from the University of New Mexico’s Maxwell Museum, and the William M.

Bass Donated Skeletal Collection housed at the University of Tennessee. Originally, 760 crania were analyzed but many were subsequently removed from analyses; Chapter 3 provides clarification. The 710 crania represent 4 pre-selected generations. To increase measurement accuracy, crania exhibiting fragmentation, trauma, or pathological conditions were not included. The individuals in the present sample were specifically chosen to fall between the ages of 20 and 60 to eliminate those who would not reflect pubertal changes and those who might show the most exaggeratedly robust cranial features in older age.

Skeletal Collections

The Hamann-Todd Collection The Hamann-Todd Skeletal Collection was begun by Dr. Carl A. Hamann, professor of anatomy, in 1893 and continued by T. Wingate Todd from 1912 to 1938 at

Case Western Reserve University in Cleveland, Ohio. It is currently housed at the

48 Cleveland Museum of Natural History. Over 3,500 individuals are represented in the collection (Jones-Kern, 1997); skeletons vary in age, sex, and ancestry, though the majority of individuals are males of European ancestry (Hamann-Todd Human Collection

Database). During the early part of the twentieth century, as the skeletons were being added to the collection, data on age-at-death, sex, stature, body mass, cranial capacity, ethnicity, and reported ancestral affinity (or “race”) were meticulously recorded. In addition, the bodies were photographed, weighed, and measured upon delivery; Jones-

Kern states that over seventy anthropometric measurements were taken (1997). These records serve as the Human Collection Database and allow the collection to be one of several skeletal collections in the United States that houses “known” individuals. That is, researchers are able to analyze these skeletons and check their estimates against reported data. In his doctoral dissertation, William Cobb provides a descriptive analysis of the individuals in the collection, asserting that 85% of males were “general laborers,” which many anthropologists consider to mean working-class people, probably from the lower or lower-middle classes (Jellema, personal communication) and most females were recorded as “housewives” (1932). Individuals were local to the Cleveland area, received either as unclaimed decedents from the Cuyahoga County Morgue or from anatomical donations at

Case Western Reserve Medical School. Cobb describes the collection amassed during

Todd’s tenure as containing “the remains of the diverse elements which have played roles in the epic human drama enacted during the last twenty years in Cleveland” (1932:81).

49 The Maxwell Documented Collection The Maxwell Museum’s Documented Skeletal Collection is housed at the

University of New Mexico and was begun by Dr. Stanley Rhine in 1975. Since its inception, the collection has amassed close to 300 skeletons from individual and family donations, as well as from the New Mexico Office of the Medical Investigator in

Albuquerque. The collection is well-known for being the largest and most documented skeletal assemblage in the Western United States. It also has more individuals who identify as Hispanic (n=10) than other U.S. collections

(http://www.unm.edu/~osteolab/coll_doc.html). The modern individuals in this collection are especially useful for forensic studies, as they may accurately reflect the larger

American population.

The Bass Collection William M. Bass established the Bass Donated Skeletal Collection in 1981 at the

University of Tennessee, Knoxville. The collection is part of the Forensic Anthropology

Center (FAC), which includes the well-known Anthropological Research Facility, or “the

Body Farm.” The Bass Collection is widely considered one of the largest (n=>1,000) and most representative collections of “known” modern human remains. Like the other two collections examined here, the Bass Collection contains many more European Europeans than any other ancestral group and about twice as many males as females. The age distribution, however, is distinct, in that there are more infants and fetal remains (n=42) than in other similar collections; birth years also impressively span from 1892 to 2011.

Sixty-six percent of bodies are donated either by the individual (arrangements are made

50 before death) or by the family of the deceased, and 33% of donations come from unclaimed individuals from the Knox County Morgue. Since many of its donations are planned, the FAC has been able to document sex, age, stature, ancestry, cause of death, body mass, and anthropometric data for each individual. In addition, “health, occupation, socio-economic status, birth information, and habitual activities” are reported

(http://fac.utk.edu/wm-bass-donated-skeletal-collection/), adding to the invaluable wealth of information for each skeleton.

Representativeness of the collections

Anthropologists often wonder whether skeletal collections are truly representative of the larger population. Many times, the very nature of collecting the individuals may cause a skewed distribution concerning sex, ancestral group, or socioeconomic class within a skeletal assemblage. Regarding this issue, Grivas and Komar write, “not every individual in a population has an equal chance of becoming part of a collection” (2008).

Certain populations are at a higher risk of becoming a medicolegal, or forensic, case, while other groups are more likely to donate their bodies for scientific use. For example, the Maxwell and Hamann-Todd collections were mostly assembled from area morgues and anatomy departments (Komar and Grivas, 2008) and The Bass Collection’s donated bodies come from individuals who typically come from a higher socioeconomic class and have higher levels of education (Wilson, et al., 2007). These disparities may have an effect on the data derived from skeletal research on these populations.

There also usually exists an overrepresentation of males in American skeletal collections; this may be due to a stigma surrounding female bodies and

51 anatomical/medical uses at the times the collections were being built (Komar and Grivas,

2008), though that fails to explain the same phenomenon seen in the more modern Bass

Collection. There may also exist a ‘donation bias,’ as different cultures may not regard body donation as an ethical and useful practice (Komar and Grivas, 2008); this could also help explain the lack of ancestral diversity seen in many U.S. skeletal collections.

Ancestral representation

This dissertation includes skeletal samples from European American and African

Hispanic or Asian descent in American skeletal collections, and since this study aims to represent the larger American population, data collection from foreign skeletal material was not pursued.

Generations and sample sizes

The crania used in this project come four generations, as defined here (Table 4).

Generation Number Number Number Number Total EA males EA females AA males AA females 1 53 93 39 36 221 2 63 58 77 33 211 3 50 53 34 31 168 4 47 33 12 18 110 Total 213 217 162 118 710 Table 4. Summary table of males, females, European Americans, and African Americans

52 The earliest generation (“Generation 1”) includes individuals born between 1864 and 1926. This sample has fewer African Americans than European Americans; this is explained by the dearth of African American skeletons in American donated collections.

The second generation (“Generation 2”) is comprised of individuals born between 1927 and 1947. Both Generations 1 and 2 include individuals known to be ‘pre-modern’ by anthropological standards; that is, the skulls from these generations are not likely to exhibit the secular changes noted in more modern crania (Jantz and Jantz, 2000). The third generation (“Generation 3”) is considered ‘modern’ by anthropological standards, in that the skulls belong to individuals born between 1948 and 1989, and may reflect

American cranial diversity in terms of secular changes in cranial size and shape (Jantz and Jantz, 2000). The final generation (“Generation 4”) included in this study consists of adults born between 1990 and 1992. This sample includes fewer individuals than the previous three, simply because of the limited availability of such recent skeletal material in American collections. This most recent generation should exhibit the latest secular trends in cranial morphology.

Age at death of skeletons used in this study

While the birth dates of the individuals in this study are important for documenting generational information, I also wanted to discriminate on the basis of age at death. I purposely asked museum curators and university collection directors to exclude skeletons younger than 20 and older than 75 for my study. Individuals older than

20 have reached and completed puberty, and will therefore exhibit the sexually dimorphic cranial traits in question. Individuals older than 75 were also omitted because research

53 suggests that as women age, their bones become more robust, which can theoretically make men and women look more similar in their cranial traits (Walker, 2008), thus confounding analyses and results.

Measurement tools To determine the outward projection of each trait, coordinate calipers were used to take each measurement. Coordinate calipers resemble traditional sliding calipers but have a middle arm that descends to capture the three-dimensional nature of an anatomical feature. In addition, coordinate calipers provide an affordable and easy-to-use alternative to the more expensive and complicated technology in use today (Pretorius, et al., 2006;

Kimmerle, et al., 2008; Garvin and Ruff, 2012). Sliding calipers were also used to measure length and width of the mastoid processes, as well as to determine the locations of Points A and B, two landmarks originally proposed here.

CHAPTER 3: METHODS

Cranial Measurements

It is well known that male skulls are generally more robust, with larger muscle attachment sites and thicker bones overall (Bass, 2005), although since there is considerable overlap between the sexes (Frayer and Wolpoff, 1985), we can expect that some female crania would exhibit more ‘male’ features and vice versa. This study, therefore, rests on the idea that in general, sex can be determined with reasonable accuracy from the cranium. The six areas of the cranium I assessed were specifically selected for their contribution to overall cranial robustness. Furthermore, the sex-based differences in these features are not only due to size, but to shape as well. These areas include four sites for muscle attachment: the external occipital protuberance, the nuchal area (or protuberances), the glabella, and the mastoid processes (Table 5, Figures 2-5).

The two other cranial traits included here are thought to be sexually dimorphic because of hormonal differences between the sexes (Iuliano-Burns, et al., 2009), and include the supraorbital ridge(s) and frontal bossing (Table 5, Figures 2-5).

55 Cranial Features Trait Cranial Landmarks and Points Used in Number Measurements

Supraorbital ridge - right 1 Points A and B (defined in this study), intermediate between frontotemporale (ft) and frontomalare temporale (fmt)

Supraorbital ridge - left 2 Points A and B (defined in this study), intermediate between frontotemporale (ft) and frontomalare temporale (fmt)

Glabella 3 Points A and B (defined in this study), intermediate between frontotemporale (ft) and frontomalare temporale (fmt)

External occipital 4 Lambda (l) to opisthion (o) protuberance

Nuchal protuberance - 5 Inion (i) to opisthion (o) right

Nuchal protuberance - 6 Inion (i) to opisthion (o) left

Nuchal protuberance - 7 Inion (i) to opisthion (o) center

Mastoid length - right 8 From the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process

Mastoid width - right 9 From the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process

Mastoid projection - 10 From the most anterior aspect of the external right auditory meatus to the most superior posterior aspect of the mastoid process

Table 5. Cranial features used in sex estimation and the proposed methods for their measurement, continued.

56 Table 5, continued

Mastoid length - left 11 From the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process

Mastoid width - left 12 From the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process

Mastoid projection - left 13 From the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process

Frontal bossing - right 14 Bregma (b) to frontomalare temporale (fmt)

Frontal bossing - left 15 Bregma (b) to frontomalare temporale (fmt)

Frontal bossing - center 16 Bregma (b) to nasion (n)

Figure 3. Lateral view of human skull. Box indicates the cranial feature measured (mastoid process). Dots indicate the landmarks used for measuring.

Figure 5. Box indicates the cranial feature(s) measured (nuchal protuberances/region). Dots indicate cranial landmarks used in measuring (i = inion; o = opisthion).

Instruments for Data Collection

Anthropologists increasingly rely on expensive and complicated instruments for three-dimensional data collection, such as three-dimensional digitizers and CT scanning technology (Pretorius, et al., 2006; Kimmerle, et al., 2008; Garvin and Ruff, 2012). While these technological innovations are certainly effective, they may not be the most practical tools to many practicing biological anthropologists. To collect data, coordinate calipers

61 were used, which provide an inexpensive and highly accessible alternative for three- dimensional skeletal data collection (Figure 6).

Figure 6. A researcher uses coordinate calipers to measure the outward projection of the glabella.

Measurement Instructions

The following explains how to perform the original measurements proposed in this dissertation. The aim of each measurement is to quantify the shape, or outward projection, of the sexually dimorphic trait. For each feature, the observer may need to move the middle arm back and forth until the largest area, or the point that projects outward the most, is found. To accurately measure the outermost projection of the supraorbital ridge, new cranial landmarks (Points A and B) were created. To find these, first place the measuring arms of sliding calipers on landmarks frontotemporale (ft) and

62 frontomalare temporale (fmt) on the right side of the skull only and record the measurement. Next, measure halfway between ft and fmt; this is the newly devised ‘Point

A,’ a point which allows for more precise measurement of the supraorbital ridge and glabella (Figure 7). Do the same for the left side; label this landmark Point B. Next, measure with coordinate calipers from Point A to Point B and record this total length. Drop the middle arm of the coordinate calipers down toward the projecting supraorbital ridge and take the largest measurement, or the point of outermost projection for the right side of the supraorbital ridge. Repeat for the left side. Use the same landmarks, Points A and B, to measure the projection of the glabella as well (Figures 8 and 6).

Figure 7. A researcher measures halfway between ft and fmt to find the newly devised cranial landmark, Point A.

Figure 8. A researcher uses coordinate calipers to measure the outward projection of the right supraorbital ridge.

To measure the external occipital protuberance, place the measuring arms of the coordinate calipers on cranial landmarks lambda (l) and opisthion (o). Drop the middle arm of the coordinate calipers down toward the point that most projects in the occipital area; this is the projection measurement (Figure 9).

Figure 9. A researcher uses coordinate calipers to measure the outward projection of the exernal occipital protuberance.

The nuchal region varies greatly in its robusticity. The researcher may see either one or two nuchal protuberances, or perhaps an enlarged nuchal crest or a group of ridges in the nuchal area. To measure the entire area appropriately, perform the following measurement for the right, left, and center nuchal regions, as each area may exhibit unique shape variations. To measure the right side of the nuchal region, place one measuring arm of the coordinate calipers on the cranial landmark inion (i) and the other on the most posterior aspect of the groove just posterior to the mastoid process. Drop the middle arm of the coordinate calipers down toward the point that most projects in the nuchal area (Figure 10). This is the projection measurement. Do the same for the left side of the nuchal region. To measure the center of the nuchal area, place the measuring arm

65 of the coordinate calipers on cranial landmarks inion (i) and opisthion (o) and drop the middle arm to the area that projects outwardly the most (Figure 11).

Figure 10. A researcher uses coordinate calipers to measure the outward projection of the right side of the nuchal region.

Figure 11. A researcher uses coordinate calipers to measure the outward projection of the center of the nuchal region.

Next, measure the outermost projection of the mastoid process. Using coordinate calipers, measure from the most anterior aspect of the external auditory meatus to the most superior posterior aspect of the mastoid process, and drop the middle arm of the calipers down to record the outermost projecting point (Fig 12).

67 Figure 12. A researcher uses coordinate calipers to measure the outward projection of the left mastoid process.

Lastly, measure the projection of the frontal bosses (even if they are not readily apparent). As with the nuchal region, perform this measurement for the right, left, and center areas of the frontal bone, as each area may exhibit unique variation in bossing. To measure the right frontal boss, place the measuring arms of the coordinate calipers on cranial landmarks bregma (b) and frontomalare temporale (fmt) and record the projection of the frontal boss. Repeat this on the other side of the skull for the left frontal boss

(Figure 13). To take this measurement for the center frontal boss, place the measuring arms of the coordinate calipers on cranial landmarks bregma (b) and nasion (n). Record the outward projection.

Figure 13. A researcher uses coordinate calipers to measure the outward projection of the frontal boss.

69 Statistical Analyses and Rationale

For statistical analyses R, SAS, and SPSS were used. R and SAS were the primary software packages employed, but SPSS was used as well to check whether the codes and functions developed in the former two agreed with the output from SPSS. First, missing data were removed since most statistical software programs do not operate properly with missing data. Then, each trait on each skull in the sample was tested for normality using Doornik and Hansen’s method (2008), which proposes an omnibus test for normality. Many of the cranial traits were not normally distributed; for those, log transformations were performed. To keep the measures on the same scale, log transformations were then performed on every trait, regardless of normality. Once the log transformations were complete, summary statistics were calculated. The mean and standard deviations for each trait are especially important here, as these were used to determine the D value (after Chakhraborty and Majumder, 1982), or the numerical degree of sexual dimorphism in a population, described in further detail next.

Summary statistics and the calculation of D values Researchers have used a multitude of differing statistical tests in research on sexual dimorphism. Some studies have taken the mean of one sex and compared it (in the form of a percentage) to the mean of another sex (Hamilton, 1982; Godde, 2015). Others test for significant differences between the means within groups and use t-tests or the

Mann-Whitney test (Hamilton, 1982). To determine whether differences in sexual dimorphism between populations are statistically significant, Relethford and Hodges

(1985) proposed a method using a linear regression and simple summary statistics

(sample size, mean, and standard deviation). Many anthropologists, however, do not

70 make use of Chakraborty and Majumder’s (1982) revision to Bennett’s measure of sexual dimorphism (1981), which is a unique, but quite logical, approach to calculating the level of sexual dimorphism in a population. Chakraborty and Majumder’s paper proposes calculating the area of non-overlap between two normally distributed overlapping curves

(each representing a biological sex). When comparing normally distributed males and females from the same population, one would assume at least some overlap between the groups. This area of overlap represents the traits in which males and females would be indistinguishable in their expression. The area of non-overlap, then, represents the sexual dimorphism present between the two groups. If there exists a large amount of overlap, the groups do not exhibit much sexual dimorphism; if there exists little or no overlap, the groups can be considered highly sexually dimorphic. The D value quantifies the level of non-overlap, or the amount that the two groups (again, males and females) differ. This is done by calculating the area under each curve and subtracting the areas of overlap.

The D values are calculated using a few steps. First, the male and female mean and the male and female standard deviations must be calculated for a given trait. Once calculated, the next step is to calculate the test statistic, as discussed in Chakraborty and

Majumder (1982). The test statistic tells a researcher how many points of intersection exist between the two curves (representing males and females). Two possibilities for points of intersection exist: either the two curves, representing males and females, intersect at one point, or they intersect twice. (Another possibility technically exists, in which the two curves do not overlap at all, but this was not the case with the present sample). If the test statistic is less than 0, only one point of intersection exists between the

71 two curves. If the test statistic is more than 0, two points of intersection are present

(Chakraborty and Majumder, 1982).

There exist two separate D calculations depending upon whether the standard deviations are equal or unequal; functions for both D calculations were written in R and are called D1 and D2, respectively (Figures 14 and 15). So, to provide an example of the process, take Trait 11 in African Americans in Generation 1, the length of the right mastoid process. The male mean, after being log transformed, is 3.23. The female mean, after being log transformed, is 3.04. The male standard deviation is 0.1 and the female standard deviation is 0.7. After plugging these values into the D1 function, results show that the test statistic is 662.49. Since the test statistic is larger than 0, and the standard deviations differ from one another, the D2 function is used in this case. The total area of non-overlap in this example, or the level of sexual dimorphism in African Americans mastoid length in Generation 1 is 0.74. Since D must fall somewhere between 0 and 1, with 1 signifying the two sexes are completely different, 0.74 indicates a high level of dimorphism in this trait in this data subset.

72 A <- 1/fs^2 - 1/ms^2 B <- fm/fs^2 - mm/ms^2 C <- fm^2/fs^2 - mm^2/ms^2 - 2*log(ms/fs) xonp <- ( B + sqrt(B^2 - A*C))/A xonn <- ( B - sqrt(B^2 - A*C))/A pmt1 = (mm - xonp)/ms qm = 1 - pnorm(pmt1) qm rm <- 1 - qm - 0.5 rm pmt2 = (mm - xonn)/ms tm <- pnorm(pmt2) tm sm <- 1 - tm -0.5 sm maleck <- qm + rm + sm + tm maleck pft1 = (fm - xonp)/fs qf <- 1 - pnorm(pft1) qf rf <- 1 - qf - 0.5 pft2 <- (fm - xonn)/fs tf <- pnorm(pft2) tf sf <- 1 - tf - 0.5 sf femck <- qf + rf + sf + tf femck D <- (qf - qm + (rm + sm ) - (rf + sf) + tf - tm)/2 Figure 14. Function for D1.

73 D2 <- function(mm, fm, ms, fs){ A <- 1/fs^2 - 1/ms^2 B <- fm/fs^2 - mm/ms^2 C <- fm^2/fs^2 - mm^2/ms^2 - 2*log(ms/fs) xonp <- ( B + sqrt(B^2 - A*C))/A xonn <- ( B - sqrt(B^2 - A*C))/A pmt1 = (mm - xonp)/ms qm = 1 - pnorm(pmt1) qm rm <- 1 - qm - 0.5 rm pmt2 = (mm - xonn)/ms tm <- pnorm(pmt2) tm sm <- 1 - tm -0.5 sm maleck <- qm + rm + sm + tm maleck pft1 = (fm - xonp)/fs qf <- 1 - pnorm(pft1) qf rf <- 1 - qf - 0.5 pft2 <- (fm - xonn)/fs tf <- pnorm(pft2) tf sf <- 1 - tf - 0.5 sf femck <- qf + rf + sf + tf femck D <- (qf - qm + (rm + sm ) - (rf + sf) + tf - tm)/2 return(D)} Figure 15. Function for D2.

T-tests After all Ds were calculated (16 Ds in four generations = 64 Ds total), paired t- tests were performed to test differences between D values. T-tests were primarily employed to test both Hypothesis 2a: “European Americans and African Americans will be significantly different in their levels of sexual dimorphism (D values)” and Hypothesis

74 3a: “Each generation will be significantly different in their levels of sexual dimorphism

(D values) from one another.”

In interpreting the results from the paired t-tests, the most important results come from the p-value and the means of the differences. The p-value from the t-test indicates whether the null hypothesis is accepted or is rejected. In this case, the null hypothesis states that the two groups in a given t-test are the same, i.e., the D values in the two groups are not significantly different from one another. In this dissertation, a small p- value (≤ .05) is considered statistical significance, thus rejecting the null hypothesis.

The paired t-tests were performed on 14 different pairings of data subsets, which are outlined in Chapter 4. The ‘means of the differences’ results indicate which of the two subsets included in a given t-test is larger or smaller on average (or, if they are the same, the result will be 0). In some cases, the means of the differences is a negative number.

Since the test is essentially subtracting the D values of one subset from another subset

(for example, African-Americans from Generation 1 minus African-Americans from

Generation 2), a negative number indicates the first subset (in this example, African-

Americans from Generation 1) has smaller D values than the second subset.

Analysis of Variance (ANOVA)

A robust and useful statistical test that can be used to determine if two groups differ significantly from one another is an analysis of variance (ANOVA) (Hair et al.,

2009). In this case, the two groups are males and females, and later, differing generations.

Three-way analyses of variance (ANOVA) were performed (on all log- transformed datasets, combined) to parse out which variables (sex, ancestry, of

75 generation) impact the trait in question. First, since ANOVAs assume normality in the data, normality tests were employed for each trait (all data combined). Interaction plots were created for the traits that were normally distributed to visualize the effects of either sex, generation, or ancestry on each trait and to determine what kind of ANOVA model to build. For example, if an interaction plot shows that ancestry has an impact on the projection of the right mastoid process, an ANOVA test can show how significant (if at all) those interactions are. Then, the ANOVA models were designed depending upon which interactions between factors were significant. The three-way ANOVAs were undertaken to test Hypothesis 4a: “Generation will have a significant effect on the measurements used in this study,” Hypothesis 4b: “Ancestry will have a significant effect on the measurements used in this study,” and Hypothesis 4c: “Sex will have a significant effect on the measurements used in this study.”

Discriminant Function Analysis (DFA) When metric measures are used, a discriminant function analysis (DFA) can systematically draw out differences between two groups based on trait similarities. An exceptionally robust test, DFA does not require variables that are normally distributed, and is thus a useful test for the skeletal sample represented in this dissertation. DFA provides a more objective method of estimating sex, and is just as accurate as using visual methods (Giles and Elliot, 1962). It is assumed in employing a discriminant function approach, however, that the independent variables used come from one of the existing reference samples (Walker, 2008). If there are no appropriate reference samples, a discriminant function is then created (Konigsberg et al., 2009). Studies have shown that using DFA on populations outside the existing reference samples is ineffective

76 (Williams et al., 2005; Ramsthaler et al., 2007). A discriminant analysis is used in this dissertation to group the measures into either male or female categories.

If using DFA, Walker suggests measuring as many features as possible, as the more traits that one can include in a DFA, the more reliable the results will be (Walker,

2008). Additionally, a principal components analysis (PCA) can be performed in conjunction with the above statistical methods to determine which of the traits for sex estimation is the most accurate (Hair et al., 2009).

A discriminant function analysis (DFA) was carried out to test whether the measurement method proposed in this dissertation is as accurate as the traditionally used, visual methods for estimating sex. Specifically, Hypothesis 1a : “The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘modern’ human crania” and Hypothesis 1b: “The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘premodern’ human crania” were tested with the DFA.

First, the premodern subset of data (Generations 1 and 2 combined) was analyzed, followed by the modern subset of data (Generations 3 and 4); Classification accuracy rates for both the premodern and modern subsets can be found in Chapter 4 (Results).

CHAPTER 4: RESULTS

Results from initial normality tests indicate most variables were not normally distributed. Log transformations were taken on the variables that were not normal; many of them were then normally distributed. The variables that were still not normal were then removed from the dataset. Log transformations were then taken on all 128 variables so that all variables would be analyzed on the same scale. Normality was then tested again on all log transformed variables and results can be found in Appendix A.

D values

Results from the D tests (Chakraborty and Majumder, 1982), including summary statistics, can be found in Table 6. The D value is only expressed between 0 and 1. The closer to 1, the higher the level of sexual dimorphism, and the closer to 0, the lower the level of sexual dimorphism. For example, in the first trait (Trait 4 - right supraorbital margin) from the first subset (European Americans from Generation 1), the D value is

0.07, indicating a very low level of sexual dimorphism (Table 6). The traits that expressed the most sexual dimorphism (0.5 or greater) are listed in Appendix B.

78 Subset Trait Name D value

AAGen1 Glabella 0.53

AAGen1 Mastoid length - right 0.74

AAGen1 Mastoid length - left 0.82

AAGen1 Mastoid width – left 0.54

EuropeansGen2 Mastoid length - left 0.67

AAGen3 External occipital protuberance 0.94

AAGen3 Mastoid length - right 0.59

AAGen4 Supraorbital ridge - right 0.55

AAGen4 Nuchal protuberance/region - center 0.92

AAGen4 Mastoid projection - right 0.55

AAGen4 Frontal bossing - right 0.68

AAGen4 Frontal bossing - left 0.69 Table 6. Traits expressing the highest levels of sexual dimorphism (> 0.5).

T-test Results

Results from paired t-tests testing the hypothesis that European Americans and

African Americans will be significantly different in their levels of sexual dimorphism (D values) are presented in Table 8. Means of differences (Table 8) can be positive or negative. If negative, this indicates that European Americans’ D values are, on average, smaller than African Americans’ D values. If the means of the differences is positive, the

African American D values are larger than the European Americans’ D values.

79 Data Subset 1 Data Subset 2 p- Means of the value differences

Europeans Generation AA Generation 1 .01 .25 1

Europeans Generation AA Generation 2 .7 .02 2

Europeans Generation AA Generation 3 .29 .09 3

Europeans Generation AA Generation 4 .09 .13 4

Europeans Generation Europeans Generation .01 .73 1 2

Europeans Generation Europeans Generation .83 .09 1 3

Europeans Generation Europeans Generation .06 -1.09 1 4

Europeans Generation Europeans Generation .13 .6 2 3

Europeans Generation Europeans Generation .99 .00 2 4

Europeans Generation Europeans Generation .01 .76 3 4

AA Generation 1 AA Generation 2 .25 .59

AA Generation 1 AA Generation 3 .56 .3

AA Generation 1 AA Generation 4 .8 -0.1

AA Generation 2 AA Generation 3 .69 -0.16

AA Generation 2 AA Generation 4 .01 -0.75

AA Generation 3 AA Generation 4 .05 -0.68 Table 7. Results from paired t-tests including p-values and means of the differences. Bold p- values indicate significant differences between groups.

80 G-plots were created for the D values to visualize change in sexual dimorphism over time (Figures 16 and 17). A color key can be found in Table 9. Since 16 traits are difficult to track in one plot, each trait has been separated out into its own plot (Figures

18-51). Missing dots in the g-plots indicate missing D values. D values were removed from the creation of g-plots and subsequent analyses of secular changes if they were not normally distributed; as a result, some of the single-trait g-plots only contain one dot.

Unfortunately, nothing can be extrapolated about change over time from those g-plots.

The traits below are labeled Traits 1-16, instead of 4-19 as they have been referred to previously in this dissertation.

Figure 16. Change in D values in European Americans over Generations 1-4. Color key in Table 9.

82 Color Trait number Trait

Chartreuse 4 Supraorbital ridge - right

Orange-red 5 Supraorbital ridge - left

Purple 6 Glabella

Light Sky Blue 7 External occipital protuberance

Hot Pink 8 Nuchal protuberance - right

Blue 9 Nuchal protuberance - left

Dark Sea Green 10 Nuchal protuberance - center

Goldenrod 11 Mastoid length - right

Coral 12 Mastoid width - right

Cadet Blue 13 Mastoid projection - right

Dark Magenta 14 Mastoid length - left

Gray 15 Mastoid width - left

Misty Rose 16 Mastoid projection - left

Olive Drab 17 Frontal bossing - right

Pale Violet Red 18 Frontal bossing - left

Turquoise 19 Frontal bossing - center Table 8. Color key for g-plots. Colors correspond to traits in g-plots. Color names generated by R.

Figure 17. Change in D values for African Americans over Generations 1-4. Color key in Table 9.

Next is a g-plot for the right supraorbital ridge among European Americans over four generations (Figure 18). Sexual dimorphism in the right supraorbital ridge remained stable and was not highly sexually dimorphic until the most recent generation.

Specifically, the right supraorbital ridge did not change in Europeans until the generation that includes the latest cohort of decedents, with birth years beginning in 1993.

Figure 18. Change in D value for Trait 1 (right supraorbital ridge) in Europeans over Generations 1-4.

Change in the sexual dimorphism of the left supraorbital ridge in European

Americans over the four generations is similar to the results from the previous trait. This is expected for the same trait from the opposite side of the cranium. Sexual dimorphism of the left supraorbital ridge remained stable until recently. Differences between males and females are more apparent only in the youngest generation.

Figure 19. Change in D value for Trait 2 (left supraorbital ridge) in Europeans over Generations 1-4.

Sexual dimorphism in the glabella in European Americans increased over time

(Figure 20). It can be inferred that over generations, the glabella has also become more sexually dimorphic in its expression.

Figure 20. Change in sexual dimorphism in the glabella in Europeans over Generations 1-4.

Figure 21 shows the change in the external occipital protuberance in European

Americans over the four generations. Since the D value for generation 3 is missing, it is more difficult to extrapolate a secular trend in this feature. One may hazard that there has been a slight increase in sexual dimorphism from Generation 1 to Generation 4.

Figure 21. Change in D value for Trait 4 in Europeans over Generations 1-4.

There is insufficient data to determine D for the right, left, and center nuchal protuberance/region. Therefore, no secular trend data are presented.

Figure 22. Change in D value for Trait 7 in Europeans over Generations 1-4.

Figure 23 shows the change in Trait 8 (the length of the right mastoid) in

European Americans over the four generations. It appears as though the level of sexual dimorphism has increased and decreased over time, and no significant inferences can be made.

Figure 23. Change in D value for Trait 8 in Europeans over Generations 1-4.

Figure 24 shows the change in Trait 9 (the width of the right mastoid process) in

European Americans over the four generations. Though the plot is missing the D value from Generation 1, it can safely be inferred that the level of sexual dimorphism has decreased over time in this feature, making males and females more similar to one another.

Figure 24. Change in D value for Trait 9 (right mastoid width) in Europeans over Generations 1- 4.

No secular trend information can be discerned for the projection of the right mastoid process in European Americans over the four generations; therefore, no plot is included here.

Figure 25 shows the change in Trait 11 (the length of the left mastoid) in

European Americans over the four generations. Even though one D value is absent, it appears as though the level of sexual dimorphism has increased and decreased over time, and no significant inferences can be made. It may be notable that the results here are similar to the results from the length of the right mastoid process.

Figure 25. Change in D value for Trait 11 in Europeans over Generations 1-4.

Although there is no data for the first generation for the width of the left mastoid,

Figure 26 shows a slight increase in the level of sexual dimorphism in the most recent generation.

Figure 26. Change in D value for Trait 12 in Europeans over Generations 1-4.

There is insufficient information to determine whether secular changes have affected the projection of the left mastoid, and the right and left frontal bosses, therefore, no secular trend data is presented.

Figure 27 shows the change in Trait 16 (center frontal bossing) in European

Americans over the four generations. Though the plot is missing the first generation’s D value, it seems as though there has been no secular change in this feature over time.

Figure 27. Change in D value for Trait 16 in Europeans over Generations 1-4.

Figure 28 shows the change in Trait 1 (the right supraorbital ridge) in African

Americans over the four generations. It can be inferred that, though the level of dimorphism in the right supraorbital ridge may have stayed constant over the first three generations, there has been a marked increase in its dimorphism since 1993.

Figure 28. Change in D value for Trait 1 in African-Americans over Generations 1-4.

Figure 29 shows the change in Trait 2 (the left supraorbital ridge) in African Americans over the four generations. The overall results are similar to its counterpart, the right supraorbital ridge (above), as it has also seen a marked increase in dimorphism over time.

Figure 29. Change in D value for Trait 2 in African-Americans over Generations 1-4.

Figure 30 shows the change in Trait 3 (the glabella) in African Americans over the four generations. It looks as though the level of dimorphism took a dive from Generation 1 to

2 but has been steadily increasing ever since.

Figure 30. Change in D value for Trait 3 in African-Americans over Generations 1-4.

Figure 31 shows the change in Trait 4 (the external occipital protuberance) in African

Americans over the four generations. Though the D value for Generation 3 is absent, it can be inferred that the level of sexual dimorphism in this feature has decreased over time.

Figure 31. Change in D value for Trait 4 in African-Americans over Generations 1-4.

Insufficient data exist for the right and left nuchal protuberance/regions in African

Americans and no plots are included here.

Figure 32 shows the change in Trait 7 (center nuchal protuberance/region) in

African Americans over the four generations. While the plot shows a marked increase in dimorphism from Generations 2 to 4, half of the D values are missing, rendering it difficult to infer secular changes.

Figure 32. Change in D value for Trait 7 in African-Americans over Generations 1-4.

Figure 33 shows the change in Trait 8 (the length of the right mastoid) in African

Americans over the four generations. It appears as though the level of sexual dimorphism has increased and decreased over time, and no significant inferences can be made.

Figure 33. Change in D value for Trait 8 in African-Americans over Generations 1-4.

Figure 34 shows the change in Trait 9 (the width of the left mastoid) in African

Americans over the four generations. It appears as though the level of sexual dimorphism has increased and decreased over time, and no significant inferences can be made.

100

Figure 34. Change in D value for Trait 9 in African-Americans over Generations 1-4.

Insufficient data exist for Trait 10 (the projection of the right mastoid process) in African

Americans, so no plot was included here.

Figure 35 shows the change in D values for Trait 11 (the length of the left mastoid process) in African Americans over the four generations. The D value from Generation 1 is missing, but, judging from the other three D values, it looks as though this feature has remained relatively stable in its dimorphism over time.

101

Figure 35. Change in D value for Trait 11 in African-Americans over Generations 1-4.

Figure 36 shows the change in D values for Trait 12 (the width of the left mastoid process) in African Americans over the four generations. The variable nature of the three

D values present, combined with the missing D value, make it difficult to make any assumptions about secular changes in this feature.

102

Figure 36. Change in D value for Trait 12 in African-Americans over Generations 1-4.

Figure 37 shows the change in Trait 13 (the projection of the left mastoid) in African

Americans over the four generations. It is difficult to make any inferences based on the D values present.

103

Figure 37. Change in D value for Trait 13 in African-Americans over Generations 1-4.

Figure 38 shows the change in Trait 14 (right frontal bossing) in African Americans over the four generations. Though the D value for Generation 3 is missing, it looks as though this feature has undergone a significant increase in its level of dimorphism since

Generation 2.

104

Figure 38. Change in D value for Trait 14 in African-Americans over Generations 1-4.

Figure 39 shows the change in Trait 15 (left frontal bossing) in African Americans over the four generations. It can be inferred from the plot that the level of sexual dimorphism has steadily increased with every generation.

105

Figure 39. Change in D value for Trait 15 in African-Americans over Generations 1-4.

Figure 40 shows the change in Trait 16 (center frontal bossing) in African Americans over the four generations. The first D value is missing, which makes it difficult to extrapolate any information on secular trends concerning this feature.

106

Figure 40. Change in D value for Trait 16 in African-Americans over Generations 1-4.

Interaction plots were created for the traits that were normally distributed to visualize the effects of either sex, generation, or ancestry on each trait and to determine what type of ANOVA to use. When looking at an interaction plot, there are two things to notice. First, look at each line on its own; each line depicts the means for a specific group. Second, look at the lines in relation to one another. If lines intersect, a strong interaction between factors is indicated. If the lines are parallel, there is little to no interaction to report.

107 Not all iterations of interaction plots are included here for the sake of brevity, but several interesting relationships are shown in Figures 41-43. For example, in Figure 41, the small discrepancy between the black males and females shows little difference between their means, while the large discrepancy between white male and female means signifies a larger difference between those means. This can be quantified by adding the interaction between ancestry and sex (ancestry*sex) into the standard ANOVA model. It can be inferred from the plot in Figure 40 that ancestry impacts the size and shape of the right supraorbital ridge.

Figure 41. Interaction plot showing relationship between male and female means from both ancestral groups for the right supraorbital ridge.

108

Another interesting interaction is the joint impact of ancestry and sex in the central nuchal region (Figure 42). The plot indicates males and females parallel one another. Lack of an intersection indicates ancestry has no bearing on this sexually dimorphic trait.

Figure 42. Interaction plot showing relationship between male and female means from both ancestral groups for the central nuchal protuberance/region.

A third example of an interaction plot used to help build the ANOVA model was the left supraorbital ridge (Figure 43). Here, Generation and ancestry were factors.

109

Figure 43. Interaction plot showing relationship between male and female means from both ancestral groups for the left supraorbital ridge.

Although it is not hypothesized that the influence of ancestry would vary by generation, this interaction was examined to consider all possible relationships. It is somewhat interesting that there is an intersection of the means in Generation 2, and then the ancestral groups divide again afterward (Figure 43). Since this relationship has no bearing on sex estimation, it was not pursued here.

Three factors were included in an ANOVA model for the right supraorbital ridge

(Table 9). After examining all possible interactions, all interactions were entered into the

ANOVA model. The interaction between ancestry and sex was significant, indicating

110 only ancestry significantly impacts (p = < 0.05) sexual dimorphism in this trait (Table 9).

That is, ancestry may affect the expression of this trait, but birth year will not.

Factors p-value

Ancestry*Sex 0.04

Sex*Generation 0.12

Generation*Ancestry 0.12 Table 9. Summary of interactions for Trait 4 (Supraorbital ridge -- right).

The left supraorbital ridge (Trait 5) shows different results as it pertains to the interaction between sex and generation (Table 10). The p-value here is 0.47, indicating birth year does not have a significant impact on the expression of this cranial feature.

Factors p-value

Ancestry*Sex 0.03

Sex*Generation 0.47

Generation*Ancestry 0.03 Table 10. Summary of interactions for Trait 5 (Supraorbital ridge -- left).

For the glabella (Trait 6), the only factor that had a significant impact on sex was

Generation (Table 11). Therefore, birth year can predict how sexually dimorphic the glabella will be.

111 Factors p-value

Ancestry*Sex 0.08

Sex*Generation 0.00

Generation*Ancestry 0.58 Table 11. Summary of interactions for Trait 6 (Glabella).

Since there did not seem to be an impact on sex from ancestry on the right mastoid width ancestry*sex was not used in the ANOVA model for this feature. Results from this

ANOVA model, which just included Generation*sex (Figure 44) are in Table 12.

Figure 44. Interaction plot showing relationship between male and female means from both ancestral groups for Trait 12 (mastoid width -- right).

112 Factors p-value

Sex*Generation 0.02

Generation*Ancestry 0.54 Table 12. Summary of interactions for right mastoid width.

The interaction plots for the projection of the mastoid process were similar to those for the right mastoid width. The summary of interactions from the ANOVA model are in

Table 13 and show that the interaction between generation number and sex is not significant, meaning for mastoid projection, birth year does not affect sex estimation. In addition, ancestry and generation are significantly related (p = 0.03), therefore birth year may have an effect on how African Americans and European Americans exhibit sexual dimorphism in this particular trait.

Factors p-value

Generation*Sex 0.36

Ancestry*Generation 0.03 Table 13. Summary of interactions for the right mastoid projection

The interaction plots for the length of the left mastoid process were similar to those for the right mastoid width and right mastoid projectio, therefore the interaction between ancestry and sex was removed from this ANOVA model. The summary of interactions

(Table 14) show that the interaction between generation number and sex is significant, though the interaction between generation and ancestry is not (though, as stated before, it is not evident that this interaction needs to be pursued).

113

Factors p-value

Sex*Generation 1.14e-11

Generation*Ancestry 0.24 Table 14. Summary of interactions for Trait 14 (Mastoid length - left).

The interaction plot for the left mastoid width showed ancestry and generation impacting sex, so the ANOVA model incorporated both Ancestry*Sex and Generation*Sex (Table

15). None of the interactions were statistically significant, indicating that neither ancestry nor generation having an effect on the expression of this particular trait.

Factors p-value

Ancestry*Sex 0.35

Sex*Generation 0.47

Generation*Ancestry 0.13 Table 15. Summary of interactions for Trait 15 (Mastoid width - left).

The interaction plots for left frontal bossing and central frontal bossing showed an impact from both ancestry and generation, so both ANOVA models included both Ancestry*Sex and Sex*Generation. Results (Tables 16 and 17) show that both ancestry and birth year have an effect on the expression of frontal bossing on the left side, as well as in the center, of the cranium.

114 Factors p-value

Ancestry*Sex 0.02

Sex*Generation 0.00

Generation*Ancestry 0.05 Table 16. Summary of interactions for Trait 18 (Frontal bossing -- left)

Factors p-value

Ancestry*Sex 0.04

Sex*Generation 0.00

Generation*Ancestry <2e-16 Table 17. Summary of interactions for Trait 19 (Frontal bossing -- center)

115

Discriminant Function Analysis

Discriminant function analyses were undertaken using all cranial traits measured to test the following two hypotheses: Hypothesis 1a, “The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘modern’ human crania” and “Hypothesis 1b: The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘premodern’ human crania.”

Classification accuracy results for the premodern crania (Generations 1 and 2 combined) can be found in Table 18. Overall, the method performed very well on the premodern skull dataset. Using Generations 1 and 2 combined, the classification accuracy was 77.8%. Females were correctly classified 81.3% of the time, and males were correctly classified 74.6% of the time. These accuracy rates are as good or better than the traditional visual assessments (Walker, 2008; Garvin, et al., 2014), and Hypothesis 1b is supported.

Overall correct 77.8%

Males correct 74.6%

Females correct 81.3% Table 18. Classification accuracy from the Discriminant Function Analysis for Generations 1 and 2 combined.

Classification accuracy results for the modern subset of crania (Generations 3 and

4 combined) can be found in Table 19. Overall, the method did not perform well, with

116 accuracy rates on par with those from using chance alone. Using Generations 3 and 4 combined, the classification accuracy was 51.1%. Females were correctly classified

56.2% of the time, and males were correctly classified 50% of the time. These accuracy rates are worse than traditional visual assessments (Walker, 2008; Garvin, et al., 2014), causing Hypothesis 1a to be rejected.

A third DFA was performed using the three traits with the highest levels of sexual dimorphism, as determined from the D values. All measurement variables were included.

These traits include the external occipital protuberance (D value = 0.94), the central nuchal region (D value = 0.92), and the left mastoid length (D value = 0.82).

Classification accuracy results using these three traits on the premodern dataset are in

Table 20. Classification accuracy results using the three traits on the modern dataset are in Table 21.

Overall accuracy 51.1%

Male accuracy 50%

Female accuracy 56.2% Table 19. Classification accuracy from the DFA for Generations 3 and 4 combined.

Overall accuracy 84%

Male accuracy 83%

Female accuracy 88% Table 20. Classification accuracy from the DFA using only external occipital protuberance, central nuchal protuberance, and the left mastoid length – premodern dataset (Generations 1 and 2 combined).

117 Overall accuracy 75%

Male accuracy 76%

Female accuracy 74% Table 21. Classification accuracy from the DFA using only external occipital protuberance, central nuchal protuberance, and the left mastoid length – modern dataset (Generations 3 and 4 combined).

118

DISCUSSION AND CONCLUSION

Hypotheses

To review, the hypotheses in this dissertation are:

 Hypothesis 1a: The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘modern’ human crania.  Null hypothesis 1a: The newly developed method will not be as accurate as standard visual methods in estimating sex of ‘modern’ human crania

 Hypothesis 1b: The newly developed method will be as accurate or more accurate as standard visual methods in estimating sex of ‘premodern’ human crania.  Null hypothesis 1b: The newly developed method will not be as accurate as standard visual methods in estimating sex of ‘premodern’ human crania

 Hypothesis 2: European Americans and African Americans will be significantly different in their levels of sexual dimorphism (D values)  Null hypothesis 2: Americans and African Americans will not be significantly different in their levels of sexual dimorphism (D values)

 Hypothesis 3: Each generation will be significantly different in their levels of sexual dimorphism (D values) from one another.  Null hypothesis 3: All generations are similar in their levels of sexual dimorphism.

 Hypothesis 4a: Generation will have a significant effect on the measurements used in this study  Null hypothesis 4a: Generation will not have a significant effect on the measurement used in this study  Hypothesis 4b: Ancestry will have a significant effect on the measurements used in this study

119  Null hypothesis 4b: Ancestry will not have a significant effect on the measurements used in this study  Hypothesis 4c: Sex will have a significant effect on the measurements used in this study  Null hypothesis 4c: Sex will not have a significant effect on the measurements used in this study

The following section will discuss the hypotheses and null hypotheses in the context of the results outlined in Chapter 4.

Results of the D tests are surprising given earlier research on sexually dimorphic cranial traits that illustrates male and female crania exhibit high levels of sexual dimorphism (Buikstra and Ubelaker, 1994; Bass, 2005; Komar and Buikstra, 2008;

Walker, 2008; Garvin, et al., 2014). D is measured between 0 and 1. The closer to 0, the lower the level of sexual dimorphism between the two groups; the closer to 1, the higher the level of sexual dimorphism. In this sample, almost every trait examined expressed at least one low D value, though some “repeat offenders” (a D value result of <0.1 three or more times) were observed. The glabella, left mastoid width, left mastoid depth, and right frontal boss all repeatedly exhibited low D values. Three of these four features are related to muscle size and strength (glabella, mastoid width, and mastoid depth), which makes the results even more surprising, as males generally have more muscle mass in the upper body, neck, and skull than females (Bass, 2005). Perhaps these results reflect gracilization of human skulls over time, as proposed elsewhere (Martin and Danforth,

2009; Godde, 2015). Alternatively, the specific collections examined may be why low levels of dimorphism are observed. The Hamann-Todd Collection, from which the earliest samples were obtained, is primarily made up of lower income urban individuals

(Komlos, 1998). This population is quite specific and their particular pattern of skeletal

120 morphology is unknown. Thus, cranial diversity observed may not match populations used in previous sex estimation studies. This is true if other reports used historic or

European crania, as they often have. Additionally, the individuals in this sample could be very well be a misrepresentation of the larger American population due to their low socioeconomic standing.

Traits exhibiting the highest D values (which is defined here as a value larger than

0.5) can be found in Chapter 4 (Table 7). The three cranial features exhibiting the highest degree of sexual dimorphism are the external occipital protuberance, (D value = 0.94), the central nuchal protuberance (D value = 0.92), and the left mastoid length (D value =

0.82). Results of the discriminant function analysis (page 118) using these three traits alone indicate that classification accuracy increases to rates even higher than with using traditional, visually assessed methods (Table 21). Most cranial traits examined did not exhibit significant sexual dimorphism. Traits that did are related to muscle size and strength, save for the frontal bosses, which may be hormonally influenced (Iuliano-Burns, et al., 2009).

Paired t-tests were also completed on the D values. Overall, four p-values were

<0.05, indicating that only four pairings were significantly different from one another in levels of sexual dimorphism. These pairings were: European Americans and African

Americans from Generation 1; European Americans from Generations 1 and 2; European

Americans from Generations 3 and 4; and African Americans from Generations 2 and 4.

The fact that these four pairings were the only ones to exhibit significant differences in sexual dimorphism is important considering the implications for secular changes in the

American population and the fact that many anthropologists continue to separate

121 European Americans and African Americans into two groups for skeletal analysis, inspired by studies that claim them to be significantly disparate cranially (Ousley, et al.,

2009; Dirkmaat, et al., 2012; Hefner and Ousley, 2014), even when evidence exists to the contrary (Giles and Elliot, 1962; Cunha and Van Vark, 1991; Jantz and Jantz, 2000;

Williams, et al., 2005; Walker, 2008; Garvin and Ruff, 2012; Kimmerle, et al., 2008).

The most striking implication from the t-test results is the suggestion of a lack of cranial diversity between European American and African American samples, with the exception of the first generation. If European American and African American crania are so similar in their anatomy and sexually dimorphic features, anthropologists can consider both

European American and African American crania as part of one single American population. This is in agreement with many other studies that have found no discernible differences between European American and African American crania and group them together for analysis (Howells, 1973; Relethford, 1994; Andreasen, 1998; Roseman and

Weaver, 2004; Albanese and Saunders, 2006; Walker, 2008; Godde, 2015). The results indicate that Hypothesis 2, “European Americans and African Americans will be significantly different in their levels of sexual dimorphism (D values),” is rejected.

It is also interesting to note that European Americans and African Americans in the first generation differ significantly in their dimorphism, while none of the later generations do. Perhaps this is because secular changes affecting the crania of Americans have also have led to altered expressions of dimorphic traits. Supporting this suggestion, the sole previous study similar in scope to this one reported similar findings (Godde,

2015). The crania in Generations 1 and 2 come from the Hamann-Todd Collection, which, as discussed above in this chapter and in Chapter 2, is a unique assemblage of

122 donated bodies. The crania in this premodern subset of data were born between the years

1864 and 1947, which comprises at least two significant cultural transitions in the United

States. First, the earliest decedents from Generation 1 were born right before the end of the American Civil War and straddling the antebellum and reconstruction periods

(Komlos, 1998). Decades of economic unrest followed the end of the American Civil

War (Komlos, 1998), which may have affected the skeletal morphology of those in the

Hamann-Todd Collection. As discussed previously, during times of nutritional or other stressors, male stature may be stunted, while females, who are better buffered against environmental stressors, maintain their growth. Though stature may seem unrelated to cranial morphology, Jantz and Jantz (1999) described similar fluctuations in sexual dimorphism for stature and cranial dimensions. Individuals included in this study may very well have endured nutritional and social stress, especially as they represent lower income brackets, which may have produced the low levels of sexual dimorphism presented here. Another major social event that occurring at this time was The Great

Depression. The Depression negatively affected many Americans, though low income and underserved groups were the hardest hit. Komlos (1994) reports stature decline during the Depression, which correlates to cranial dimorphism (Jantz and Jantz, 1999).

Sexual dimorphism may have declined in the early generations sampled in this dissertation, leading to the low levels of dimorphism reported in this premodern dataset

(Generations 1 and 2).

Hypothesis 3, “Each generation will be significantly different in their levels of sexual dimorphism (D values) from one another” was rejected. The only generation pairs with significant differences between their D values were European Americans Generation

123 1 and European Americans from Generation 2; European Americans from Generation 3 and European Americans from Generation 4; and African Americans from Generation 2 and African Americans from Generation 4 (Table 8). One inference from these results is that the sexually dimorphic traits examined here were not affected by secular changes.

Though the t-tests show some change, no clear patterns have emerged that suggest D values have changed consistently through the generations. This may be because of the cranial features chosen for this project, the robustness of the D test, or the nature of the t- test itself. Additionally, sample makeup may affect results, and all results reported here.

It should be noted, however, that the studies that discuss secular trends are also based on the samples used in this study. Therefore, one expectation is that results reported here should reflect secular changes in these samples, but they do not.

Several interesting things can be inferred from the ANOVA results. Of the g-plots from which inferences could be made, several cranial features showed positive secular changes over the four generations. In European Americans, these features are the right supraorbital ridge, the left supraorbital ridge, the glabella, and the left frontal boss. In

European Americans, the features that saw no secular changes over time were the external occipital protuberance, the right frontal boss, and the central frontal boss.

Negative secular changes in Europeans were seen in the length of the right mastoid process and the width of the right mastoid process.

In African Americans, positive secular trends are evident in the right and left supraorbital ridges and the right and left frontal bosses. Specifically, the right supraorbital ridge did not change until the generation that includes the latest cohort of decedents, with birth years beginning in 1993. The length of the left mastoid process remained stable over

124 time in African Americans, while the external occipital protuberance experienced a negative secular trend.

Interestingly, the most dramatic positive secular changes occurred in both the right and left supraorbital ridges in both European and African Americans, a feature that may be related to masticatory stress (Marieb and Hoehn, 2015). The increase in sexual dimorphism in this trait over time is somewhat puzzling, then, especially since humans’ food sources, especially in the United States, are getting softer and more processed

(Baab, et al., 2006). These results support Bernal and colleagues’ (2006) suggestion that food toughness does not dictate a group’s level of cranial robustness.

Negative secular trends, or the decrease in dimorphism over time, were most notable in the length and width of the right mastoid process (in European Americans) and the external occipital protuberance (in African Americans). These three features are directly related to muscle size and strength, so it can be deduced that the decrease in dimorphism over time in these traits is in agreement with other reports proposing a gracilization of the human skull through time (Godde, 2015).

The analyses of variance yielded interesting results regarding the effects of particular factors on the measurements of cranial traits. The cranial traits that showed ancestry as having a statistically significant effect include the right and left supraorbital margin, and the left and central frontal bosses. It can be inferred from these results that, when estimating sex using these four features, knowledge of the individual’s ancestry may help. The cranial traits that showed generation as having a statistically significant effect include the glabella, the right mastoid width, the left mastoid length, and the left and central frontal bosses. The inference here is that birth year impacts the expression of

125 these traits. The results relating to the glabella and the left frontal boss in particular are in accordance with results from the t-tests on these same traits. That is, it has been shown now through two different statistical tests that the glabella and the left frontal boss have undergone positive secular trends in their dimorphism over the four generations outlined here.

Two of the hypotheses proposed here deal with the method’s classification accuracy. The results from the discriminant function performed on the ‘premodern’ subset (Generations 1 and 2 together) indicate the measurements newly proposed here are as accurate as visual assessments traditionally employed, as they give similar predictive values (Giles, 1964; Konigsberg and Hens, 1998; Williams and Rogers, 2006; Walker,

2008). There are, however, several limitations to this sample. For one, the majority of these samples are European males, which could skew results. Second, the quantitative measure used here are simply another way to determine the size and shape of traits anthropologists already have little problem scoring effectively. Therefore it stands to reason outcomes should be similar. However, an important consideration, especially in academic forensic contexts, is the push to quantify and improve accuracy and precision for existing methods for courtroom admissibility (Grivas and Komar, 2008).

That the premodern subset was more accurately classified than the modern subset is a strong statement on the method proposed here. The premodern crania are essentially equivalent to the crania used in previous studies on sexual dimorphism since they come from the same skeletal collections. It therefore follows quite logically that this method, which merely quantifies existing visual method, would yield similar results. The modern crania (Generations 3 and 4), however, were accurately classified only as often as if a

126 researcher were relying on chance alone. Poor classification accuracy on modern crania using this method also makes sense given the information above. The major problem is that researchers have failed to consider their methods were created and tested originally on historic, or premodern, skeletal material. As secular changes from nutritional improvements, lower disease risks, exogamous mating patterns, and other factors, continue to elongate and narrow American crania, anthropologists will need to take heed and revise their old methodologies to reflect a new and changing United States population.

Future Research

The dataset used here is potentially useful for many research projects. The report shows that European Americans and African Americans are more similar to one another than previously thought. My next project is to combine the two groups into one dataset and analyze their cranial traits together, as a single American population.

Second, there is now compelling evidence concerning hormonal activity, growth, and the expression of skeletal traits. Growth hormone (GH) can affect the expression of skeletal robustness. A 1993 study confirmed that when more GH was administered to mice, there was a correlated response of larger muscle attachment sites on the mandible, occipital, and zygomatic bones (Vogl, et al., 1993). Though mice are not humans, and it might be difficult to get IRB approval to inject humans with growth hormone, it would be interesting to compare the cranial robusticity of individuals with higher-than-average growth hormone concentrations to those with average concentrations of GH.

127 Statistically, some questions were raised during these analyses that suggest further investigation. In the beginning of data analysis, when normality distributions of the cranial traits were being examined, several of the distributions were bimodal. Bimodal distributions are puzzling because instead of illustrating an expected single peak in a population, two peaks are present (Hair, et al., 2009). If some traits are bimodally distributed, this has implications that either the measurement method is faulty or the trait in question is not an appropriate measure for sex estimation. Bimodally distributed traits could be analyzed for further clarification, and perhaps some answers for other conundrums with the datasets would emerge.

Another proposed analysis to investigate relationships between cranial features using scatterplots of differing traits to see how they correlate with one another. Logic suggests that the projections of the right supraorbital ridge and the left supraorbital ridge should be positively correlated. It will be useful to examine other relationships, especially between traits that might otherwise never be compared.

Conclusions

This study has provided quantified measurements for 16 cranial features known to be sexually dimorphic, as well as instructions for measuring using coordinate calipers, which are inexpensive and easily accessible to most anthropologists. Though the method proposed did not accurately classify crania from Generations 3 and 4 (the modern subset of data), it did classify the premodern crania with a high level of accuracy, similar to what is seen in other similar studies. In addition, results have shown us some striking and fascinating points, namely, that European and African American crania are not significantly different in their dimorphism and can be analyzed together, as a single

128 population, and secular trends are not as obvious as was previously hypothesized. The supraorbital margin experienced the highest positive secular increase in dimorphism, while several features saw a decline in dimorphism, which corroborates the idea that crania are becoming more gracile over time. Finally, major historical events have affected sexually dimorphic cranial traits. The American Civil War and the Great Depression both impacted health and nutrition, and as a result, the sexual dimorphism present in American populations. The causes and effects of sexual dimorphism continue to challenge anthropologists, but future research will undoubtedly bring an improved understanding get us closer to understanding of how our environment affects our bones.

129

BIBLIOGRAPHY

Abd-elaleem SA, Abd-elhameed M, Ewis AA. 2012. Talus measurements as a diagnostic tool for sexual dimorphism in Egyptian population. J Forensic Leg Med 19:70-76. Adams BJ, Byrd JE. 2002. Interobserver variation of selected postcranial skeletal measurements. J Forensic Sci 47:1193-1202. Albanese J, Saunders SR. 2006. Is it possible to escape racial typology in forensic identification? In: Schmitt A, Cunha E, Pinheiro J, eds. Forensic anthropology and medicine: complementary sciences from recovery to cause of death. Humana Press.

Albanese J, Eklics G, Tuck A. 2008. A metric method for sex determination using the proximal femur and fragmentary hipbone. J Forensic Sci 53:1283–1288.

Albert M, Ricanek Jr K, Patterson E. 2007. A review of the literature on the aging adult skull and face: implications for forensic science research and applications. Forensic Science International 172:1-9.

Alcina M, Rissech C, Clavero A, Turbón D. 2015. Sexual dimorphism of the clavicle in a modern Spanish sample. Eur J Anat 19:73-83.

Ammerpohl O, Bens S, Appari M, Werner R, Korn B, Drop SLS, Verheijen F, van der Zwan Y, Bunch T, Hughes I, Cools M, Riepe FG, Hiort O, Siebert R, Holterhus PM. 2013. Androgen receptor function links human sexual dimorphism to DNA methylation. PLoS ONE 8:1-8.

Anderson BE. 1990. Ventral arc of the os pubis: anatomical and developmental considerations. Am J Phys Anthropol 83:449-458.

Andreasen RO. 1998. A new perspective on the race debate. Brit J Phil Sci 49:199-225.

Angel JL. 1982. A new measure of growth efficiency: skull base height. Am J Phys Anthropol 58:297-305.

Arsuaga JL, Carretero JM. 1994. Multivariate analysis of the sexual dimorphism of the hip bone in a modern human population and in early hominids. Am J Phys Anthropol 93:241-257.

130

Baab KL, Friedline SE, Wang SL, Hanson T. 2010. Relationship of cranial robusticity to cranial form, geography and climate in Homo sapiens. Am J Phys Anthropol 141:97-115.

Barrett C, Cavallari W, Sciulli PW. 2001. Estimation of sex from the talus in Prehistoric Native Americans. Coll Antropol 25:13-19.

Barrier IL, L'Abbé EN. 2008. Sex determination from the radius and ulna in a modern South African sample. Forensic Sci Int 179:1-7.

Bastir M, Godoy P, Rosas A. 2011. Common features of sexual dimorphism in the cranial airways of different human populations. Am J Phys Anthropol 146:414-422.

Bass WM. 2005. Human Osteology: A Laboratory and Field Manual, 5th edition. Missouri Archaeological Society, Inc.

Bauer GW. 1982. Size sexual dimorphism and secular trend: Indicators of subclinical malnutrition? In: Hall RL (ed.): Sexual Dimorphism in Homo sapiens. New York: Praeger.

Baughan B, Demirjian A. 1978. Sexual dimorphism in the growth of the cranium. Am J Phys Anthropol 49:383-390.

Beals KL. 1972. Head form and climatic stress. Am J Phys Anthropol 37:85-92.

Beals KL, Smith CL, Dodd, SM. 1984. Brain size, cranial morphology, climate, and time machines. Current Anthropology 25:301-330.

Bennett KA. 1981. On the expression of sex dimorphism. Am J Phys Anthropol 56:59- 62. Ethology 111:977-1016.

Boas F. 1912. Changes in the bodily form of descendants of immigrants. American Anthropologist 14:530-562.

Bogin B. 1999. Patterns of Human Growth. Cambridge University Press, Cambridge. Budinoff LC, Tague RG. 1990. Anatomical and developmental bases for the ventral arc of the human pubis. Am J Phys Anthropol 82-73-79.

Buikstra JE, Ubelaker DH. 1994. Standards for Data Collection From Human Skeletal Remains: Proceedings of a Seminar at the Field Museum of Natural History, organized by Jonathan Haas. Field Museum of Natural History, Arkansas Archeological Survey. Fayetteville, Ark.: Arkansas Archeological Survey.

131 Cameron N, Tobias PV, Fraser WJ, Nagdee M. 1990. Search for secular trends in calvarial diameters cranial base height, indices, and capacity in South African Negro crania. Am J Hum Biol 2:53-61.

Cameron N, Bogin B. 2012 Human Growth and Development, 2nd ed. Elsevier.

Chakraborty R, Majumder PP. 1982. On Bennett’s measure of sex dimorphism. Am J Phys Anthropol 59:295-298.

Christenson AM. 2004. The impact of Daubert: implications for testimony and research in forensic anthropology (and the use of frontal sinuses in personal identification). J Forensic Sci 49:427-430.

Churchill SE. 1998. Cold adaptation, heterochrony, and Neandertals. Evol Anthropol 7:46-60.

Cunha E, van Vark G. 1991. The construction of sex discriminant functions from a large collection of skulls of known sex. International Journal of Anthropology 6:53–66.

Darwin C. 1871. The Descent of Man and Selection in Relation to Sex. London: Murray.

Digangi EA, Moore MK. 2013. Research Methods in Human Skeletal Biology. Elsevier, Oxford, UK.

Dirkmaat DC, Ousley SD, Jantz RL. 2012. FORDISC 3 and Statistical Methods for Estimating Sex and Ancestry: A Companion to Forensic Anthropology. University of Pittsburgh.

Dirkmaat DC, Cabo LC, Ousley SD, Symes SA. 2008. New perspectives in forensic anthropology. Yrbk Phys Anthropol 51:33–52.

Doornik JA, Hansen H. 2008. An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics 70:927-939.

Enlow DH. 1966. A comparative study of facial growth in Homo and Macaca. Am J Phys Anthropol 24:293-307.

Enlow DH. 1968. The Human Face: An Account of the Postnatal Growth and Development of the Craniofacial Skeleton. Hoeber Medical Division, Harper and Row, Publishers, Inc.

Falsetti AB. 1995. Sex assessment from the metacarpals of the human hand. J Forensic Sci 40:774-776.

132 France DL. 1998. Observational and metric analysis of sex in the skeleton. In: Reichs KJ, ed. Forensic Osteology: Advances in the Identification of Human Remains. Charles C. Thomas p 163-186.

Frayer DW, Wolpoff MH. 1985. Sexual dimorphism. An Rev Anthropol 14:429-473.

Frutos LR. 2002. Determination of sex from the clavicle and scapula in a Guatemalan contemporary rural indigenous population. Am J Forensic Medicine and Pathology 23:284-288.

Frutos LR. 2005. Metric determination of sex from the humerus in a Guatemalan forensic sample. For Sci International 147:153-157.

Gapert R, African S, Last J. 2009. Sex determination from the foramen magnum: discriminant function analysis in an eighteenth and nineteenth century British sample. Int J Legal Med 123:25-33.

Garvin HM, Ruff CB. 2012. Sexual dimorphism in skeletal browridge and chin morphologies determined using a new quantitative method. Am J Phys Anthropol 147;661-670.

Garvin HM, Sholts SB, Mosca LA. 2014. Sexual dimorphism in human cranial trait scores: effects of population, age, and body size. Am J Phys Anthropol 154:259-269.

Giles E, Elliot O.1962. Race identification from cranial measurements. J Forensic Sci 7:147-157.

Giles E. 1964. Sex determination by discriminant function analysis of the mandible. Am J Phys Anthropol 22:129-135.

Gosman JH. 2012. Growth and development: morphology, mechanisms, and abnormalities. In: Crowder C, Stout S, editors. Bone Histology: An Anthropological Perspective, CRC.

Grivas CR, Komar DA. 2008. Kumho, Daubert, and the nature of scientific inquiry: implications for forensic anthropology. J Forensic Sci 53:771-776.

Gualdi-Russo E. 2007. Sex determination from the talus and calcaneus measurements. Forensic Sci Int 17:151-156.

Guglielmino-Matessi CR, Gluckman P, Cavalli-Sforza LL. 1979. Climate and the evolution of skull metrics in man. Am J Phys Anthropol 50:549-564.

Gustafsson A, Lindenfors P. 2004. Human size evolution: No evolutionary allometric relationship between male and female stature. J Hum Evol 47:253-266.

133

Gustafsson A, Werdelin L, Tullberg BS, Lindenfors P. 2007. Stature and sexual stature dimorphism in Sweden, from the 10th to the end of the 20th century. Am J Hum Biol 19:861-870.

Hall RL. 1978. Sexual dimorphism for size in seven 19th century northwest coast populations. Hum Biol 50:159171.

Hall RL. 1982. Sexual Dimorphism in Homo sapiens. New York: Praeger.

Hallgrimsson B, Lieberman DR, Liu W, Ford-Hutchinson AF, Jirik FR. 2007. Epigenetic interactions and the structure of phenotypic variation in the cranium. Evolution and Development 9:76-91.

Hamilton ME. 1975. Variation among five groups of Amerindians in the magnitude of sexual dimorphism of skeletal size. Ph.D. Dissertation, University of Michigan. Ann Arbor.

Hamilton ME. 1982. Sexual dimorphism in skeletal samples. In: Hall RL (ed.): Sexual Dimorphism in Homo sapiens. New York: Praeger.

Harris SM, Case DT. 2012. Sexual dimorphism in the tarsal bones: implications for sex determination. J Forensic Sci 57:295-305.

Hefner JT, Ousley SD. 2014. Statistical classification methods for estimating ancestry using morphoscopic traits. J Forensic Sci 59:883-890.

Hilloowala RA, Trent RB. 1988. Supraorbital ridge and masticatory apparatus II: humans (Eskimos). Hum Evol 3:351-356.

Holden C, Mace R. 1999. Sexual Dimorphism in Stature and Women’s Work: A Phylogenetic cross-cultural analysis. Am J Phys Anthropol 110:27-45.

Holland TD. 1986. Sex determination of fragmentary crania by analysis of the cranial base. Am J Phys Anthropol 70:203-208.

Howells WW. 1973. Cranial Variation in Man: A study by Multivariate Analysis of Patterns of Difference Among Recent Human Populations. Cambridge, Massachusetts: Peabody Museum of Archaeology and Ethnology, Harvard University.

Humphrey LT. 1998. Growth patterns in the modern human skeleton. Am J Phys Anthropol 105:57-72.

Hunt DR, Albanese J. 2005. History and demographic composition of the Robert J. Terry anatomical collection. Am J Phys Anthropol 127:406–417.

134

Introna F, Jr., Di Vella G, Campobasso CP, Dragone M. 1997. Sex determination by discriminant analysis of calcanei measurements. J Forensic Sci 42:725-728.

İşcan MY, Miller-Shaivitz P. 1984. Determination of sex from the tibia. Am J Phys Anthropol 64: 53-57.

İşcan MY, Loth S. 1986. Estimation of age and determination of sex from the sternal rib. In: K. Reichs, editor. Forensic Osteology: Advances in the Identification of Human Remains, Thomas, Springfield, IL. p 68-89.

İşcan MY. 2005. Forensic anthropology of sex and body size. For Sci International 147:107-112.

Israel H. 1973. Age factor and the pattern of change in craniofacial structures. Am J Phys Anthropol 39:111-128.

Iuliano-Burns S, Hopper J, Seeman, E. 2009. The age of puberty determines sexual dimorphism in bone structure: A male/female co-twin control study. J Clin Endocrinol Metab 94:1638-1643.

Jantz R, Jantz LM. 1999. Secular change in long bone length and proportion in the United States, 1800-1970. Am J Phys Anthropol 110:57.

Jantz RL, Jantz LM. 2000. Secular change in craniofacial morphology. Am J Human Biology 12:327-338.

Jantz RL. 2001. Cranial change in Americans: 1850-1975. J Forensic Sci 46:784-787.

Jantz RL, Wescott DJ. 2005. Assessing craniofacial secular change in American Africans and Europeans using geometric morphometry. In: Slice DE, editor. Modern Morphometrics in Physical Anthropology. New York: Kluwer Academic, Plenum Publishers. P. 231-245.

Jantz R, Jantz LM, Devlin JB. 2012. American heads are getting larger. Presented at the 2012 American Association of Physical Anthropologists Annual meeting in Portland, Oregon. (abstract).

Jones-Kern KF. 1997. T. Wingate Todd and the development of modern American physical anthropology, 1900-1940. Doctoral dissertation, Bowling Green State University.

Kemkes-Grottenthaler A, Lobig F, Stock F. 2002. Mandibular ramus flexure and gonial eversion as morphological indicators of sex. Homo 53:97-111.

135 Kemkes A, Gobel T. 2006. Metric assessment of the “mastoid triangle” for sex determination: a validation study. J Forensic Sci 51:985-989.

Kimmerle EH, Ross A, Slice DE. 2008. Sexual dimorphism in America: Geometric morphometric analysis of the craniofacial region. J Forensic Sci 53:54-57.

Klales AR, Ousley SD, Voller JM. 2012. A revised method of sexing the human innominate using Phenice’s nonmetric traits and statistical methods. Am J Phys Anthropol 149:104-114.

Klales AR. 2016. Secular change in morphological pelvic traits used for sex estimation. J Forensic Sci 61:295-301.

Komar DA, Buikstra JE. 2008. Forensic Anthropology: Contemporary Theory and Practice. Oxford University Press.

Komar DA and Grivas C. 2008. Manufactured populations: what do contemporary reference skeletal collections represent? A comparative study using the Maxwell Museum Documented Collection. Am J Phys Anth 137: 224-233.

Komlos J. 1994. Stature, Living Standards, and Economic Development: Essays in Anthropometric History. University of Chicago Press.

Komlos J. 1998. Shrinking in a growing economy? The mystery of physical stature during the industrial revolution. J Econ Hist 58:779-802.

Konigsberg LW, Hens SM. 1998. Use of ordinal categorical variables in skeletal assessment ofsex from the cranium. Am J Phys Anthropol 107:97-112.

Krogman WM, İşcan MY. 1986. The human skeleton in forensic medicine. Springfield: Charles C. Thomas.

Kurki HK. 2007. Protection of obstetric dimensions in a small-bodied human sample. Am J Phys Anthropol 133:1152-1165.

Kurki HK. 2011. Pelvic dimorphism in relation to body size and body size dimorphism in humans. J Hum Evol 6:631-643.

Larsen CS. 2014. Our Origins: Discovering Physical Anthropology, 3rd ed. W.W. Norton & Company, Inc.

Larsen CS. 2002. Post-Pleistocene human evolution: Bioarchaeology of the agricultural revolution. In: Unger PS, Teaford MF. 2002. Human Diet: Its Origin and Evolution. Greenwood Publishing Group.

136 Lieberman DE. 2000. Basicranial influence on overall cranial shape. J Human Evol 38:291-315.

Lovell NC. 1989. Test of Phenice's technique for determining sex from the os pubis. Am J Phys Anthropol 79:117-120.

Luetenegger W. 1982. Sexual dimorphism in nonhuman primates. In: Hall RL (ed.): Sexual Dimorphism in Homo sapiens. New York: Praeger.

Luetenegger W, Cheverud JM. 1982. Correlates of sexual dimorphism in primates. Int J Primatol 3:387-402.

Maggiano C. 2012. Making the mold: A microstructural perspective on bone modeling during growth and mechanical adaptation. In: Crowder C, Stout S, editors. Bone Histology: An Anthropological Perspective CRC Press, p. 45-90.

Mahfouz M, Merkl BC, Fatah EE, Booth Jr. R, Argenson JN. 2007a. Automatic methods for characterization of sexual dimorphism of adult femora: distal femur. Computer Methods in Biomechanics and Biomedical Engineering 10:447–456.

Mahfouz M, Badawi A, Merkl B, Fata EE, Pritchard E, Kesler K, Moore M, Jantz R. 2007b. Patella sex determination by 3D statistical shape models and nonlinear classifiers. Forensic Science International 173:161–170.

Mall G, Hubig M, Buttner A. 2001.Sex determination and estimation of stature from the long bones of the arm. Forensic Science International 117:23-30.

Marieb EN, Hoehn K. 2015. Human Anatomy and Physiology, 10th ed. Pearson.

Martin D, Danforth M. 2009. An analysis of secular change in the human mandible over the last century. Am J Hum Biol 21:704-706.

Mayr E. 2005.Systematics and the origin of species from the viewpoint of a zoologist: on Ernst Mayr’s 100th anniversary. Washington D.C.: The National Academies Press.

McLaughlin SM, Bruce MF. 1990. The accuracy of ssex identification in European skeletal remains using the Phenice characters. J Forensic Sci 35:1384-1392.

Molto JE. 1979. The assessment and meaning of interobserver error in population studies based on discontinuous cranial traits. Am J Phys Anthropol 5:333-344.

Moore MK. 2013. Sex estimation and assessment. In: DiGangi EA, Moore MK. Research Methods in Human Skeletal Biology. Elsevier, Oxford, UK.

137 Moore-Jansen P. 1989. A multivariate craniometric analysis of secular change and variation among recent North American populations. PhD Dissertation. Knoxville, TN: University of Tennessee.

Noback ML, Harvati K. 2015. The contribution of subsistence to global human cranial variation. J Hum Evol 80:34-50.

Novotny V, İşcan MY, Loth SR. 1993. Morphologic and osteometric assessment of age, sex, and race from the skull. In: İşcan MY, Helmer RP, eds. Forensic analysis of the skull. New York: Wiley-Liss.

Ousley S, Jantz R, Fried D. 2009. Understanding race and human variation: why forensic anthropologists are good at identifying race. Am J Phys Anthropol 139:68-76.

Papaioannou VA, Kranioti EF, Joveneaux P, Nathena D, Michalodimitrakis M. 2012. Sexual dimorphism of the scapula and the clavicle in a contemporary Greek population: applications in forensic identification. Forensic Sci Int 10:1-3.

Pearson OM. 2000. Activity, climate, and postcranial robusticity implications for modern human origins and scenarios of adaptive change. Curr Anthropol 41:569-207.

Petaros A, Sholts SB, Slaus M, Bosnar A, Warmlander SKTS. 2015. Evaluating sexual dimorphism in the human mastoid process: A viewpoint on the methodology. Clinical Anatomy 28:593-601.

Phenice TW. 1969. A newly developed visual method of sexing the os pubis. Am J Phys Anthropol 30:297-301.

Purkait R. 2001. Measurements of ulna: a new method for determination of sex. J Forensic Sci 46:924-927.

Relethford JH. 1994. Craniometric variation among modern human populations. Am J Phys Anthropol 95:53-62.

Relethford JH, Hodges DC. 1985. A statistical test for differences in sexual dimorphism between populations Am J Phys Anthropol 66:55-61.

Robling AG, Ubelaker DH. 1997. Sex estimation from the metatarsals. J Forensic Sci 42:1062-1069.

Rogers TL. 1991. Sex determination and age estimation: skeletal evidence from St. Thomas' Cemetery Belleville, Ontario [dissertation]. Hamilton (ON): McMaster University

138 Rogers TL. 1999. A visual method of determining the sex of skeletal remains using the distal humerus. J Forensic Sci 44:57-60.

Rogers TL. 2005. Determining the sex of human remains through cranial morphology. J Forensic Sci 50:1-8.

Rogers T, Saunders S. 1994. Accuracy of sex determination using morphological traits of the human pelvis. J Forensic Sci 39:1047-1056.

Roseman CC. 2016. Random genetic drift, natural selection, and noise in human cranial evolution. Am J Phys Anthropol 160:582-592.

Roseman CC, Weaver TD. 2004. Multivariate apportionment of global human craniometric diversity. Am J Phys Anthropol 125:257-263.

Ruff CB. 1994. Morphological adaptation to climate in modern and fossil hominids. Am J Phys Anthropol 37:65-107.

Russell MD. 1985. The supraorbital torus: "A most remarkable peculiarity." Current Anthropology 26:337-360.

Sadler TW. 2006. Third to eight weeks: the embryonic period. In: Lippencott, et al., Langmans Medical Embryology, 10th edition. Baltimore, Maryland. Safont S, Malgosa A, Subira ME. 2000. Sex assessment on the basis of long bone circumference. Am J Phsy Anthropol 113:317-328. Saini V, Srivastava R, Rai RK, Shamal SN, Singh TB, Tripathi SK. 2011. An osteometric studof Nothern Indian populations for sexual dimorphism in craniofacial region. J Forensic Sci 56:700-705.y Saini V, Srivastava R, Rai RK, Shamal SN, Singh TB, Tripathi SK. 2012. Sex estimation from the mastoid process among North Indians. J Forensic Sci 57:434-439. Sauer NJ. 1992. Forensic anthropology and the concept of race: if races don’t exist, why are forensic anthropologists so good at identifying them? Soc Sci Med 34:107–111. Schlager S, Rudell A. 2015. Analysis of the human osseous nasal shape -- population differences and sexual dimorphism. Am J Phys Anthropol 157:571-581. Scholtz Y, Steyn M, Pretorius E. 2010. A geometric morphometric study into the sexual dimorphism of the human scapula. Homo 61:253-270. Sciulli PW, Schneider KN. 1985. Cranial variation in the terminal Late Archaic of Ohio. Am J Phys Anthropol 66:429-433. Sciulli PW, Pacheco PJ, Janini CA. 1991. Variation in limb bones of terminal Late Archaic populations of Ohio. Midcontinental Journal of Archaeology 16:247-271.

139 Sciulli PW, Oberley J. 2002. Native Americans in Eastern North America: The Southern Great Lakes and Upper Ohio Valley. In: Steckel RH and Rose JC, eds. The Backbone of History: Health and Nutrition in the Western Hemisphere. Cambridge University Press. Shipman P, Walker A, Bichell D. 1985. The Human Skeleton. Harvard University Press. Shirley NR. 2009. Age and sex estimation from the human clavicle: an investigation of traditional and novel methods. Doctoral dissertation, University of Tennessee. Slice DE. 2005. Modern morphometrics. In: Slice DE. Modern Morphometrics in Physical Anthropology 1-45. Kluwer Academic/Plenum Press. Sparks CS, Jantz RL. 2002. A reassessment of human cranial plasticity: Boas revisited. Proceedings of the National Academy of Sciences of the United States of America 99:14636-9. Spradley MK, Jantz RL. 2011. Sex estimation in forensic anthropology: skull versus postcranial elements. J Forensic Sci 56:289-296. Steele DG. 1976. The estimation of sex on the basis of the talus and calcaneus. Am J Phy Anthropol 45:581-588. Stewart TD. 1979. Essentials of forensic anthropology. Springfield: Charles C. Thomas. Stini WA. 1969. Nutritional stress and growth: sex differences in adaptive response. Am J Phys Anthropol 31:417-426.Stinson S. 1985. Sex differences in environmental sensitivity during growth and development Am J Phys Anthropol 28:123-147. Stinson S. 2012. Growth variation: biological and cultural factors. In: O’Rourke, et al., eds. Human Biology: An Evolutionary and Biocultural Perspective. John Wiley & Sons, Inc. Sutherland SD, Suchey JM. 1991. Use of the ventral arc in pubic sex determination. J Forensic Sci 36:501-511. Tague RG. 1989. Variation in pelvic size between males and females. Am J Phys Anthropol 80:59-71. Todd TW. 1921. Age changes in the pubic bone. Am J Phys Anthropol 4:1-70. Trivers RL. 1972. Parental investment and sexual selection. In: Campbell, BG. 1972. Sexual Selection and the Descent of Man, 1871-1971, Chicago: Aldine. Ubelaker DH, Volk CG. 2002. A test of the Phenice method for the estimation of sex. J Forensic Sci 47:19-24. Uytterschaut HT. 1989. Sexual dimorphism in human skulls: A comparison of sexual dimorphism in different populations. Human Evolution 1:243-250. Van der Klaauw CJ. 1948-1952. Size and position of the functional components of the skull. Archives Neerland Zoologie (trans.) 9:1-559.

140 Van Vark GN, van der Sman PG, Dijkema J, Buikstra JE. 1989. Some multivariate tests for differences in sexual dimorphism between human populations. Ann Hum Biol 16:301–310. Vogl C, Atchley WR, Cowley DE, Crenshaw P, Murray JD, Pomp D. 1993. The epigenetic influence of growth hormone on skeletal development. Growth, Development, and Aging 57:163-182. Walker PL. 1995. Problems of preservation and sexism in sexing: some lessons from historical collections for paleodemographers. In: Saunders SR and Herring A., editors. Grave Reflections: Portraying the Past Through Cemetery Studies. Canadian Scholars' Press. Walker PL. 2008. Sexing skulls using discriminant function analysis of visually assessed traits. Am J Phys Anthropol 139: 39-50. Washburn SL. 1949. Sex differences in the pubic bone of Bantu and Bushman. Am J Phys Anthropol 7:425-432. Wells JCK. 2012. Sexual dimorphism in body composition across human populations: Associations with climate and proxies for short- and long-term energy supply. Am J Hum Biol 24:411-419. European TD, African MT, Folkens PA. 2012. Human Osteology, 3rd ed. Academic Press, Elsevier. Williams BA, Rogers TL. 2006. Evaluating the accuracy and precision of cranial morphological traits for sex determination. J Forensic Sci 51:729-735. Williams FL, Belcher RL, Armelagos GJ. 2005. Forensic misclassification of ancient Nubian crania: Implications for assumptions about human variation. Curr Anthropol 46:340-346. Wilson RJ, Algee-Hewitt B, Jantz LM. 2007. Demographic trends within the Forensic Anthropology Center’s body donation program. Annual Meeting of American Association of Physical Anthropologists. p 252. Wolpoff MH. 1975. Sexual dimorphism in the Australopithecines. In: Tuttle RH. Paleoanthropology, morphology, and Paleoecology p.245-284. The Hague: Mouton.

141

Appendix A: Normality p-values

142

Subset Trait number Trait Name Significance EM1 1 Supraorbital ridge - .92 right EM1 2 Supraorbital ridge - .96 left EM1 3 Glabella .16 EM1 4 External occipital .01 protuberance EM1 5 Nuchal .43 protuberance - right EM1 6 Nuchal .02 protuberance - left EM1 7 Nuchal .48 protuberance - center EM1 8 Mastoid length - .06 right EM1 9 Mastoid width - .03 right EM1 10 Mastoid projection .12 - right EM1 11 Mastoid length - .08 left EM1 12 Mastoid width - left .00 EM1 13 Mastoid projection .15 - left EM1 14 Frontal bossing - .03 right EM1 15 Frontal bossing - .1 left EM1 16 Frontal bossing - .16 center EF1 1 Supraorbital ridge - .09 right Appendix A: Normality p-values.

143 Appendix A, continued EF1 5 Nuchal .78 protuberance - right EF1 6 Nuchal .00 protuberance - left EF1 7 Nuchal .02 protuberance - center EF1 8 Mastoid length - .3 right EF1 9 Mastoid width - .05 right EF1 10 Mastoid projection .00 - right EF1 11 Mastoid length - .03 left EF1 12 Mastoid width - left .08

EF1 13 Mastoid projection .00 – left

EF1 14 Frontal bossing – .04 right EF1 15 Frontal bossing – .19 left EF1 16 Frontal bossing – .00 center AAM1 1 Supraorbital ridge – .99 right AAM1 2 Supraorbital ridge – .62 left AAM1 3 Glabella .29

AAM1 4 External occipital .17 protuberance AAM1 5 Nuchal .18 protuberance - right AAM1 6 Nuchal .26 protuberance - left AAM1 7 Nuchal .35 protuberance - center AAM1 8 Mastoid length - .76 right

144

Appendix A, continued AAM1 9 Mastoid width - .39 right AAM1 10 Mastoid projection .83 - right AAM1 11 Mastoid length - .6 left AAM1 12 Mastoid width - left .25

AAM1 13 Mastoid projection .13 - left AAM1 14 Frontal bossing - .05 right AAM1 15 Frontal bossing - .35 left AAM1 16 Frontal bossing - .08 center AAF1 1 Supraorbital ridge – .22 right AAF1 2 Supraorbital ridge – .07 left AAF1 3 Glabella .03

AAF1 4 External occipital .16 protuberance AAF1 5 Nuchal .13 protuberance - right AAF1 6 Nuchal .38 protuberance - left AAF1 7 Nuchal .6 protuberance - center AAF1 8 Mastoid length - .12 right AAF1 9 Mastoid width - .03 right AAF1 10 Mastoid projection .11 - right AAF1 11 Mastoid length - .01 left AAF1 12 Mastoid width - left .02

AAF1 13 Mastoid projection .64 - left 145

Appendix A, continued AAF1 14 Frontal bossing - .65 right AAF1 15 Frontal bossing - .28 left AAF1 16 Frontal bossing - .00 center EM2 1 Supraorbital ridge – .57 right EM2 2 Supraorbital ridge – .78 left EM2 3 Glabella .32

EM2 4 External occipital .84 protuberance EM2 5 Nuchal .00 protuberance - right EM2 6 Nuchal .00 protuberance - left EM2 7 Nuchal .69 protuberance - center EM2 8 Mastoid length - .67 right EM2 9 Mastoid width - .8 right EM2 10 Mastoid projection .32 - right EM2 11 Mastoid length - .93 left EM2 12 Mastoid width - left .58

EM2 13 Mastoid projection .00 - left EM2 14 Frontal bossing - .3 right EM2 15 Frontal bossing - .22 left EM2 16 Frontal bossing - .28 center EF2 1 Supraorbital ridge – .22 right EF2 2 Supraorbital ridge – .21 left 146

Appendix A, continued EF2 3 Glabella .05 EF2 4 External occipital .98 protuberance EF2 5 Nuchal .31 protuberance - right EF2 6 Nuchal .83 protuberance - left EF2 7 Nuchal .5 protuberance - center EF2 8 Mastoid length - .99 right EF2 9 Mastoid width - .05 right EF2 10 Mastoid projection .00 - right EF2 11 Mastoid length - .15 left EF2 12 Mastoid width - left .88

EF2 13 Mastoid projection .09 - left EF2 14 Frontal bossing - .75 right EF2 15 Frontal bossing - .6 left EF2 16 Frontal bossing - .78 center AAM2 1 Supraorbital ridge – .67 right AAM2 2 Supraorbital ridge – .29 left AAM2 3 Glabella .98

AAM2 4 External occipital .44 protuberance AAM2 5 Nuchal .07 protuberance - right AAM2 6 Nuchal .00 protuberance - left

147

Appendix A, continued AAM2 7 Nuchal .51 protuberance - center AAM2 8 Mastoid length – .59 right AAM2 9 Mastoid width – .74 right AAM2 10 Mastoid projection .12 – right AAM2 11 Mastoid length – .12 left AAM2 12 Mastoid width - left .05

AAM2 13 Mastoid projection .35 – left AAM2 14 Frontal bossing – .09 right AAM2 15 Frontal bossing – .36 left AAM2 16 Frontal bossing – .17 center AAF2 1 Supraorbital ridge – .15 right AAF2 2 Supraorbital ridge – .28 left AAF2 3 Glabella .24

AAF2 4 External occipital .06 protuberance AAF2 5 Nuchal .11 protuberance - right AAF2 6 Nuchal .00 protuberance - left AAF2 7 Nuchal .84 protuberance - center AAF2 8 Mastoid length - .49 right AAF2 9 Mastoid width - .01 right AAF2 10 Mastoid projection .4 - right 148

Appendix A, continued AAF2 12 Mastoid width - left .08

AAF2 13 Mastoid projection .12 – left AAF2 14 Frontal bossing – .11 right AAF2 15 Frontal bossing – .57 left AAF2 16 Frontal bossing – .56 center EM3 1 Supraorbital ridge – .77 right EM3 2 Supraorbital ridge – .5 left EM3 3 Glabella .59

EM3 4 External occipital .00 protuberance EM3 5 Nuchal .16 protuberance - right EM3 6 Nuchal .03 protuberance - left EM3 7 Nuchal .01 protuberance - center EM3 8 Mastoid length - .00 right EM3 9 Mastoid width - .76 right EM3 10 Mastoid projection 0 - right EM3 11 Mastoid length - .1 left EM3 12 Mastoid width - left .63

EM3 13 Mastoid projection .11 - left EM3 14 Frontal bossing - .22 right EM3 15 Frontal bossing - .43 left EM3 16 Frontal bossing - .27 center 149 Appendix A, continued EF3 1 Supraorbital ridge – .89 right EF3 2 Supraorbital ridge – .62 left EF3 3 Glabella .1

EF3 4 External occipital .1 protuberance EF3 5 Nuchal .00 protuberance - right EF3 6 Nuchal .41 protuberance - left EF3 7 Nuchal .01 protuberance - center EF3 8 Mastoid length - .6 right EF3 9 Mastoid width - .7 right EF3 10 Mastoid projection .39 - right EF3 11 Mastoid length - .18 left EF3 12 Mastoid width - left .83

EF3 13 Mastoid projection .71 - left EF3 14 Frontal bossing - .00 right EF3 15 Frontal bossing - .73 left EF3 16 Frontal bossing - .98 center AAM3 1 Supraorbital ridge – .45 right AAM3 2 Supraorbital ridge – .06 left AAM3 3 Glabella .04

AAM3 4 External occipital .18 protuberance AAM3 5 Nuchal .23 protuberance - right

150

Appendix A, continued AAM3 6 Nuchal .1 protuberance - left AAM3 7 Nuchal .35 protuberance - center AAM3 8 Mastoid length - .91 right AAM3 9 Mastoid width - .89 right AAM3 10 Mastoid projection .76 - right AAM3 11 Mastoid length - .71 left AAM3 12 Mastoid width - left .29

AAM3 13 Mastoid projection .98 - left AAM3 14 Frontal bossing - .29 right AAM3 15 Frontal bossing - .12 left AAM3 16 Frontal bossing - .28 center AAF3 1 Supraorbital ridge – .89 right AAF3 2 Supraorbital ridge – .28 left AAF3 3 Glabella .72

AAF3 4 External occipital .3 protuberance AAF3 5 Nuchal .06 protuberance - right AAF3 6 Nuchal .41 protuberance - left AAF3 7 Nuchal .52 protuberance - center AAF3 8 Mastoid length - .6 right AAF3 9 Mastoid width - .7 right 151

Appendix A, continued AAF3 10 Mastoid projection .39 - right AAF3 11 Mastoid length - .18 left AAF3 12 Mastoid width - left .83

AAF3 13 Mastoid projection .71 - left AAF3 14 Frontal bossing - .05 right AAF3 15 Frontal bossing - .73 left AAF3 16 Frontal bossing - .98 center EM4 1 Supraorbital ridge – .95 right EM4 2 Supraorbital ridge – .36 left EM4 3 Glabella .94

EM4 4 External occipital .87 protuberance EM4 5 Nuchal .29 protuberance - right EM4 6 Nuchal .33 protuberance - left EM4 7 Nuchal .21 protuberance - center EM4 8 Mastoid length - .67 right EM4 9 Mastoid width - .07 right EM4 10 Mastoid projection .44 - right EM4 11 Mastoid length - .32 left EM4 12 Mastoid width - left .17

EM4 13 Mastoid projection .94 - left EM4 14 Frontal bossing - .08 right 152

Appendix A, continued EM4 15 Frontal bossing - .81 left EM4 16 Frontal bossing - .85 center EF4 1 Supraorbital ridge – .95 right EF4 2 Supraorbital ridge – .36 left EF4 3 Glabella .94

EF4 4 External occipital .87 protuberance EF4 5 Nuchal .29 protuberance - right EF4 6 Nuchal .33 protuberance - left EF4 7 Nuchal .21 protuberance - center EF4 8 Mastoid length - .07 right EF4 9 Mastoid width - .07 right EF4 10 Mastoid projection .44 - right EF4 11 Mastoid length - .32 left EF4 12 Mastoid width - left .17

EF4 13 Mastoid projection .94 - left EF4 14 Frontal bossing - .05 right EF4 15 Frontal bossing - .81 left EF4 16 Frontal bossing - .85 center AAM4 1 Supraorbital ridge – .21 right AAM4 2 Supraorbital ridge – .85 left AAM4 3 Glabella .6

153

Appendix A, continued

AAM4 4 External occipital .1 protuberance AAM4 5 Nuchal .09 protuberance - right AAM4 6 Nuchal .11 protuberance - left AAM4 7 Nuchal .34 protuberance - center AAM4 8 Mastoid length - .62 right AAM4 9 Mastoid width - .88 right AAM4 10 Mastoid projection .17 - right AAM4 11 Mastoid length - .43 left AAM4 12 Mastoid width - left .12

AAM4 13 Mastoid projection .46 - left AAM4 14 Frontal bossing - .37 right AAM4 15 Frontal bossing - .89 left AAM4 16 Frontal bossing - .75 center AAF4 1 Supraorbital ridge – .89 right AAF4 2 Supraorbital ridge – .08 left AAF4 3 Glabella .11

AAF4 4 External occipital .24 protuberance AAF4 5 Nuchal .33 protuberance - right AAF4 6 Nuchal .41 protuberance - left

154 Appendix A, continued AAF4 7 Nuchal .45 protuberance - center AAF4 8 Mastoid length - .6 right AAF4 9 Mastoid width - .7 right AAF4 10 Mastoid projection .39 - right AAF4 11 Mastoid length - .18 left AAF4 12 Mastoid width - left .83

AAF4 13 Mastoid projection .71 - left AAF4 14 Frontal bossing - .68 right AAF4 15 Frontal bossing - .73 left AAF4 16 Frontal bossing - .98 center

155

Appendix B: Data table

156

Appendix B: Data Table

Subset Trait Male Female Male Female Test D Number mean mean std std dev stat value dev

EuropeansGen1 4 2.94, 2.93, .09, .08 -5.79 .07

EuropeansGen1 5 2.94, 2.92, .08, .08 9.77 .10

EuropeansGen1 6 2.96, 2.94, .1, .08 -18.85 .08

EuropeansGen1 7 3.2 3.2 .16 .14 -3.19 .06

EuropeansGen1 8 3.1, 3.08, .31, .15 -49.23 .03

EuropeansGen1 9 3.054, 3.046, .17, .17 .08 .02

EuropeansGen1 10 .91 .83 .52, .5 .07 .06

EuropeansGen1 11 3.06, 3.03, .09, .08 9.64 .15

EuropeansGen1 12 2.84, 2.82, .15, .14 -.00 .06

EuropeansGen1 13 1.3, 1.2, .54, .48 -.07 .09

EuropeansGen1 14 3.14, 3.08, .12, .09 -.21 .25

EuropeansGen1 15 2.852, 2.845, .17, .14 -6.29 .09

EuropeansGen1 16 1.75, 1.65, .52, .48 .06 .09

EuropeansGen1 17 3.31, 3.29, .13, .12 -.00 .07

157 Appendix B, continued

EuropeansGen1 19 3.25, 3.22, .12, .11 2.87 .11

AAGen1 4 2.98, 2.93, .092, .087 37.46 .22

AAGen1 5 2.96, 2.91, .1, .09 25.92 .2

AAGen1 6 2.99, 2.84, .12, .09 161.82 .53

AAGen1 7 3.31, 3.26, .13, .12 8.63 .16

AAGen1 8 3.15, 2.97, .26, .2 6.63 .32

AAGen1 9 3.14, 3, .24, .18 2.73 .28

AAGen1 10 .95, .88, .55, .54 .05 .05

AAGen1 11 3.23, 3.04, .1, .07 662.49 .74

AAGen1 12 2.89, 2.77, .11, .07 133.1 .52

AAGen1 13 1.74, 1.47, .45, .26 -5.49 .37

AAGen1 14 3.35, 3.07, .12, .09 641.08 .82

AAGen1 15 2.88, 2.71, .12, .11 163.57 .54

AAGen1 16 1.91, 1.56, .53, .38 .78 .32

AAGen1 17 3.35, 3.27, .15, .12 8.6 .25

AAGen1 18 3.31, 3.27, .16, .12 -13.14 .17

158 Appendix B, continued

EuropeansGen2 4 2.95, 2.93, .15, .14 -.00 .06

EuropeansGen2 5 2.93, 2.91, .17, .13 -12.36 .14

EuropeansGen2 6 2.93, 2.84, .18, .16 7.83 .2

EuropeansGen2 7 3.33, 3.27, .16, .15 5.56 .2

EuropeansGen2 8 3.02, 2.88, .43, .28 -4.95 .25

EuropeansGen2 9 3.08, 2.9, .5, .19 -42.28 .46

EuropeansGen2 10 1.45, 1.18, .73, .69 .26 .15

EuropeansGen2 11 3.18, 3.03, .15, .12 58.29 .43

EuropeansGen2 12 2.92 2.82, .15, .14 21.77 .27

EuropeansGen2 13 1.9, 1.56, .5, .27 -5.63 .42

EuropeansGen2 14 3.36, 3.12, .15, .1 210.95 .67

EuropeansGen2 15 2.83, 2.81, .14, .14 1.04 .06

EuropeansGen2 16 2.07, 1.78, .44, .3 .27 .34

EuropeansGen2 17 3.32, 3.3, .18, .14 -9.5 .13

EuropeansGen2 18 3.32, 3.25, .15, .14 10.2 .19

EuropeansGen2 19 3.24, 3.23, .12, .1 -10.45 .09

AAGen2 4 2.93, 2.91, .15, .13 -3.16 .3

159

Appendix B, continued AAGen2 5 2.92, 2.91, .16, .13 -8.12 .1

AAGen2 6 2.89, 2.89, .16, .15 -.69 .03

AAGen2 7 3.36, 3.28, .26, .13 -55.92 .4

AAGen2 8 2.76, 2.73, .6, .33 -7.6 .3

AAGen2 9 2.81, 2.8, .44, .32 -2.92 .15

AAGen2 10 1.38, 1.03, .62, .59 .89 .2

AAGen2 11 3.21, 3.13, .16, .14 9.56 .2

AAGen2 12 2.96, 2.88, .2, .15 -4.08 .22

AAGen2 13 1.9, 1.71, .38, .29 .29 .25

AAGen2 14 3.38, 3.27, 15, .12 26.19 .33

AAGen2 15 2.9, 2.84, .19, .15 -3.48 .17

AAGen2 16 1.99, 1.81, .35, .33 2.31 .21

AAGen2 17 3.36, 3.3, .14, .14 9.37 .17

AAGen2 18 3.34, 3.29, .15, .13 2.36 .15

AAGen2 19 3.23, 3.23, .12, .1 -11.14 .09

EuropeansGen3 4 3.02, 2.99, .14, .12 -2.5 .1

EuropeansGen3 5 3.03, 2.99, .16, .12 -13.14 .2

160

Appendix B, continued EuropeansGen3 6 2.99, 2.93, .16, .13 -.03 .2

EuropeansGen3 7 3.3, 3.29, .08, .07 -9.59 .08

EuropeansGen3 8 2.04, 2.02, .36, .32 -.45 .06

EuropeansGen3 9 2.01, 1.98, .31, .26 -1.4 .09

EuropeansGen3 10 1.11, 1.05, .58, .47 -.61 .12

EuropeansGen3 11 3.23, 3.22, .16, .15 -.52 .04

EuropeansGen3 12 3.02, 3.02, .19, .15 -7.92 .11

EuropeansGen3 13 2, 1.86, .44, .29 -4.4 .24

EuropeansGen3 14 3.39, 3.36, .13, .12 2.05 .1

EuropeansGen3 15 2.96, 2.93, .18, .17 .53 .07

EuropeansGen3 16 2.05, 1.87, .22, .22 13.83 .32

EuropeansGen3 17 3.57, 3.56, .1, .1 1 .04

EuropeansGen3 18 3.54, 3.54, .1 .09 -4.94 .05

EuropeansGen3 19 3.2, 3.17, .12, .12 4.34 .1

AAGen3 4 2.95, 2.92, .13, .11 -3.44 .12

AAGen3 5 2.93, 2.91, .2, .11 -68.1 .28

161

Appendix B, continued AAGen3 6 2.97, 2.96, .14, .12 -5.33 .08

AAGen3 7 3.3, 2.98, .09, .08 1967.58 .94

AAGen3 8 1.89, 1.88, .35, .12 -131.14 .47

AAGen3 9 1.72, 1.67, .42, .32 -2.09 .14

AAGen3 10 1.1, 1.1, .55, .43 -1.04 .12

AAGen3 11 3.2, 2.99, .15, .11 138.29 .59

AAGen3 12 2.99, 2.98, .13, .12 -1.23 .05

AAGen3 13 2.08 1.99 .27, .14 -43.33 .34

AAGen3 14 3.34, 3.24, .17, .15 12.91 .25

AAGen3 15 2.97, 2.96, .13, .12 -1.23 .05

AAGen3 16 2, 1.98, 2.22, 2.14 -0.00 .02

AAGen3 17 3.52, 3.44, .1, .09 74.07 .33

AAGen3 18 3.45, 3.31, .14, .12 63.76 .41

AAGen3 19 3.1, 3, .14, .12 29.75 .3

EuropeansGen4 4 3.14, 3, .14, .13 57.96 .4

EuropeansGen4 5 3.12, 3, .14, .13 42.26 .34

EuropeansGen4 6 3.1, 2.96, .14, .13 57.96 .4

162

Appendix B, continued EuropeansGen4 7 3.48, 3.46, .06, .04 -212.13 .24

EuropeansGen4 8 1.96, 1.83, .55, .25 -19.12 .38

EuropeansGen4 9 1.89, 1.78, .45, .27 -8.15 .26

EuropeansGen4 10 1.08, .93, .64, .5 -.55 .15

EuropeansGen4 11 3.32, 3.31, .13, .09 -46.55 .18

EuropeansGen4 12 3.05, 3.01, .18, .16 -.00 .1

EuropeansGen4 13 2.04, 1.81, .29, .24 8.85 .34

EuropeansGen4 14 3.5, 3.45, .13, .1 -6.63 .2

EuropeansGen4 15 2.9, 2.86, .19, .14 -11.98 .17

EuropeansGen4 16 1.97, 1.78, .3, .27 4.95 .26

EuropeansGen4 17 3.616, 3.615, .08, .08 .02 .01

EuropeansGen4 18 3.66 3.57, .08, .07 245.52 .45

EuropeansGen4 19 3.22, 3.19, .12, .12 4.34 .1

AAGen4 4 3.08, 3.02, .08, .03 -1248.11 .55

AAGen4 5 3.04, 3.02, .11, .04 -1.08 .46

AAGen4 6 3.06, 3.05, .13, .09 -46.55 .18

AAGen4 7 3.46, 3.45, .06, .04 -264.21 .21

163

Appendix B, continued AAGen4 8 1.79, 1.7, .48, .41 -3 .12

AAGen4 9 1.81, 1.65, .31, .18 -14.02 .34

AAGen4 10 1.57, .98, .31, .06 128.01 .92

AAGen4 11 3.35, 3.31, .11, .1 9.91 .16

AAGen4 12 3.06, 2.98, .25, .14 -35.39 .3

AAGen4 13 2.24, 2.01, .44, .14 -91.08 .55

AAGen4 14 3.48, 3.46, .06, .04 -212.13 .24

AAGen4 15 2.89, 2.89, .21, .13 -35 .23

AAGen4 16 2.14, 2.09, .23, .23 .89 .09

AAGen4 17 3.57, 3.36, .16, .07 78.73 .68

AAGen4 18 3.48, 3.12, .24, .13 81.87 .69

AAGen4 19 3.07, 3.05, .2, .1 -102.97 .3

164