Cranial Sexual Dimorphism and the Population Specificity of Anthropological Standards

Cranial sexual dimorphism and the population specificity of anthropological standards

Alexandra Dillon (BSc, GDipForSci)

Centre for Forensic Science

University of Western Australia

This thesis is presented for the degree of

Master of Forensic Science

2014

DECLARATION

I declare that the research presented in this thesis, for the Master of Forensic Science at the University of Western Australia, is my own work. The results of the work have not been submitted for assessment, in full or part, within any other tertiary institute, except where due acknowledgement has been made in the text.

………………………………………………………………...

Alexandra Dillon

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ABSTRACT

When skeletal remains are referred to the forensic anthropologist it will ultimately require the formulation of a biological profile, which includes the estimation of sex. Modern humans are sexually dimorphic, which means that there are anatomical differences between males and females, due in part to muscle size and attachment variations. These variations are expressed osteologically, particularly in the cranium. All human populations are sexually dimorphic, however the degree of sexual dimorphism, attributed to influences such as genetics and climate, varies within and between populations.

The primary aims of the present thesis are to quantify sexual dimorphism in Western Australia crania and to evaluate the effect on classification accuracy when applying non-population specific sex estimation standards. Until recently, forensic anthropologists in Western Australia had little choice but to apply morphometric standards developed for foreign populations for skeletal sex estimation. The effect of applying these standards is relatively under researched and thus requires quantification and interpretation; the latter will help guide forensic practice in other jurisdictions.

The sample comprises 300 adult cranial multislice computerized tomography (MSCT) scans equally distributed by sex. Each scan is obtained from a medical PACS database that houses data from various hospitals in Western Australia. Although the ancestry of the sample is unknown, it is assumed that this sample is approximately representative of the current Western Australian population. A total of 26 linear measurements are taken in each cranium. Prior to data collection, a precision test is performed to quantify measurement error; bilateral asymmetry is also statistically evaluated. Sexual dimorphism is analysed using a series of ANOVA’s and discriminant function analyses. The magnitude of cranial sexual dimorphism in the Western Australian sample is then compared to various foreign populations using a series of t-tests. Western Australian individuals are then classified using a variety of foreign published standards. This is performed to assess the level of classification accuracy achieved and the sex bias. This v

provided insights useful for evaluating the forensic applicability of foreign classification statistics.

Measurement error was quantified in the precision test; 25/26 measurements were found to have a relative technical error of measurement (rTEM) of < 5%. Coefficient of reliability (R) values were all above 0.70, with 20/26 measurements having values over 0.90 (mean = 0.93). Maximum cranial breadth had both a relatively high rTEM value (6.55) and low coefficient of reliability (0.71). No significant bilateral asymmetry was observed. It was found that all measurements in the Western Australian population were larger in males than in females. All measurements were significantly sexually dimorphic (P <0.01-0.001), with the exception of orbital height and maximum frontal breadth.

A stepwise discriminant function analysis was then performed for the Western Australian population (cross-validated accuracy: 88.7%; sex bias: 4.00%). The mean measurement values were compared to other foreign populations. The Western Australian female population is on average larger in cranial size, whilst the male population is similar (or smaller) than the comparative populations. The effect of applying foreign sex estimation standards to Western Australian data was explored. A lower overall accuracy (as low as 50%), and a much larger sex bias than originally stated (18.7 to 100%) was found. When these functions are adjusted using the Western Australian33 data, the accuracy range increases to 76.7 to 88.0% (sex bias 2.7 to 4.0%). It is clear that population variation in the expression of sexual dimorphism exists.

The present research shows that accurate cranial sex estimation can be achieved when using population specific standards, however, the application of non-population standards results in an unacceptably low accuracy and a high sex bias, therefore, where available, population specific standards should always be used. The application of standards that are deemed ‘similar’ also results in low sex estimation accuracy, which can have ramifications that include the incorrect identification of remains and the misdirection of forensic investigative resources. Therefore, for the accurate

estimation of sex in the human adult cranium, population specific standards (if and where possible) should always be applied.

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ACKNOWLEDGEMENTS

This project has been completed due to the encouragement and support of many important people surrounding me. I would like to take this opportunity to thank these people for their contribution in helping me complete this project.

Professional Acknowledgements

Firstly, I would like to thank my co-ordinating supervisor Professor Daniel Franklin, and co-supervisor Ambika Flavel. Their consistent support and guidance throughout this entire process has been immeasurable, and for that I am forever grateful. Thank you for teaching me all I have learned over the last eighteen months.

I would also like the thank Winthrop Professor Ian Dadour for his help over the last three years as a student at the Centre for Forensic Science. Thanks are also due to Algis Kuliukas, Bonnie Knott, Bernadine De Beaux and the other staff at the Centre for Forensic Science for their assistance and administrative support.

Personal Acknowledgements

First and foremost, I wish to thank my parents, Shane and Daphne Dillon, for their constant support over the past seven years as a student at the University of Western Australia. They have never let an opportunity pass me, and because of this I have had the best experiences anyone could ever hope for. Thank you for everything.

To my family and friends, thank you for being the greatest anyone could ever ask for. Your encouragement and love means everything to me.

TABLE OF CONTENTS

DECLARATION...... iii

ABSTRACT ...... v

ACKNOWLEDGEMENTS ...... ix

TABLE OF CONTENTS ...... xi

LIST OF FIGURES ...... xv

LIST OF TABLES ...... xvi

CHAPTER ONE: Introduction ...... 1

1.1. Background to the study ...... 1

1.2. Aims ...... 2

1.3. Sexual dimorphism ...... 3

1.3.1. Sexual dimorphism in the skull ...... 4

1.3.2. Sexual dimorphism in other areas of the body ...... 4

1.4. Population specificity ...... 5

1.4.1. The need for population specific standards ...... 6

1.5. Collecting information from MSCT scans ...... 7

1.5.1. MSCT databases as a proxy for modern human skeletal collections ... 8

1.6. Sources of data ...... 9

1.7. Limitations ...... 9

1.8. Thesis format ...... 9

CHAPTER TWO: A Brief Introduction to Human Cranial Anatomy ...... 11

2.1. Introduction ...... 11

2.2. Cranial anatomy ...... 11

2.2.1. Skeletal ...... 11

2.2.2. Musculature ...... 13

2.3. Bone growth and sexual dimorphism ...... 17

CHAPTER THREE: A Review of Literature Relating to Sex Estimation in the Skull ...... 19

3.1. Introduction ...... 19

3.2. Studies validating the use of MSCT scans in lieu of dry bone...... 19

3.2.1. Verhoff et al. (2008) ...... 19

3.2.2. Franklin et al. (2013a) ...... 20

3.3. Sex estimation standards for the skull ...... 20

3.3.1. Giles and Elliot (1963) ...... 21

3.3.2. Steyn and Iscan (1998) ...... 22

3.3.3. Franklin et al. (2005) ...... 23

3.3.4. Kranioti et al. (2008) ...... 23

3.3.5. Spradley and Jantz (2011) ...... 24

3.3.6. Ogawa et al. (2013) ...... 25

3.3.7. Franklin et al. (2013b) ...... 26

3.4. Summary ...... 26

CHAPTER FOUR: Materials and Methods ...... 27

4.1. Introduction ...... 27

4.2. Materials ...... 27

4.2.1. Study Sample ...... 27

4.2.2. Ethics ...... 28

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4.3. Methods – craniometric measurements ...... 29

4.3.1. Landmarks ...... 29

4.3.2. Measurements ...... 34

4.4. Methods - statistical analyses ...... 39

4.4.1. Precision test ...... 39

4.4.2. Basic descriptive statistics ...... 41

4.4.3. Classification statistics ...... 42

4.4.3.1 Discriminant function analysis ...... 42

4.4.4. Population specificity of sexing standards ...... 43

CHAPTER FIVE: Results ...... 45

5.1. Introduction ...... 45

5.2. Precision test ...... 45

5.3. Bilateral asymmetry ...... 47

5.4. Cranial sexual dimorphism in the Western Australian population ...... 47

5.4.1.e On -way ANOVA ...... 47

5.4.2. Stepwise discriminant analysis ...... 50

5.5. Population variation in cranial sexual dimorphism ...... 50

5.5.1. Morphometric variation ...... 55

5.6. Classification accuracy of foreign standards ...... 59

CHAPTER SIX: Discussion and Conclusions ...... 61

6.1. Introduction ...... 61

6.2. Precision test ...... 61

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6.3. Bilateral asymmetry ...... 63

6.4. Cranial sexual dimorphism ...... 64

6.4.1. Population variation in the expression and magnitude of cranial sexual dimorphism ...... 66

6.5. Population specificity of anthropological standards ...... 71

6.5.1. Outcomes of applying foreign standards ...... 72

6.6. Forensic applications...... 73

6.7. Limitations ...... 74

6.8. Future research ...... 75

6.9. Conclusions ...... 76

REFERENCES ...... 77

APPENDIX I: Unpaired T-test raw data of the Western Australian population and other populations ...... 93

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LIST OF FIGURES

Figure 2.1: The bones of the human skull ...... 12 Figure 2.2: The muscles of the skull ...... 16 Figure 2.3: Muscle attachments of the occipital bone ...... 17 Figure 4.1: Fronto-lateral, lateral, frontal and basal view of cranial landmarks ...... 33 Figure 4.2: Fronto, lateral and posterior views of a 3D volume rendered cranium ...... 33 Figure 4.3: Frontal and lateral views of cranial measurements ...... 36 Figure 4.4: Frontal and base view of cranial measurements...... 37 Figure 4.5: Midsagittal view of cranial measurements ...... 38 Figure 5.1: Comparison of male and female mean cranial measurements...... 49 Figure 5.2: Mean difference of cranial measurements between Western Australian and published studies for males...... 56 Figure 5.3: Mean difference of cranial measurements between Western Australian and published studies for females...... 58

LIST OF TABLES

Table 2.1: The muscles of the cranium and their origin, insertion and movement...... 15 Table 3.1: Measurements used to formulate discriminant functions in selected published papers...... 22 Table 4.1: Age distribution in years for males and females...... 27 Table 4.2: Landmark codes and definitions to be used in the present study...... 31 Table 4.3: Inter-landmark linear measurements from 3D landmark data ...... 34 Table 5.1: Intra-observer error based on the 4x4 precision test...... 46 Table 5.2: Comparison of mean left and right cranial measurements ...... 47 Table 5.3: Descriptive statistics and comparisons of mean cranial measurements...... 48 Table 5.4: Stepwise discriminant analysis of all cranial measurements...... 50 Table 5.5: Significance of sexual dimorphism in bizygomatic breadth in a variety of global populations compared to Western Australia...... 51 Table 5.6: Significance of sexual dimorphism in opisthion-glabella length in a variety of global populations compared to Western Australia...... 52 Table 5.7: Significance of sexual dimorphism in basion-nasion length in a variety of global populations compared to Western Australia...... 52 Table 5.8: Significance of sexual dimorphism in glabello-occipital length in a variety of global populations compared to Western Australia...... 53 Table 5.9: Significance of sexual dimorphism in mastoid height in a variety of global populations compared to Western Australia...... 54 Table 5.10: Significance of sexual dimorphism in biauricular length in a variety of global populations compared to Western Australia...... 54 Table 5.11: Significance of sexual dimorphism in minimum frontal breadth in a variety of global populations compared to Western Australia...... 55 Table 5.12: Accuracy and sex bias of discriminant functions when applied to the Western Australian sample ...... 60

Table Appendix I. 1: Comparison of mean male measurements...... 93 Table Appendix I. 2: Comparison of mean female measurements...... 100

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CHAPTER ONE

Introduction

1.1. Background to the study

A forensic anthropologist examines skeletal tissues and uses techniques based in physical anthropology to extract information about a decedent and the circumstances surrounding their death (Pickering & Bachman 2009). A forensic anthropologist will examine human remains that are either fully skeletonised or partially fleshed (both complete and/or fragments) when other forensic specialists, such as a pathologist, request assistance to estimate demographic information and/or interpretations of skeletal trauma. A forensic anthropologist may also identify (if present) the nature of pathology and taphonomic changes, in addition to assisting in recovering buried or surface remains (Cattaneo 2007). Aside from working at a crime scene, a forensic anthropologist may also assist in the identification of human remains from multiple- fatality incidents (disaster victim identification) such as airplane crashes, natural disasters and acts of terrorism. Forensic anthropologists are also required to assess living people, mostly in cases involving ascertaining whether illegal immigrants are at an age of legal responsibility (Dirnhofer et al. 2006; Ramsthaler et al. 2010).

The first task of an anthropological assessment is to determine whether the referred remains are in fact bone or a non-osseous material. This is often readily apparent if the specimen is large enough to visually identify its biological nature. Smaller fragments can be inspected microscopically; the key determinant of bone is a compact surface with some graininess (Hillier & Bell 2007). The next objective is to determine whether the bones are human or non-human. This will involve macroscopic (visual) methods, such as comparing the specimen to a reference skeletal collection. If human, a biological profile will then be formulated; this consists of estimations of sex, age, ancestry and stature (Ross & Manneschi 2011).

Modern humans are sexually dimorphic, which at the most basic level, means that there are anatomical differences between males and females. These morphological variations are expressed through sex-specific osteological features (Gonzalez et al. 2007), which are due to (in part) muscle size and attachment variations between the sexes (Walker 2008). Secondary sexual characteristics (morphological features present in the body after puberty) provide the basis for sex estimation of adults (Wilson et al. 2008).

There are two primary approaches in attempting to estimate skeletal sex: non-metric (morphoscopic) and metric (morphometric). Non-metric analyses involve visually comparing defined attributes to standards based on composite visual images and descriptions (e.g. estimating the sex of an individual using the scoring system for dimorphic cranial traits, such as Acsadi and Nemeskeri (1970), and Buikstra and Ubelaker (1994)). These methods, however, are inherently subjective as they are highly experience dependent (Walrath et al. 2004). Metric analyses involve taking measurements from defined osteometric landmarks on the bone, which are analysed using published standards (statistical models developed for a specific population). These methods are discussed further in Chapter Three. The present thesis is concerned with population specific standards for the estimation of skeletal sex, with specific reference to the crania of modern Western Australian individuals.

1.2. Aims

The overall objective of this project is to explore the issue of the population specificity of anthropological standards as applied to a contemporary Western Australian population. The specific aims of this thesis are: i) Statistical quantification of sexual dimorphism in Western Australian crania

The first aim is to explore how modern Western Australian individuals vary from other global populations in relation to the expression and magnitude of cranial sexual dimorphism. The most sexually dimorphic regions of the cranium is statistically quantified by analysing linear morphometric data acquired from multi-slice computed

tomography (MSCT) scans. That data then has further utility in exploring geographical variations in cranial sexual dimorphism. ii) To quantify and evaluate the effect on classification accuracy of applying non- population specific standards to a Western Australian population

Forensic anthropologists in Western Australia have (until recently) had little choice but to apply morphometric standards developed for foreign populations (e.g. North American and South African White) for skeletal sex estimation. In this study, a selection of foreign standards are applied to the Western Australian cranial data to determine why there is an incorrect classification of sex between populations. A series of statistical analyses are performed to quantify which measurements are more (or less) dimorphic and how accurately they classify sex, when compared to published standards for other geographical populations. Classification performance (e.g. accuracy and sex bias) is also evaluated.

1.3. Sexual dimorphism

Sexual dimorphism describes size and morphological differences between males and females (Iscan 2005). Sex-specific attributes span a continuum of morphological characteristics and metric measures in the human skeleton (Loth & Iscan 2000). All populations are sexually dimorphic, although the extent of the sex differences between males and females is variable within each population (Bennett 1981; Walker 2008). Previous research (e.g. Dayal et al. 2008; Franklin et al. 2012a; Green & Curnoe 2009; Hsiao et al. 2010; Scheuer 2002) has demonstrated that the male skeleton is more robust and has larger muscle attachment sites than females in the same population. Males, therefore, generally have greater maximum lengths and diameters of long bones than their female counterparts (Green & Curnoe 2009). There is, however, a large overlap between the sexes in the distribution of most features used to estimate skeletal sex, so any individual that falls into the middle of the overall range cannot be accurately sexed (Rosing et al. 2007). The following sections consider sexual dimorphism in specific anatomical regions.

1.3.1. Sexual dimorphism in the skull

The skull displays a high level of sexual dimorphism in some populations. The degree of sexual dimorphism in the skull can be attributed to genetics, nutrition, cultural variation and muscle attachments (Iscan et al. 1995; Manoel et al. 2009; and see Chapter Six). Some of the differences between male and female skulls are displayed through variances in robusticity: adult females are generally more gracile than males and female skulls tend to retain prepubertal traits; conversely, males tend to exhibit larger bony features and robustness (Green & Curnoe 2009; Hsiao et al. 2010). Muscle attachment sites are believed to contribute to size and shape differences of osteological features of the cranium. For example, the size of the superciliary ridges and glabellar region of the frontal bone, as well as the mastoid process, are affected by the size of the muscles that are attached to them (Green & Curnoe 2009). The robusticity of male muscles is due to normal testosterone levels producing a greater muscle mass (Loth & Iscan 2000), and this in turn leads to larger areas of muscle attachment (see also Chapter Two).

1.3.2. Sexual dimorphism in other areas of the body a) Pelvis

The pelvis includes the left and right os coxa and the sacrum. The mature os coxa is formed by the fusion (at around the age of puberty) of three bones: the ilium, ischium and pubis. The pelvis contains the most characteristics used for estimating sex, as it is the part of the human skeleton that has a specific morphology to facilitate childbirth (Bruzek 2002). This is shown through shape differences in the pelvis; the male pelvis is adapted for bipedalism, whereas the female pelvis is adapted for a compromise between bipedalism and a passage large enough for a fetal head to pass through the birth canal (Scheuer 2002).

Techniques for estimating sex in the pelvis fall into two broad categories: morphoscopic and morphometric (see above). Phenice (1969) visually assessed three traits (ventral arc, subpubic concavity and medial aspect of the ischiopubic ramus) in the pubic bone and claimed that the method was over 95% accurate and not

experience dependent. Lovell (1989) tested this technique and reported an accuracy of 83%. Gonzalez et al. (2009) analysed the greater sciatic notch and ischiopubic morphology using morphometric techniques based on landmark and three- dimensional (3D) semi-landmark data. They found that sex estimation accuracy in the pelvis was frequently above 90%, however females were misclassified more frequently than males. b) Long bones

Morphoscopic and morphometric analysis of the long bones of the body (humerus, femur, tibia, fibula, ulna and radius) can be performed to estimate sex. This is most commonly achieved through the statistical analysis of linear measurements. Generally speaking, long bones in females have a shorter maximum length and smaller midshaft circumference than males (Krogman 1978). Steyn and Iscan (1999) studied sexual dimorphism in the humerus by taking six measurements in a South African population sample. The accuracy was high: 96% in the White population and 95% in the Black population. Different measurements were more sexually dimorphic in the White (epicondylar breadth and head diameter) and Black (head diameter and humerus length) populations, which highlights issues surrounding variation in the expression of sexual dimorphism (see below).

1.4. Population specificity

Geographical, temporal and secular variation occurs between (and within) populations. This means that a population in one area will be genetically and phenotypically different from a population in a geographically removed area. The same principle is applied to temporally diverse populations; individuals from a population living 100 years ago will be genetically and morphologically different to individuals from a modern population living today. Both the general robusticity and gracility of male and female skeletal remains, and the scale of sex-related differences, depend on the particular geographical population (Rosing et al. 2007).

Different populations express sexual dimorphism in diverse ways and at different magnitudes. Although generally male skulls are larger and have more prominent muscle attachment regions, substantial population differences do exist (Walker 2008). For example, when studying supraorbital ridge development, the characteristics that identify a male European are also found in female Australian Aboriginals (Brothwell 1981). In light of this variation, standards have been formulated for several different geographical populations; this provides more accurate and statistically robust estimations, as the standards are developed from data representative of individuals from that specific geographical population (Harrison et al. 1977). When these standards are applied to individuals foreign to the reference sample, the accuracy and reliability of the subsequent estimation of sex is greatly reduced (Franklin et al. 2012a).

Aside from geographical differences, temporal differences have also been documented (e.g. Belcher & Armelagos 2005; Goose 1981; Iscan et al. 1995; Nagaoka et al. 2008). These are skeletal morphological changes that occur over a period of time, rather than over a geographical area. Iscan et al. (1995) studied sexual dimorphism in Japanese crania and showed that modern females had become larger, with no size increase found in males. This means that the magnitude of sexual dimorphism has decreased. Their study also demonstrated that more recent populations change at a faster rate than their ancestors. Nagaoka et al. (2012) examined evidence for temporal differences in cranial dimensions in a Japanese population. Cranial measurements (such as maximum cranial length, maximum cranial breadth and upper facial height) were shown to be significantly different, compared to data from populations from different time periods. This is an excellent example of temporal change; discriminant functions based on samples that are temporally removed should be used cautiously and as a last resort. The results of these studies again reiterate the need for contemporary population specific standards (see below).

1.4.1. The need for population specific standards

The application of discriminant functions to populations different than those from which they are developed leads to significant errors (Bigoni et al. 2010; Walker 2008; Franklin et al. 2012a). For example, the application of discriminant functions that are

based on weakly dimorphic populations should be used cautiously on populations showing a higher degree of sexual dimorphism (Calcagno 1980). An inaccurate sex estimation can result from inter-population differences in the expression of sexual dimorphism, as some populations have robust individuals of both sexes, while others have gracile individuals of both sexes (Green & Curnoe 2009). This highlights the need for standards developed specifically for certain populations.

Franklin et al. (2012a) applied foreign standards (North American Caucasians (Giles & Elliot 1963) and South African Caucasians (Steyn & Iscan 1998)) to a Western Australian population and found that although sex was estimated accurately overall (83% and 80% respectively), there was an unacceptable sex bias (31% and 36% respectively). The latter bias occurs when the accuracy of correct classifications of males and females are markedly different. This again highlights one of the known problems associated with the application of foreign standards.

1.5. Collecting information from MSCT scans

Hounsfield and Cormack first developed MSCT scanning technology in the early 1970s (Bolliger et al. 2008). MSCT scans allow for the 3D reconstruction of the human body, and provide a detailed visualization of internal and external structures, while being minimally invasive and non-destructive (Ramsthaler et al. 2010). The data collected from these scans allows for visualization in situ, as well as the capability for easy data storage, and the opportunity to re-examine data at any time. MSCT scanning also provides images that are more suitable for presentation in court, rather than graphic autopsy photographs (Bolliger et al. 2008; Dedouit et al. 2007; Douglas et al. 1997; Leth 2009). There are, however, some disadvantages to the use of MSCT scans in a forensic context. Although they are excellent for the visualization of skeletal elements and soft tissue, foreign bodies and gases can be lost in the images, as they have an inferior contrast resolution to soft tissue (Bolliger et al. 2008).

MSCT scans are especially useful in forensic anthropology as they have a high spatial resolution, which allows high quality image reconstruction and 3D modelling (Brough

et al. 2012). Franklin et al. (2013a) examined whether volume-rendered MSCT scans of the skull accurately represented the actual physical specimen and also evaluated the accuracy of cranial measurements taken from these scans. Calliper and MSCT scan derived measurements of the skull (using the computer program OsiriX®) were statistically compared. They found that although traditional bone measurements were more precise than those acquired in MSCT scans, the overall differences between the two methods were insignificant relative to sample variance (Franklin et al. 2013a). A similar finding was made in other earlier studies (e.g. Verhoff et al. 2008; Ramsthaler et al. 2010). Scanning is also advantageous in that no bone preparation is required and there is no damage to the osseous material, as well as being able to be applied to living individuals. Aside from metric analyses of the cranium, MSCT scans are also used to visually assess sex in crania, as shown by Ramsthaler et al. (2010), who morphoscopically evaluated traditional sex characteristics (e.g. mastoid process and glabella) and achieved a correct sex assignment accuracy of 84 to 85%.

1.5.1. MSCT databases as a proxy for modern human skeletal collections

MSCT scans offer a solution to the continuing problem of the lack of skeletal collections representative of modern populations as a whole (Franklin et al. 2013a). There are no documented modern skeletal collections in Western Australia, thus there is a clear need for an alternative source of data to develop population standards, and for the analysis of population differences; hence the use of MSCT scans in the present study. MSCT scans have been used as a proxy for documented skeletal collections in multiple studies in Western Australia (e.g. Franklin et al. 2012b; Franklin et al. 2012c; Franklin et al. 2013a, Franklin et al. 2013b). It has also been stated that “…future anthropological studies should be directed to the use of fresh corpses to better understand local populations” (Iscan 2005, p.110). Clearly MSCT scans are representative of individuals residing within a current population as a whole, and also allow large samples (which are frequently unavailable) to be studied.

1.6. Sources of data

Data for this research was collected from cranial MSCT scans of 300 adults (≥ 18 years of age) equally distributed by sex. Each scan was obtained from a medical PACS (picture archiving and communication system) database that contains scans from various hospitals in Western Australia. The only available information about the subjects is their age and sex. Although information of the subjects’ ancestry is not available (see below), it is generally assumed that the scans are a representative sample of the Western Australian population as a whole (see Franklin et al. 2012a; Franklin et al. 2012c). Ethical approval was granted from the University of Western Australia’s Human Research Ethics Committee (Project No RA/4/1/4362) prior to receipt of scans and/or data collection.

1.7. Limitations

The MSCT scans used in this project omit any personal information about the subjects, except for their age and sex; the specific ancestry of any individual is also unknown. This information is excluded in most medical modalities because the ancestry of a patient is normally not medically relevant when these scans are taken, and irrespective, individuals often identify their ancestry as their cultural, rather than population affinity. It is thus assumed that this sample of 300 scans is a representative sample of a contemporary Western Australian population, however it cannot be statistically documented (see Section 6.7).

1.8. Thesis format

This thesis is presented in six chapters. Chapters Two and Three are the literature review chapters: Chapter Two will briefly introduce the anatomy of the cranium; and Chapter Three reviews the literature on sex estimation standards developed for various global populations. Chapter Four outlines the material studied and methods used to analyse the data collected in this study. Chapter Five presents the results from the analysis performed on the data. The discussion and final conclusions pertaining to the results in Chapter Five are presented in Chapter Six. 9

CHAPTER TWO

2. A Brief Introduction to Human Cranial Anatomy

2.1. Introduction

The basis of physical/forensic anthropology is a fundamental understanding of human skeletal anatomy. Such knowledge is duly required when a sex assessment is performed, whether in an actual bone, or 3D MSCT reconstructed, specimen. The purpose of this chapter is to briefly consider the anatomy and musculature of the human skull.

2.2. Cranial anatomy

2.2.1. Skeletal

The human skull can be divided into separate anatomical regions: the cranium, which is the skull without the mandible; neurocranium, which is the skull without the bones of the face; the calvaria (the bones that make up the skull cap); the cranial base (the bones of the floor of the cranial vault); and the viscerocranium (the bones of the face and the mandible) (Bastir et al. 2006; Bigoni et al. 2010; Pickering 2009; Steyn & Iscan 1998; White et al. 2012). The data analysed in the present research thesis are derived from measurements taken in cranial MSCT scans; thus the following sections accordingly review basic cranial morphology.

The human cranium (Figure 2.1) comprises 21 bones, eight paired and five unpaired, including: the frontal bone; two parietal; occipital; two temporal; sphenoid; two maxilla; two nasal; two zygomatic; ethmoid; two lacrimal; two inferior nasal concha; two palatine bones; and the vomer. The bones unite at unmovable joints called sutures (Smith et al. 2002).

Figure 2.1: The bones of the human skull (Romanes 1996).

2.2.2. Musculature

Skeletal muscles are soft tissues that attach to bones by tendons (Romanes 1996). There are groups of muscles that attach to the skull; they are responsible for facial expression, mastication and movement of the head and neck, and contribute to the expression of cranial sexual dimorphism (Celbis et al. 2001; see also Chapter One). There are two types of skeletal muscle attachment: the origin and insertion. The origin of a muscle is the bone that does not move when the muscle shortens; insertion of a muscle is the bone that moves when the muscle is shortened, and can sometimes be the insertion of multiple muscles (e.g. coronoid process of the mandible is the insertion of masseter and temporalis). The functions of a selection of (potentially sexually dimorphic) relevant muscles in the cranium are described below in Table 2.1 and Figures 2.2 and 2.3.

i) Facial expression

Numerous subcutaneous voluntary muscles control facial expression. They are found in the face, scalp and neck, and include procerus, nasalis, depressor supercilii, zygomaticus major, zygomaticus minor and buccinators, to mention a few (see Figure 2.3). Generally, these muscles originate from skeletal elements and insert into the skin (Woodburne & Burkel 1988). ii) Mastication

The muscles of mastication are responsible for movements of the jaw. The muscles of mastication include masseter, temporalis, and medial and lateral pterygoids (the insertions for mastication muscles are found to be more hyper-robust than other muscle attachments in the skull) (see Figure 2.3). Combined with the hinge action of the temporomandibular joint, these muscles aid in the opening and powerful closing of the jaw (Woodburne & Burkel 1988). They also assist in chewing and grinding movements.

iii) Head movement/stability

The movement and stability of the head and neck is controlled by several important muscles. These include sternocleidomastoid, trapezius, semispinalis, rectus capitis

posterior major and minor, and splenius capitis (see Figure 2.3). Whilst aiding in keeping the neck stable, they allow for movements in many planes of direction.

Table 2.1: The muscles of the cranium and their origin, insertion and movement.

Muscle Origin Insertion Movement Procerus Fascia over nasal bone The skin between the eyebrows Frowning

Nasalis Maxilla Nasal bone Elevates nostril corners Depressor supercilii Medial orbital rim Medial aspect of the orbit Eyebrow depression Zygomaticus major Zygomatic bone Muscles around mouth Up and lateral movement of mouth corners Zygomaticus minor Zygomatic bone Skin of upper lip Upper lip elevation Buccinator Alveolar processes; Temporomandibular Orbicularis oris Cheek compression joint Masseter Zygomatic arch Coronoid process of the mandible Opening and protraction of the mouth Temporalis Parietal bone Coronoid process of the mandible Opening and retraction of the mouth Sternocleidomastoid Manubrium; Medial clavicle Mastoid process; Superior nuchal line Cervical rotation and flexion Trapezius Lateral clavicle and scapula Thoracic spinous process; Nuchal ligament Movements of the scapula Semispinalis Cervical and thoracic column Between superior and inferior nuchal line Head extension Rectus Capitis Atlas Inferior nuchal line Neck flexion Splenius Capitis Nuchal ligament; Cervical and thoracic Mastoid process Extend and flex the head column

Figure 2.2: The muscles of the skull (Romanes 1996).

Figure 2.3: Muscle attachments of the occipital bone (Romanes 1996).

2.3. Bone growth and sexual dimorphism

Cortical bone serves as an attachment site for muscles; histological differences, such as increased number of Haversian systems (osteons arranged to the long axis of compact bone), are often present in human bone in areas of significant biomechanical force (Hillier & Bell 2007; Petrtyl et al. 1996). As bone material changes during growth, its architecture is modified to support the most beneficial performance of that bone (Berryman et al. 2010). Bone is organized to resist loads of force imposed by functions of attaching muscles; therefore new bone is deposited where it is needed and resorbed from where it is not (Berryman et al. 2010).

Hormone related changes in the body lead to sexual dimorphism in many tissues, including bone (Cox & Mays 2000). Androgens, such as testosterone, directly induce higher rates of bone formation through molecular and cellular mechanisms (Wilson et al. 1981). Androgens induce muscle formation by stimulating muscle protein synthesis, and since testosterone is more abundant in males, extra muscle mass results in male

bones supporting higher compressive forces, therefore leading to more robust bone attachment sites (Cabo et al. 2010; Veldhuis et al. 2005). In males, puberty generally starts two years later than females. In these two years males have extra somatic growth, during which time there is acceleration in the growth of muscle mass, and as a consequence, changes on the skeletal form occur at muscle attachment sites as a response to pulling forces (Cox & Mays 2000).

CHAPTER THREE

3. A Review of Literature Relating to Sex Estimation in the Skull

3.1. Introduction

Sexual dimorphism in the skull has been extensively analysed in a variety of specific populations, however standards have not been developed for all global populations. Furthermore, many existing sexing standards were developed using skeletal collection from the early 20th Century or otherwise potentially biased samples. Traditionally, if standards have not been published for a particular population, data from populations that are deemed biologically ‘similar’ are used. The following chapter reviews literature relating to the validation of using MSCT scans in lieu of dry bone, and published standards potentially used by forensic anthropologists for the estimation of skeletal sex. A specific emphasis is placed on metric sex estimation methods. The majority of such publications are based on datasets taken from dry bone. A fundamental premise of this study is that MSCT scans can be used in lieu of dry bone; at least two research groups have independently established this and those validation studies are also reviewed in this chapter.

3.2. Studies validating the use of MSCT scans in lieu of dry bone

This section reviews research relating to quantifying the variation between measurements derived from MSCT scans and those taken in dry bone. Such research is important towards demonstrating that MSCT scans are an accurate representation of the scanned bone specimen. The latter highlights the applicability of using MSCT scans in lieu of actual bone specimens in situations where there is limited access to documented collections of human skeletons (e.g. Australia).

3.2.1. Verhoff et al. (2008)

Verhoff et al. compared conventional methods of acquiring measurements in dry bone to measurements taken from 3D reconstructed MSCT scans. The aim was to determine

whether measurements from a MSCT scan are a viable alternative to measurements taken from dry bone. The sample consisted of four anonymised skulls from the Anatomical Institute of the University of Bern (Switzerland). A total of 33 measurements adapted from Martin and Saller (1957) were taken in the physical specimens. Those skulls were then subjected to MSCT scanning (slice thickness 1.25mm). The scans were then volume rendered and 33 measurements taken. Upper facial breadth and maximum frontal breadth (11 and 6 mm respectively) had the largest variation between the MSCT and dry bone measurements. Some of the variation between measurements was attributed to a high level of inter-observer variation. It was concluded that “…equivalent osteometric values can be obtained from 3D reconstructions based on MSCT scans with a slice thickness of 1.25mm and from conventional manual examinations” (Verhoff et al. 2008, p. 152).

3.2.2. Franklin et al. (2013a)

Franklin et al. aimed to quantify differences (inter- and intra-observer error) between measurements taken in dry bone specimens compared to the same measurements acquired in MSCT scans. Six reference skulls were subjected to MSCT scanning at a slice thickness of 0.9mm. A total of 40 3D landmarks were acquired using OsiriX® in the MSCT scans, from which 23 linear inter-landmark measurements were calculated; the same measurements were taken in the corresponding dry bone specimens. The measurements in the dry skull were then compared to the inter-landmark distance measurements of the MSCT scan of the same specimen. It was found that the differences were negligible (a mean measurement difference of 0.31mm). The authors concluded that the results of this study “…demonstrates that precise measurements can be acquired in 3D reconstructed MSCT cranial scans” (Franklin et al. 2013a, p. 517).

3.3. Sex estimation standards for the skull

This section discusses previous research concerning the estimation of sex; it is these studies that are used as a point of comparison to the data acquired in the present study. This section reviews research that has acquired cranial data using a variety of

acquisition methods, such as virtual reconstruction measurement (see above) and measurement of dry bone specimens.

3.3.1. Giles and Elliot (1963)

Giles and Elliot examined 408 North American Black and White adult skulls: 221 males and 187 females. This sample comprised individuals from the Terry (Washington University School of Medicine) and the Todd Collections (Western Reserve University School of Medicine). All specimens were between 21 to 75 years of age. A total of 11 measurements (Table 3.1) were taken in each skull, however only nine were used in the formation of discriminant functions. Ten of the original 11 measurements were adapted from Hooton (1946). These variables were selected based on ease of measurement, as well as their potential sexual dimorphism (Giles & Elliot 1963).

Mean measurement values were all larger for the male individuals. A total of 21 discriminant functions were formulated, 15 of which relate specifically to the White population. The stated accuracy of these functions ranges from 81.3 to 88.8%. The highest accuracy for the White population was Function 4 (glabello-occipital length, maximum width, basion-bregma height, basion-nasion length, maximum diameter bizygomatic, basion-prosthion, prosthion-nasion height and mastoid length) with 88.8% of the sample classified correctly. Function 16 (glabello-occipital length, maximum width, maximum diameter bizygomatic and mastoid length) was the least accurate (81.3%) discriminant function for the White population. The sex bias is not published in this paper and it is not stated whether the discriminant functions are cross-validated. The authors then applied these discriminant functions to a sample of Native American Indian skulls to determine whether the accuracy of sex estimation was comparable. It was found that the discriminant functions were accurate (up to 92.7%), but only if the sectioning point was adjusted to compensate for the mean size differences between populations.

Table 3.1: Measurements used to formulate discriminant functions in selected published papers.

Measurement Published Study i ii iii iv v vi vii Basion Bregma Height X X X X X X X Basion-Nasion Length X X X X

Basion-Prosthion Length X X

Biauricular Breadth X X

Bifrontal Breadth X X Biorbital Breadth X

Bizygomatic Breadth X X X X X X X Foramen Magnum Breadth X X X

Foramen Magnum Length X Interorbital Breadth X Mastoid Height X X X X X

Maximum Cranial Breadth X X X

Maximum Cranial Length X X X X X X X Maximum Frontal Breadth X X X

Minimum Frontal Breadth X X

Nasal Breadth X X X

Nasal Height X X X

Nasion-Prosthion Height X X X X X

Orbital Breadth X

Orbital Height X X

Frontal Chord X

Occipital Chord X

Palate External X

Parietal Chord X X

i. Giles & Elliot 1963; ii. Steyn & Iscan 1998; iii. Franklin et al. 2005; iv. Kranioti et al. 2008; v. Spradley & Jantz 2011; vi. Ogawa et al. 2013; vii. Franklin et al. 2013b

3.3.2. Steyn and Iscan (1998)

Steyn and Iscan examined cranial sexual dimorphism in 91 (44 males and 47 females) South African White individuals. The mean age was 66 years for the males and 67 years for the females. Twelve cranial (Table 3.1) and five mandibular measurements (adapted from Martin and Saller (1957)) were taken in each specimen. Bizygomatic breadth was the most dimorphic measurement (F = 68.78, p <0.001). The least dimorphic measurement was maximum frontal breadth (F = 11.39, p <0.001). A total of four discriminant functions were developed. The reported cross-validated

accuracies for these functions ranged from 81.1 to 85.7%. Function 1 (bizygomatic breadth, basion-nasion length, nasal height, cranial length, nasal breadth and basion- bregma height) was the most accurate, with 86.4% of males and 85.1% of females (85.7% overall) correctly classified, with a sex bias of 1.3%. Function 3 (bizygomatic breadth, nasal height and nasion-prosthion height) was the least accurate, with 79.5% of males and 82.6% of females (81.1% overall) classified correctly with a sex bias of -3.1%.

3.3.3. Franklin et al. (2005)

Franklin et al. examined cranial sexual dimorphism in an indigenous South African population. The sample comprised 332 individuals: 182 males and 150 females from the Dart Collection at the University of the Witwatersrand. A digitiser was used to collect 3D landmark coordinates, from which eight measurements (Table 3.1) were calculated and used to produce sex discriminant functions. Measurement definitions primarily follow Giles and Elliot (1963). The mode of data collection meant that the definition of three of the measurements had to be slightly modified: cranial breadth, bizygomatic breadth and mastoid length.

Bizygomatic breadth was the most dimorphic measurement (t = 12.564, p = <0.001). The least dimorphic measurement was cranial breadth (t = 3.839, p = <0.001). A total of eight (stepwise) discriminant functions were produced; cross-validated accuracy for all discriminant functions was between 77 to 80%. Function 4 (maximum cranial length, basi-bregmatic height, bizygomatic breadth, mastoid length and maxillo- alveolar breadth) was the most accurate, with 76% of males and 84% of females correctly classified (80% overall), with a sex bias of -8%. The least accurate was Function 7 (maximum cranial length and bizygomatic breadth), with 77% of males and 80% of females classified correctly (78% overall), with a sex bias of -3%.

3.3.4. Kranioti et al. (2008)

Kranioti et al. examined cranial sexual dimorphism in a Cretan population from two cemeteries; St Konstantinos and Pateles, Heraklion, Crete. The sample consisted of 178 individuals: 90 males and 88 females. The mean age was 68.94 for the males and 73.21

for the females. A total of 16 measurements adapted from Martin and Saller (1957) were taken in each skull (Table 3.1). The most dimorphic measurement was bizygomatic breadth (F = 126.57, p <0.001). The least dimorphic measurement was nasal breadth (F = 5.17, p <0.05).

A total of six demarking measurements were published and the cross-validated accuracies for these ranged from 70.20 to 81.9% (sex bias of -6.17 to 0.56%). A total of two (stepwise) discriminant functions were formulated. Function 1 (bizygomatic breadth, maximum cranial length, nasion-prosthion height, mastoid height and nasal breadth) was the most accurate, with 87.2% of males and 86.9% of females correctly classified (87.10% overall), with a sex bias of 0.3%. The least accurate was Function 2 (maximum cranial length, basion-bregma height, mastoid height, foramen magnum breadth and maximum vault breadth), with 85.56% of males and 79.55% of females classified correctly (82.60% overall), and a sex bias of 6.01%. It was found that Cretan crania are more similar in size to North American, than they are to South African, whites.

3.3.5. Spradley and Jantz (2011)

Spradley and Jantz examined sexual dimorphism in North American Black and White populations. The sample comprised 704 individuals (430 males and 274 females) from the Forensic Anthropology Data Bank. Individuals are adult (over 18 years) and were born after 1930 (there is no published mean age for this sample). A total of 24 cranial measurements (Table 3.1) were recorded in each specimen. The most sexually dimorphic measurement was bizygomatic breadth (cross-validated correct sex estimation accuracy of 75 to 78%) and the least dimorphic was orbital height (cross- validated accuracy of 44 to 47%) in both populations.

Two (stepwise) discriminant functions were formulated; one for each population. Function 1 for the Black population (bizygomatic breadth, mastoid height, biauricular breadth, upper facial height, minimum frontal breadth, foramen magnum breadth, orbital height and nasal height) correctly classified 90.57% of males and 90.7% of females (90.64% overall), with a sex bias of -0.13%. Function 2 for the White

population (bizygomatic breadth, basion-nasion length, mastoid height, nasal height, basion-bregma height, minimum frontal breadth, biauricular breadth, glabella-occipital length, frontal chord, parietal chord and occipital chord) resulted in 91.53% of males and 88.49% of females (90.07% overall) correctly classified, with a sex bias of 3.04%. Although the classification accuracy of each function for the two populations was similar, different measurements are used to formulate each standard, which highlights population variation in cranial dimorphism.

3.3.6. Ogawa et al. (2013)

A series of cranial measurements were taken in a modern Japanese population in order to develop a set of population specific discriminant functions for sex estimation. Ogawa examined a sample of 113 adult individuals: 73 male and 40 female. The mean age of the individuals was not published. The measurements used in this study were taken from forensic anthropological records at the National Research Institute of Police in Japan. A selection of 10 of the original 25 Martin and Saller (1957) cranial and mandibular measurements (Table 3.1) were used, selected based on their ‘robust’ inter- and intra-observer reliability.

The most sexually dimorphic measurement was cranial base length (F = 69.92, p = <0.001), and the least was maximum cranial breadth (F = 24.27, p = <0.001). Eight discriminant functions (1 stepwise and 7 direct) were developed for the cranium using six measurements. The reported cross-validated classification accuracy ranged from 84.8 to 88.1%. When the cranial discriminant functions were validated against a hold- out sample from the same population (25 males and 25 females), a classification accuracy of 86.7 to 91.5% was achieved. Function 1 (stepwise) was the most accurate (maximum cranial length, cranial base length, maximum frontal breadth, and bizygomatic breadth) with 91.3% of males and 90.9% of females (91.1% overall) correctly classified, with an allocated sex bias of 0.4%. Function 4 was the least accurate (maximum cranial length, cranial base length, upper facial breadth, and bizygomatic breadth) with 87.0% of males and 86.4% of females (86.7% overall) correctly classified, with a sex bias of 0.6%.

3.3.7. Franklin et al. (2013b)

Franklin et al. formulated cranial metric sex estimation standards for a Western Australian population. The sample consisted of high resolution MSCT scans of 400 individuals (200 males; 200 females); the male specimens were aged between 18 to 79 years, and the females between 18 to 93 years. A total of 31 landmarks were used to calculate 18 linear measurements (Table 3.1). All but two (frontal breadth and orbit height) were found to be significantly dimorphic. The most sexually dimorphic measurement was bizygomatic breadth (F = 362.22, p <0.001), and the least was maximum frontal breadth (F = 0.48, p = NS). Fifteen discriminant functions (7 direct, 7 multiple and 1 stepwise) were developed, with cross-validated accuracies ranging from 70.8 to 90.0%. Function 15 (stepwise; glabello-occipital length, bizygomatic breadth and mastoid height) was the most accurate, with a cross-validated accuracy of 90% and a sex bias of -2.2%. Function 13 (stepwise; orbit breadth, orbit height and bifrontal breadth) was the least accurate, with a cross-validated accuracy of 70.8% and a sex bias of -1.6%.

3.4. Summary

This chapter reviewed a selection of literature on population specific sex estimation standards for a number of global populations. Bizygomatic breadth was frequently the most sexually dimorphic measurement, and as a result is used as a demarking point for estimating skeletal sex in multiple papers, which is beneficial in cases where only partial skeletal elements are recovered. Maximum cranial breadth was frequently the least dimorphic measurement. The papers reviewed here also demonstrate the applicability of using medical scans to formulate population specific standards, as these can be used as proxy for actual bone specimens, which is particularly important for jurisdictions where there is a paucity of documented skeletal collections available for study.

CHAPTER FOUR

4. Materials and Methods

4.1. Introduction

The present study involves the acquisition of measurements in 3D volume rendered crania of modern Western Australian individuals. This chapter outlines the materials used in this study, including the sources of data and associated ethical considerations. The landmark and measurement criteria, as well as the methodology for their acquisition, are also outlined. Finally, the statistical analyses performed on the acquired data are outlined, including the precision test, descriptive and classification statistics.

4.2. Materials

4.2.1. Study Sample

Data are collected from cranial MSCT scans of 300 adults (≥ 18 years of age) equally distributed by sex (Table 4.1). Each scan was obtained from a medical PACS database populated with medical scans from major Western Australian hospitals (e.g. Sir Charles Gairdner Hospital; Royal Perth Hospital; Fremantle Hospital). The scans are anonymised prior to receipt to protect patient confidentiality; the only available information about the subjects is their age and sex.

Table 4.1: Age distribution (in years) for males and females.

Sex N Min. Max. Mean SD Male 150 18 79 41.06 15.87 Female 150 18 93 47.75 19.43

Specific information regarding the ancestry of each individual is also not available – such information is not deemed medically relevant and it is thus not collected. It is

assumed, however, that this sample is approximately representative of the current Western Australian population. A study by Flavel and Franklin (unpublished) confirms that the sample is primarily Caucasian (78.61%), which accords with ethnicity frequency data for Western Australia from the Australian Bureau of Statistics (2012). i) Inclusion/exclusion criterion

MSCT scans offer excellent resolution for the visualization of the skeleton (Bolliger et al. 2008). As this study uses scans already in the Western Australian PACS database, a strict selection criteria was enforced. Only those scans with slice thickness ≤2mm, reconstructed retrospectively from the volumetric raw data, are used. The morphology of important cranial landmarks are lost on the scans when the slice thickness is higher; this is because the spatial resolution of these scans is low (Dirnhofer et al. 2006), which means that the lines of the image are not closely joined, allowing for parts of the scan to be missing or distorted. Further, scans missing key regions of the skull are excluded, as are scans that have obvious deformities and/or serious trauma.

4.2.2. Ethics

It is necessary to obtain approval from the relevant organisation to ensure compliance with ethical guidelines for research involving human subjects (Lindorff 2010). Ethical approval was obtained before any data collection was performed in the present study. Human research ethics approval was granted by the University of Western Australia’s Human Research Ethics committee on 22nd August 2012; this project has been appended to Professor Daniel Franklin’s UWA HREC approved (RA/4/1/4362) research program.

4.3. Methods – craniometric measurements

4.3.1. Landmarks a) Landmark definition

Landmarks are defined as geometric locations of osteological points that are biologically homologous (Uysal et al. 2005; Bookstein 1991). Landmarks can ideally be identified accurately and precisely, otherwise they have limited anthropological value, as landmarks must be unambiguously defined and repeatable (Richtsmeier et al. 1995). The OsiriX® software can be used to collect 3D (x, y and z) coordinates (see below). Traditional cranial measurements and angles are then calculated from these 3D coordinates. The ability to accurately locate different landmarks is inherently related to their specific definition and the related biology. To that end, Bookstein (1991) devised a taxonomy that defines three types of landmarks:

Type 1: landmarks whose homology between specimens is maintained by strong evidence, such as the meeting of structures or local unusual histology, e.g. meeting of the coronal and sagittal sutures at bregma (Bookstein 1991; O’Higgins 2000).

Type 2: landmarks whose homology between specimens is supported only by geometric evidence, e.g. prosthion, defined as the point on the maxillary alveolar process that projects most anteriorly in the midline (Bookstein 1991; O’Higgins 2000).

Type 3: landmarks that have at least one deficient coordinate, meaning that they can be reliably located to an outline or surface, but not a specific location, e.g. euryon, defined as the most widely separated points on the two sides of the skull (Bookstein 1991; O’Higgins 2000).

Type 1 landmarks are most reproducible and the least reproducible are Type 3 (O’Higgins 2000), especially in 3D volume-rendered specimens, as you cannot manually feel the specimen for maximum curvatures (Franklin et al. 2013a). Franklin et al. (2013a) also determined that Type 3 landmarks were less precise, having a relatively higher degree of inter- and intra-observer error compared to Type 1 landmarks (e.g.

euryon, frontoparietal temporale and frontotemporale). The majority of the landmarks in this study are Type 2 or Type 3.

In the present study 39 osteometric landmarks were selected based on the required landmarks being acquired accurately using the 3D approach (OsiriX®; and see below Table 4.2, Figure 4.1), as well as their inclusion in previously published research used for comparison in this study. Due to the methodological approach (3D volume rendered specimens), a selection of landmarks required modification (or clarification) to their definition. For example, when locating the point at the opisthocranion, which is a Type 3 landmark, it relies on a convex posterior surface, however, in the event that the posteriormost surface is flat in this study, the protocol has been established that the point is to be placed in the midpoint of that surface. Variation also occurs at bregma and lambda; if extra bones (wormian bones) are present, the landmarks are then placed where the sutures would normally intersect, typically in the middle of the wormian bone. b) Landmark acquisition – OsiriX®

The DICOM (digital imaging and communication in medicine) images for each individual are viewed in OsiriX®; this is a stand-alone application for the MacOS X operating system (Rosset & Spadola 2004). OsiriX® is used to reconstruct and visualise MSCT scans (Figure 4.2), as well as MRI (magnetic resonance imaging) and PET (positron emission tomography) scans. This software is first used to perform 3D volume rendering of the scan slices, allowing the surface of the crania to be visualized. This software also allows the user to place and name landmarks in the reconstructed image, and export the 3D (x, y and z) coordinates in csv (comma separated values) format for further analysis. The x, y and z coordinates are based on dimensions established at the time the MSCT image is acquired (Aldridge et al. 2005; Williams & Richtsmeier 2003); the unit of measure for the x and y axes (in the horizontal plane) are the same and are the size of the pixels in the rows and columns, whereas the z axis is determined by the slice thickness (Corner et al. 1992). OsiriX® allows the 3D volume rendered scan to be orientated in any plane, which aids in ensuring the landmarks are placed in their correct locations (Hennessy & Stringer 2002). Once these osteometric 30

landmarks are placed in the skull, this data is exported in csv format, which is then imported into MorphDb (see below).

Table 4.2: Landmark codes and definitions used in the present study.

Landmark Name Code Landmark Description Bilateral Landmarks Alarei,ii al The most lateral point on the nasal aperture. A point on the lateral aspect of the root of the Auriculareii au zygomatic process at the deepest incurvature, wherever it may be. Apex of the lacrimal fossa, as it impinges on the Dacryoniii d frontal bone. Intersection of the most anterior surface of the Ectoconchioni ec lateral border of the orbit and a line bisecting the orbit along its long axis. The most lateral point on the outer surface of Ectomalarei ecm the alveolar margins, usually opposite the middle of the upper second molar tooth. The most widely separated points on the two Euryoni eu sides of the skull. Measured approximately perpendicular to the Foramen Magnum Lateralisiv fml maximum length and recorded at the widest transverse diameter of the foramen. Most lateral point on the fronto-zygomatic Frontomalare Temoraleii fmt suture. Fronto-parietal suture at the intersection of the Frontoparietal Temporaleiii fpt superior temporal line. Point located forward and inward on the Fronto- temporalei ft superior temporal line directly above the zygomatic process of the frontal bone. Mastoidalei ms The most inferior point on the mastoid. The point on the inferior orbital margin that meets with the short axis of the orbit, Orbital Margin Inferioriv oi perpendicular to the superior orbital margin, for an inside measurement. The point on the superior orbital margin that meets with the short axis of the orbit, Orbital Margin Superioriv os perpendicular to the inferior orbital margin, for an inside measurement. The highest point on the superior margin of the Porioniii po external auditory meatus. Zygioni,ii zy The most lateral point on the zygomatic arch. Table continued ...

Landmark Name Code Landmark Description Midline Landmarks The point of intersection of the coronal and Bregmai b sagittal sutures. The midpoint of the anterior margin of the Basioni ba foramen magnum in the mid-sagittal plane. The most forward projecting point of the forehead in the midline at the level of the Glabellai,iii g supraorbital ridges and above the nasofrontal suture. Junction of occipital and parietal bones in the Lambdai l midline. The junction of the internasal suture with the Nasionv,vi n nasofrontal suture. The point where a line drawn between the Nasospinaleii ns inferiormost points of the nasal aperture crosses the midsagittal line. Point where midsagittal plane intersects the Opisthionvi o posterior margin of the foramen magnum. Instrumentally determined most posterior Opisthocranionvi op point of the skull not on the external occipital protuberance. The most anterior point in the midline on the Prosthionv pr alveolar processes of the maxilla.

Key: i. Bass 1987; ii. Buikstra & Ubelaker 1994; iii. de Villiers 1968; iv. Franklin et al. 2013a; v. Howells 1973; vi. Wood & Lynch 1996.

Figure 4.1: Fronto-lateral, lateral, frontal and basal view of cranial landmarks (key shown in Table 4.2) (adapted from Franklin 2005).

Figure 4.2: Frontal, lateral and posterior views of a 3D volume rendered cranium (Male, 28 years old). 33

4.3.2. Measurements i) Measurement data

From the 39 landmarks a total of 26 linear measurements were calculated using MorphDb, which is a Windows based program that was developed at the Centre for Forensic Science (UWA). This software facilitates the storage and analysis of landmark coordinates of multiple individuals (see below). The selection of measurements was dependent on those most commonly used in previously published research and thus their suitability for comparison with the results of this study. The measurements are described in Table 4.3, and illustrated in Figures 4.3-4.5.

Table 4.3: Inter-landmark linear measurements acquired in the present study data (see Table 4.2 for landmark definition).

Measurement Landmarks Code Definition Basion-Bregma ba-b BBH Direct distance from bregma to basion. Heighti Basion-Nasion ba-n BNL Direct length from nasion to basion. Lengthi Basion- Nasospinale ba-ns BNS Maximum length from basion to nasospinale. Lengthi Basion-Prosthion From basion to the most anterior point on the pr-ba BPL Lengthii maxilla in the median sagittal plane. Bi-orbital Direct distance between right and left ec-ec EKB Breadthi ectoconchion. Biauricular The least exterior breadth across the roots of au-au AUB Breadthi the zygomatic processes. Bifrontal Direct distance between the two external fmt-fmt FMB Breadthi points on the frontomalare suture. Bizygomatic Direct distance between most lateral points zy-zy ZYB Breadthi of the zygomatic arches. Foramen The distance between the lateral margins of Magnum fml-fml FOB the foramen magnum at the points of Breadthi greatest lateral curvature. Foramen ba-o FOL Direct distance from basion to opisthion. Magnum Lengthi Direct distance from bregma to nasion taken Frontal Chordi b-n FRC in the midsagittal plane.

Table continued ...

Measurement Landmarks Code Definition Glabello- Distance between glabella and g-op GOL Occipital Lengthi opisthocranion in the midsagittal plane. Interorbital Direct distance between right and left d-d DKB Breadthi dacryon. The vertical projection of the mastoid process Mastoid Height oi|po|ms MDH (ms) below and perpendicular to the eye-ear (left)i (Frankfort) plane (po-oi). Maximum Maximum width of the skull perpendicular to eu-eu XCB Cranial Breadthi midsagittal plane wherever it is located. Maximum Maximum breadth at the coronal suture fpt-fpt FRB Frontal Breadthiii perpendicular to the medial plane. Minimum Direct distance between the two ft-ft WFB Frontal Breadthi frontotemporale. Nasal Breadthi al-al NLB Maximum breadth of the nasal aperture. The direct distance from nasion to the Nasal Heighti n-ns NLH midpoint of a line connecting the lowest points of the inferior margin of the notches. Nasion- n-pr NPH Direct distance from nasion to prosthion. Prosthion Heighti Direct distance from lambda to opisthion Occipital Chordi l-o OCC taken in the midsagittal plane. Opisthion- The maximum distance from opisthion to o-g OPL Glabella Lengthiv glabella in the midline. Orbital Breadth Laterally sloping distance from dacryon to d-ec OBB (Left) i ectoconchion. Orbital Height Direct distance between the superior and oi-os OBH (Left)i inferior orbital margins. Maximum breadth across the alveolar Palate External borders of the maxilla measured on the ecm-ecm MAB Breadthi lateral surfaces at the location of the second maxillary molars. Direct distance from bregma to lambda taken Parietal Chordi b-l PAC in the midsagittal plane.

Key: i. Buikstra & Ubelaker 1994; ii. Hooton 1946; iii. Howells 1973; iv. Giles & Elliot 1963.

a) Frontal view

b) Lateral view

Figure 4.3: Frontal and lateral views of cranial measurements (adapted from Franklin 2005).

a) Frontal view

b) Base view

Figure 4.4: Frontal and base view of cranial measurements (adapted from Franklin 2005). 37

Figure 4.5: Midsagittal view of cranial measurements; measurements in red are chords (adapted from Buikstra & Ubelaker 1994).

ii) Measurement calculation - MorphDB

The x, y and z coordinates for each osteometric landmark exported from OsiriX® are imported into MorphDb. This program calculates user defined inter-landmark linear measurements (or angles) from landmark coordinate data. The software also stores age and sex information for each individual. As the data collection component of the project is taken blind to the biological profile (e.g. sex and age unknown), it is only at this point that the information is available to the researcher.

4.4. Methods - statistical analyses

The following sections describe the precision test and the statistical methods that are used to analyse the measurement data. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 19.0.

4.4.1. Precision test i) Introduction

Metric data can vary, both when collected by one, or multiple, observers. Prior to primary data collection the measurement acquisition method must be evaluated by its stated error, as well as its accuracy and precision (Franklin et al. 2012c). High levels of measurement error can lead to erroneous results (Goto & Mascie-Taylor 2007) and is thus quantified prior to data collection so that problematic individual measurements can be identified and adjusted accordingly (e.g. changing measurement definitions or collection protocols (Franklin et al. 2013a)). Therefore, intra-observer error must be minimized to achieve increased precision and accuracy (Mueller & Martorell 1988), which will result in more reliable data. Intra-observer error shows the variation of repeated measurements on the same specimen performed by the same observer (Perini et al. 2005). It is widely recognized that it is impossible to estimate landmark positions without error (Lyman & VanPool 2009; Chen et al. 2004). Accordingly, therefore, a precision test was conducted prior to primary data collection to assess intra-observer error. ii) Method

The precision test (intra-observer error) involved measuring four randomly selected crania (MSCT scans - two males and two females) and then re-measuring the same specimens every second day, a total of four times each. The time gap between each re- measurement minimizes the possibility of recalling results from the previous assessment. The following statistics are calculated: technical error of measurement (TEM); relative TEM (rTEM); and the Coefficient of Reliability (R).

Technical error of measurement (TEM)

The technical error of measurement, which is an accuracy index, represents measurement quality, control and precision (Perini et al. 2005). It is the standard deviation between repeated measurements taken by the same person on the same subject at different times. The main contribution to a high TEM is random flaws in measurement and recording techniques (Lyman & VanPool 2009). The formula is as follows:

√Σ푑2 푇퐸푀 = 푥푁 d = The difference between (x) measurements

N = The number of replicates

푥 = The number of subjects measured

Relative technical error of measurement (rTEM)

The rTEM is a coefficient of variation and provides an estimate of the error level relative to the size of the individual measurement (Goto & Mascie-Taylor 2007). It is expressed as a percentage of the magnitude of error of the average size of the dimension under study (Lyman & VanPool 2009). The formula is as follows:

푇퐸푀 푟푇퐸푀 = [ ] × 100 푉퐴푉

VAV = variable average value (the overall mean of replicate values from all test subjects for a certain variable) (Perini et al. 2005).

Coefficient of reliability (R)

The coefficient of reliability (R) is the proportion of inter-subject variance that is affected by measurement error (Reynolds et al. 2008). It estimates the degree of intra- observer precision. The value ranges from 0 (not reliable) to 1 (complete reliability) (Goto & Mascie-Taylor 2007). The formula is as follows:

푇퐸푀2 푅 = 1 − [ ] 푆2

푆2 = The population variance of a character

4.4.2. Basic descriptive statistics

Summary statistics were calculated including mean, standard deviation and range of measurement values. i) Comparison of means

Analysis of variance - ANOVA

The analysis of variance (ANOVA), quantifies the deviation from the mean across groups (Pietrusewsky 2008). This is used in this study to compare variances within the Western Australian sample. This includes testing the significance of differences between male and female measurement data. It is also used to analyse the presence of bilateral asymmetry in cranial measurements (orbital breadth; orbital height; and mastoid height). t-test

An independent sample t-test is used to compare two mean scores of the same dependent variable from two different groups. A t-test will be performed using the mean measurement values of the Western Australian sample as compared to the mean measurement values of other populations used as a basis of comparison in this study. Significant values are calculated using the mean, standard deviation and n (sample size). These values are all that are available from published standards as they do not give the raw measurement values.

4.4.3. Classification statistics

4.4.3.1 Discriminant function analysis

The major application for discriminant function analysis (DFA) in forensic anthropology is to assign an unknown specimen to one or more groups (Pietrusewsky 2008). Discriminant function analysis uses variables to discriminate between two or more groups and can also be used to determine which variable (or variables) are the most accurate group classification predictors (Hill & Lewicki 2006). The DFA uses the independent variables to make a prediction of which group an individual belongs. The basis of DFA is to weight and combine multiple variables so that the inter-correlations of those variable(s) are used and the ratio of between-group variance to the within- group variance is maximized (Pietrusewsky 2008). The sectioning point is used for classifying an unknown specimen into one group based on their discriminant function score. In cases where there are an equal number of cases per group, the sectioning point is zero. When the group numbers are different, however, the value is equal to the mean of the two group centroids (Patriquin et al. 2005). There are two different DFA approaches – direct and stepwise. i) Direct DFA

Direct discriminant function analysis is used when the variables analysed are selected by the user based on specific individual requirements. In direct DFA, selection of measurement (or variable) may be related to providing functions suitable for fragmental or isolated skeletal remains. ii) Stepwise DFA

A stepwise DFA automatically selects measurements that, when combined, provide maximal classification. Forward selection begins with a constant mean and introduces variables that minimize the overall Wilks’ Lambda individually until no further additions improve classification accuracy (Ramsey & Schafer 2002). Backward elimination involves using all possible variables, and at each step, the variable with the smallest F-ratio (ratio of two mean squares) is removed until no more variables can be removed without decreasing the accuracy (Ramsey & Schafer 2002). Put simply, at 42

each step, the measurement that most accurately discriminates between groups is entered (or removed) into the discriminant function first (Pietrusewksy 2008), until the addition of variables no longer increases the accuracy.

4.4.4. Population specificity of sexing standards

The sex of the Western Australian individuals were classified using a variety of foreign published standards. This was performed to assess the level of classification accuracy achieved and the sex bias. This provides insights useful for evaluating the forensic applicability of foreign classification statistics. i) Selection of discriminant functions

The comparative discriminant functions used in this study had to fulfil three criteria:

a. That the stated accuracy of sex estimation was greater than 75%; b. That the sex bias was less than 7%; c. That the discriminant functions have measurements in common to those collected in the present study.

Only the functions with the highest accuracy were selected from each published paper. A sex bias of 5% is ideal when using sex estimation standards in forensic anthropology, as it is the acceptable misclassification of sex relative to males and females. Some of the discriminant functions used for comparison in this study, however, did not have a sex bias of less than 5%. To increase the number of functions available for comparison, a limit of less than 7% was used.

CHAPTER FIVE

5. Results

5.1. Introduction

This chapter presents the results of the statistical analyses of the cranial data from the Western Australian population towards achieving the aims listed in Chapter One:

 Statistical quantification of sexual dimorphism in Western Australian crania  To quantify and evaluate the effect on classification accuracy of applying non-population specific standards to a Western Australian population.

An intra-observer precision test was first performed in order to validate the methodology of data collection. The results of the precision test, followed by the analysis of bilateral asymmetry, are reported. This is followed by the analysis of size differences between populations. Finally, the accuracy of foreign standards are explored, both when applied in their original format and when using the same combinations of measurements used in these functions to formulate Western Australian specific sex estimation functions.

5.2. Precision test

Table 5.1 presents the results of the 4x4 intra-observer precision test based on the analysis of the measurements acquired in the 3D cranial MSCT scans. All measurements had coefficients of reliability (R) and relative technical error of measurement (rTEM) values of >0.75 and <5% respectively. The only exception was for maximum cranial breadth (R = 0.71; rTEM = 6.55%). The majority of the remaining measurements have a coefficient of reliability over 0.90 and ranging from 0.76 to 0.99 (mean = 0.93). The lowest R value was 0.76 (frontal chord) and the highest R value was 0.99 (multiple measurements - see Table 5.1). The lowest TEM value was 0.34 (orbital height) and the highest was 5.75 (maximum cranial breadth). The lowest rTEM value

was 0.28% (maximum cranial length) and the highest was 6.55% (maximum cranial breadth). The mean rTEM for the cranial measurements was 1.26%.

Table 5.1: Intra-observer error (TEM; R; rTEM) based on the 4x4 precision test.

Technical Error of Coefficient of Measurement Relative TEM (%) Measurement (TEM) Reliability (R) BBH 1.04 0.95 0.75 BNL 0.64 0.99 0.62 BNS 0.79 0.98 1.13 BPL 0.49 0.99 0.53 GOL 0.53 0.99 0.28 EKB 0.83 0.92 0.86 AUB 0.60 0.99 0.48 WFB 0.70 0.97 0.82 FMB 0.58 0.98 0.56 ZYB 0.62 0.99 0.49 FOB 0.43 0.88 1.31 FOL 0.37 0.84 0.92 FRC 1.31 0.76 1.16 NLB 1.30 0.94 1.26 NLH 0.71 0.99 0.93 DKB 0.58 0.85 2.95 MHL 0.51 0.99 1.56 OPL 0.50 0.99 0.34 XCB 5.75 0.71 6.55 FRB 1.22 0.97 1.25 NPH 1.81 0.97 1.95 OCC 0.72 0.94 0.70 OBB 0.61 0.86 1.45 OBH 0.34 0.97 0.94 MAB 0.80 0.99 1.47 PAC 1.99 0.91 1.64

Note: Key to measurements is shown in Table 4.3.

5.3. Bilateral asymmetry

Bilateral asymmetry was evaluated using one-way ANOVA (Table 5.2). The ANOVA assumptions of normality and homogeneity of variance were not violated. Of the three variables tested (orbital height, orbital breadth and mastoid height), none were statistically significantly different between the left and right sides. When comparing the mean values, the largest bilateral difference was only 0.6mm (orbital breadth). Based on no evidence of bilateral asymmetry, the left side values for those measurements are used in all subsequent analyses.

Table 5.2: Comparison of mean left and right cranial measurements (in mm).

Left Right Measurement n Mean SD n Mean SD F P-value Orbital Breadth 300 41.59 2.29 300 41.99 2.20 4.698 0.031NS Orbital Height 300 35.51 2.01 300 35.36 2.05 0.860 0.354NS Mastoid Height 300 30.85 4.12 300 31.18 4.06 0.989 0.320NS

Key: ns = not significant.

5.4. Cranial sexual dimorphism in the Western Australian population

Descriptive statistics for the male and female cranial measurements are presented and illustrated in Table 5.3 and Figure 5.1 respectively. It is evident that males are on average larger than females for all measurements; the significance of sexual dimorphism is further explored below.

5.4.1. One-way ANOVA

The statistical significance of sexual dimorphism in the Western Australian sample was quantified using ANOVA (Table 5.3). Bizygomatic breadth was the most dimorphic measurement (F = 232.46; p = <0.001), followed by opisthion-glabella length (F = 173.38; p = <0.001), and basion-nasion length (F = 144.57; p = <0.001). The least sexually dimorphic measurements were minimum frontal breadth (F = 0.35; p = ns) and orbital height (F = 2.46; p = ns).

Table 5.3: Descriptive statistics and comparisons of mean cranial measurements (in mm). Male (n=150) Female (n=150) Measurement Mean SD Range Mean SD Range F R Square P-value BBH 140.17 5.96 123.9-157.6 134.46 5.12 119.8-148.0 79.32 0.210 *** BNL 105.95 5.02 93.6-117.1 99.55 4.15 87.8-111.7 144.57 0.327 *** BNS 97.15 5.59 84.1-112.6 91.78 4.76 76.9-107.0 80.05 0.212 *** BPL 97.67 5.65 84.2-110.9 91.83 5.57 76.4-107.2 80.57 0.213 *** EKB 99.27 3.79 88.7-110.8 94.98 3.80 84.2-105.1 95.71 0.243 *** AUB 125.03 4.78 111.0-140.0 119.41 4.58 103.6-133.9 107.90 0.266 *** FMB 106.73 3.95 96.2-116.2 101.91 4.28 90.4-112.9 102.51 0.256 *** ZYB 131.90 4.64 120.2-144.0 123.66 4.72 113.1-138.3 232.46 0.438 *** FOB 32.40 2.26 27.2-38.6 31.27 2.44 25.8-36.8 16.90 0.054 *** FOL 38.80 2.29 31.7-44.9 37.44 2.57 30.9-46.6 22.87 0.071 *** GOL 190.27 7.72 168.0-206.7 180.36 6.91 160.8-196.0 135.84 0.313 *** DKB 20.87 2.31 16.4-27.9 19.30 2.33 14.4-26.4 34.25 0.103 *** MHL 32.99 3.69 22.5-41.2 28.69 3.34 20.6-38.1 112.25 0.274 *** XCB 132.95 6.11 114.7-147.4 130.43 6.62 104.8-150.4 11.80 0.038 *** FRB 97.35 10.07 72.9-121.4 96.68 9.34 67.5-119.2 0.35 0.001 NS WFB 99.90 4.18 87.1-110.5 96.79 4.62 84.6-111.0 37.28 0.111 *** NLB 25.14 2.11 20.8-35.5 24.36 2.07 19.3-29.6 10.42 0.034 *** NLH 53.68 3.04 44.7-60.9 50.41 2.66 43.8-56.4 97.92 0.247 *** NPH 68.75 4.27 56.7-78.7 64.39 4.45 53.1-75.7 74.58 0.200 *** OPL 148.82 5.58 133.7-162.9 140.81 4.93 124.8-155.6 173.38 0.368 *** OBB 42.45 2.18 36.9-47.9 40.72 2.05 34.8-47.8 49.85 0.143 *** OBH 35.70 2.03 30.7-40.3 35.32 1.97 30.2-40.4 2.46 0.008 NS MAB 58.12 5.83 40.7-72.8 54.64 6.23 37.8-67.1 25.04 0.078 *** FRC 113.72 5.13 99.4-127.1 108.95 4.72 96.8-120.8 70.10 0.190 *** PAC 122.09 6.57 104.2-143.6 117.01 6.28 96.8-136.3 46.99 0.136 *** OCC 99.72 5.29 87.4-113.1 97.27 4.84 83.4-113.4 17.54 0.056 *** Key: ***p < 0.001; **p < 0.01; *p < 0.05; NS = not significant. Key to measurements is shown in Table 4.3. 48

200 Male (n=150) 180 Female (n=150) 160

140

120

100

80 Measurement(mm) 60

0 BBH BNL BNS BPL EKB AUB FMB ZYB FOB FOL GOL DKB MHL XCB FRB WFB NLB NLH NPH OPL OBB OBH MAB FRC PAC OCC

Figure 5.1: Comparison of male and female mean cranial measurements (in mm). Key to measurements in Table 4.3; standard error bars are shown.

5.4.2. Stepwise discriminant analysis

The 26 cranial measurements from the Western Australian population were subjected to stepwise discriminant function analysis; a total of six measurements were selected (bizygomatic breadth, opisthion-forehead length, mastoid height, biauricular breadth, minimum frontal breadth and glabello-occipital length). The accuracy of correct classification according to sex (after cross-validation) was 88.7%, with a sex bias of 4.00% (Table 5.4). This analysis establishes a ‘baseline’ classification accuracy for this population and also assists in identifying the measurements that are most sexually dimorphic.

Table 5.4: Stepwise discriminant analysis of all cranial measurements.

Group centroid and Correctly Sex Equation sectioning point assigned bias (ZYB x 0.232) + (OPL x 0.070) + ♂1.188 ♂136/150 (MHL x 0.098) + (AUB x -0.117)+ [0.00] ♀130/150 4.00% (WFB x -0.063) + (GOL x 0.034) + ♀-1.188 [88.7%] -28.599

Key to measurements in Table 4.3.

5.5. Population variation in cranial sexual dimorphism

Mean measurement data from the Western Australian sample and other studied populations were compared using a series of unpaired t-tests. Seven measurements were chosen for comparison; these were the most dimorphic measurements in the Western Australian population according to the ANOVA statistical data (see above), six of the seven were also selected in the stepwise discriminant function analysis. Measurements are examined according to levels of apparent dimorphism (high to low). The aim here is to evaluate the magnitude of dimorphism in those measurements relative to a selection of other global populations. The assumption followed is that a higher t-value implies that the probability of the male/female difference being the same gets smaller (sexual dimorphism increases as t-values increase).

i) Bizygomatic breadth (ZYB)

Bizygomatic breadth is significantly different between males and females in each population; the t values range from 8.12 to 19.13 (Table 5.5). This measurement is most dimorphic in the North American White population (t = 19.13), followed by the Western Australian population (t = 15.25) and the North American Black population (t = 13.30). It is least dimorphic in the South African White (t = 8.42) and Japanese populations (t = 8.12).

Table 5.5: Significance of sexual dimorphism in bizygomatic breadth in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 131.90 123.66 15.25 *** North American White (Giles)i 131.92 122.70 11.93 *** North American Black (Giles)i 133.25 124.40 13.30 *** South African Whiteii 128.90 121.90 8.42 *** South African Blackiii 122.60 115.70 12.56 *** North American White (Spradley)iv 129.80 121.01 19.13 *** North American Black (Spradley)iv 130.76 122.43 10.56 *** Japanesev 136.50 129.00 8.12 *** Cretanvi 130.54 122.07 11.62 ***

Key: ***p < 0.001; i. Giles & Elliot (1963); ii. Steyn & Iscan (1998); iii. Franklin et al. (2005); iv. Spradley & Jantz (2011); v. Ogawa et al. (2013); vi. Kranioti et al. (2008).

ii) Opisthion-glabella length (OPL)

Opisthion-glabella length is significantly different between males and females in each population; the t values range from 10.07 to 13.17 (Table 5.6). This measurement is most dimorphic in the Western Australian (t = 13.17) followed by the North American White population (t = 10.70). It is least dimorphic in the North American Black population (t = 10.07).

Table 5.6: Significance of sexual dimorphism in opisthion-glabella length in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 148.82 140.81 13.17 *** North American White (Giles)i 156.60 148.80 10.70 *** North American Black (Giles)i 153.95 147.03 10.07 ***

Key: ***p < 0.001. References shown in Table 5.5.

iii) Basion-nasion length (BNL)

Basion-nasion length is significantly different between males and females in each population; the t values range from 6.89 to 15.23 (Table 5.7). This measurement is most dimorphic in the North American White (t = 15.23) followed by the Western Australian population (t = 12.03). It is least dimorphic in the South African White population (t = 6.89).

Table 5.7: Significance of sexual dimorphism in basion-nasion length in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 105.95 99.55 12.03 *** North American White (Giles)i 100.60 95.07 8.87 *** North American Black (Giles)i 101.47 96.31 8.47 *** South African Whiteii 102.40 96.20 6.89 *** North American White (Spradley)iv 106.12 99.51 15.23 *** North American Black (Spradley)iv 104.02 98.86 7.38 *** Japanesev 103.80 96.30 8.38 *** Cretaniv 102.11 96.25 7.17 ***

Key: ***p < 0.001. References shown in Table 5.5.

iv) Glabello-occipital length (GOL)

Glabello-occipital length is significantly different between males and females in each population; the t values range from 7.33 to 13.91 (Table 5.8). This measurement is

most dimorphic in the North American White (t = 13.91) followed by the Western Australian population (t = 11.71). It is least dimorphic in the South African White population (t = 7.33).

Table 5.8: Significance of sexual dimorphism in glabello-occipital length in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 190.27 180.36 11.71 *** North American White (Giles)i 181.33 171.45 9.93 *** North American Black (Giles)i 185.89 177.84 9.41 *** South African Whiteii 187.70 179.00 7.33 *** South African Blackiii 185.50 178.50 10.29 *** North American White (Spradley)iv 188.04 178.52 13.91 *** North American Black (Spradley)iv 187.17 177.49 9.23 *** Japanesev 179.40 169.40 7.55 *** Cretanvi 181.07 172.89 8.32 ***

Key: ***p < 0.001. References shown in Table 5.5.

v) Mastoid height (MHL)

Mastoid height is significantly different between males and females in each population; the t values range from 4.02 to 12.34 (Table 5.9). It is most dimorphic in the North American White (t = 12.34) followed by the North American Black population (t = 10.91). It is least dimorphic in the South African White population (t = 4.02). vi) Biauricular breadth (AUB)

Biauricular breadth is significantly different between males and females in each population; the t values range from 7.07 to 12.24 (Table 5.10). It is most dimorphic in the North American White (t = 12.24) followed by the Western Australian population (t = 10.39). It is least dimorphic in the North American Black population (t = 7.07).

Table 5.9: Significance of sexual dimorphism in mastoid height in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 32.99 28.69 10.58 *** North American White (Giles)i 28.07 25.21 7.12 *** North American Black (Giles)i 30.32 26.35 10.91 *** South African Whiteii 34.00 30.90 4.02 *** South African Blackiii 35.09 32.10 8.13 *** North American White (Spradley)iv 31.65 27.45 12.34 *** North American Black (Spradley)iv 32.11 28.45 6.61 *** Cretanvi 31.69 28.56 5.79 ***

Key: ***p < 0.001. References shown in Table 5.5.

Table 5.10: Significance of sexual dimorphism in biauricular length in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 125.03 119.41 10.39 *** North American White (Spradley)iv 123.07 117.19 12.24 *** North American Black (Spradley)iv 121.30 115.84 7.07 ***

Key: ***p < 0.001. References shown in Table 5.5.

vii) Minimum frontal breadth (WFB)

Minimum frontal breadth is significantly different between males and females in each population; the t values range from 3.69 to 6.33 (Table 5.11). This measurement is most dimorphic in the North American White (t = 6.33) followed by the Western Australian population (t = 6.11). It is least dimorphic in the North American Black population (t = 3.69), where this measurement is considerably less significant than the other populations.

Table 5.11: Significance of sexual dimorphism in minimum frontal breadth in a variety of global populations compared to Western Australia.

Male mean Female Population t p (mm) mean (mm) Western Australia 99.90 96.79 6.11 *** South African Whiteii 97.80 93.60 4.59 *** North American White (Spradley)iv 96.65 93.69 6.33 *** North American Black (Spradley)iv 95.96 93.06 3.69 ** Cretanvi 96.33 93.23 4.58 ***

Key: ***p < 0.001, **p<0.01. References shown in Table 5.5.

5.5.1. Morphometric variation

To explore levels of morphometric variation, the mean measurement values of the Western Australian sample were compared to those of six foreign populations (North American White, North American Black, Japanese, Cretan, South African White and South African Black) (Figures 5.2 and 5.3). These populations were chosen because they have published sex estimation discriminant functions using comparable cranial measurements.

The mean difference in the male sample is shown in Figure 5.2; positive values represent larger mean measurement values than the Western Australian population and vice versa. It is evident that the Western Australian male population is most similar to the North American White population (Spradley & Jantz 2011), followed by the South African White population (Steyn & Iscan 1998). The most divergent populations are the North American Black populations (Spradley & Jantz 2011; Giles & Elliot 1963). The Western Australian male sample is larger in all instances for the following measurements: (AUB, FOL, GOL, WFB, OBB, OBH and PAC), and smaller in all instances for the following measurements: (FRB, OPL and MAB). All other measurements are a combination of smaller and larger means when compared to the other populations.

-5

-10

-15 BBH BNL BNS BPL EKB AUB FMB ZYB FOB FOL GOL DKB MHL XCB FRB WFB NLB NLH NPH OPL OBB OBH MAB FRC PAC OCC

Giles & Elliot (1963) North American Black Giles & Elliot (1963) North American White Steyn & Iscan (1998) South African White Franklin et al. (2005) South African Black Kranioti et al. (2008) Cretan Spradley & Jantz (2011) North American Black Spradley & Jantz (2011) North American White Ogawa et al. (2012) Japanese

Figure 5.2: Mean difference (in mm) of cranial measurements between Western Australian and published studies for males (where 0 represents the Western Australian mean measurement). Key to measurements in Table 4.3. 56

The Western Australian female population is most similar to the North American White population (Figure 5.3 - Spradley & Jantz 2011). It is least similar to the North American Black populations (Giles & Elliot 1963; Spradley & Jantz 2011) and the Cretan population (Kranioti et al. 2008). The Western Australian female sample is larger in all instances for the following measurements: (AUB, FOL, GOL, WFB, NLH, OBB, OBH and PAC), and smaller in all instances for the following measurements: (DKB, FRB, OPL and MAB).

A series of unpaired sample t-tests were used to test for significant mean size differences (Appendix I.1 and I.2). The majority of the male Western Australian cranial measurements differed significantly to the foreign populations (Appendix I.1). The mean measurements that were not significant include: bizygomatic breadth – North American White population (t = 0.03; p = ns); bifrontal breadth – North American Black population (t = 0.58; p = ns); and basion-nasion length – North American White population (t = 0.36; p = ns).

The majority of the female Western Australian cranial measurements differed significantly to the foreign populations examined (Appendix I.2). The mean measurements that were not significant included: mastoid height – Cretan population (t = 0.28; p = ns); bifrontal breadth – North American Black population (t = 0.19; p = ns); and basion-nasion length – North American White population (t = 0.08; p = ns).

-5

-10

-15 BBH BNL BNS BPL EKB AUB FMB ZYB FOB FOL GOL DKB MHL XCB FRB WFB NLB NLH NPH OPL OBB OBH MAB FRC PAC OCC

Figure 5.3: Mean difference (in mm) of cranial measurements between Western Australian and published studies for females (where 0 represents the Western Australian mean measurement). Key to measurements in Table 4.3.

5.6. Classification accuracy of foreign standards

The effect of applying foreign sex classification statistics to the Western Australian population was evaluated by subjecting the measurement data to a selection of published standards. Table 5.12 presents the stated accuracy of the 17 functions examined, the actual accuracy of those published discriminant functions (as applied to the Western Australian data) and finally the cross-validated accuracy of discriminant functions formulated using the same measurements as the published functions, but based on a discriminant function analysis of the Western Australian population.

When directly applied to the Western Australian population, all published functions exhibit a much larger sex bias than originally stated (18.7 to 100%, compared to stated sex bias of 0 to -6.90%). The overall accuracy of the functions decreases when applied to the Western Australian data in all instances, falling as low as 50% with a sex bias of 100%, using the Cretan population standards (Kranioti et al. (2008) functions 1 and 2). A -100% sex bias was found when using the Spradley and Jantz (2011) White population standards (see Table 5.12). The highest accuracy was 83.3% using Function 3 of Steyn and Iscan (1998) (sex bias of 18.7%), which is the only function that achieved a higher accuracy than the stated accuracy published in the original paper.

The last column in Table 5.12 represents a series of discriminant function analyses, (using the same variables as the foreign functions previously required) as applied to the Western Australian sample. This was performed to assess the classification performance of those measurements and to allow a meaningful comparison to the same foreign standards. Measurement combinations, therefore, were based on the selection of measurements used for the formulation of discriminant functions in the comparative publications. Overall, each function now performed with an acceptable level of error: the accuracy ranged from 76.7 to 88.0%, and the sex bias ranged from -4.00 to 2.70% (Table 5.12).

Table 5.12: Accuracy and sex bias of discriminant functions when applied to the Western Australian sample (including stated accuracy and potential accuracy from modified functions).

Published Accuracy Direct Application to WA WA Specific Discriminant Functions Reference and population Stated Stated Achieved Achieved Function Sex Bias Sex Bias Accuracy Sex Bias Accuracy Accuracy Giles & Elliot 1963 4 88.80% not specified 206/300 (68.9%) 60.00% 254/300 (84.7%) -1.30% North American White Giles & Elliot 1963 6 86.00% not specified 235/300 (78.3%) 36.70% 254/300 (84.7%) -1.30% N. American Black & White Giles & Elliot 1963 7 87.70% not specified 198/300 (66.0%) 65.40% 258/300 (86.0%) -1.40% North American Black 1 85.70% 1.30% 235/300 (78.3%) 34.00% 261/300 (87.0%) 0.60% Steyn & Iscan 1998 2 83.50% 5.50% 200/300 (66.6%) -36.00% 237/300 (79.0%) 2.00% South African White 3 81.10% -3.10% 250/300 (83.3%) 18.70% 256/300 (85.3%) 2.70% 5 79.00% -3.00% 208/300 (69.3%) 60.00% 256/300 (85.3%) 0.00% Franklin et al. 2005 6 79.00% 0.00% 212/300 (70.7%) 54.70% 254/300 (84.7%) -1.30% South African Black 7 78.00% -3.00% 190/300 (63.3%) 72.00% 256/300 (85.3%) -2.70% 8 77.00% -6.00% 177/300 (59.0%) 82.00% 248/300 (82.7%) 2.70% Kranioti et al. 2008 1 87.10% 0.31% 150/300 (50.0%) 100% 256/300 (85.3%) -1.30% Cretan 2 82.60% 6.06% 150/300 (50.0%) 100% 230/300 (76.7%) -4.00% Spradley & Jantz 2011 1 90.64% -0.13% 154/300 (51.3%) -97.40% 257/300 (85.7%) 0.70% North American Black Spradley & Jantz 2011 2 90.01% 3.04% 150/300 (50.0%) -100% 264/300 (88.0%) 2.60% North American White 3 87.20% -6.90% 212/300 (70.6%) -48.00% 245/300 (81.7%) -2.00% Ogawa et al. 2013 5 88.60% -3.50% 180/300 (60.0%) -78.00% 252/300 (84.0%) 2.60% Japanese 8 91.50% 1.10% 185/300 (61.7%) -68.70% 239/300 (79.7%) -2.00% 60

CHAPTER SIX

6. Discussion and Conclusions

6.1. Introduction

The primary aims of this project were to quantify sexual dimorphism in Western Australian crania and to assess the effect on classification accuracy of applying non- population specific standards. Prior to data collection, measurement error and bilateral asymmetry were assessed; those results are briefly interpreted and discussed. Cranial sexual dimorphism is thereafter examined in relation to its magnitude and expression relative to various global populations. The effect on classification accuracy was also explored through the application of foreign standards to the Western Australian population; those results are accordingly discussed. Finally, the limitations of this study, potential for future research and the final conclusions are presented.

6.2. Precision test

A precision test was undertaken to evaluate the accuracy and precision of the measurement acquisition method employed. As previously discussed (see Section 4.4), the precision test involved measuring four randomly selected crania (MSCT scans) and then re-measuring the same specimen every second day, a total of four times each, using the computer software OsiriX®. Twenty-five out of 26 cranial measurements were found to have a relative technical error of measurement (rTEM) of < 5%. Coefficient of reliability (R) values are all above 0.70, with 20/26 measurements having values over 0.90 (mean = 0.93). Maximum cranial breadth had both a relatively high rTEM value (6.55) and low coefficient of reliability (0.71) (see Table 5.1). Despite the latter measurement falling slightly outside the aforementioned limits it was included in this study, due to its almost universal application in foreign discriminant functions.

As mentioned above, maximum cranial breadth was the least precise measurement taken in the MSCT cranial scans. Maximum cranial breadth is defined by the distance between the two bilateral euryon landmarks; the latter is a Type 3 landmark, which by definition are landmarks that have at least one deficient coordinate, meaning that they can be located to an outline or surface, but not at a specific location (Bookstein 1991). It has been well established that landmarks located on curvatures are less precise in contrast to Type 1 landmarks (e.g. Ross & Williams (2008); Lagravere et al. (2010)), which are located on the meetings of structures, such as the coronal and sagittal sutures at the bregma landmark. This is demonstrated in this study, with those measurements defined from Type 1 landmarks (e.g. nasal height: R = 0.99) being acquired more accurately than measurements defined from Type 3 landmarks (e.g. foramen magnum length: R = 0.84) (see Table 5.1).

Published studies (e.g. Franklin et al. (2013a)) have included precision tests in their statistical analyses, deemed to be vital because high levels of error can skew results leading to unreliable data. Franklin et al. (2013a) performed a precision test with the same method (measurements taken in MSCT scans acquired using OsiriX®) on the same population sample as the present study. The authors included Type 3 landmarks in their study, irrespective of their relatively lower measurement accuracy. For example, the two landmarks used to measure foramen magnum length (basion and opisthion) are both defined as Type 3 landmarks. In the precision test for their study, the coefficient of reliability value for measurements defined by Type 3 landmarks were clearly lower (e.g. foramen magnum length: R = 0.746) than those measurements defined by at least one Type 1 landmark (e.g. basion-bregma height: R = 0.980). These results parallel the measurement precision data acquired in the present study. It is important to note, however, that Franklin et al. (2013a) suggest that it is likely that the error associated with any measurement in a precision test will decrease during data collection; this is due to the measurer developing further experience with the specific set of landmarks.

6.3. Bilateral asymmetry

In the present study there are three bilateral measurements; orbital breadth, orbital height and mastoid height. An assessment of bilateral asymmetry was undertaken to evaluate whether left or right measurement values could be used interchangeably in the derived sex discriminant functions. None of the measurements were found to express significant bilateral asymmetry, thus implying that left or right values can be used interchangeably in situations where a cranium may be damaged or partially missing (e.g. if the left zygomatic arch is missing on a cranium, the right may be substituted and vice versa).

There are relatively few published studies specifically examining bilateral asymmetry in the human skull. Franklin et al. (2012a) assessed asymmetry in bilateral cranial measurements as part of a study comparing the application of traditional and geometric morphometric analyses. The latter study examined a subset of the same population sample as the present study. No statistically significant asymmetry was found in the bilateral measurements tested, which included the same measurements used in the present study. Trinkaus (1978) examined bilateral asymmetry in human cranial non-metric traits (e.g. supra-orbital foramen; highest nuchal line present; and presence/absence of parietal notch) in a variety of populations (Indigenous Australian, Amerindian, North African and European). Trinkaus found that asymmetry in human non-metric traits frequently occurs. Non-metric traits, however, are unlikely to have a flow-on effect of introducing bilateral morphometric variation.

In contrast to the human skull, there is an abundance of publications that have examined bilateral asymmetry in the postcranial skeleton (e.g., upper and lower limbs (Krishan 2011; Trinkaus et al. 1994)). Biomechanical stress is suggested as the single most influential factor leading to bilateral asymmetry in the human skeleton (Trinkaus 1978). When a mechanical force acts upon bone, it remodels in response to the stress, which will affect the expression of most non-metric traits through changes in skeletal proportions (Trinkaus 1978) (e.g. mechanical stimulation of the stroke arm in tennis players results in increased bone density (Krahl et al. 1994) and cortical thickness (Jones et al. 1977)).

Krishan and Sharma (2007) examined the accuracy of estimating stature in a North Indian population based on hand and foot dimensions. They found that hand breadth exhibits statistically significant bilateral asymmetry across the sexes, however no other asymmetry occurs. The authors suggests that the bilateral asymmetry is due to handedness (defined as the increasing use of a dominant limb during development, which creates greater dimension on the dominant side with higher activity levels (Trinkaus et al. 1994)), as the width of the hand is affected by musculature contracting on the bone, and this will occur more frequently on the dominant hand.

Trinkaus et al. (1994) examined humeral bilateral asymmetry in Euroamerican, Amerindian and Japanese populations. The authors found little evidence of asymmetry in humeral length, but apparent asymmetry in diaphyseal cross-sectional size and shape, which is suggested as being related to mechanical remodelling in response to handedness. The two examples explored here show evidence of bilateral asymmetry in the human skeleton, however each is due to long-term repetitive mechanical loading. Side dominance is obviously not expected in the cranium and morphometric asymmetry in this bone is only likely to occur under the influence of pathology or trauma, therefore non-pathological skulls would not be expected to express bilateral asymmetry (Trinkaus 1978).

6.4. Cranial sexual dimorphism

Sexual dimorphism is a term used to describe size and morphological differences between males and females (Iscan 2005). The magnitude of sexual dimorphism will vary between and within populations (Walker 2008). This is due to differences in energetic intake, body composition, nutrition and genetics (Kimmerle et al. 2008). There are different patterns of growth between males and females; females usually complete growth by the time they reach puberty, however, males have a later growth spurt after puberty (Bulygina et al. 2006). A greater degree of sexual dimorphism will be observed in a population if nutritional resources are available to sustain the later adolescent male growth spurt (Humphrey 1998; Rogers 2005).

The significance of differences of Western Australian mean male and female cranial measurements were assessed using one-way ANOVA (Table 5.3). Males were larger than females for all variables, which is an observation common to all of the comparative populations examined (e.g. Cretan (Kranioti et al. 2008); Japanese (Ogawa et al. 2013); South African White (Steyn & Iscan 1998)). Only 2/26 measurements (maximum frontal breadth and orbital height) were not significantly sexually dimorphic in the Western Australian population. These measurements were, however, significantly dimorphic in the comparative populations (e.g. maximum frontal breadth: South African White (Steyn & Iscan 1998), Japanese (Ogawa et al. 2013), and Cretan (Kranioti et al. 2008); and orbital height: North American Black and White (Spradley & Jantz (2011)).

The cranial measurements acquired from the Western Australian sample were compared to other global populations. A total of 19/26 measurements in the Western Australian female population were, on average, larger than females from the comparative populations. When the same variables are examined in the male population, a total of 17/26 measurements are similar (or smaller) relative to the comparative populations. This shows that the Western Australian female population is comparatively larger, whilst the male population is more similar, in size to the other global populations examined.

Bizygomatic breadth was found to be the single most dimorphic measurement in the Western Australian population. The muscles that are located in this cranial region are zygomaticus major and minor, and masseter, which are primarily involved in facial expression and mastication. Masseter is an important muscle of mastication, and structures relating to mastication are generally the last to grow (Rogers 2005), meaning these muscles are more likely to be affected by the later male growth spurt. It is therefore not unexpected that bone structures relating to these muscles will be larger in males relative to females. The latter was clearly evident in the Western Australian population, in addition to other populations (e.g. North American White (Spradley & Jantz 2011), and North American Black (Giles & Elliot 1963)).

The measurement of mastoid height was also highly sexually dimorphic in the Western Australian population. This measurement is also strongly dimorphic in the North American White (Spradley & Jantz 2011) and North American Black (Giles & Elliot 1963) populations (see Table 5.9). The muscles in the mastoid region (sternocleidomastoid, splenius and longissimus) are larger and attach over a greater area in males relative to females (Romanes 1986), therefore it is not biologically unreasonable that this cranial region is sexually dimorphic. For example, male crania are generally larger than female crania, and muscles such as sternocleidomastoid that insert into the mastoid process are involved in maintaining the balance and stability of the head (Watson & Trott 1993). Due to this size difference between male and female crania, males require relatively larger muscles, and therefore larger insertion sites, to support, move and stabilize the head (Watson & Trott 1993).

The relationship between muscle size and skeletal form is well established in the published literature (e.g. Gionhaku & Lowe (1989); Benington et al. (1999); Kitai et al. (2002)). There is a general consensus that muscle size and development significantly affects skeletal form (size and shape). Kitai et al. (2002) also suggest that muscle volume will influence the size of muscle attachment sites in the cranium. The Western Australian female population has on average a larger cranium than the comparative female individuals from other global populations, and it is not unreasonable to suggest that if the cranium is larger, it will require more robust muscles to support its stabilization and articulation to the vertebral column (Watson & Trott 1993). This leads to, as the aforementioned studies suggest, more robust muscle attachment sites.

6.4.1. Population variation in the expression and magnitude of cranial sexual dimorphism

The relative magnitude of cranial sexual dimorphism within global populations was compared (see Tables 5.5 to 5.11). It was found that the North American White population (Spradley & Jantz 2011) was the most dimorphic, followed by the Western Australian population. The least dimorphic of the studied populations was the South African White sample (Steyn & Iscan 1998). There are many factors that affect the expression and magnitude of sexual dimorphism. For example, individuals within a

population experiencing high levels of environmental stress (e.g. malnutrition) are expected to have a lower magnitude of sexual dimorphism than individuals from a non-stressed population. This is suggested as being related to the availability of resources that facilitate the later male growth spurt (Stinson 1985).

Access to nutrition is associated with the expression of sexual dimorphism, although many studies examining this phenomenon have observed differences primarily in adult stature (Wolfe & Gray 1982). As previously discussed, growth spurts during adolescence, particularly those affected by outside environmental pressures, are in part related to the expression of sexual dimorphism in adults. As discussed above, sexual dimorphism also arises in part due to differences in muscle size between the sexes; importantly, muscle development is inhibited by malnutrition (Stini 1971). Females are generally less affected by environmental pressures, such as malnutrition, than their male counterparts. This is shown in a study by Tanner (1962) of war- associated famine in Stuttgart, in which the stature and weight of children from 1911 to 1953 were examined. Girls appeared to be buffered from the effects of malnutrition in comparison to boys during the same period; there is a uniform increase in growth over time, however when malnutrition occurs, this trend is reversed (Harrison et al. 1977). Therefore, in environments where malnutrition is present, the male population is likely to experience inhibited muscle growth, likely resulting in a decrease in sexual dimorphism.

Australia is a first-world country (United Nations Development Program 2012). This categorization is based on factors that include the rates of education and life expectancy. A first-world country will have higher rates of education and high life expectancy, whilst the opposite is expected in a third-world country. Because of this classification, it is assumed that Australia, as a first-world country, will have better access to nutrition than second- or third-world countries. Research shows that individuals in low socio-economic countries have an increased risk of consuming diets that are of a lower quality; e.g., choosing foods that are lower in fibre and higher in fat (Subar et al. 1995; Johannson et al. 1999; Hupkens et al. 2000). Education rates are also correlated with nutrition; healthier diets are more common in societies where the

rate of education is higher (Johansson et al. 1999). The Western Australian population consumes a varied diet, due in part to the growing environments and seasons (Miller et al. 1997).

Australian women are known to have a healthier, more favourable diet than men; females are more likely to consume a balanced diet, consisting of more fruit and vegetables, higher in fibre and lower in fat, than their male counterparts (McNaughton et al. 2008; Johannson et al. 1999). This may potentially explain why Western Australian females are larger in size when compared to other global populations, whilst the males are similar in size. This, combined with the aforementioned factors that lead to good nutrition (e.g. education and availability), show that the Western Australian population is well nourished with a diet that is well balanced (Pollard et al. 2007). Therefore, the pressures of malnutrition are unlikely to have an effect on growth rates in the Western Australian population.

The present study identified that the South African White population was the least dimorphic. This may (in part) be due to sample bias in the Steyn and Iscan (1998) study; their sample only included individuals with birth dates ranging from 1863-1936, meaning that associated adolescent growth spurts ensued between the 1870s-1940s. Environmental pressures during this time were heightened compared to that of the modern day, and the availability of nutrition and medicine is incomparable (Coovadia et al. 2009). In contrast, the Western Australian population is contemporary and thus represents the current situation, where environmental factors such as nutrition and health are stable and widely available (Wilson et al. 1995; Schieber et al. 1991). These factors combined may account for a temporally removed population, such as the South African White sample, being comparatively less sexually dimorphic than the modern Western Australian population.

Another influence affecting the magnitude and expression of sexual dimorphism is the relative division of sex-specific socio-cultural roles. When males and females within a population perform equal amounts of physical labour, the levels of mechanical stress upon the body are similar. These populations will therefore exhibit a decrease in

physical differences between the sexes (Wilczak 1998). In Australia, the sex inequality gap is closing (Rawstron 2012). Australian census data shows that the female labour force has increased from 37.1 to 59% from 1971-2011, however the male labour force has decreased from 81 to 72% over the same time period (Australian Bureau of Statistics 2003). The sex-specific physical activity gap has also closed in the Australian society; females are now involved in more active leisure activities, including the participation of ‘strenuous’ activities historically reserved for males (Lenskyj 1998). There is a presumption that modernization has improved the sex equality gap in all societies. Although this is true for many countries, such as Australia, it must be stated that there are many societies where cultural and historical sex roles do still exist (e.g. farming communities in Finland (Silvasti 2003)). Although sexual dimorphism is expected to be more apparent in these sex divided societies, the sex inequality gap is closing in Australia, therefore it is perhaps possible that the magnitude of sexual dimorphism in Western Australia may, over many future generations, be reduced.

Climate can also affect the expression of sexual dimorphism. Studies suggest that nasal shape is associated with climate; individuals indigenous to warmer climates are known to have a relatively narrow nasal breadth, and those from colder climes have a relatively wider nasal breadth (e.g. Beals et al. 1984; Roseman 2004). Beals et al. (1984) also suggest that individuals from populations indigenous to colder climates will have a relatively broader cranium than those in warmer climates, due to thermoregulation pressures. For example, Arctic people will have a larger maximum cranial breadth measurement than people living in warmer climates. Although these studies show that temperature can affect the size and shape of the cranium in different ways, Harvati and Weaver (2006a) suggest that shape changes in the cranium will be more apparent in a colder, than in a warmer, environment.

Western Australia has a ‘Mediterranean-type’ climate that is characterized by wet, cold winters and dry, hot summers (Ludwig & Asseng 2006; Australian Bureau of Meteorology 2013). According to the findings of other studies (e.g. Beals et al. 1984; Roseman 2004), we would expect individuals from the Western Australian population to have narrower skulls than individuals from a population in a colder climate. When

cranial breadth measurements (maximum cranial breadth and maximum frontal breadth) are compared to other populations, Western Australians have a relatively smaller maximum cranial and frontal breadths than all the other populations (e.g. Japanese (Ogawa et al. 2013); Cretan (Kranioti et al. 2008); South African White (Steyn & Iscan 1998); North American White (Spradley & Jantz 2011; Giles & Elliot 1963); and North American Black (Spradley & Jantz 2011; Giles & Elliot 1963)), with the exception of the South African Black population (Franklin et al. 2005) (see Figure 5.2 and 5.3). This is perhaps not unexpected given that South Africa has a much more diverse climate than Western Australia, from cool and tropical winters, to hot and dry summers (Thomas et al. 2007). Whilst there is a clear difference identified, the exact reason it occurs remains uncertain. The above, however, supports the assertion that climate does have an influence on the expression and magnitude of sexual dimorphism.

Genetics can also affect cranial sexual dimorphism. This is shown through size and shape differences observed between African Americans and European Americans in North America. These are two genetically disparate populations that live in the same environment, however, their cranial shape and size has remained different over time (Mielke et al. 2011). This suggests that genetic factors are a stronger influence on cranial variation than climatic factors (von Cramon-Taubadel 2011). Based on interpreting the genetic ancestry of the Western Australian population, it would be expected that cranial sexual dimorphism in that population would be most similar to the South African White (Steyn & Iscan 1998), North American White (Spradley & Jantz 2011; Giles & Elliot 1963) and Cretan (Kranioti et al. 2008) populations, as according to the Australian Bureau of Statistics (2012), the Western Australian population is primarily Caucasian (79%). The Western Australian population is similarly dimorphic to the North American White and Cretan populations, however the South African White population is much less dimorphic. This is likely due to a temporal bias (see above) in the study of the South African White population (Steyn & Iscan 1998). The similarities between the Western Australian population and the North American White and Cretan populations perhaps indicate that population history has a significant effect on the magnitude of cranial sexual dimorphism, as suggested by Harvati and Weaver (2006b).

Population variation in sexual dimorphism is clearly a complex phenomenon. There are a multitude of factors that likely affect the expression of sexual dimorphism in the Western Australian population. There is an assessable degree of sexual dimorphism within this population, and its expression is not due entirely to only one influence. Many factors (known and unknown) are likely to affect cranial form (e.g. climate as well as genetics - Harvati & Weaver 2006b), however the degree in which these factors affect individual populations clearly varies.

6.5. Population specificity of anthropological standards

The successful application (high accuracy and low sex bias) of discriminant functions to estimate skeletal sex is dependent upon two factors: firstly, the size variation between populations for each sex; and secondly, the degree of sexual dimorphism within populations (Calcagno 1980). Populations that exhibit low accuracy for sex estimation show more similarities between the sexes and therefore are less dimorphic. The Western Australian population can be classified with a high degree of expected cross- validated accuracy (88.4%; sex bias: 4%) when compared to other populations (e.g. Cretan: cross-validated accuracy: 87.10%, sex bias: 0.31% (Kranioti et al. 2008); South African White: cross-validated accuracy: 85.70%, sex bias: 1.30% (Steyn & Iscan 1998); South African Black: cross-validated accuracy: 79.00%, sex bias: -3.00% (Franklin et al. 2005); and North American Black: cross-validated accuracy: 87.70% (Giles & Elliot 1963)).

Having established that the Western Australian population has a measureable degree of sexual dimorphism, and that there is a difference in the magnitude of sexual dimorphism between this population and others, it becomes necessary to assess the effect on classification accuracy of applying non-population specific standards to the Western Australian sample. This section discusses the accuracy of the direct application of foreign standards, as well as the formulation of these standards using the Western Australian population data.

6.5.1. Outcomes of applying foreign standards

Applying foreign standards to the Western Australian population has been demonstrated in this study to result in a low correct classification rate and high sex bias (see Table 5.12). However, when these functions are adjusted to the Western Australian population by substituting measurement coefficients to formulate population specific discriminant functions, these functions perform at an acceptable level, with both high accuracy and low sex bias (see Table 5.12). This shows that the measurements themselves are dimorphic between populations; however, the actual values of the measurements, and the loading of discriminant functions coefficients, vary between populations. This means that the overall shape of the cranium varies between populations, as well as their size.

As previously demonstrated in Chapter Five, the application of foreign standards to the Western Australian population results in accuracy rates of 50.0 to 83.3%, with associated sex biases ranging from 18.7 to 100%. When the same combinations of measurements are used to formulate discriminant functions for the Western Australian population, however, the accuracy range increases to 76.7 to 88.0%, with associated sex bias values ranging from 0.0 to 4.0%. This increase in overall accuracy shows that that the combinations of these measurements are valid choices for quantifying sexual dimorphism in different populations; however, the function coefficients must be adjusted for each population.

Kranioti et al. (2008) compared cranial measurements from their Cretan population to a North American and South African White population. It was found that the Cretan population is more similar in size to the North American White, and least similar to the South African White, population. Both populations demonstrate larger cranial length values than the Cretan population, however, the Cretan population had a larger mean maximum frontal breadth. The authors also compared their results to a Middle to Late Helladic population, also from Crete. Six measurements were compared, and it was found that the Middle to Late Helladic population was much smaller than the modern sample for all but one cranial measurement (maximum cranial length). This highlights the need for not only geographically specific population standards, but also temporally

contemporary standards as well; e.g. modern standards developed from contemporary individuals to account for the secular trend of size increases observed in a multitude of European countries (e.g. Mediterranean countries, Poland and the United Kingdom) (Harrison et al. 1977). The present study performed similar comparisons and elucidated many differences in cranial sexual dimorphism between populations (see above and Chapter Five). The latter results support the findings of Kranioti et al. (2008). This shows that not only is the Western Australian population different from all of the other studied global populations examined in this study, but also highlights the fact that those global populations are all different from each other.

6.6. Forensic applications

The testing and development of population specific standards is important to the field of forensic anthropology. In cases where skeletal remains are referred for anthropological assessment, a biological profile will be formulated to aid in establishing identity (Ross & Manneschi 2011). If sex estimation accuracy is low (particularly if using foreign standards), this could lead to an incorrect estimation of that biological attribute. If this is the case, then other biological profile characteristics (such as age and stature) are also likely to be estimated incorrectly, as some methods use sex as a classifying factor (e.g. the Suchey-Brooks symphysis age scoring system). This leads to an incorrect biological profile, which has major consequences in a forensic investigation. Ultimately, it could result in a considerable waste of investigative resources that will be directed inefficiently.

In response to the paucity of anthropological standards for Australia, standards for cranial sex estimation have been formulated by Franklin et al. (2013b) and additionally explored in the present study. The use of these standards can aid in the formulation of an accurate biological profile for human remains referred for anthropological assessment in Western Australia. The standards developed here display high accuracy and low sex bias, which makes them highly applicable for forensic investigations in Western Australia.

6.7. Limitations

The present study only included subjects over the age of 18 years, as the rate of growth of sub-adult individuals differs within and between the sexes. Further, sex estimation in the immature skeleton has been demonstrated to be relatively inaccurate (e.g. Franklin et al. 2007; Scheuer 2002; Buck & Vidarsdottir 2004), as prior to puberty, the human skeleton is largely androgynous. A larger sample would also be beneficial for the formulation of sex estimation standards for the Western Australian population and if possible, with known ancestry, to replicate the frequencies reported by the Australian Bureau of Statistics. This would result in sex estimation standards with higher discriminatory power because it would be possible to reduce the effect of ethnic variation in cranial form.

As noted above, the MSCT scans used in this study omit personal information about the subjects, except for age and sex. This means that the specific ancestry of individuals is unknown. This information is omitted because the ancestry of a patient is not normally medically relevant. Further, individuals may also identify their ancestry as their cultural, rather than population, affinity. In an unpublished study by Flavel and Franklin, 201 anonymised MSCT scans from Western Australia were assessed using CRANID to estimate ancestry. It was found that 78.61% of the scans were most likely of European ancestry, whilst the 2011 census data reports a frequency of 79.51%. This means that the sample of MSCT scans is likely representative of the Western Australian population as a whole. As the same database analysed in the Flavel and Franklin study was used in the present study, this assumption of a ‘representative population’ is followed, albeit this has not been statistically documented.

The present study could be replicated to include a broader range of comparative literature to further analyse population differences in cranial sexual dimorphism. This would include the use of standards developed from a wider range of populations from Asia, Northern Africa, Europe and South America. This would also include not only comparing foreign standards to a Western Australian sample, but also comparing foreign populations to each other.

Depending on the age of individuals studied, bone pathologies, such as osteoporosis can affect sexual dimorphism. The latter pathology is more common in postmenopausal women (35%) than men (19%), which could alter the expression of sexual dimorphism (Riggs et al. 2002). Osteoporosis results in bone loss, which could affect measurement data, especially if a larger proportion of individuals suffered from osteoporosis in the studied sample than in the wider population. Osteoporosis, and other pathologies related to bone loss, can be attributed to smoking, alcohol abuse, inactivity, poor nutrition and a lack of Vitamin D (Riggs et al. 2002). Aside from bone pathologies, Loth and Henneberg (1996) found that the loss of teeth in females leads to remodelling of the alveolar process so that it resembles the shape and thickness of the male jaw. It is expected that the magnitude of sexual dimorphism will thus decrease (or be inaccurate) in samples including subjects of a postmenopausal age or exhibiting significant tooth loss, therefore (if possible) these subjects should be excluded. Also, with the wide availability of healthcare in Western countries, it is expected that secular, rather than geographical, variations will occur within the sample, such as orthodontic changes (Martin & Danforth 2009).

6.8. Future research

It would be beneficial to test the applicability of Western Australian sex estimation standards to individuals from other states and territories in Australia. Other studies have attempted to apply discriminant function statistics developed from one population and compared it to a related population (e.g. Cretan discriminant functions applied to an archaeological sample also from Crete (Kranioti et al. (2008)). It is expected that the application of Western Australian standards to other Australian individuals from other states and territories will result in a lower accuracy rate than that observed in this study. Once this difference in accuracy is estimated, it would be advantageous to investigate what differences, whether shape and/or size, account for the change in accuracy rates. This could be achieved by performing a 3D geometric morphometric study to investigate the population differences in the overall size and shape of the skull, using multivariate statistical techniques (e.g. principle component analysis). 75

In the future it would be beneficial to analyse Western Australian cranial measurement data using Fordisc® 3.0 and/or CRANID, which are computer programs designed to assign sex and ancestry to unknown individuals, using craniofacial measurements (Jantz & Ousley 2005; Wright 1992). The benefit of using this program would be to observe what ancestries were selected for the sample of MSCT scans from Western Australia. This again would likely highlight population differences between geographical areas depending on what ancestries were selected.

6.9. Conclusions

The current research clearly establishes that the application of population specific standards yield the most accurate results for the estimation of sex. In a population where there are no specific sex estimation standards available, statistics from populations deemed ‘similar’ are used, as this is the only available information (Franklin et al. 2013a). This study shows that although a population may be deemed ‘similar’, it does not mean that using these standards will provide an accurate estimation of sex. Although applying some of the foreign population standards to a Western Australian sample produced high classification rates, in each case the sex bias was so high that the results were deemed unreliable. The availability of modified standards that offer reliable sex estimation, with a high and quantified degree of accuracy and low sex bias, are essential to the fields of biological and forensic anthropology.

This study shows that accurate sex estimation of the cranium can be achieved when using population specific standards. It has added to previous knowledge of the existence of population differences, and highlights examples of morphological population specificity and the effects of using foreign sex estimation standards. It is clearly demonstrated in this research that failure to apply appropriate standards will result in low accuracy and a high sex bias. Therefore, for the accurate estimation of sex in the human adult cranium, population specific standards (if and where possible) should always be applied.

REFERENCES

Acsadi, G & Nemeskeri, J 1970, History of human life span and mortality, Akadémiai Kiadó, Budapest.

Aldridge, K, Boyadjiev, SA, Capone, GT, Deleon, VB & Richtsmeier, JT 2005, ‘Precision and error of three-dimensional phenotypic measures acquired from 3dMD photogrammetric images’, American Journal of Medical Genetics, vol. A, no. 138, pp. 247-253.

Australian Bureau of Meteorology 2013, Climate statistics for Australian locations, Australian Government Bureau of Meteorology, Available from: .

Australian Bureau of Statistics 2003, Australian social trends, Australian Bureau of Statistics, Available from:.

Australian Bureau of Statistics 2012, Ancestry, Australian Bureau of Statistics, Available from: .

Bass, WM 1987, Human osteology: a laboratory and field manual of the human skeleton, Missouri Archaeological Society, Springfield.

Bastir, M, Rosas, A & O’Higgins, P 2006, 'Craniofacial levels and the morphological maturation of the human skull', Journal of Anatomy, vol. 209, no. 5, pp. 637-654.

Beals, KL, Smith, CL, Dodd, SM, Angel, JL, Armstrong, E, Blumenberg, B, Girgis, FG, Turkel, S, Gibson, KR, et al. 1984, 'Brain size, cranial morphology, climate, and time machines [and comments and reply]', Current Anthropology, vol. 25, no. 3, pp. 301- 330.

Belcher, RL & Armelagos, GJ 2005, 'Forensic misclassification of ancient Nubian crania: implications for assumptions about human variation', Current Anthropology, vol. 46, no. 2, pp. 340-346. 77

Benington, PC, Gardener, JE & Hunt, NP 1999, 'Masseter muscle volume measured using ultrasonography and its relationship with facial morphology', The European Journal of Orthodontics, vol. 21, no. 6, pp. 659-670.

Bennett, KA 1981, 'On the expression of sex dimorphism', American Journal of Physical Anthropology, vol. 56, no. 1, pp. 59-61.

Berryman, HE, Lanfear, AK & Shirley, NR 2010, ‘The biomechanics of gunshot trauma to bone: research considerations within the present judicial climate’, in Companion to forensic anthropology, eds D Dirkmaat, Blackwell Publishing Ltd, West Sussex, pp. 390-400.

Bigoni, L, Veleminska, J & Bruzek, J 2010, 'Three-dimensional geometric morphometric analysis of cranio-facial sexual dimorphism in a Central European sample of known sex', HOMO - Journal of Comparative Human Biology, vol. 61, pp. 16-32.

Bolliger, SA, Thali, MJ, Ross, S, Buck, U, Naether, S & Vock, P 2008, 'Virtual autopsy using imaging: bridging radiologic and forensic sciences. A review of the Virtopsy and similar projects', European Radiology, vol. 18, no. 2, pp. 273-282.

Bookstein, FL 1991, Morphometric tools for landmark data: geometry and biology, Cambridge University Press, Cambridge.

Brothwell, DR 1981, Digging up bones: the excavation, treatment, and study of human skeletal remains, Cornell University Press, New York.

Brough, AL, Rutty, GN, Black, S & Morgan, B 2012, 'Post-mortem computed tomography and 3D imaging: anthropological applications for juvenile remains', Forensic Science, Medicine, and Pathology, vol. 8, pp. 270-279.

Bruzek, J 2002, 'A method for visual determination of sex, using the human hip bone', American Journal of Physical Anthropology, vol. 117, no. 2, pp. 157-168.

Buck, TJ & Vidarsdottir, US 2004, 'A proposed method for the identification of race in sub-adult skeletons: a geometric morphometric analysis of mandibular morphology', Journal of Forensic Sciences, vol. 49, no. 6, pp. 1159-1164.

Buikstra, JE & Ubelaker, DH 1994, Standards for data collection from human skeletal remains: proceedings of a seminar at the Field Museum of Natural History, Arkansas Archaeological Survey, Fayetteville.

Bulygina, E, Mitteroecker, P & Aiello, L 2006, 'Ontogeny of facial dimorphism and patterns of individual development within one human population', American Journal of Physical Anthropology, vol. 131, no. 3, pp. 432-443.

Cabo, LL, Brewster, CP & Azpiazu, JL 2010, ‘Sexual dimorphism: interpreting sex markers’, in Companion to forensic anthropology, eds D Dirkmaat, Wiley- Blackwell Publishing Ltd, Chichester, pp. 248-287.

Calcagno, J 1980, 'On the applicability of sexing human skeletal material by discriminant function analysis', Journal of Human Evolution, vol. 10, pp. 189-198.

Cattaneo, C 2007, 'Forensic anthropology: developments of a classical discipline in the new millennium', Forensic Science International, vol. 165, no. 2, pp. 185-193.

Celbis, O, Iscan, MY, Soysal, Z & Cagdir, S 2001, 'Sexual diagnosis of the glabellar region', Legal Medicine, vol. 3, no. 3, pp. 162-170.

Chen, Y-J, Chen, S-K, Chung-Chen Yao, J & Chang, H-F 2004, 'The effects of differences in landmark identification on the cephalometric measurements in traditional versus digitized cephalometry', The Angle Orthodontist, vol. 74, no. 2, pp. 155- 161.

Coovadia, H, Jewkes, R, Barron, P, Sanders, D & McIntyre, D 2009, 'The health and health system of South Africa: historical roots of current public health challenges', The Lancet, vol. 374, no. 9692, pp. 817-834.

Corner, BD, Lele, S & Richtsmeier, JT 1992, 'Measuring precision of three-dimensional landmark data', Medicine, vol. 1500, pp. 21205-21219.

Cox, M & Mays, S 2000, Human osteology: in archaeology and forensic science, Cambridge University Press, Cambridge.

Dayal, MR, Spocter, MA & Bidmos, MA 2008, 'An assessment of sex using the skull of black South Africans by discriminant function analysis', Journal of Comparative Biology, vol. 59, pp. 209-221. de Villiers, H 1968, The skull of the South African Negro: a biometrical and morphological study, Witwatersrand University Press, Witswatersrand.

Dedouit, F, Telmon, N, Costagliola, R, Otal, P, Joffre, F & Rougé, D 2007, 'Virtual anthropology and forensic identification: report of one case', Forensic Science International, vol. 173, no. 2, pp. 182-187.

Dirnhofer, R, Jackowski, C, Vock, P, Potter, K & Thali, MJ 2006, 'VIRTOPSY: minimally invasive, imaging-guided virtual autopsy', Radiographics, vol. 26, no. 5, pp. 1305- 1333.

Douglas, KS, Lyon, DR & Ogloff, JRP 1997, 'The impact of graphic photographic evidence on mock jurors: decisions in a murder trial', Law and Human Behaviour, vol. 21, no. 5, pp. 485-501.

Flavel, A & Franklin, D (unpublished), ‘Testing assumptions of ancestry in a modern Western Australian population: morphometric analyses using CRANID’, unpublished.

Franklin, D 2005, Cranial variation in Southern Africa: a series of inter-landmark and three-dimensional multivariate morphometric studies. Ph.D thesis, University of Western Australia.

Franklin, D, Freedman, L & Milne, N 2005, 'Sexual dimorphism and discriminant function sexing in indigenous South African crania', Journal of Comparative Biology, vol. 55, pp. 213-228.

Franklin, D, Freedman, L, Milne, N & Oxnard, CE 2007, ‘Geometric morphometric study of population variation in indigenous southern African crania’, American Journal of Human Biology, vol. 19, no. 1, pp. 20-33.

Franklin, D, Flavel, A, Kuliukas, A, Oxnard, CE & Marks, MK 2012a, ‘Cranial sexual dimorphism and anthropological standards: preliminary investigation in a Western Australian population’, in Proceedings of the American Academy of Forensic Sciences, vol. 18, pp. 361-362.

Franklin, D, Cardini, A, Flavel, A & Kuliukas, A 2012b, 'The application of traditional and geometric morphometric analyses for forensic quantification of sexual dimorphism: preliminary investigations in a Western Australian population', International Journal of Legal Medicine, vol. 126, pp. 549-558.

Franklin, D, Flavel, A, Kuliukas, A, Cardini, A, Marks, MK, Oxnard, CE & O’Higgins, P 2012c, 'Estimation of sex from sternal measurements in a Western Australian Population', Forensic Science International, vol. 217, no. 230, pp. 230.e1-230.e5.

Franklin, D, Cardini, A, Flavel, A, Kuliukas, A, Marks, MK, Hart, R, Oxnard, CE & O’Higgins, P 2013a, 'Concordance of traditional osteometric and volume- rendered MSCT interlandmark cranial measurements', International Journal of Legal Medicine, vol. 127, pp. 505-520.

Franklin, D, Cardini, A, Flavel, A & Kuliukas, A 2013b, ‘Estimation of sex from cranial measurements in a Western Australian population’, Forensic Science International, vol. 229, no. 1-3, pp. 158e.1-158e.8.

Giles, E & Elliot, O 1963, 'Sex determination by discriminant function analysis of crania', American Journal of Physical Anthropology, vol. 21, no. 1, pp. 53-68.

Gionhaku, N & Lowe, A 1989, 'Relationship between jaw muscle volume and craniofacial form', Journal of Dental Research, vol. 68, no. 5, pp. 805-809.

Gonzalez, PN, Bernal, V & Perez, SI 2009, 'Geometric morphometric approach to sex estimation of human pelvis', Forensic Science International, vol. 189, no. 1-3, pp. 68-74.

Gonzalez, PN, Bernal, V, Perez, SI & Barrientos, G 2007, 'Analysis of dimorphic structures of the human pelvis: its implications for sex estimation in samples without reference collections', Journal of Archaeological Science, vol. 34, no. 10, pp. 1720-1730.

Goose, D 1981, 'Changes in human face breadth since the mediaeval period in Britain', Archives of Oral Biology, vol. 26, pp. 757-758.

Goto, R & Mascie-Taylor, CGN 2007, 'Precision of measurement as a component of human variation', Journal of Physiological Anthropology, vol. 26, no. 2, pp. 253- 256.

Green, H & Curnoe, D 2009, 'Sexual dimorphism in Southeast Asian crania: a geometric morphometric approach', HOMO-Journal of Comparative Human Biology, vol. 60, no. 6, pp. 517-534.

Harrison, GA, Weiner, JS, Tanner, JM & Barnicot, N 1977, Human biology: an introduction to human evolution, variation and growth, Oxford University Press, Oxford.

Harvati, K & Weaver, TD 2006a, 'Human cranial anatomy and the differential preservation of population history and climate signatures', The Anatomical Record, vol. 288A, no. 12, pp. 1225-1233.

Harvati, K & Weaver, TD 2006b, 'Reliability of cranial morphology in reconstructing Neanderthal phylogeny', in Neanderthals revisited: new approaches and perspectives, eds K Harvati & T Harrison, Springer, Dordrecht, pp. 239-254.

Hennessy, R & Stringer, C 2002, 'Geometric morphometric study of the regional variation of modern human craniofacial form', American Journal of Physical Anthropology, vol. 117, pp. 37-48.

Hill, T & Lewicki, P 2006, Statistics: methods and applications: a comprehensive reference for science, industry, and data mining, StatSoft Inc, Tulsa.

Hillier, ML & Bell, LS 2007, 'Differentiating human bone from animal bone: a review of histological methods', Journal of Forensic Sciences, vol. 52, no. 2, pp. 249-263. 82

Hooton, EA 1946, Up from the ape, Macmillan, New York.

Howells, W 1973, ‘Cranial variation in man. A study by multivariate analysis of patterns of difference, among recent human populations’, Papers of the Peabody Museum of Archaeology and Ethnology, vol. 67, pp. 1-259.

Hsiao, TH, Tsai, SM, Chou, ST, Pan, JY, Tseng, YC, Chang, HP & Chen, HS 2010, 'Sex determination using discriminant function analysis in children and adolescents: a lateral cephalometric study', International Journal of Legal Medicine, vol. 124, no. 2, pp. 155-160.

Humphrey, LT 1998, 'Growth patterns in the modern human skeleton', American Journal of Physical Anthropology, vol. 105, no. 1, pp. 57-72.

Hupkens, CL, Knibbe, RA & Drop, MJ 2000, 'Social class differences in food consumption: the explanatory value of permissiveness and health and cost considerations', The European Journal of Public Health, vol. 10, no. 2, pp. 108- 113.

Iscan, MY 2005, 'Forensic anthropology of sex and body size', Forensic Science International, vol. 147, pp. 107-112.

Iscan, MY, Yoshino, M & Kato, S 1995, 'Sexual dimorphism in modern Japanese crania', American Journal of Human Biology, vol. 7, no. 4, pp. 459-464.

Jantz, RL & Ousley, SD 2005, ‘Fordisc 3.0: personal computer forensic discriminant functions’, Knoxville: University of Tennessee.

Johansson, L, Thelle, DS, Solvoll, K, Bjorneboe, G-EA & Drevon, CA 1999, 'Healthy dietary habits in relation to social determinants and lifestyle factors', British Journal of Nutrition, vol. 81, pp. 211-220.

Jones, HH, Priest, JD, Hayes, WC, Tichenor, CC & Nagel, DA 1977, 'Humeral hypertrophy in response to exercise', Journal of Bone and Joint Surgery America, vol. 59, no. 2, pp. 204-208.

Kimmerle, E, Ross, A & Slice, D 2008, 'Sexual dimorphism in America: geometric morphometric analysis of the craniofacial region', Journal of Forensic Sciences, vol. 53, no. 1, pp. 54-57.

Kitai, N, Fujii, Y, Murakami, S, Furukawa, S, Kreiborg, S & Takada, K 2002, 'Human masticatory muscle volume and zygomatico-mandibular form in adults with mandibular prognathism', Journal of Dental Research, vol. 81, no. 11, pp. 752- 756.

Krahl, H, Michaelis, U, Pieper, H-G, Quack, G & Montag, M 1994, 'Stimulation of bone growth through sports: a radiologic investigation of the upper extremities in professional tennis players', The American Journal of Sports Medicine, vol. 22, no. 6, pp. 751-757.

Kranioti, E, Iscan, MY & Michalodimitrakis, M 2008, 'Craniometric analysis of the modern Cretan population', Forensic Science International, vol. 180, pp. 110.e1- 110.e5.

Krishan, K 2011, 'Marked limb bilateral asymmetry in an agricultural endogamous population of North India', American Journal of Human Biology, vol. 23, no. 5, pp. 674-685.

Krishan, K & Sharma, A 2007, 'Estimation of stature from dimensions of hands and feet in a North Indian population', Journal of Forensic and Legal Medicine, vol. 14, no. 6, pp. 327-332.

Krogman, W 1978, The human skeleton in forensic medicine, Charles C Thomas Publisher, Ltd., Springfield.

Lagravere, MO, Low, C, Flores-Mir, C, Chung, R, Carey, JP, Heo, G & Major, PW 2010, 'Intraexaminer and interexaminer reliabilities of landmark identification on digitized lateral cephalograms and formatted 3-dimensional cone-beam computerized tomography images', American Journal of Orthodontics and Dentofacial Orthopedics, vol. 137, no. 5, pp. 598-604.

Lenskyj, HJ 1998, ‘Inside sport or on the margins? Australian women and the sport media', International Review for the Sociology of Sport, vol. 33, no. 1, pp. 19-32.

Leth, PM 2009, 'Computerized tomography used as a routine procedure at postmortem investigations', The American Journal of Forensic Medicine and Pathology, vol. 30, no. 3, pp. 219-222.

Lindorff, M 2010, 'Ethics, ethical human research and human research ethics committees', Australian Universities’ Review, vol. 52, no. 1, pp. 51-59.

Loth, S & Iscan, MY 2000, 'Anthropology: sex determination', in Encyclopedia of forensic sciences, ed. JA Siegal, Elsevier, Oxford, pp. 252-260.

Loth, SR & Henneberg, M 1996, 'Mandibular ramus flexure: a new morphologic indicator of sexual dimorphism in the human skeleton', American Journal of Physical Anthropology, vol. 99, no. 3, pp. 473-485.

Lovell, NC 1989, 'Test of Phenice's technique for determining sex from the os pubis', American Journal of Physical Anthropology, vol. 79, no. 1, pp. 117-120.

Ludwig, F & Asseng, S 2006, 'Climate change impacts on wheat production in a Mediterranean environment in Western Australia', Agricultural Systems, vol. 90, no. 1, pp. 159-179.

Lyman, RL & VanPool, TL 2009, 'Metric data in archaeology: a study of intra-analyst and inter-analyst variation', American Antiquity, vol. 74, no. 3, pp. 485-504.

Manoel, C, Prado, F, Caria, P & Groppo, F 2009, 'Morphometric analysis of the foramen magnum in human skulls of Brazilian individuals: its relation to gender', Brazilian Journal of Morphological Sciences, vol. 26, no. 2, pp. 104-108.

Martin, D & Danforth, ME 2009, 'An analysis of secular change in the human mandible over the last century', American Journal of Human Biology, vol. 21, no. 5, pp. 704-706.

Martin, R & Saller, K 1957, Lehrbuch der anthropologie, Gustav Fischer, Stuttgart.

McNaughton, SA, Ball, K, Crawford, D & Mishra, GD 2008, 'An index of diet and eating patterns is a valid measure of diet quality in an Australian population', The Journal of Nutrition, vol. 138, no. 1, pp. 86-93.

Mielke, JH, Konigsberg, LW & Relethford, J 2011, Human biological variation, Oxford University Press, Oxford.

Miller, MR, Pollard, C & Coli, T 1997, 'Western Australian Health Department recommendations for fruit and vegetable consumption–how much is enough?', Australian and New Zealand Journal of Public Health, vol. 21, no. 6, pp. 638-642.

Mueller, WH & Martorell, R 1988, ‘Reliability and accuracy of measurement’ in Anthropometric standardization reference manual, eds TG Lohman, AF Roche & R Martorell, Human Kinetic Books, Illinois, pp. 83-86.

Nagaoka T, Shizushima, A, Sawada, J, Tomo, S, Hoshino, K, Sato, H & Hirata, K 2008, 'Sex determination using mastoid process measurements: standards for Japanese human skeletons of the medieval and early modern periods', Anthropological Science, vol. 116, no. 2, pp. 105-113.

Nagaoka, T, Kawakubo, Y & Hirata, K 2012, 'Evidence for temporal and social differences in cranial dimensions in Edo‐period Japanese', International Journal of Osteoarchaeology, vol. 22, pp. 409-422.

O’Higgins, P 2000, ‘The study of morphological variation in the hominid fossil record: biology, landmarks and geometry’, Journal of Anatomy, vol. 197, pp. 103-120.

Ogawa, Y, Imaizumi, K, Miyasaka, S & Yoshino, M 2013, 'Discriminant functions for sex estimation of modern Japanese skulls', Journal of Forensic and Legal Medicine, vol. 20, no. 4, pp. 234-238.

OsiriX® Imaging Software, 2014, Advanced open-source PACS workstation DICOM viewer, Available from: .

Pallant, J 2011, SPSS survival manual 4th ed., Allen & Unwin, Crows Nest.

Patriquin, M, Steyn, M & Loth, S 2005, 'Metric analysis of sex differences in South African black and white pelves', Forensic Science International, vol. 147, no. 2, pp. 119-127.

Perini, TA, de Oliveira, GL, Ornellas, JS & de Oliveira, FP 2005, 'Technical error of measurement in anthropometry', Revista Brasileira de Medicina do Esporte, vol. 11, no. 1, pp. 81-85.

Petrtyl, M, Hert, J & Fiala, P 1996, 'Spatial organization of the haversian bone in man', Journal of Biomechanics, vol. 29, no. 2, pp. 161-169.

Phenice, M 1969, ‘A newly developed visual method of sexing in the Os Pubis’, American Journal of Physical Anthropology, vol. 30, pp. 297-301.

Pickering, RB & Bachman, D 2009, The use of forensic anthropology, CRC, Boca Raton.

Pietrusewsky, M 2008, ‘Metric analysis of skeletal remains: methods and applications’ in Biological anthropology of the human skeleton, eds MA Katzenberg & SR Saunders, John Wiley & Sons Inc., New Jersey, pp. 487-532.

Pollard, CM, Miller, MR, Daly, AM, Crouchley, KE, O’Donoghue, KJ, Lang, AJ & Binns, CW 2007, 'Increasing fruit and vegetable consumption: success of the Western Australian go for 2&5 campaign', Public Health Nutrition, vol. 11, no. 3, pp. 314- 320.

Ramsey, FL & Schafer DW 2002, The statistical sleuth: a course in methods of data analysis, Duxbury Press, Belmont.

Ramsthaler, F, Kettner, M, Gehl, A & Verhoff, M 2010, 'Digital forensic osteology: morphological sexing of skeletal remains using volume-rendered cranial CT scans', Forensic Science International, vol. 195, no. 1, pp. 148-152.

Rawstron, K 2012, 'Diverging paths: occupational sex segregation, Australia, and the OECD', The Australian Sociological Association Annual Conference 2012: Emerging Equalities, vol.1, pp. 1-12.

Reynolds, M, Franklin, D, Raymond, MA & Dadour, I 2008, 'Bloodstain measurement using computer-fitted theoretical ellipses: a study in accuracy and precision', Journal of Forensic Identification, vol. 58, no. 4, pp. 469-484.

Richtsmeier, JT, Paik, CH, Elfert, PC, Cole, TM & Dahlman, HR 1995, ‘Precision, repeatability, and validation of the localization of cranial landmarks using computed tomography scans’, Cleft Palate-Craniofacial Journal, vol. 32, pp. 217- 227.

Riggs, BL, Khosla, S & Melton, LJ 2002, 'Sex steroids and the construction and conservation of the adult skeleton', Endocrine Reviews, vol. 23, no. 3, pp. 279- 302.

Rogers, TL 2005, 'Determining the sex of human remains through cranial morphology', Journal of Forensic Sciences, vol. 50, no. 3, pp. 1-8.

Romanes, G 1996, Cunningham's manual of practical anatomy, Oxford Medical Publications, Oxford, UK.

Roseman, CC 2004, 'Detecting interregionally diversifying natural selection on modern human cranial form by using matched molecular and morphometric data', Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 35, pp. 12824-12829.

Rosing, F, Graw, M, Marré, B, Ritz-Timme, S, Rothschild, M, Rötzscher, K, Schmeling, A, Schröder, I & Geserick, G 2007, 'Recommendations for the forensic diagnosis of sex and age from skeletons', HOMO-Journal of Comparative Human Biology, vol. 58, no. 1, pp. 75-89.

Ross, AH & Manneschi, M 2011, 'New identification criteria for the Chilean population: estimation of sex and stature', Forensic Science International, vol. 204, pp. 206.e1-206.e3.

Ross, AH & Williams, S 2008, 'Testing repeatability and error of coordinate landmark data acquired from crania*', Journal of Forensic Sciences, vol. 53, no. 4, pp. 782- 785.

Rosset, A & Spadola, L 2004, 'OsiriX: an open-source software for navigating in multidimensional DICOM images', Journal of Digital Imaging, vol. 17, no. 3, pp. 205-216.

Scheuer, L 2002, 'A blind test of mandibular morphology for sexing mandibles in the first few years of life', American Journal of Physical Anthropology, vol. 119, no. 2, pp. 189-191.

Schieber, GJ, Poullier, J-P & Greenwald, LM 1991, 'Health care systems in twenty-four countries', Health Affairs, vol. 10, no. 3, pp. 22-38.

Silvasti, T 2003, 'Bending borders of gendered labour division on farms: the case of Finland', Sociologia Ruralis, vol. 43, no. 2, pp. 154-166.

Smith, DR, Limbird, KG & Hoffman, JM 2002, 'Identification of human skeletal remains by comparison of bony details of the cranium using computerized tomographic (CT) scans', Journal of Forensic Sciences, vol. 47, no. 5, pp. 937-939.

Spradley, M & Jantz, RL 2011, 'Sex estimation in forensic anthropology: skull versus postcranial elements', Journal of Forensic Sciences, vol. 56, no. 2, pp. 289-296.

Steyn, M & Iscan, MY 1998, 'Sexual dimorphism in the crania and mandibles of South African whites', Forensic Science International, vol. 98, pp. 9-16.

Steyn, M & Iscan, MY 1999, 'Osteometric variation in the humerus: sexual dimorphism in South Africans', Forensic Science International, vol. 106, no. 2, pp. 77-85.

Stini, WA 1971, 'Evolutionary implications of changing nutritional patterns in human populations', American Anthropologist, vol. 73, no. 5, pp. 1019-1030.

Stinson, S 1985, 'Sex differences in environmental sensitivity during growth and development', American Journal of Physical Anthropology, vol. 28, no. S6, pp. 123-147.

Subar, AF, Heimendinger, J, Patterson, BH, Krebs-Smith, SM, Pivonka, E & Kessler, R 1995, 'Fruit and vegetable intake in the United States: the baseline survey of the five a day for better health program', American Journal of Health Promotion, vol. 9, no. 5, pp. 352-360.

Tanner, JM 1962, Growth at adolescence, Blackwell, Oxford.

Thomas, DS, Twyman, C, Osbahr, H & Hewitson, B 2007, 'Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa', Climatic Change, vol. 83, no. 3, pp. 301-322.

Trinkaus, E 1978, 'Bilateral asymmetry of human skeletal non‐metric traits', American Journal of Physical Anthropology, vol. 49, no. 3, pp. 315-318.

Trinkaus, E, Churchill, SE & Ruff, CB 1994, 'Postcranial robusticity in Homo. II: humeral bilateral asymmetry and bone plasticity', American Journal of Physical Anthropology, vol. 93, no. 1, pp. 1-34.

United Nations Development Program 2012, Human Development Index and its components, United Nations, Available from:.

Uysal, S, Gokharman, D, Kacar, M, Tuncbilek, I & Kosar, U 2005, 'Estimation of sex by 3D CT measurements of the foramen magnum', Journal of Forensic Sciences, vol. 50, no. 6, pp. 1310-1314.

Veldhuis, JD, Roemmich, JN, Richmond, EJ, Rogol, AD, Lovejoy, JC, Sheffield-Moore, M, Mauras, N & Bowers, CY 2005, 'Endocrine control of body composition in infancy, childhood, and puberty', Endocrine Reviews, vol. 26, no. 1, pp. 114-146.

Verhoff, MA, Ramsthaler, F, Krähahn, J, Deml, U, Gille, RJ, Grabherr, S, Thali, MJ & Kreutz, K 2008, 'Digital forensic osteology - possibilities in cooperation with the Virtopsy® project', Forensic Science International, vol. 174, no. 2-3, pp. 152-156. von Cramon‐Taubadel, N 2011, 'The relative efficacy of functional and developmental cranial modules for reconstructing global human population history', American Journal of Physical Anthropology, vol. 146, no. 1, pp. 83-93. 90

Walker, P 2008, 'Sexing skulls using discriminant function analysis of visually assessed traits', American Journal of Physical Anthropology, vol. 136, pp. 39-50.

Walrath, DE, Turner, P & Bruzek, J 2004, 'Reliability test of the visual assessment of cranial traits for sex determination', American Journal of Physical Anthropology, vol. 125, no. 2, pp. 132-137.

Watson, DH & Trott, PH 1993, ‘Cervical headache: an investigation of natural head posture and upper cervical flexor muscle performance’, Cephalalgia, vol. 13, no. 4, pp. 272-284.

White, T, Black, M & Folkens, P 2012, Human osteology, Academic Press, San Diego.

Wilczak, CA 1998, 'Consideration of sexual dimorphism, age, and asymmetry in quantitative measurements of muscle insertion sites', International Journal of Osteoarchaeology, vol. 8, no. 5, pp. 311-325.

Williams, FLE & Richtsmeier, JT 2003, 'Comparison of mandibular landmarks from computed tomography and 3D digitizer data', Clinical Anatomy, vol. 16, no. 6, pp. 494-500.

Wilson, JD, George, FW & Griffin, JE 1981, 'The hormonal control of sexual development', Science, vol. 211, pp. 1278-1284.

Wilson, RM, Runciman, WB, Gibberd, RW, Harrison, BT, Newby, L & Hamilton, JD 1995, 'The quality in Australian health care study', Medical Journal of Australia, vol. 163, no. 9, pp. 458-471.

Wilson LA, Macleod, N & Humphrey LT 2008, ‘Morphometric criteria for sexing juvenile human skeletons using the ilium’, Journal of Forensic Sciences, vol. 53, no. 2, pp. 269-279.

Wolfe, LD & Gray, JP 1982, 'A cross-cultural investigation into the sexual dimorphism of stature', in Sexual dimorphism in Homo sapiens: a question of size, eds R Hall, Praeger Publishers, Westport, pp. 197-230.

Wood, CG, & Lynch, JM 1996, ‘Sexual dimorphism in the craniofacial skeleton of modern humans’, in Advances in Morphometrics, eds LF Macus, M Corti, A Loy, GJP Naylor & DE Slice, Plenum Press, New York, pp. 407-414.

Woodburne, RT and Burkel, WE 1988, Essentials of human anatomy, Oxford University Press, Oxford.

Wright, R 1992, ‘Correlation between cranial form and geography in Homo sapiens: CRANID – a computer program for forensic and other applications’, Archaeology in Oceania, vol. 27, pp. 105-112.

APPENDIX I: Unpaired T-test raw data of the Western Australian population and other populations

Table Appendix I. 1: Comparison of mean male measurements (in mm). Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) BBH Mean 140.17 132.09 134.32 136.80 132.40 139.70 137.13 141.39 142.20 MD 8.08 5.85 3.37 7.77 0.47 3.04 -1.22 -2.03

t-test t = 11.40 t = 8.06 t = 3.52 t = 12.42 t = 0.63 t = 4.03 t = 2.16 t = 2.45 p <0.0001 p <0.0001 p < 0.001 p <0.0001 p = ns p <0.0001 p = 0.031 p = 0.015 BNL Mean 105.95 101.47 100.60 102.40 102.01 104.02 106.12 103.80

MD 4.48 5.35 3.55 3.94 1.93 -0.17 2.15

t-test t = 7.33 t = 9.08 t = 4.22 t = 6.40 t = 3.18 t = 0.36 t = 3.05

p <0.0001 p <0.0001 p <0.0001 p <0.0001 p < 0.001 p = ns p = 0.002

BNS Mean 97.15 MD

t-test

i. Giles & Elliot (1963) North American Black; ii. Giles & Elliot (1963) North American White; iii. Steyn & Iscan (1998) South African White; iv. Franklin et al. (2005) South African Black; v. Kranioti et al. (2008) Cretan; vi. Spradley & Jantz (2011) North American Black; vii. Spradley & Jantz (2011) North American White; viii. Ogawa et al. (2013) Japanese. MD = mean difference

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) BPL Mean 97.67 102.96 95.40 95.40 93.11 103.94 97.31 MD -5.29 2.27 2.27 4.56 -6.27 0.36

t-test t = 7.16 t = 2.94 t = 2.37 t = 6.29 t = 8.42 t = 0.58

p <0.0001 p = 0.003 p = 0.019 p <0.0001 p <0.0001 p = ns

EKB Mean 99.27 97.86 99.99 97.38 MD 1.41 -0.72 1.89

t = 2.66 t = 1.33 t = 4.75 t-test p = 0.008 p = ns p <0.0001

AUB Mean 125.03 121.23 123.07 MD 3.80 1.96

t = 6.18 t = 3.83 t-test p = 0.0001 p <0.0001

FMB Mean 106.73 107.06 105.04 105.70 MD -0.33 1.69 1.03

t = 0.58 t = 2.85 t = 1.84 t-test p = ns p < 0.01 p = 0.068

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) ZYB Mean 131.90 133.25 131.92 128.90 122.60 130.54 130.76 129.80 136.50 MD -1.35 -0.02 3.00 9.30 1.36 1.14 2.10 -4.60

t-test t = 2.19 t = 0.03 t = 3.80 t = 16.73 t = 2.11 t = 1.84 t = 4.14 t = 6.83 p = 0.030 p = ns p <0.001 p <0.001 p = 0.036 p = 0.067 p <0.0001 p < 0.0001 FOB Mean 32.40 31.37 36.24 31.98 MD 1.03 -3.84 0.42

t-test t = 3.11 t = 12.13 t = 1.81 p = 0.002 p <0.0001 p < 0.01 FOL Mean 38.80 36.19 29.93 37.09 MD 2.61 8.87 1.71

t-test t = 7.84 t = 26.95 t = 5.83 p <0.0001 p <0.0001 p <0.0001

GOL Mean 190.27 185.89 181.33 187.70 185.50 181.07 187.17 188.04 179.40 MD 4.38 8.94 2.57 4.77 9.20 3.10 2.23 10.87

t-test t = 4.90 t = 9.62 t = 2.06 t = 5.97 t = 9.36 t = 4.92 t = 2.98 t = 10.35 p <0.0001 p <0.0001 p = 0.041 p <0.0001 p <0.0001 p<0.0001 p < 0.01 p <0.0001

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) DKB Mean 20.87 23.67 21.04 MD -2.80 -0.17

t = 7.96 t = 0.64 t-test p <0.0001 p = ns

MHL Mean 32.99 30.32 28.07 34.00 35.09 31.69 32.11 31.65 MD 2.67 4.92 -1.01 -2.10 1.30 0.88 1.34

t-test t = 6.45 t = 11.79 t = 1.62 t = 5.43 t = 2.64 t = 1.86 t = 3.68

p <0.0001 p <0.0001 p = ns p <0.0001 p = 0.009 p = 0.063 p <0.001

XCB Mean 132.95 139.35 143.01 123.20 137.64 136.27 140.01 145.90 MD -6.40 -10.06 9.75 -4.69 -3.32 -7.06 -12.95

t-test t = 9.20 t = 12.98 t = 14.84 t = 5.57 t = 4.48 t = 11.14 t = 15.15

p <0.0001 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p <0.0001

FRB Mean 97.35 119.50 122.46 120.90 MD -22.15 -25.11 -23.55

t-test t = 14.05 t =21.60 t = 18.72

p <0.0001 p<0.0001 p <0.0001

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) WFB Mean 99.90 97.80 96.33 95.96 96.68 MD 2.10 3.57 3.94 3.22

t-test t = 2.98 t = 6.21 t = 6.80 t = 6.88

p = 0.003 p <0.0001 p <0.0001 p <0.0001

NLB Mean 25.14 27.23 24.27 24.80 23.98 26.15 23.77 MD -2.09 0.87 0.34 1.16 -1.01 1.37

t-test t = 7.69 t = 3.31 t = 0.93 t = 3.81 t = 3.60 t = 6.67

p <0.0001 p = 0.001 p = ns p = 0.0002 p < 0.001 p <0.0001

NLH Mean 53.68 53.70 51.60 52.29 53.00 MD -0.02 2.08 1.39 0.68

t = 0.04 t = 5.13 t = 3.40 t = 2.25 t-test p = ns p <0.0001 p < 0.001 p < 0.01

NPH Mean 68.75 73.35 70.76 71.30 67.20 69.38 72.55 71.36 MD -4.60 -2.01 -2.55 1.55 -0.63 -3.80 -2.61

t-test t = 8.43 t = 3.67 t = 3.55 t = 3.14 t = 0.90 t = 6.97 t = 5.94 p <0.0001 p = 0.0003 p = 0.0005 p = 0.002 p = ns p <0.0001 p <0.0001

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) OPL Mean 148.82 153.95 156.60 MD -5.13 -7.78

t-test t = 7.54 t = 6.15

p <0.0001 p <0.0001 OBB Mean 42.45 40.26 41.11 MD 2.19 1.34

t-test t = 7.26 t = 5.65

p <0.0001 p <0.0001

OBH Mean 35.70 35.13 33.77 MD 0.57 1.93

t-test t = 1.99 t = 8.88

p = 0.047 p <0.0001

MAB Mean 58.12 65.39 59.87

MD -7.27 -1.75

t-test t = 1.69 t = 2.72

p <0.0001 p = 0.007

Table Appendix I. 1 [continued]: Comparison of mean male measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=108) (n=113) (n=44) (n=182) (n=90) (n=323) (n=107) (n=73) FRC Mean 113.72 112.90 114.30 MD 0.82 -0.58

t-test t = 1.15 t = 1.85

p = ns p = ns

PAC Mean 122.09 116.55 118.04 MD 5.54 4.05

t-test t = 6.23 t = 5.80

p <0.0001 p<0.0001

OCC Mean 99.72 98.80 100.47 MD 0.92 -0.75

t-test t = 1.12 t = 1.39

p = ns p = ns

Table Appendix I. 2: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) BBH Mean 134.46 126.68 127.45 130.50 126.90 132.47 131.75 134.57 134.00 MD 7.78 7.01 3.96 7.56 1.99 2.71 -0.11 0.46 t-test t = 1.13 t = 9.94 t = 4.59 t = 12.64 t = 2.55 t = 3.43 t = 0.20 t = 0.52 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p = 0.0113 p < 0.001 p = ns p = ns BNL Mean 99.55 96.31 95.07 96.20 96.25 98.86 99.51 96.30 MD 3.24 4.48 3.35 3.30 0.69 0.04 3.25 t-test t = 6.11 t = 7.66 t = 4.84 t = 4.76 t = 1.08 t = 0.08 t = 4.38 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p = ns p = ns p <0.0001

BNS Mean 91.78 MD t-test

BPL Mean 91.83 98.72 90.48 90.00 88.76 98.63 92.17 MD -6.89 1.35 1.83 3.07 -6.80 -0.34 t-test t = 9.94 t = 1.74 t = 2.01 t = 4.07 t = 7.86 t = 0.53

p <0.0001 p = 0.083 p = 0.046 p <0.0001 p <0.0001 p = ns i. Giles & Elliot (1963) North American Black; ii. Giles & Elliot (1963) North American White; iii. Steyn & Iscan (1998) South African White; iv. Franklin et al. (2005) South African Black; v. Kranioti et al. (2008) Cretan; vi. Spradley & Jantz (2011) North American Black; vii. Spradley & Jantz (2011) North American White; viii. Ogawa et al. (2013) Japanese. MD = mean difference 100

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) EKB Mean 94.98 93.14 95.20 92.89 MD 1.84 -0.22 2.09

t =3.48 t = 0.38 t = 4.84 t-test p = 0.0006 p = ns p <0.0001 AUB 115.84 117.19 Mean 119.41

MD 3.57 2.22

t = 4.96 t = 4.35 t-test p <0.0001 p <0.0001 FMB Mean 101.91 101.77 100.04 99.80 MD 0.14 1.87 2.11

t = 0.19 t = 4.02 t = 2.82 t-test p = ns p < 0.0001 p = 0.0054 ZYB Mean 123.66 124.40 122.71 121.90 115.70 122.07 122.43 121.01 129.00 MD -0.74 0.95 1.76 7.96 1.59 1.23 2.65 -5.34

t-test t = 1.26 t = 1.41 t = 2.36 t = 14.97 t =2.54 t = 1.73 t = 5.45 t = 6.23 p = ns p = ns p = 0.019 p <0.0001 p = 0.012 p = 0.08 p <0.0001 p <0.0001

101

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) FOB Mean 31.27 28.85 34.49 30.06 MD 2.42 -3.22 1.21

t = 7.31 t = 8.78 t = 4.35 t-test p <0.0001 p <0.0001 p <0.0001 FOL Mean 37.44 34.49 28.53 35.50 MD 2.95 8.91 1.94 t-test t = 8.86 t = 22.90 t = 6.99 p <0.0001 p <0.0001 p <0.0001 GOL Mean 180.36 177.84 171.45 179.00 178.50 172.89 177.49 178.52 169.40 MD 2.52 8.91 1.36 1.86 7.47 2.87 1.84 10.96 t-test t = 3.01 t = 9.40 t = 1.22 t = 2.58 t = 8.24 t = 2.97 t = 2.35 t = 8.88 p = 0.003 p <0.0001 p = ns p = 0.010 p <0.0001 p = 0.003 p = 0.019 p <0.0001 DKB Mean 19.30 22.49 20.00 MD -3.19 -0.70

t = 8.00 t = 2.51 t-test p <0.0001 p = 0.012

102

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) MHL Mean 28.69 26.35 25.21 30.90 32.10 28.56 28.45 27.45 MD 2.34 3.48 -2.21 -3.41 0.13 0.24 1.24

t-test t = 7.06 t = 7.94 t = 3.79 t = 8.87 t = 0.28 t = 0.47 t = 3.25 p <0.0001 p <0.0001 p = 0.0002 p <0.0001 p = ns p = ns p < 0.01

XCB Mean 130.43 134.05 138.71 120.60 133.92 132.70 136.05 140.60 MD -3.62 -8.28 9.83 -3.49 -2.27 -5.62 -10.17

t-test t = 4.54 t = 9.43 t = 12.97 t = 4.09 t = 2.53 t = 8.94 t = 8.88 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p = 0.012 p <0.0001 p <0.0001

FRB Mean 96.68 115.50 118.99 114.60 MD -18.82 -22.31 -17.92

t = 13.00 t = 20.46 t = 11.79 t-test p <0.0001 p <0.0001 p <0.0001 WFB Mean 96.79 93.60 93.23 93.06 93.69 MD 3.19 3.56 3.73 3.10

t = 4.10 t = 5.79 t = 5.25 t = 5.87 t-test p <0.0001 p <0.0001 p <0.0001 p <0.0001

103

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) NLB Mean 24.36 26.15 23.12 22.90 23.16 25.12 22.59 MD -1.79 1.24 1.46 1.20 -0.76 1.77

t-test t = 6.97 t = 4.15 t = 4.23 t = 4.29 t = 2.67 t = 8.06 p <0.0001 p <0.0001 p <0.0001 p <0.0001 p = 0.008 p <0.0001

NLH Mean 50.41 49.80 48.2 48.21 49.52 MD 0.61 2.21 2.20 0.89

t-test t = 1.43 t = 5.91 t = 5.54 t = 2.77 p = ns p <0.0001 p <0.0001 p = 0.006

NPH Mean 64.39 68.07 66.33 66.00 64.00 64.12 66.45 66.59 MD -3.68 -1.94 -1.61 0.39 0.27 -2.06 -2.20

t-test t = 6.60 t = 3.22 t = 2.09 t = 0.76 t = 0.38 t = 3.15 t = 4.44 p <0.0001 p = 0.0015 p = 0.038 p = ns p = ns p < 0.01 p <0.0001

OPL Mean 140.81 147.03 148.40 MD -6.22 -7.59

t-test t = 11.17 t = 10.79 p <0.0001 p <0.0001

104

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) OBB Mean 40.72 38.41 38.98 MD 2.31 1.74 t-test t = 7.48 t = 7.24 p <0.0001 p <0.0001

OBH Mean 35.32 34.38 33.31 MD 0.94 2.01 t-test t = 3.20 t = 8.68 p < 0.01 p <0.0001

MAB Mean 54.64 62.80 57.03 MD -8.16 -2.39 t-test t = 12.46 t = 3.08 p <0.0001 p = 0.002

FRC Mean 108.95 107.94 109.92 MD 1.01 -0.97 t-test t = 1.34 t = 1.83 p = ns p = 0.068

105

Table Appendix I. 2 [continued]: Comparison of mean female measurements (in mm).

Standard Measure WA i ii iii iv v vi vii viii (n=150) (n=79) (n=108) (n=47) (n=150) (n=88) (n=203) (n=71) (n=40) PAC Mean 117.01 112.19 113.60 MD 4.82 3.41

t = 5.18 t = 4.89 t-test p <0.0001 p <0.0001 OCC Mean 97.27 96.65 98.01 MD 0.77 -0.74

t = 0.80 t = 1.27 t-test p = ns p = ns

106