Patterns of Morphological Integration in Modern Human Crania: Evaluating Hypotheses of Modularity Using Geometric Morphometrics

Home , Brow ridge, Dacryon, Infratemporal crest, Sphenoparietal suture, Sphenozygomatic suture, Stephanion

PATTERNS OF MORPHOLOGICAL INTEGRATION IN MODERN HUMAN CRANIA: EVALUATING HYPOTHESES OF MODULARITY USING GEOMETRIC MORPHOMETRICS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Adam Kolatorowicz

Graduate Program in Anthropology

The Ohio State University

2015

Dissertation Committee:

Jeffrey K. McKee, Advisor

Paul W. Sciulli

Samuel D. Stout

Mark Hubbe

Copyrighted by

Adam Kolatorowicz

2015

ABSTRACT

This project examines patterns of phenotypic integration in modern human cranial morphology using geometric morphometric methods. It is theoretically based in the functional paradigm of craniofacial growth and morphological integration. The hypotheses being addressed are: 1) cranial form is influenced by secular trends, sex, and phylogenetic history of the population and 2) integration patterns wherein the basicranium is the keystone feature best explains the relationships among in cranial modules.

Geometric morphometric methods were used to collect and analyze three- dimensional coordinate data of 152 endocranial and ectocranial landmarks from 391 anatomically modern human crania. These crania are derived from temporally historic and recent groups in the United States spanning both sexes and across several ancestral groups. Landmark data were subjected to generalized Procrustes analysis and then areas of shape variation were identified via principal components analysis of shape coordinates.

Discriminant function analysis and canonical variate analysis identified regions that can be used to separate groups. Temporal period, ancestry, and sex all have significant effects on mean shape. Age-at-death accounts for a small proportion of the total variation. Modern individuals have higher, narrower vaults with highly arched palates

ii and historic individuals have short, wider vaults with shallower palates. The forehead, brow ridges, and cheek shape were closely associated with sexual dimorphism. Variation in both the vault and face allowed for separation of ancestral groups with concomitant inferior movement of the anterior basicranium in the median plane.

Three major hypotheses of modularity were tested based on functional demands of cranial modules, functional-developmental fields, and the basicranium. Comparing covariance structures of partitions of landmark subsets revealed that the cranium is more integrated when considering functional demands of cranial components origins of cranial components and is less modular when considering developmental origins. Special sensory modules are the most independent units in the cranium. Depending on the definition of cranial modules, results may be quite different and not comparable across studies.

This project integrates anthropology, evolutionary anatomy, and developmental biology. It makes a significant contribution to our understanding of integration patterns in the modern human cranium and highlights differences among theoretical frameworks of integration. The findings can be used for individual identification in medicolegal contexts and clinical applications for surgical treatment of craniofacial-related disorders and injuries. Future research will include examining patterns of morphological integration in non-human primates.

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Dedicated to my parents.

ACKNOWLEDGEMENTS

I would like to acknowledge the faculty and staff at multiple institutions for the role they played in making this dissertation possible. First and foremost, my dissertation committee chair Dr. Jeff McKee and committee members Dr. Sam Stout, Dr. Paul Sciulli, and Dr. Mark Hubbe in the Department of Anthropology at The Ohio State University provided thoughtful comments and constructive criticism throughout the process. Dr.

Mike Warren and Mr. Carlos Zambrano from the C.A. Pound Human Identification Lab at the University of Florida – Gainesville graciously let me borrow their lab’s digitizer. It is good to know people in high places when funding does not come through. Dr. Amanda

Agnew in the Division of Anatomy at the Ohio State University granted access to the skeletal teaching collection and workspace used for the observer error study. Dr. Tim

Gocha was instrumental in establishing working relationships in the Division of

Anatomy.

Thanks to Mr. Lyman Jellema, Collections Manager of the Hamann-Todd

Osteological Collection at the Cleveland Museum of Natural History, for being a most gracious host while I collected data. Dr. Sabrina Curran helped to break up the monotony of data collection with some entertaining and fruitful conversations about anthropology, evolution, and methodology. Dr. Dawnie Wolfe Steadman facilitated access to the W. M.

Bass Donated Skeletal Collection at the University of Tennessee – Knoxville and Ms.

v Heli Maijanen provided logistical support while I collected data. Finally, I appreciate the patience and knowledge base of staff members in the Department of Anthropology at

Ohio State. Ms. Elizabeth Freeman, Mr. Wayne Miller, and Ms. Jean Whipple offered administrative and logistical support during many steps while I completed this project.

I am indebted to my family and friends who supported me through this journey.

In particular, I am grateful for the comradery fostered by The Juices of Fire, the collegiality of fellow graduate students at Ohio State, and the support system created by

Ms. Cheryl Lynn Wyckoff. Above all, I would like to thank my Dad. He was a true renaissance man who sought to better understand the world around him. He had broad interests in art, science, and technology with formal and informal study in astronomy, biology, culture, chemistry, computer science, engineering, history, mathematics, paleontology, and physics. As a jack of all trades and master of many, he inspired me to be curious about the world. Although he is no longer with us to read this dissertation I know that he would appreciate my contribution to our understanding of the pale blue dot we inhabit.

VITA

2000...... A.A. Anthropology, College of Lake County

2002...... B.S. Anthropology, Northern Illinois University

2003 to 2004 ...... Graduate Teaching Assistant, Department of Biology, University of Indianapolis

2004 to 2006 ...... Adjunct Instructor, Department of Biology, University of Indianapolis

2006...... M.S. Human Biology, University of Indianapolis

2006 to 2007 ...... Graduate Teaching Associate, Department of Anthropology, The Ohio State University

2008...... Graduate Research Associate, Department of Anthropology, The Ohio State University

2008 to 2013 ...... Graduate Teaching Associate, Department of Anthropology, The Ohio State University

2011...... Fellow, Forensic Science Academy, Joint POW/MIA Accounting Command – Central Identification Laboratory, Joint Base Pearl Harbor – Hickam

2010 to present ...... Adjunct Instructor, Department of Social Science, Department of Biological and Physical Sciences, Columbus State Community College

2014...... Lecturer, Department of Anthropology, The Ohio State University

vii 2014 to present ...... Adjunct Instructor, Department of Sociology and Anthropology, Ohio University

PUBLICATIONS

Gocha TP, Ingvoldstad ME, Kolatorowicz A, Cosgriff-Hernandez MTJ, Sciulli PW. 2015. Testing the applicability of six macroscopic skeletal aging techniques on a modern Southeast Asian sample. Forensic Sci Int 249:318.e1-7.

FIELDS OF STUDY

Major Field: Anthropology

Minor Field: Anatomy

Minor Field: Statistical Data Analysis

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TABLE OF CONTENTS

ABSTRACT ...... ii DEDICATION ...... iv ACKNOWLEDGEMENTS ...... v VITA ...... vii TABLE OF CONTENTS ...... ix LIST OF TABLES ...... xii LIST OF FIGURES ...... xiii

CHAPTER 1: INTRODUCTION ...... 1 1.1 PURPOSE ...... 4 1.2 SIGNIFICANCE OF RESEARCH ...... 5 1.3 HYPOTHESES ...... 7 1.4 ORGANIZATION OF DISSERTATION ...... 8 1.5 SUMMARY ...... 9

CHAPTER 2: DEVELOPMENT, GROWTH, AND FUNCTION OF THE SKULL .....11 2.1 DEFINING COMPONENTS OF THE SKULL ...... 12 2.2 EMBRYOLOGICAL ORIGINS ...... 15 2.2.1 Embryological Tissues Giving Rise to Cranial Bones ...... 16 2.2.2 Basicranium...... 17 2.2.3 Vault ...... 18 2.2.4 Face ...... 19 2.3 CRANIOFACIAL GROWTH AND DEVELOPMENT ...... 21 2.3.1 Basicranium ...... 23 2.3.2 Vault ...... 24 2.3.3 Face ...... 26 2.4 FUNCTIONAL PARADIGM OF CRANIOFACIAL GROWTH ...... 28 2.5 FACTORS INFLUENCING CRANIAL FORM ...... 29 2.5.1 Heredity ...... 29 2.5.2 Climate ...... 33 2.5.3 Masticatory Stress ...... 36 2.5.4 Nutrition ...... 37 2.5.5 Posture ...... 38 2.5.6 Oral Breathing ...... 39 ix 2.6 SUMMARY ...... 40

CHAPTER 3: MORPHOLOGICAL INTEGRATION AND MODULARITY ...... 41 3.1 MODULES IN THE HUMAN CRANIUM ...... 41 3.2 EVOLUTIONARY CHANGES IN CRANIAL AND BRAIN FORM ...... 46 3.2.1 Vertebrates ...... 46 3.2.2 Hominoids ...... 47 3.2.3 Trends in Hominin Cranial Form ...... 48 3.3 PATTERNS OF HOMINID CRANIOFACIAL INTEGRATION ...... 50 3.3.1 Cranial Base ...... 51 3.4 ISSUES IN CURRENT RESEARCH ...... 57 3.5 SUMMARY ...... 59

CHAPTER 4: METHODS FOR DESCRIBING CRANIAL FORM ...... 61 4.1 NON-METRIC AND METRIC METHODS ...... 61 4.2 MEASURING SIZE, SHAPE, AND FORM ...... 63 4.2.1 Estimating Size and Shape ...... 69 4.3 LANDMARK MORPHOMETRICS ...... 72 4.4 USING LANDMARKS TO DESCRIBE CRANIAL FORM ...... 75 4.5 ACQUIRING AND ANALYZING LANDMARK DATA ...... 78 4.5.1 Acquisition of Landmark Data ...... 78 4.5.2 Analyzing Landmark Data ...... 82 4.6 APPLYING LANDMARK MORPHOMETRICS TO HUMAN CRANIAL FORM ...... 84 4.7 SUMMARY ...... 86

CHAPTER 5: MATERIALS AND METHODS ...... 88 5.1 LANDMARKS ...... 88 5.2 SAMPLE ...... 90 5.3 EQUIPMENT ...... 94 5.4 DATA COLLECTION PROTOCOL ...... 96 5.5 ERROR STUDY ...... 97 5.6 DATA ANALYSIS ...... 99 5.6.1 Assumptions ...... 100 5.6.2 Missing Data ...... 101 5.6.3 Generalized Procrustes Analysis ...... 102 5.6.4 Observer Error ...... 103 5.6.5 Outliers ...... 104 5.6.6 Principal Components Analysis ...... 104 5.6.7 Procrustes ANOVA ...... 105 5.6.8 Regression Analysis ...... 106 5.6.9 Discriminant Function Analysis ...... 106 x 5.6.10 Canonical Variate Analysis ...... 109 5.6.11 Covariation of Landmark Subsets ...... 110 5.7 SUMMARY ...... 117

CHAPTER 6: RESULTS ...... 119 6.1 OBSERVER ERROR ...... 119 6.2 MISSING DATA ...... 122 6.3 GENERALIZED PROCRUSTES ANALYSIS ...... 123 6.4 OUTLIERS ...... 126 6.5 PRINCIPAL COMPONENTS ANALYSIS ...... 129 6.6 PROCRUSTES ANOVA ...... 141 6.7 REGRESSION ANALYSIS ...... 141 6.8 DISCRIMINANT FUNCTION ANALYSIS ...... 142 6.9 CANONICAL VARIATE ANALYSIS ...... 150 6.10 COVARIATION OF LANDMARK SUBSETS ...... 160 6.11 SUMMARY ...... 164

CHAPTER 7: DISCUSSION ...... 166 7.1 MORPHOLOGICAL VARIATION IN HUMAN CRANIA ...... 167 7.1.1 Sex ...... 167 7.1.2 Ancestry ...... 169 7.1.3 Time Period ...... 179 7.2 PATTERNS OF MORPHOLOGICAL INTEGRATION ...... 181 7.3 LIMITATIONS ...... 186 7.4 FUTURE RESEARCH AND BROADER IMPACT ...... 190 7.5 SUMMARY ...... 194

CHAPTER 8: CONCLUSIONS ...... 196

WORKS CITED ...... 200 APPENDIX A: LANDMARK DEFINITIONS ...... 216 APPENDIX B: LANDMARK LOCATIONS ...... 222 APPENDIX C: LANDMARK COLLECTION ORDER FOR MAIN STUDY ...... 227 APPENDIX D: OBSERVER ERROR LANDMARK COLLECTION ORDER ...... 229 APPENDIX E: R SCRIPT FOR ESTIMATING MISSING LANDMARKS ...... 231

LIST OF TABLES

Table 3.1: Proposed modules of the hominin cranium ...... 46

Table 5.1: Number of landmarks used to quantify shape of cranial modules...... 89

Table 5.2: Sample demographics ...... 92

Table 5.3: Sample age-at-death distributions...... 92

Table 5.4: Landmarks not recorded or included in error study ...... 98

Table 6.1: Observer error Procrustes ANOVA for centroid size ...... 121

Table 6.2: Observer error Procrustes ANOVA for shape...... 122

Table 6.3: Amount of variance explained by principal components ...... 132

Table 6.4: Classifier effects Procrustes ANOVA for shape ...... 141

Table 6.5: Regression analysis of age effects on shape ...... 142

Table 6.6: Test for difference between mean shapes of groups ...... 143

Table 6.7: Cross-validation classification results for collections ...... 145

Table 6.8: Cross-validation classification results for females and males ...... 145

Table 6.9: Precision of decision rules for classification ...... 146

Table 6.10: Amount of variation among groups explained by canonical variates ...... 152

Table 6.11: Mahalanobis distances among groups in canonical variate analysis ...... 154

Table 6.12: Procrustes distances among groups in canonical variate analysis ...... 155

Table 6.13: Evaluation of modularity hypotheses ...... 160

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LIST OF FIGURES

Figure 2.1: Patterns of remodeling that move bones in skull during growth ...... 22

Figure 3.1: Hypothetical shape changes over time between two modules ...... 42

Figure 3.2: Cranial base angle ...... 52

Figure 4.1: Axes of rotation in coordinate measuring machine ...... 80

Figure 5.1: MicroScribe® 3DX coordinate measuring machine in home position with measurement axes ...... 95

Figure 5.2: Data collection workspace setup ...... 95

Figure 5.3: A priori hypothesis of modularity based on functional modules ...... 111

Figure 5.4: A priori hypothesis of modularity based on developmental fields in functional-developmental modules ...... 112

Figure 5.5: A priori hypothesis of modularity based on special sensory fields in functional-developmental modules ...... 113

Figure 5.6: A priori hypothesis of modularity based on basicranial module ...... 114

Figure 5.7: Evaluating hypothesis of modularity ...... 115

Figure 5.8: Delaunay triangulation of configuration to identify spatially contiguous landmarks and partitions ...... 116

Figure 6.1: Procrustes ANOVA landmark configuration for measurement error ...... 120

Figure 6.2: Multivariate regression of missing landmarks reflected on shape subspace ...... 123

Figure 6.3: Paired and median landmarks matching during Procrustes fit ...... 124

Figure 6.4: Landmark configuration after Procrustes fit ...... 125 xiii Figure 6.5: Checking for errors in landmark collection order ...... 127

Figure 6.6: Examples of normal individual and extreme outlier ...... 128

Figure 6.7: Cumulative distribution of Procrustes distances from average ...... 129

Figure 6.8: Covariance matrix correlation between collections ...... 131

Figure 6.9: Covariance matrix correlation between sexes ...... 131

Figure 6.10: Percent variance explained by principal components ...... 132

Figure 6.11: Scatterplots of PC1 and PC2 scores ...... 134

Figure 6.12: Scatterplots of PC1 and PC2 scores separated by collection ...... 135

Figure 6.13: Scatterplots of PC1 and PC2 scores separated by sex ...... 136

Figure 6.14: Scatterplots of PC1 and PC2 scores separated by ancestry ...... 137

Figure 6.15: Shape changes for PC1 ...... 139

Figure 6.16: Shape changes for PC2 ...... 140

Figure 6.17: Scatterplot of regression scores ...... 142

Figure 6.18: Cross-validated discriminant function scores distribution for collections .144

Figure 6.19: Cross-validated discriminant function scores distribution for females and males ...... 144

Figure 6.20: Discriminant function shape differences between collections ...... 148

Figure 6.21: Discriminant function shape differences between sexes ...... 149

Figure 6.22: Scatterplots of canonical variates 1-6 scores ...... 153

Figure 6.23: Shape changes associated with canonical variates 1-3 ...... 158

Figure 6.24: Shape changes associated with canonical variates 4-6 ...... 159

Figure 6.25: RVM distributions of random landmark partitions for evaluating hypotheses of modularity ...... 163

xiv Figure 7.1: Hypothetical changes associated with hyper-male and hyper-female shapes ...... 169

Figure 7.2: Comparing concepts of modularity and integration ...... 184

Figure B.1: Ectocranial landmarks, lateral ...... 223

Figure B.2: Ectocranial landmarks, anterior ...... 224

Figure B.3: Ectocranial landmarks, inferior ...... 225

Figure B.4: Endocranial landmarks ...... 226

CHAPTER 1: INTRODUCTION

Humans are unusual biological organisms and social beings. Humans possess a suite of unique social and biological features. While individual traits may be shared with other organisms, the combination of these traits make humans distinct from other primates and mammals. To some degree, we have overcome our biological limitations by developing a set of behaviors that buffers us against the perturbations arising from the environment and our own biology (Larsen, 1997). The behaviors exist in a social world that we have created for ourselves, outside of, but overlapping with, the physical world.

The biocultural approach taken by anthropologists to gain a deeper, more complete understanding of the human condition affords a unique perspective on the intersections of biology, behavior, and the environment.

Considering only biology, as a species, humans are a physically weak, slow walking group of Primates, the last remaining species in the Hominin tribe. We are identified by reduced dentition, relatively large brains, and a bipedal locomotor pattern.

These features arose out of a mosaic evolution in which several traits appeared in a symphony of concomitant changes. We are lightly muscled and cannot move quickly across the landscape. Human bodies are relatively hairless, imparting no protection from the elements and limiting humans to tropical climates. We are somewhat defenseless from the perspective of usual evolutionary responses among animals. Humans do not possess protective armor, horns, antlers, or claws. We cannot change directions quickly 1 to avoid predators. Humans have become the dominant species on the planet despite all these biological limitations. Human culture has allowed the species to create clothing to protect against the weather allowing us to travel to all corners of the world. Humans fashion armor and tools to protect and attack other animals and themselves.

Humans are the only habitually striding, tailless, featherless bipeds that exist today (Lieberman, 2011). Bipedal locomotion, a hallmark of the human condition, may be an energetically efficient way to move across long distances (Bramble and Lieberman,

2004); however, a bipedal “lifestyle” imparts a number of functional deficits to its adopters. Human are susceptible to choking, herniated intervertebral disks, hemorrhoids, varicose veins, and a multitude of foot-related problems. Humans experience lower back pain, weak abdominal muscles (e.g. inguinal hernias), obstetric difficulties, and feeble knees. Despite these functional deficits hominins maintained a bipedal locomotive pattern, suggesting it is a highly advantageous trait that increased the fitness of individuals. The shift from quadrupedal to obligate bipedal locomotion resulted in a series of changes in more than just upper and lower limbs. Modifications occurred elsewhere in the body as a result of this new way of traversing the landscape with some idiosyncratic alterations to the head. Dramatic changes occurred in the head as it became repositioned on top of the vertebral column.

Humans have oddly shaped and sized heads compared to other animals. The typical vertebrate skull is long and low with a flat cranial base. The brain case is smaller than the face with the face positioned in front of the brain. There is a long, protruding rostrum. Quadrupeds support their heads with great nuchal musculature as the head is suspended from the trunk in a cantilevered system. The spinal cord exits the skull at the 2 back. Throughout human evolution, the human skull became shorter and higher with a more flexed cranial base. The brain case became larger than the face with the face becoming less projecting and more orthognathic, positioned beneath the front of the brain instead of in front of the brain. The human head is relatively large for our body size and is supported by the cervical vertebrae as it sits balanced on top of the neck. The spinal cord exits the skull through the inferior base. Considering the mosaic nature of human evolution, we must consider the changing size and shape of the brain relative to the body throughout evolutionary time. This also affected the size and shape of the skull, a system whose shape reflects its primary functions of support and protection.

Out of all the components of the human skeleton, the skull can tell one the most about an individual. It contains the most accurate information on age, sex, ancestry, diet, locomotion, and behavior (via brain shape). The postcranial skeleton offers a great deal of information regarding locomotion, sex, stature, and age. However, this information is scattered throughout the postcranial skeleton and must be pieced together. Therefore, the cranium is the best source of phenotypic information for variability (Bruner et al., 2004).

When skeletal remains are recovered, whether in a forensic, historic, prehistoric, or paleontological context, quite often, there is relatively little of the postcranial skeleton recovered and more of the cranium as it contains some of the thickest and densest bones in the body. At the same time the cranium also includes the smallest, most fragile bones that are paper thin. The hominin fossil record is no exception in that most of the fossil material discovered comes from the cranium. Paleoanthropologists have dedicated much of their time to understanding human evolution by studying cranial variation across temporal and geographic space. 3 1.1 PURPOSE

This research examines modular variation in the human cranial base to explain the nature and level of variability in modern human populations and identify the role of the cranial base in patterns of modularity. The theoretical framework for this project is grounded in two principals: morphological integration and the functional paradigm of cranial growth. These will be used to test hypotheses concerning susceptibility of the skull to macro- and micro-evolutionary forces (Bastir and Rosas, 2005). Morphological integration recognizes that biological structures are related to one another in complex ways in which change in one component may cause modification in another component

(Olson and Miller, 1958). Change in one part of the human skull generally does not have a major effect on other parts, meaning it is functionally and developmentally flexible.

The functional paradigm of cranial growth, also known as the functional matrix hypothesis, states that cranial shape reflects the primary tasks of support and protection

(Moss and Young, 1960). The basicranium has been chosen because it is unique in that it connects and overlaps the major functional cranial components and is evolutionarily conservative; therefore, it may be used as a proxy for phylogenetic relationships (von

Cramon-Taubadel, 2011). Additionally, the basicranium has the most influence over integration of other functional modules including the vault, nasal cavity, pharynx, orbits, and oral cavity (Ross and Ravosa, 1993; Lieberman et al., 2000ab).

A modern and an historic population from the United States have been chosen to assess the levels of modularity in the human cranium. The term “modern” has two meanings in the context of this research. When “modern” is used in reference to the human species (e.g., the modern human cranium) I am referring to anatomically modern 4 Homo sapiens; whereas, when “modern” is used to describe a sample it implies that the individuals comprising the sample died within the last fifty years. An “historic” sample refers to a population containing individuals that died more than fifty years ago. The modern sample from Tennessee comprises individuals that died beginning in the 1980s until the present and the historic sample from Cleveland represents individuals that died around the turn of the twentieth century. The difference in time period was selected to examine the possible effects of secular changes in the cranium (Jantz and Jantz, 2000;

Wescott and Jantz, 2005; Little et al., 2006). Also, male and female adults from

European American and African American ancestral groups were selected to examine the possible effects of ancestry, sex, and age at death on basicranial form.

1.2 SIGNIFICANCE OF RESEARCH

The proposed research contributes to the understanding of human evolution, variation, and developmental biology. It will add to the growing body of knowledge on the nature and origins of human skeletal variability. Appreciating levels of variation in past and present populations and documenting those changes is an essential component in identifying selective pressures responsible for evolution and/or secular change

(McGuigan and Blows, 2010). Understanding the degree of variation and the cranial complexes responsible for that variation, among and between human populations, is central to understanding hominin evolution. Much of the current research in biological anthropology has abandoned the genocentric view of variation and incorporated plasticity and developmental acclimatization into research models of variation (González-José et al., 2001; Uliajszek, 1997; Weiss and Buchanan, 2003). The proposed research also 5 adopts a development-first view of variability with the addition of adult basicranial form as an exemplification of phylogenetic history.

Factors influencing cranial development affect the adult phenotype more than the genotype itself (Little et al., 2006). While it has been recognized that climate is a major factor influencing variable cranial form in different human populations (Cunningham and

Wescott, 2002; Hernandez et al., 1997), modern cranial variation is due to factors related to cultural buffering, such as nutrition or diet consistency, which affect an individual within one’s lifetime (Larsen, 1997; Little et al., 2006; Stefán, 1999). Biocultural factors influencing the evolution of hominins have had a confounding effect on the interpretation of morphological data.

The results of this study will contribute to the debate on adaptive and plastic traits of the skeleton and their roles in human evolution. The efficacy of geometric morphometrics will also be demonstrated as a method to capture and describe the complex morphology of the cranial base. Geometric morphometrics maintains the three- dimensional relationships of a structure throughout data collection and analysis making for a unique viewpoint on organismal form. This study examines both ectocranial and endocranial surfaces of the base; whereas other studies may only examine either the outside or inside of the base, not both.

The research synthesizes approaches in developmental biology, evolutionary theory, traditional biological anthropology methods for skeletal identification, and emerging technologies. Broader impacts include assessing models used to describe hominin evolution, applications for identifying unknown human skeletal remains in medicolegal contexts, and clinical applications for surgery involving the cranial base. 6 1.3 HYPOTHESES

This dissertation tests two primary hypotheses based upon the concept that there are biological differences in cranial form in human populations. These differences are reflections of the populations’ phylogenetic relationships, or population history. The first hypothesis is that there will be differences in the shape of the cranial base. The goal is to document specific areas of the basicranium that vary significantly in recent populations based upon population history. Age-at-death and sex will also be considered as factors to explain observed variation. Additionally, basicranial form in modern populations will be more similar to one another than historic populations due to gene flow. Historic populations were less likely to interbreed due to social separation of ancestral groups whereas modern populations are more likely to interbreed as a result of changing social norms.

The second hypothesis is that the cranial base can be treated as a separate functional module from the vault and face. The basicranium is the first part of the skull to begin development and to some degree directs the development of the face and vault.

Modularity based on the functional modules and functional-developmental modules will be compared to the proposed basicranial modularity. Following the functional paradigm of craniofacial growth (Moss and Young, 1960) the midline base will be seen as an independent unit. Although it overlaps with the facial and neurocranial complexes it is still somewhat autonomous.

Altogether, it is predicted that modern cranial base morphology can be explained by ancestral lineages and the cranial base is not functionally integrated with the neurocranial and facial modules of the skull. It is expected that the sample will display 7 low levels of phenotypic integration, being consistent with the overall hominin pattern

(Lieberman et al., 2000b). The modern human cranium will be highly modularized, that is, different functional components will not be closely related to one another.

Specifically, the facial skeleton will be uncoupled from the neurocranial skeleton

(Polanski and Franciscus, 2006). Also, the midline cranial base will be closely related to the face and neurocranium, but the lateral portions of the base will not be closely related to the face and neurocranium (Bruner and Ripani, 2008). This is due to the functional, embryological, and developmental relationships among these structures (Enlow, 1968).

1.4 ORGANIZATION OF DISSERTATION

This dissertation is divided into eight chapters. The first chapter introduces the framework for this research and its significance to our understanding of human cranial variation and evolution. The goals of the work along with the hypotheses being testing are briefly described. The second chapter reviews development and growth of the skull from embryological origins to the end of the life cycle with focus on the vault, face, and basicranium. The functional paradigm of craniofacial growth is elucidated as one of the two primary theories by which this research is founded. Factors that influence craniofacial growth and development are summarized within a biocultural context.

Chapter three introduces the concept of morphological integration, as defined from genetic, developmental, and functional perspectives. Morphological integration serves as the second primary theory used as a basis for this dissertation. The functional modules of the human skull will be defined by relating soft and skeletal tissues. Chapter three also surveys eight million years of primate cranial evolution with close scrutiny on trends in 8 the hominin skull. Documented patterns of craniofacial integration as it relates to changes in basicranial morphology and secular changes in modern humans is addressed.

Chapter five summarizes the materials and methods used to conduct this study. The chapter describes the study samples, instrumentation, and statistical analyses employed, with a focus on geometric morphometrics as an approach to describe shape variation.

Chapter six presents the results of the analysis used to test the hypotheses. Chapter seven is a discussion of the results as they pertain to the hypotheses with explanations for why or why not the results conform to expected outcomes. Directions for future work within the context of human evolution are presented, applications of this work to fields other than evolutionary studies are made. The final chapter, chapter eight, offers conclusions drawn from this research and how the findings can be applied to the larger field of anthropology to improve our understanding of the human condition.

1.5 SUMMARY

The human skull is examined because it contains a wealth of information regarding population history and phylogenetic relationships. It is a complex structure serving multiple functions including protection and support of the soft tissues that call the skull their home. Additionally, the skull develops from several different embryological tissues. These tissues within the face and vault are connected via the cranial base. The position of the base and its importance in sustaining a link between the brain and the rest of the body places a number of functional demands on the structure. Its role in hominin evolution is just as complex as its organization. By documenting levels of cranial variability in modern humans and assessing the degree of modularity we can more 9 carefully evaluate models of hominin evolution. This information can be used to aid in the identification of individuals in a forensic context or potentially guide physicians in their treatment of cranial disorders. Two main hypotheses are presented to examine the levels of variability in historic and modern populations with the cranial base as the focus of this research. The hypotheses test whether there are morphological differences between these populations separated by time and lineage. Furthermore, the cranial base will be examined as an independent functional unit within the context of multiple modular approaches. Finally, an overview of this dissertation was presented including a summary of each forthcoming chapter.

CHAPTER 2: DEVELOPMENT, GROWTH, AND FUNCTION OF THE SKULL

The skull is one of the most complex structures in the entire human skeleton, serving many functions which revolve around protection and support of the brain and special sensory organs. Additionally, the skull allows the brain to communicate with tissues, glands, and organs in the head, neck, and rest of the body. Embedded within or attached to the twenty-two bones that form the skull (frontal bone, two parietal bones, occipital bone, two temporal bones, two maxilla, two zygomatic bones, ethmoid, sphenoid, vomer, two inferior nasal concha, two nasal bones, two lacrimal bones, two palatine bones, and the mandible, not including the six auditory ossicles or the hyoid bone), special sensory organs detect environmental stimuli that provide information on light (vision), sound waves (audition), position of the head (equilibrium), chemicals in the air (olfaction), and chemicals present in solids and liquids we ingest (gustation).

Elsewhere in the head, neck, and remaining body regions, general sensory organs provide information related to temperature, touch, pressure, pain, stretch, and chemicals in both the internal and external environments.

The essential information garnered from the special sensory organs such as the eyes, ears, nose, and tongue combined with general sensory information derived from internal organs, skin, muscles, tendons, and ligaments is received and integrated by the brain to affect a response. If any of these stimuli are noxious, or harmful to the body, as

11 in excessive stretch of a joint capsule or increase in blood pH beyond normal parameters, a reflexive or defensive reaction may be initiated by the brain to protect the body. Or, if stimuli are non-noxious, the brain will use the information to maintain homeostatic function. The brain and other organs within the head must be protected so that they may carry out their functions of stimuli detection, information integration, and response to stimuli. The skull and its many bones serve this protective purpose. Adult human cranial morphology and its variation in modern populations can only be understood through an examination of its anatomy, embryological origins, and functions.

This chapter will outline the structure of the adult cranium by taking a systems approach to reduce the inherent complexity of the cranium. The cranium will be divided into three smaller regions by describing the bones of the vault, face, and basicranium.

The embryological origins and development of the skull will be traced followed by a discussion of craniofacial growth. Next, the functional paradigm of craniofacial growth will be used as the foundation to enumerate the specific functions of the cranial subsystems by relating soft tissues to bones. Finally, factors influencing cranial form such as heredity, climate, masticatory stress, nutrition, posture, and oral breathing will be introduced.

2.1 DEFINING COMPONENTS OF THE SKULL

The skull is divided into a portion that surrounds and protects the brain (vault and base) and a portion that forms the face. The parts are derived from different embryological tissues and undergo different ossification processes. Endochondral ossification (cranial base) involves the formation of a cartilaginous precursor model 12 which is later replaced by bone in multiple ossification centers. Bones developing through intramembranous ossification (vault and face) are derived from a membranous sheath of mesenchymal tissue that surrounds organs, spaces, or tissues (Lieberman,

2011). The growth of the structures inside the sheath stimulate the bone to form.

Mesenchyme serves as a precursor for many other tissue in the lymphatic and cardiovascular systems as well as many connective tissues.

The neurocranium is composed of eight bones that protect the brain: the unpaired frontal, occipital, ethmoid, and sphenoid bones and the paired parietal and temporal bones. The neurocranium is subdivided into a membranous, flat portion that surrounds the brain as a vault, also known as the desmocranium in the embryo, and a cartilaginous portion, also known as the embryological chondrocranium, which forms the cranial base

(Sadler, 2006). The desmocranium includes the frontal, parietals, and squamous portions of the temporal bones and is formed through intramembranous ossification. The chondrocranium includes the occipital, body and lesser wings of the sphenoid, and petrous portions of the temporal bones and is formed through endochondral ossification.

The cranial base is the most complex substructure within the skull. It connects the vault above with the face below and serves a major integration center of the skull

(Lieberman, 2000a). Dozens of foramina allow for blood vessels and nerves to pass between the head and neck. Muscles attach to its outer surface to move the head, lower, jaw, and throat. It protects the delicate organs of hearing and equilibrium and serves as a foundation on which the brain grows. The face is suspended from the anterior portion of the base including the mandible, which articulates with the middle basicranium. Parts of the frontal, ethmoid, sphenoid, temporal, and occipital bones form the base through 13 endochondral ossification. The base is divided into three fossae as defined on the endocranial surface: anterior, middle, and posterior.

The anterior cranial fossa is made up of parts of the frontal, sphenoid, and ethmoid bones extending anteriorly from the foramen cecum (the remnant of the end of the neural tube) posteriorly to the lesser wing of the sphenoid with the cribriform plate of the ethmoid in between. The olfactory bulbs and the frontal lobes of the brain are supported by the anterior cranial fossa. The middle cranial fossa is centrally located around the hypophyseal fossa of the sphenoid bone with the greater wings extending laterally. The temporal lobes are supported by the middle cranial fossa. The posterior cranial fossa consists of the spheno-occipital clivus of the sphenoid, the petrous pyramids of the temporal bones, and the basioccipital. The brainstem and cerebellum are supported within this posterior cranial fossa. Finally, a separate but related midline cranial base is defined along the median plane by an anterior and posterior section. The prechordal, anterior section is found between foramen cecum and the deepest point of the hypophyseal fossa (known as sella). The postchordal, posterior section is found between sella and the most posterior point on the anterior margin of the foramen magnum (a point known as basion). Further details of the uniquely acute angle in humans formed between the anterior and posterior cranial base will be discussed in Chapter 3, within the context of evolutionary anatomy.

The viscerocranium (facial skeleton) is composed of fourteen bones formed via intramembranous ossification: the unpaired mandible and vomer as well as the paired nasal, lacrimal, maxilla, zygomatic, inferior nasal concha, and palatine bones (Sadler,

2006). The face can be further subdivided into lower, middle, and upper sections. The 14 upper face is made of the forehead which consists entirely of the frontal bone. The middle face comprises the maxilla including the upper dentition with contributions from the nasal, lacrimal, ethmoidal, sphenoidal, palatine, and zygomatic bones. The lower face is made entirely of the mandible including the lower dentition.

2.2 EMBRYOLOGICAL ORIGINS

Cranial form is canalized very early in development along with the neural tube.

Bones of the head are derived from three main embryonic tissues: paraxial mesoderm, lateral plate mesoderm, and the neural crest (Sperber et al., 2010). These tissues arise from the three germ layers (endoderm, mesoderm, and ectoderm) that are a result of gastrulation occurring during the third week of gestation (Sadler, 2006). After fertilization, formation of the zygote is followed by a transformation into a hollow ball of cells known as the blastula. It will take three days for this hollow ball to become a solid mass of cells called a morula (Sperber et al., 2010). Beginning at day five, the cells form the blastocyst and begin to differentiate into an outer layer known as the trophoblast (later to become the placenta) and an inner mass called the embryoblast (later to transform into all body tissues) (Sperber et al., 2010). The blastocyst implants in the uterine wall around the ninth day to undergo further development. The embryoblast comprises two layers of cells: 1) the hypoblast which will become the amniotic sac and 2) the epiblast which will develop into the three germ layers (Sadler, 2006).

By the fifteenth day of development, a primitive streak forms along the midline of the blastula to establish bilateral symmetry (Sadler, 2006). The epiblast then migrates into this groove forming three new layers of cells, the endoderm, mesoderm, and 15 ectoderm. These three layers become suspended between a yolk sac and amniotic sac.

The endoderm will later form the lining of most of the alimentary canal, the lining of the glands opening into the digestive tract such as the pancreas and liver, the lining of the urinary system, and lining of the respiratory tract (Sadler, 2006). The mesoderm is located between the superficial ectoderm and deep endoderm. This embryological tissue gives rise to structures/tissues such as the notochord, bone, cartilage, fat, the circulatory system, lymphatic, linings of the coelom, and the dermal and hypodermal layers of skin

(Sadler, 2006). Mesoderm further differentiates to paraxial mesoderm and lateral plate mesoderm which will become bones of the cranium. The third germ layer, ectoderm, differentiates into the epidermis, appendages of the skin, nervous tissue, teeth, and parts of the eye (Sadler, 2006). The neural crest component of ectoderm specifically will develop into other cranial bones.

2.2.1 Embryological Tissues Giving Rise to Cranial Bones

The paraxial mesoderm and lateral plate mesoderm form by approximately the seventeenth day of gestation and are derivatives of the embryonic mesoderm (Sadler,

2006). Cells of the mesoderm form a thickened plate close to the midline creating the paraxial mesoderm while cells located more laterally remain thin and create the lateral plate mesoderm. The formation of sphenoid body and base of the occipital are induced by the notochord. Lateral plate mesoderm gives rise to the parietals, posterior occipital, and the petrous portion of the temporal. The neural crest forms by the fourth week and is a derivative of the embryonic ectoderm (Sadler, 2006). The remainder of the neurocranium and the entire face is derived from neural crest cells. 16 Later in development, larger structures such as the facial prominences and pharyngeal arches will guide development of the face (Sperber et al., 2010). The neurocranium is first to reach adult size and proportion indicating early canalization. The face, however, begins developing later, making it susceptible to influence from the environment. Early modification of the timing and rate of growth fields results in novel phenotypes that may be of evolutionary significance (Ponce de León and Zollikofer,

2001; Lieberman et al., 2002).

2.2.2 Basicranium

Evidence of the embryonic basicranium (chondrocranium) appears following the

28th day of gestation when the beginnings of the brain and eye have formed (Sadler,

2006). Mesenchymal cells condense under these tissues and by the 40th day begin forming the separate cartilages of the base. Mesenchymal cells in front of the notochord originate from the paraxial mesoderm (prechordal cartilages) whereas those mesenchymal cells behind the notochord arise from the neural crest (postchordal cartilages) (Lieberman, 2011). A third group of sensory cartilages form around the otic and olfactory capsules. Five prechordal cartilages produce the nasal region below the brain, the hypophyseal fossa, greater wings of sphenoid, lesser wings of sphenoid, and the midline ethmoid. Two postchordal cartilages will form the basioccipital and a ring around the foramen magnum. The sensory cartilages will form the petrous portion of the temporal and the walls of the nasal cavity along with the vomer.

Endochondral ossification of the chondrocranium occurs through 41 ossification centers in a process that starts caudally, proceeds rostrally, and ends laterally (Lieberman, 17 2011). The sphenoid bone also has endochondral and membranous portions fusing across

19 centers that begin at eight weeks (Sperber et al., 2010). The cartilaginous portion of the occipital bone begins ossification at ten weeks and will fuse with the membranous portion two weeks later (Sperber et al., 2010). Around 16 weeks the petrous and styloid portions of the temporal bones begin ossification within 21 ossification centers (Sperber et al., 2010). The ethmoid is created through three ossification centers forming at four months with one centering on the perpendicular plate and crista galli and two others within the nasal capsular cartilages (Sperber et al., 2010). Another pair of cartilages detaches from the ethmoid to form the inferior nasal conchae (Lieberman, 2011).

2.2.3 Vault

The embryonic vault (desmocranium) begins formation soon after the chondrocranium within the ectomeninx membrane of the brain (Sperber et al., 2010).

The mesenchymal cells differentiate into an inner endocranium (inner table) and an outer ectocranium (outer table) with trabecular structures in between (diploë). Further development is stimulated by tensile forces at the sutures and attachments of the dural infoldings via the falx cerebri and tentorium cerebelli (Lieberman, 2011). (Sutures are the synarthrotic fibrous joints connecting bones of the skull.) These tensile forces are produced by rapid growth of the brain, having the greatest influence over early vault shape.

Intramembranous ossification of the desmocranium begins at eight weeks

(Sperber et al., 2010). The parietals are derived from two ossification centers each. The frontal forms as a left and right element with a single ossification center each and then 18 fuses after birth. The contribution of the temporals to the vault begins with the squamous and tympanic ring. The squamous portion forms though a single ossification center at eight weeks whereas the tympanic ring forms through four separate centers starting at three months. These two portions will fuse with the petrous portion just before birth

(Lieberman, 2011). The squamae of the greater wings of the sphenoid and the pterygoid plates form separately from the endochondral portion. The squamous occipital forms from two centers above the tentorium cerebelli and will fuse with the endochondral portion at the superior nuchal line. Finally, additional intersutural, Wormian bones may form between the vault sutures and are extremely variable in size, number, and location

(Hauser and DeStefano, 1989).

2.2.4 Face

The embryonic face (splanchnocranium) develops through three organizing principals: 1) migration of general sets of cells, 2) coalescence around spaces and organs, and 3) prepatterning and induction by signal centers (Lieberman, 2011). A two-layered membrane forms from a thickened mass of endodermal cells that projects beyond the trilaminar disk after gastrulation occurs. This oropharyngeal membrane lies at the cranial end of the disk and above the stomodeum, a depression at the end of the pharynx which will later locate the mouth. Neural crest cells begin to migrate toward the stomodeum and in separate streams of cells will differentiate as induced by signaling centers (Sadler,

2006). The prosencephalic center, derived from the mesoderm, is first to signal cells to become the frontonasal prominence around the eyes and nose. Next, the rhobmbencephalic center induces cells that are migrating toward the middle and lower 19 face eventually becoming the branchial arches. Endodermal, mesodermal, and ectodermal components of the arches will develop into muscle forming groups, lining of the pharynx, and organs related to the oral cavity, respectively (Lieberman, 2011).

Three pairs of facial prominences then begin to form and move toward the midline from the left and right sides proceeding dorsal to ventral (Sadler, 2006). Above the stomodeum, the frontonasal prominence interacts with the lens and olfactory placodes, thickened regions of the ectoderm that will induce formation of the eye and olfactory epithelium. The eminence grows down between the newly forming eyes. The olfactory placodes then stimulates the frontonasal eminence to produce lateral nasal prominences. Below the stomodeum, the maxillary and mandibular prominences migrate ventrally as derivatives of the first branchial arch and other nearby tissues transforming into the palate, upper jaw, upper lip, and lower jaw (Lieberman, 2011). The maxillary process specifically unites in the front and joins with the nasal processes to form the primary palate thus separating the oral and nasal cavities.

Intramembranous ossification of the facial bones begins at six weeks beginning with the lower jaw (Sperber et al., 2010). The middle and upper face starts ossifying at eight weeks. Maxillary, zygomatic, orbitonasal, and nasopalatine ossifications centers guide later facial development. The maxillary body forms a shelf of bone surrounding the teeth with the palatine bones at the back of the palate. The zygomatic center forms the zygomatic arch connecting with the temporal bone with nearby smaller centers contributing to the wings of the sphenoid and the pterygoid plates. Nasals, lacrimals, and perpendicular plates of the palatines form from nasopalatine centers surrounding the nasal cavity. The ethmoid then shapes the remaining nasal cavity. The face is fully 20 formed at 25 weeks (Sperber et al., 2010). At the end of in utero development the supraorbtial regions connect the face with anterior cranial fossa, the nasal region connects the middle face with the basicranium through the ethmoid and sphenoid, and the lower face is connected to the cranial base through the mandible (Lieberman, 2011). These relationships will persist throughout life as parts of the face begin to function as specialized modules integrated throughout the cranium

2.3 CRANIOFACIAL GROWTH AND DEVELOPMENT

The human skull follows two different growth trajectories as related to the major functions of the neurocranium, basicranium, and viscerocranium (Sperber et al., 2010).

The vault and cranial base follow a neural trajectory along with the brain in which the majority of growth and development takes place within the first six years. The face follows a skeletal, or somatic, trajectory with the rest of the body and continues growing beyond the teen years. Variation in cranial form arises from four primary factors (Enlow and Hans, 2008:26):

1. Fundamental differences in the pattern of the fields of resorption and

deposition, that is, the distribution of the growth fields in an individual person.

2. The specific placement of the boundaries between growth fields; that is, the

size and shape of any given growth field.

3. The differential rates and amounts of deposition and resorption throughout

each field.

4. The timing and growth activities among different fields.

21 Change in form of the cranium during growth and development arises from movement of the bones by three patterns of remodeling: drift, displacement, and rotation

(Figure 2.1). In drift, deposition of bone occurs on one surface and on the opposite surface resorption occurs, effectively moving the entire bone or feature in one direction without losing its function (Enlow and Hans, 2008). For example, a ball bearing implanted on the roof of the mouth in a juvenile will become surrounded by bone and appear in the nasal cavity as the palate drifts inferiorly (Enlow and Bang, 1956). In displacement, a bone is pushed farther away from other bones it articulates with it by deposition on one end and lack of resorption (Enlow and Hans, 2008). The mandible is displaced anteriorly as the posterior margin of the ramus deposits bone, pushing the chin forward. Lastly, rotation occurs as differential patterns of deposition and resorption transpire on opposite sides of a central axis (Lieberman, 2011). The petrous pyramids rotate about the skull’s longitudinal axis beginning in a sagittal orientation and ending in a coronal orientation, for instance. Drift, displacement, and rotation ensue within and between the basicranium, neurocranium, and viscerocranium.

Figure 2.1: Patterns of remodeling that move bones in skull during growth. (a) Drift, (b) Displacement, and (c) Rotation. 22 2.3.1 Basicranium

The postnatal basicranium grows regionally in the anterior, middle, and posterior cranial fossae. The foramina penetrating the base, conducting nerves and blood vessels, surround the brainstem, reaching adult size soon after birth. The fossae will widen mediolaterally, elongate anteroposteriorly, and deepen. The elongation and widening causes the petrous pyramids to rotate. All the while the angle between the anterior and posterior cranial base will decrease. Basicranial growth will cease by 20 years of age

(Sperber et al., 2010).

Lieberman (2011) outlines six major processes of growth and development in the basicranium: 1) anteroposterior growth, 2) mediolateral growth, 3) superoinferior growth, 4) angulation in the sagittal plane, 5) petrous rotation, and 6) position and orientation of the foramen magnum. The processes are influenced by the functions of the base such as providing an articulation with the mandible or creating a gantry by which the face is suspended. Anteroposterior growth occurs at the spheno-occipital synchondrosis, the mid-sphenoid synchondrosis, and the spheno-ethmoid synchondrosis.

(A synchondrosis is a hyaline cartilaginous joint.) Deposition within sutures of the lateral cranial base and posterior drift of the foramen magnum contribute to the lengthening.

Mediolateral growth occurs by drift of the lateral margins and by intramembranous ossification within its sutures. Inferior drift of most of the cranial fossae contribute to superoinferior growth.

The angle between the prechordal and postchordal base undergoes changes in its angulation. Prenatally, the base is angled at 150° (Sperber et al., 2010) and begins a process of flexion. After birth, the basicranium rapidly flexes another 10° by year two 23 and then stabilizes around 134° (Lieberman and McCarthy, 1999). Related to this flexion is the lengthening of the anterior base as the frontal lobe develops and a shortening of the posterior portion. This is contrary to what takes place in chimpanzees where the cranial base continues to extend after birth from 142° to 157° with a concomitant shortening of the anterior portion and lengthening of the posterior portion (Lieberman and McCarthy,

1999). The human petrous pyramids will move from a sagittal orientation (135-140° from the midline) to a more coronal orientation (125° from the midline) (Jeffrey and

Spoor, 2002) by deposition within adjacent sutures. This rotation may be explained by a solution to the spatial packing problem posed by an increase in endocranial volume

(Spoor 1997, Jeffrey and Spoor, 2002), to be discussed further in Chapter 3.

The last process of growth and development in the basicranium relates to the locomotor pattern that postnatal, hairless, tailless, habitually striding primates will adopt.

The human foramen magnum is located closer to the center of the cranial base and positioned in an inferior and slightly anterior orientation. This unique position and orientation occurs from a short posterior cranial base, a more flexed base, and a larger posterior cranial fossa. Chimpanzees have a more posteriorly located and posteriorly- facing foramen magnum resulting from long posterior base, an extended base, and no posterior fossa.

2.3.2 Vault

Changes in size and shape of the postnatal vault (neurocranium) is patterned by an anteroposterior elongation, mediolateral widening, growing taller, and thickening

(Lieberman, 2011). As the neurocranium grows and develops it interacts with the face 24 and base. The vault will be 95% complete by ten years of age (Sperber et al., 2010). The human vault rests on top of the face and extends posteriorly beyond the foramen magnum to accommodate the grossly overinflated brain relative to body size. Other animals have a face located anterior to the brain. Growth happens through three major processes: “(1) growth within sutures; (2) rotations and displacements that occur from drift; and (3) thickening of the vault bones” (Lieberman, 2011:115).

Intracranial pressure from the growing brain and cerebrospinal fluid in the dural venous sinus system stimulate bone deposition along the sutures and on the margins of the fontanelles, cartilaginous membranes found at the intersections of sutures to accommodate the rapidly growing brain. Deposition in sagitally oriented sutures produces widening, deposition in coronally oriented sutures elongates the vault, and deposition in transversely oriented sutures produces heightening. There is differential patterning of resorptive and depository fields in the vault. The bones superior to the tentorium cerebelli, a transversely oriented dural infolding that retains the cerebellum within the posterior cranial fossa, have depository fields on both the endocranial and ectocranial surfaces; thus, only thickening can occur. Bones inferior to the tentorium cerebelli have an ectocranial depository field and an ectocranial resorptive field. As such, they grow via drift. The dissimilarity in vault growth patterning between superior and inferior vault bones is a result of their disparate embryological origins. The final neurocranial growth process, thickening, results from deposition of bone on both the outer an inner tables which proceeds along a skeletal growth trajectory. Thickening of the vault continues into adulthood past cessation of brain growth.

25 2.3.3 Face

Growth of the postnatal face (viscerocranium) is characterized by orthognathism

(lack of protrusion of the lower face in relation to the orbits) and relatively little projection beyond the anterior portion of the vault and base (Lieberman, 2011). In other words, the face is pushed beneath the frontal lobe and is flat, contrary to a pattern of facial projection and prognathism in other primates. As described earlier, the face can be divided into individual capsules or spaces, each with a slightly different pattern of growth and development. The supraorbital region, part of the frontal bone, overlaps with the vault and the anterior cranial base. It drifts anteriorly in response to brain and eyeball expansion. The orbits grow from outward drift centering around the optic capsule as the eyeballs expand as well as intersutural growth between the sphenoid, ethmoid, lacrimal, frontal, zygomatic, and maxillary bones. As the eyeballs are neural projections that form early in development with the brain, they exert a strong influence on the midfacial bones

(Lieberman, 2011). The nasal cavity forms from an ethmomaxillary complex that grows downward and forward (Enlow, 1968). Sutural growth transpires between the frontal, zygomatic, nasal, maxillary, and temporal bones while drift occurs at the inferior and lateral margins of the nasal cavity. The superior and inferior nasal cavity margins in humans are parallel because the anterior cranial base is relatively horizontal and long.

Chimpanzees have much shorter and non-parallel superior margins.

Four pairs of paranasal sinuses are formed in the frontal, sphenoid, maxillary, and ethmoid bones as osteoclasts migrate through the bone (Enlow, 1968). Laterally, the zygomatic arches are formed by a joining of the temporal and zygomatic bones where growth proceeds in the zygomaticotemporal suture and through outward drift as the 26 cranium elongates and widens. Last, the oral cavity is formed by the maxillary and mandibular arches. The maxillary arch grows inferiorly from the palate, anteriorly from the middle cranial fossa, and is heavily influenced by the ethmomaxillary complex

(Lieberman, 2011). As the palate is displaced anteriorly it pulls along the maxillary arch and the developing teeth contained therein. The dominant mechanisms of anterior growth is deposition along posterior margins of the maxillary tuberosities and deposition in coronally oriented sutures that elongate palate (Lieberman, 2011). The anterior surface of the maxilla is a resorptive field which prevents the face from protruding, counteracting the anterior displacing effects of the maxillary tuberosity deposition (Enlow, 1968). The mandibular arch grows independently and is attached to the base at the temporomandibular joint where it must match its growth to that of the upper jaw. It grows anteriorly from the posterior cranial fossa. Bone is deposited on the posterior margins of the mandibular body and pushes the mandible anteriorly to match the anterior movement of the maxillary arch (Enlow, 1968). The ramus also gets taller and pushes the mandible inferiorly from the base to match the downward growth of the midface. The body drifts laterally to widen the arch and bone is deposited on the inferior margin to heighten the body.

The complex interactions between the facial capsules and spaces during growth and development produce a face that is flat and non-projecting, a derived feature of

Homo sapiens (Lieberman, 2011). The upper face concludes growth around 12 years while the middle and lower face continues growing until somewhere between 18 and 25 years (Sperber et al., 2010).

27 2.4 FUNCTIONAL PARADIGM OF CRANIOFACIAL GROWTH

The functional paradigm of craniofacial growth states that cranial shape reflects the primary functions of support and protection (Moss and Young, 1960). This functional matrix hypothesis places cranial growth under strong influence of mechanical input rather than genetic factors. Functions of the skull are carried out by functional cranial components. A functional cranial component is made of a functional matrix and skeletal unit. Skeletal units give support to the functional matrices. The functional matrix is made of all the soft tissues, organs, and cavities necessary to carry out a particular function. Functional matrices may be classified as either periosteal or capsular.

Muscles that attach to a bone through a periosteum create periosteal matrices, an example being the muscles of mastication (temporalis, masseter, medial pterygoid, and lateral pterygoid muscles) that have attachments on the mandible, sphenoid bone, temporal bones, and zygomatic bones. The muscles and bones are related to the function of chewing. A functional cavity within the skull is a capsular matrix. The paranasal sinuses found within the frontal, ethmoid, sphenoid, and maxillary bones are hollow chambers that serve to lighten the skull and have direct connections to the nasal cavity via a mucous membrane to act as resonators. Other examples of specific functions include hearing, equilibrium, taste, vision, olfaction, and respiration.

The orbital cavity, its soft tissues, and the function of vision are used here as an initial example of all structures that are influenced by a single function. The functional matrix related to vision is classified as a capsular matrix and consists of the orbital cavity itself and its contents (eyeball with related overlying conjunctiva, six extraocular muscles with supporting annular tendon, lacrimal gland, special sensory and general sensory 28 nerves, blood vessels, and adipose tissue). The skeletal units include bones that comprise the orbital cavity and protect the soft tissues: orbital plate of the frontal, perpendicular plate of the ethmoid, greater and lesser wings of the sphenoid, lacrimal, and frontal process and orbital plate of the maxilla. The bones develop and grow around the soft tissues related to vision and create a four-walled chamber that protects the soft tissues contained therein. Components of the orbital functional matrix and the linked skeletal units work together to make vision possible.

2.5 FACTORS INFLUENCING CRANIAL FORM

Craniofacial development and growth must not be simply viewed as a deterministic process, rather, as a dynamic process influenced by the internal environment, genes, and the external environment. It is more appropriate to view genetics and development in a covariant relationship. Intrinsic developmental variation is a key source of phenotypic variation. This may be included in the level of mechanistic constraint or deemed an entirely new level. No matter which level one considers, just as with developmental morphogenetic fields, complex phenotypes are a result of interactions at all levels. Several biological, environmental, biomechanical, and cultural factors such as heredity, climatic stress, masticatory stress, nutrition, oral breathing, and posture have been proposed as explanations for variants of adult cranial morphology.

2.5.1 Heredity

Heritability is the proportion of observed variation in a trait that can be attributed to genetic variation. Broad-sense or narrow-sense heritability may be calculated 29 depending upon whether or not both additive and dominance components are considered.

Broad-sense heritability (H2) measures the proportion of phenotypic variance that is additive and dominant in a population:

2 A

Narrow-sense heritability measures the proportion of phenotypic variance that is additive:

2 A

(Because studies of quantitative trait evolution employ only narrow-sense heritability, from this point forward ‘heritability’ will refer to narrow-sense only.)

Heritability is applicable to populations with the same allele frequencies and the same environment. One must assume that traits are normally distributed. h2 is given as a value between ‘0’ and ‘1’. It can be estimated by crossing two pure lines. If h2 = 1 then the offspring have the same trait means as their parents. Higher values indicate that the genetic component is high and the environment has no effect on the phenotype.

Therefore, variation in the phenotype is due to the genes. If h2 = 0 then the offspring have the same trait mean as the population. Lower values indicate that the genetic component is low and the environment has a large effect on the phenotype. High h2 does not imply rapid evolution. Rather, it signifies that the trait is more susceptible to the effects of differential fertility than a trait with lower heritability (Clark, 1956).

Heritability estimates also can be used as a measure of ecosensitivity, genetically determined size, and polymorphic variability (Henneberg and Lewicki, 1978).

Features that are important to fitness have low heritability and low genetic variation (Falconer and Mackay, 1996; Lynch and Walsh, 1998; Ridley, 2004; Roff, 30 1997; Self and Leamy, 1978). Variation in important features may lead to disadvantageous character states and decreased fitness. However, if one compares mean- standardized measures, the traits which are important to fitness are more variable than other morphological traits (Houle, 1992). This may be more informative as to the maintenance of genetic and quantitative variation. More specifically, comparing standardized estimates of VG reveals how fast a character will respond to selective pressures. Using the coefficient of additive genetic variation (CVA) may be more appropriate for examining evolvability and variability (Houle, 1992). CV fitness traits have higher genetic variability. The low h2 can be explained by a large residual component or fitness traits change over a lifetime. Other factors including large number of loci, more environmental variable than considered, and more interactions may produce higher than expected variability (Houle, 1992). This does not negate the use of h2 estimates; they are still required to make predictions.

It is unwise to use a single measure of heritability to model the evolution of a quantitative trait in a previous population. One cannot account for environmental changes. One of the most difficult aspects of calculating h2 is assessing the effect of the environmental component on the phenotype. Multiple measures of heritability can provide a more complete and accurate picture of what selective force are acting, if any, on a population. Without knowing which environmental condition existed in previous generation one cannot accurately model the evolution of a quantitative trait. A trait may appear to be under directional selection but not evolve because the environmental component may not affect fitness (Alatalo et al., 1990).

31 Estimating heritability requires one to have accurate pedigrees to establish familial relationships. Let us return to our previous question: How does one answer specific genetic evolutionary questions in fossil species if all that one has is the phenotype? It has been found that genotypic (G) and phenotypic (P) correlation matrices are strikingly similar (Cherverud, 1988). One can cautiously apply quantitative genetic theory to paleontological questions. A survey of vertebrate morphology heritability literature revealed an average h2 value of 0.35. Multiplying the phenotypic correlation matrix by this factor yields a prediction of the genetic component (G = h2P). The similarity between phenotypic and genetic correlations was verified in a reexamination of

Boas’ anthropometric data (Konigsberg and Ousley, 1995). The proportionality of P and

G has implications for phenotypic allometry studies, predicting phenotypic responses to selection, and biological distance analyses (Konigsberg and Ousley, 1995).

Many studies have examined heritability in past human skeletal populations.

Once again, determining the genetic component calls for pedigrees. A skeletal sample in an Austria cemetery with known genealogies has provided researchers with data that sheds new light on selective forces acting on the human cranium. The individuals died over a 180 year period during from the 1600s through the 1800s. The first study

(Sjøvold, 1984 in Carson, 2006b) utilized least squares regression to estimate h2. This method has a tendency to overestimate values and the maximum-likelihood approach is preferred now (Shaw, 1987). It assumes no correlation of the environment between parents and offspring, all traits are autosomal, and male and female variances are equal.

Carson (2006ab) reevaluated the collection using the estimated the heritability of continuous and discrete traits in a historic Austrian population. Thirty-three ratio scale 32 and 55 semi-continuous ordinal scale traits were observed on 298 crania with known pedigrees. Metric variables have an h2 range of 0.0 (bifrontal breadth) to 0.867 (external alveolar breadth). The general pattern revealed facial measures as being less heritable than neurocranial measures (0.268 and 0.304 respectively). Non-metric variables have an h2 range of 0.0 (metopic suture presence/absence) to 1.0 (supraorbital foramen presence/absence) with no evident pattern. Metric data suggest that many parts of the cranium are not experiencing selection. The existing variation is more attributable to neutral genetic drift (Strauss and Hubbe, 2010; Betti et al. 2010; Smith, 2011). This may be true for more recent populations that are buffered from natural selection by culturally mediated behaviors (Larsen, 1995, 1997). However, earlier hominin populations were not protected by cultural buffering and therefore were subject to selective forces. The small additive genetic component of phenotypic variance in modern populations informs us that differences in cranial form represent features that once had adaptive significance.

Several hypotheses have been proposed to explain the differences.

2.5.2 Climate

Perhaps the most widely known, used, and debated hypothesis to explain cranial form differences is climatic stress (Beals et al., 1984; Eckhardt, 1987; Havarti and

Weaver, 2006; Hernández et al., 1997; awłowski, 2005; Roseman, 2004; Steegmann et al., 2002). Hypotheses tend to revolve around concepts of heat production and retention in relation to body/cranial shape. Bergmann’s (1847) and Allen’s (1877) rules explain variation in body sizes and proportions of warm-blooded animals. Bergmann’s rule states that organisms that live in cold climates tend to have greater body mass and 33 organisms living in warm climates tend to have smaller body mass. Allen’s rule states that organisms living in cold climates tend to have short, thick extremities and organisms living in warm climates tend to have long, thin extremities. Heat production is a function of volume. Heat retention is a function of surface area and volume. We can apply these rules to cranial shape. A long, low vault would theoretically radiate heat more efficiently because there is more surface area per unit volume while a taller, rounded vault would theoretically conserve heat better.

Some have found a strong correlation exists between head shape and climate

(Beals et al., 1984). Forty percent of the variation in head shape can be explained by bioclimatic differences. Reduced energy from the sun, lower absolute humidity (vapor pressure), and the rigors of a cold winter are proposed as the major factors that influenced cranial shape. Hominins became more brachycephalic (increased breadth) as populations migrated to colder regions and more dolichocephalic in warmer regions. Hominins experienced a “thermodynamic life crisis” associated with differential reproduction resulting from shape adaptations to prevent hypothermia and/or heat stroke (Beals et al.,

1984). Morphological data from 10 of owells’ populations ( owells, 1973) were used to estimate the additive genetic variance (Roseman, 2004). The effects of natural selection account for half of the among region variance. Populations were selected for increased thermoregulatory capacity in extreme cold climates. A later study by Betti and colleagues (2010) found that cranial breadth, facial breadth, and measurements describing nasal and orbital openings are most highly correlated with climate. In these instances natural selection is most evident in populations living in extremely cold climates.

Additionally, it has been shown that by large the neurocranium represents neutral genetic 34 variation whereas the face specifically reflects climatic differences (Havarti and Weaver,

2006). These findings were supported by Hubbe and others (2009) demonstrating that the neurocranium is indicative of phylogenetic relationships whereas facial morphology is influenced by selective forces in populations living in areas such as Northeast Asia,

North America, and Northern Europe, all cold climates.

Brachycephalization due to climatic stress may be linked to an allometric increase in overall body mass whereby increased heat loss is a cost of encephalization

( awłowski, 2005). eat loss increases linearly as ambient temperature decreases. This may have created a selective pressure for increased neonatal weight. The resulting lower ratio of surface area to body mass acted as protection against heat loss at lower temperatures. Despite the strong correlation between cranial form and climate found by some researchers, one needs to consider physiological adaptations and acclimatization

(Eckhardt, 1987; Hernández et al., 1997; Steegmann et al., 2002). An organism may appear to be morphologically climate-adapted in extreme environments only (Hernández et al., 1997). It may be a case of reduced thermosensitivity.

One case that links physiological adaptations to anatomical adaptations is the correlation between cross sectional area of sensory nerve cranial foramina and climate

(Visser and Dias, 1999). The hypothesis states that these foramina are smaller in colder climates. The smaller size is a function of a small sensory nerve comprising fewer axons.

Fewer axons indicate reduced sensation to temperature. If skin thermosensitivity is lower in cold-derived populations then the cold stress required to elicit a heat-generating response should be greater. Responses include vasoconstriction, increased metabolic rate, increased muscle tone, and shivering, all of which are energetically costly. Crania 35 from prehistoric New Zealand populations (Maori and Moriori) living in a cool, wet environment were compared to a modern Indian population living in a hot, wet environment. The New Zealanders’ foramina were significantly smaller than the Indian sample. Reduced thermosensitivity was selectively advantageous as body temperature could drop lower before the heat-generating mechanisms would be activated (Visser and

Dias, 1999). This process could have conserved energy and/or lowered caloric intake requirements.

2.5.3 Masticatory Stress

The transition to an agricultural society shifted human diet from relatively coarse, unprocessed foods to soft, processed foods (Ciochon et al., 1997; González-José et al.,

2005; Lahr, 1996; Larsen, 1995; Lieberman et al., 2002; Little et al., 2006; Sardi et al.,

2006; Van Gerven et al., 1977). Softer foods place less demand on the facial skeletal complex resulting in a more gracile, differently proportioned face. This trend produced higher, narrower vaults with a slight narrowing and heightening of the face. The temporalis muscle, which primarily elevates and secondarily protracts and retracts the mandible via its insertion on the coronoid process, is responsible for producing tensile forces in the vault (Lieberman, 2011). The origins of temporalis include the frontal, parietal, and temporal bones along the temporal lines. In response to the tensile forces the bones drift laterally during growth. The other major masticatory muscle, masseter, whose origin is on the marginal process of the maxilla extending posteriorly along the zygomatic arch pulls down on the maxilla and zygomatic as it elevates the mandible by inserting on the mandibular ramus. These biomechanical forces creates a wider, more 36 robust face. A causal link between diet coarseness and cranial robusticity cannot be found in earlier human populations. One can only find correlation. However, animal models can be used to test the hypothesis by measuring strain and observing its effect on cranial growth. Few have approximated actual human dietary coarseness.

Food processing techniques cause decreased mandibular and maxillary growth

(Lieberman et al., 2004). A sample of rock hyrax was split into one group that was fed dry/raw food and the other that was fed cooked/processed food. The rock hyrax is a small-bodied, herbivorous mammal the size of a guinea pig, native to sub-Saharan and southern Africa as well as southwest Asia. It is most closely related to elephants. It was used by Lieberman and colleagues because the postcanine teeth are located posterior to the orbits, just as in humans; whereas other primate species have some postcanine teeth anterior to the orbits. Concomitant changes in growth and development of the skull were observed. This study addressed two problems with previous research. Traditional animal model studies compare groups that are fed hard food to groups fed very soft, almost, liquid diets. This does not reflect the probable dietary consistency of early humans.

Also, site-specific strain and growth are not analyzed in differently shaped faces.

Animals raised on a cooked food diet have smaller, gracile skulls whereas animals raised on a raw food diet have larger, more robust skulls (Lieberman et al., 2004). These results validate the masticatory stress hypothesis.

2.5.4 Nutrition

The amount of dietary protein as an environmental component has significant effects on cranial phenotype (Ramírez-Rozzi et al., 2005). Two groups of squirrel 37 monkeys with different diets were followed from juvenile to adult stages. One was fed a high-protein (HP) diet and the other a low-protein (LP) diet. Longitudinal measurements of the cranium were taken. The changing relationship between size and shape over time was examined in each group. The HP group had a reduced neurocranium in relation to the facial skeleton. The LP group experienced an overall reduction in size, but not shape.

Malnutrition slows growth and development and tend to affect facial growth more than neurocranial growth (Ramírez-Rozzi et al., 2005). The excess protein in the HP group induced heterochrony whereby the face accelerated passed the neurocranium. This produced a differently proportioned face in the HP monkeys whereas the LP monkeys are proportioned dwarfs compared to the average monkey. Heterochrony is usually ascribed to genetic factors only; yet, this study provides evidence that environment alone can induce change in developmental timing.

2.5.5 Posture

The anatomical commitment to a habitually bipedal gait caused a cascade of effects throughout the hominin body, both anatomically and physiologically. In the cranium, upright posture changed the foramen magnum position to a more anterior location as the head was balanced over the vertebra column with a concomitant reduction in nuchal musculature. Posture has also been attributed as an environmental component of overall cranial shape (Mizoguchi, 1999). A series of modern Japanese women was analyzed to study the relationship between head shape and position of the vertebrae.

Cranial breadth, length, and height are strongly correlated with vertebral size and sacral breadth. Breadth and height are not correlated to rib dimensions. Yet, cranial length, a 38 measure of dolicocephaly, is correlated with costal chords, a measure of the anterior- posterior diameter of the thorax, of ribs 1-7. Non-human primates have deep thoracic cages and humans have shallow thoracic cages. Although correlations exist, one is left without a causal explanation. The relationship could be the result of biomechanical interactions between the cranium and thorax or pleiotropic gene effects.

2.5.6 Oral Breathing

Mouth breathing due to infection, asthma, or general obstruction of the nares has been proposed as an environmental component affecting cranial form (Corruccini et al.,

1985). This is associated with a more general condition in lesser developed countries

(LDCs) known as maxillary and facial collapse syndrome. It results in malocclusion and a highly arched palate. This condition is attributed to the Westernization of LDCs through increased disease incidence and processed, softer foods. People living in LDCs on average have poorer nutrition and higher incidences of disease than people living in industrialized countries (Corruccini et al., 1985). Three anthropometric measurements

(bigonial diameter, facial height, and bimaxillary breadth) were taken from modern rural and urban populations in both northern India and central Kentucky. Indian children are chronically ill and primarily employ oral breathing. Kentucky children are healthier and primarily employ nasal breathing. No statistically significant differences in head shape were found between mouth breathers and nose breathers. On the other hand, differences were found between rural and urban children. This is attributed to a harder rural diet and a softer urban diet. This environmental effect narrows and lengthens the middle third of

39 the face during growth and development. While type of breathing does not affect cranial shape, once again masticatory stress plays a role.

2.6 SUMMARY

The function of the skull was reviewed in the context of cranial structure, growth, and development. The cranium begins development shortly after the appearance of the brain and will become the most complex system within the skeleton. An examination of the bones of the cranium reveals its primary functions of support and protection for the brain and special sensory organs. The keystone for this integrated system is the basicranium which supports the brain above and attaches the face below. Additionally, it serves as a limiting factor for the shape and size of the face and vault. Information, blood, air, food, liquid, and biomechanical forces pass through or near the cranial base.

The form of the skull is influenced throughout the life cycle by factors such as climate, nutrition, posture, and heredity. However, it is masticatory stress that has the greatest effect on overall calvarial and facial architecture within the lifetime of an individual.

These factors may affect the entire cranium or one of its separate modules. The extent of effects rippling through the cranium is determined by the level of integration and modularity.

CHAPTER 3: MORPHOLOGICAL INTEGRATION AND MODULARITY

This chapter introduces the concept of phenotypic integration as a framework for understanding patterns of variation in the human skull, within an evolutionary context.

First, different hypotheses for segmenting the cranium into modules will be presented.

Next, specific evolutionary changes in the hominid brain and skull will be highlighted, including recent secular trends in modern humans, with an emphasis placed on the cranial base. As was discussed in Chapter 2, the basicranium is the keystone of the skull. Major hypotheses that attempt to explain the marked flexion of the human cranial base will be elucidated to reiterate the complex relationships between the base, vault, and face.

Finally, considerations for studies of morphological integration will be discussed.

3.1 MODULES IN THE HUMAN CRANIUM

The concept of morphological integration was first formalized more than half a century ago (Olson and Miller, 1958). Integration is defined as “the association of elements through a set of causal mechanisms so that the change in one element is reflected by a change in another” (González-José et al., 2004:75). Another procedure describing the relationships among units is modularization, the process by which individual traits or functional units acquire a degree of genetic and developmental independence, where traits in the same module evolve dependently and traits in different

41 modules evolve independently of one another (Polanski and Franciscus, 2006). An organism, body systems, or an individual structure can be integrated at one of several levels that overlap one another: functional, genetic, or developmental (Cheverud, 1982).

Different patterns of modularity may arise depending on the integrative approach taken.

Figure 3.1 provides three examples of relationships between two hypothetical modules that over time change in their shape. Part (a) depicts two modules that are independent of one another and hence modularity is high. One module remains static while the other changes. Parts (b) and (c) show positive and negative relationships in which a change in one module induces change in the other. These systems would be more integrated.

(a) (b) (c)

Figure 3.1: Hypothetical shape changes over time between two modules. (a) Independent modules. (b) Positive dependence between modules. (c) Negative dependence between modules.

Functional phenotypic integration focuses primarily on the purpose served by the organ, tissue, or system. For example, the lymphatic and cardiovascular systems are closely related in their movement of fluids and in their immune response. This 42 relationship is quite conspicuous as all interstitial fluid and leaked plasma collected from the body drains into systemic circulation via the right subclavian vein. They are less related to the integumentary system which serves to protect, prevent water loss, perceive general sensation, regulate temperature, and excrete wastes; although, skin is highly vascularized. In the case of the cranium, the optic module and all tissues contained therein serve to protect and support the special sense of vision whereas the otic complex protects the tissues related to hearing and equilibrium.

Several structural, functional modules have been identified in the cranium (Table

3.1). Bastir and Rosas (2004) assign the lower facial complex a major role in producing patterns of variation. In a later study, Bastir and Rosas (2005) also recognized the posterior face as a functional unit. Lieberman et al. (2002) discovered three regions of the cranium contributing to shape differences among “archaic” and “anatomically modern” humans: 1) anterior cranial base length, 2) anterior cranial base flexion, and 3) middle cranial fossa width. They credited these modules to the facial retraction and neurocranial globularity of anatomically modern humans. Still, two functional complexes, the neurocranium and face, have been identified as being the most heritable in modern populations along genetic integrative patterns (Carson, 2006).

Other researchers divide these two major functional complexes into four minor complexes each (González-José et al., 2005). The neural complex comprises anteroneural, midneural, postneural, and otic complexes. The first three divisions correspond to the anterior, middle, and posterior cranial fossae, respectively, including the soft tissue contents of each. The otic complex refers to the organs of hearing and

43 equilibrium in the petrous portions of the temporal bones. The facial complex comprises optic, respiratory, masticatory, and alveolar complexes. The optic complex corresponds to the functional matrices and skeletal units of the orbital cavity responsible for vision.

The tissues that make up the nasal cavity are part of the respiratory complex. Bones included are the nasals, ethmoid, nasal conchae, vomer, and maxilla. The masticatory complex represents the oral cavity, in particular the maxillae and palatines. The last minor functional facial complex is alveolar which corresponds to the teeth and the bone immediately surrounding the teeth in the maxillae. These eight minor complexes must be studied individually then pooled together to understand the nature of cranial growth and variability.

Another way to divide the cranium into modules is by the shared functional- developmental relationships (Table 3.1). Seven functional-developmental modules have been identified by combining the embryological origins of certain bones of the cranium with the functions they serve, including specific sensory functions (von Cramon-

Taubadel, 2011). The dermatocranium consists of bones that have origins and develop via intramembranous ossification (Sperber et al., 2010). The chondrocranium consists of bones that have origins and develop via endochondral ossification (Sperber et al., 2010).

Additionally, this model identifies three sensory-defined functional modules based on olfaction, hearing and equilibrium, and vision. This modularity scheme rectifies the disparity in research which separates strictly functional modules with those that are more strictly developmentally defined. The differences between the functional modules would affect how one would interpret interactions among modules of the cranium.

A third division of the cranium into module is proposed here that recognizes the profound influence the cranial base has on integrating the rest of the skull. The hypothesis that the basicranium is a module relatively independent of the vault and face will be tested as compared to the functional matrix hypothesis and functional- developmental hypothesis.

Regardless of which modular hypothesis one uses, the basicranium has the most influence over integration of other functional modules including the vault, nasal cavity, pharynx, orbits, and oral cavity (Lieberman et al., 2000ab). The extent of influence suggests that the basicranium is canalized early in development being induced by the neural tube at 28 days after fertilization (Sperber et al., 2010). The cranium does not only follow a genetically predetermined trajectory for shape and size. Variation in cranial form is produced from uncontrolled muscular contraction during early development (Zelditch et al., 2004a). Canalization of the cranium is due to increased neuromuscular control during growth and development.

Table 3.1: Proposed modules of the hominin cranium.

Modularity Hypothesis Major Complex Minor Complex

Functional Modules Neurocranium Anteroneural Midneural Postneural Otic Face Optic Respiratory Masticatory Alveolar Functional-Developmental Dermatocranium Vault Modules Face Chondrocranium Nasal Auditory Orbit Basicranial Module Basicranium Vault Face

3.2 EVOLUTIONARY CHANGES IN CRANIAL AND BRAIN FORM

The fossil record reveals definite evolutionary trends in cranial and brain shape.

Changes in the general vertebrate form will be addressed followed by a detailed look at the hominoid cranium. Further trends in early and late hominin evolution will then be discussed ending with a brief look at secular trends occurring in recent human populations.

3.2.1 Vertebrates

The brain has undergone major changes in evolutionary history. This created a concomitant change in cranial form. Let us use extant animals as a model for different

46 stages in brain/cranial change. Four stages are apparent: 1) rhombencephalization, 2) cerebralization, 3) corticalization, and 4) gyrification (Mooney et al., 2002). The brain of the first vertebrates was dominated by a large rhombencephalon. This structure is composed of the medulla oblongata, pons, and cerebellum which are respectively responsible for basic visceral function, relaying messages in the brainstem, and coordinating motor activity. The next stage involved an increase in size of the forebrain among mammals. The cerebrum is responsible for many higher cognitive functions such as memory and planning. The outer cerebral layer, the cortex, then began to expand in higher mammals. This structure contains neuron cell bodies. Finally, the primate family witnessed an increase in the surface area of the cortex called gyrification. This process effectively increased the number of neurons that could be packed in to a finite cerebral volume. A marked cerebellar lateral expansion in hominoids over monkeys occurred

(MacLeod et al., 2003). The associated changes in cranial form include increased globularity, reduction of the facial complex, and increased basicranial flexion. The processes of cerebralization and gyrification are accentuated in the hominoid line.

3.2.2 Hominoids

Endocasts reveal several modifications in the brain from Australopithecines through early Homo to modern humans (Bruner, 2004). First, the surface area greatly increases creating more complex neural circuitry. There is a lateral expansion of the parietal lobes and an antero-lateral expansion of the frontal lobes. The parietal lobes are mainly responsible for somatosensory integration while the frontal lobes are responsible

47 for higher cognitive processes and fine motor control. Changes in these lobes are associated with speech production and comprehension (Bruner, 2004). Modified brains produce altered cranial phenotypes. Of particular interest is the location of the facial complex relative to the neurocranium. The neurocranium is considerably larger and more globular in anatomically modern humans compared to chimpanzees or archaic humans

(Lieberman et al., 2004). Also, the frontal lobes are positioned more directly over the face in humans. The facial complex is smaller and more projecting in chimpanzees.

3.2.3 Trends in Hominin Cranial Form

Cranial homogeneity has increased throughout hominin history (Cunningham and

Wescott, 2002; González-José et al., 2004). Hundreds of thousands of years ago, small populations of humans were reproductively isolated. As they moved into new environments they developed new cranial adaptations. Populations living in warm climates developed short, wide nasal apertures and dolichocephalic crania in order to radiate heat. Humans living in colder climates developed tall, narrow nasal apertures and brachycephalic crania with flat faces to retain heat (Hernandez et al., 1997). In general, the genus Homo has experienced a fronto-parietal expansion with a reduction of the lower face associated with the anterior/posterior development of the neurocranium

(Bruner et al., 2004). Repositioning the upper face relative to the cranial base resulted in the loss of a sloping forehead and large browridges (O’ iggins, 2000).

As founding populations grew, gene flow increased and genetic drift decreased.

The advent of complex cultural systems has buffered humans from some selective

48 pressures (Larsen, 1997; Stefan, 1999). In the absence of selection, plasticity has a greater influence on cranial form than genetics (Little et al., 2006). Increased brachycephalization and decreased facial projection are evident from the fossil record. A large facial complex is associated with a robust masticatory complex.

Secular change in cranial form has been noted within the past several thousand years with an overall trend towards increased brachycephalization (Larsen, 1997; Jantz and Jantz, 2000; Wescott and Jantz, 2005; Little et al., 2006). This trend has produced higher, narrower vaults with a slight narrowing and heightening of the face. These changes have been primarily ascribed to changes in health and masticatory stress

(Hernández et al., 1997; Larsen, 1997; Lieberman et al., 2002). The transition to an agricultural society shifted the diet from relatively coarse foods to soft foods. Softer foods place less demand on the masticatory complex resulting in a more gracile, differently proportioned, facial skeleton. Following the functional paradigm of craniofacial growth, lower biomechanical demands would decrease canalization, thereby creating more variation (Sardi et al., 2006). Why do we see a decrease in variability over time? Systemic factors such as poor nutrition and less growth hormone from sedentism reduce cranial diversity (Sardi et al., 2006). Another cause of secular change include heterosis (increase in growth, size, fecundity, and function in offspring over parents), socioeconomic status, posture, and allometric increase in height (Little et al., 2006).

Simply put, secular changes are morphological changes in response to a demographic transition to industrialized societies (Jantz, 2001; Gravlee et al., 2003; Wescott and Jantz,

2005). The interregional selective pressures that were once believed to produce

49 contemporary cranial diversity are now assigned a limited role and restricted to cold climates (Roseman and Weaver, 2004). Today, population structure and history are used to explain modern diversity (Hanihara et al., 2003; Roseman, 2004).

3.3 PATTERNS OF HOMINOID CRANIOFACIAL INTEGRATION

Extant primate species (Pan troglodytes, Gorilla gorilla, and Homo sapiens) display unique patterns of integration (Lieberman et al., 2000b; Polanski and Franciscus,

2006). Covariation among measurements of structural modules may be used to measure respective levels of integration or modularization. Facial and neurocranial major functional complexes are uncoupled across taxa. In other words, they represent large cranial structural modules. Changes in one module have little to no effect on the other.

On the other hand, the minor functional complexes (anteroneural, midneaural, postneural, and otic) that comprise the neurocranial complex are coupled across taxa. Thus far, chimpanzees, gorillas, and humans share similar patterns of integration. Differences appear in the facial complex. Chimpanzees and gorillas display high levels of integration in the face. The optic, respiratory, masticatory, and alveolar complexes are closely linked to each other. Conversely, humans exhibit higher levels of modularity. The minor facial complexes are functionally and genetically more independent from one another. A change in one complex will not have a drastic effect on the others. It has been proposed that greater modularity is selectively advantageous because of increased flexibility

(Polanski and Franciscus, 2006). As such, a change in one module may not have deleterious effects on other modules with different functions.

3.3.1 Cranial Base

The basicranium has the most influence over integration of other functional modules including the vault, nasal cavity, pharynx, orbits, and oral cavity (Ross and

Ravosa, 1993; Lieberman et al., 2000ab). The extent of influence suggests that it is canalized early in development. The cranial base mediates interaction between the neurocranium and face. It provides the brain with a connection with the rest of the body through the neck. It is perhaps the most architecturally complex portion of the cranium.

Several foramina communicate nerves and vessels to and from the head via the base. The base can be divided into anterior and posterior components (Figure 3.2). The anterior cranial base (ACB) begins at foramen cecum (a pit on the cribriform plate between the crista galli and the endocranial wall of the frontal bone) and extends posteriorly to sella

(the centerpoint of the sella turcica). This approximates the floor of the anterior cranial fossa. The posterior cranial base (PCB) extends from sella to basion (the lowest point on the anterior margin of the foramen magnum). A flexed basicranium is defined as one in which the pre-sellar and post-sellar portions form and angle of less than 180 degrees. If the angle is obtuse the base is less flexed and if the angle is acute the base has greater flexion.

If the brain were to increase in size then there must be more space in the endocranial cavity to accommodate the larger brain. The basicranium can either increase in size itself to create a larger platform for the brain to rest on or it can flex so the ends of the base get closer to one another. The first option is not feasible because of the significance of the cranial base as both a structural and functional constituent of the skull.

Any change in the base would result in changes of the functional components it connects.

Of particular detriment is if the base got larger the face would have to be larger.

Therefore, the second option is more practical. Endocranial volume can increase to house a larger brain while the roof and posterior wall of the face can stay the same size thereby providing no interference with mastication or respiration, essential functions of the face.

(a) (b)

Figure 3.2: Cranial Base Angle (a). A = foramen cecum, B = sella, C = basion, the dashed line is the cranial base angle. Interpetrosal Angle (b). The two solid lines mark the attachment of the tentorium cerebelli to the petrous pyramids. The dashed line is the interpetrosal angle.

There are many more interactions between cranial structures besides the brain and cranial base. Changes in the cranial base are limited by other minor functional

52 complexes. The size and shape of the orbital complex cannot be compromised by the cranial base. A great deal of importance was placed on stereoscopic vision in primates

(Ross CF, 2000). Hence, drastic changes would be maladaptive. The respiratory complex would be affected by changes in the orbital complex because they share many of the same skeletal units. A decreased basicranial angle impinges on nasopharyngeal structures. As the posterior cranial base encroaches on the face the nasopharynx becomes smaller. This effectively decreases airway size. Maintaining a patent airway may have acted as a major limiting factor in cranial base morphology (Jeffery, 2003). It has been hypothesized that orthograde posture (Ross and Ravosa, 1993; Jeffery and Spoor, 2002;

Jeffery, 2003), overall facial architecture (Ross and Ravosa, 1993; Lieberman et al.,

2004), and vocalization (Lieberman and McCarthy, 1999) also played roles in controlling basicranial growth.

Four hypotheses have been proposed to explain why basicranial flexion had increased over the course of hominin evolution: spatial packing, postural behavior, facial retraction, and vocalization. The increased flexion is identified as functionally adaptive or structurally constrained (Ross CF et al., 2004). Either it provided some increase in fitness of the individual or it was a by-product of change somewhere else as an effect of phenotypic integration. These hypotheses indicate that basicranial flexion is related to other aspects of craniofacial evolution including vision, respiration, speech, and encephalization.

Humans display a marked decrease in the angle between the anterior and posterior cranial base as brain size increased throughout evolutionary history (Ross and Ravosa,

1993; Lieberman and McCarthy, 1999; Lieberman et al., 2000ab; Jeffery and Spoor,

2002; Jeffery, 2003; Jeffery and Spoor, 2004; Ackermann, 2005). Also, the angle between the petrosal pyramids at the point of attachment for the tentorium cerebelli increased (Spoor, 1997). Extant Homo and Pan display different degrees of flexion throughout ontogeny (Lieberman and McCarthy, 1999). Humans are born with a base that continues to flex up to the fourth year of life and then extends slightly until around

16 years old. Chimpanzees are born with a more extended base than humans which continues to extend until adult size is reached. Differences in ontogeny across hominin taxa can be used to explain the paedomorphic features of humans and test hypotheses of neoteny and heterochrony (Penin et al., 2002). The larger brain sizes of more recent hominins created a spatial packing problem in a general sense within the entire endocranial cavity, in the space below the tentorium cerebelli, and as a result of different rates of expansion in the cerebrum and cerebellum. The spatial packing hypothesis and its components treat basicranial flexion as functional and structural changes in cranial architecture.

As both the cerebrum and the cerebellum expanded laterally, subsequent accommodations in the cranial base were required. This is known as general spatial packing which resulted in more acute cranial base angle (Ross and Ravosa, 1993). The angle was found to be highly correlated with the encephalization index (Ross and Ravosa,

1993). Correlations are weaker than other factors and at fetal developmental stages the base can vary independently of brain growth (Lieberman et al., 2000). This is most likely related to neuromuscular canalization of cranial form (Zelditch, et al., 2004a) or other

54 unknown factors that influence shape at different stages of development (Jeffrey, 2003).

The general spatial packing hypothesis can be refuted when hydro- or microcephalic individuals are included in analysis because they have the same degree of flexion as average individuals with dramatically different neurocranial shape.

Infratentorial packing occurred exclusively below the tentorium cerebelli where cerebellar expansion created changes in the posterior cranial fossa. The cerebellum is responsible for coordinating muscular contraction and linking together visual stimuli with spatial awareness and memory. Its expansion caused the long axis of the petrosal pyramids to move from a sagittal orientation to a more coronal orientation effectively increasing the interpetrosal angle. This hypothesis posits that cerebral expansion did not cause associated basicranial angles decrease. Generally, this hypothesis has been discarded (Ross and Ravosa, 1993, Jeffrey, 2003).

Another functional explanation is that of differential encephalization where the cerebrum and cerebellum expanded at dissimilar rates throughout hominoid evolution

(Jeffery and Spoor, 2002). Therefore, there was differential packing related to gyrification, which contributed to changes in the cranial base. The notion is that an expanding brain must maintain a spherical shape so that axon lengths and surface area to volume ratio is minimized. Transitioning to a more spherical brain created greater flexion. Cranial base angle is strongly correlated with a rounder shape (Lieberman et al.,

2000a). Hominins took this one step further by further increasing surface area to pack in even more neurons within relatively the same volume. This process resulted in the undulating surface of the modern human brain with its deep sulci. More neurons mean

55 more processing power and more connection possibilities for complex cognitive routines

(Bruner, 2004).

The second major hypothesis that attempts to explain dramatic levels of basicranial flexion in humans is postural behavior. Primates are a unique order in that they rely heavily on color, stereoscopic vision. When a quadrupedal animal assumes an orthograde posture the visual field is rotated above the horizon. To compensate for this the foramen magnum shifts anteriorly so that the cranium may balance over the vertebral column and nuchal musculature can be reduced. One would expect the visual field to be linked to an orbital kyphosis in humans. Correlations between post-sellar angulation and visual field orientation have been noted (Ross and Ravosa, 1993; Strait and Ross CF,

1999). A relationship between orthograde posture and visual field, however, has not been demonstrated.

Facial retraction is a third hypothesis. Most non-human primates have a more projecting, taller face with a greatly extended cranial base. This relationship may be structurally explained. As the base flexed, from brain expansion, over evolutionary time the face was rotated or pulled underneath the anterior cranial base. The axes of the orbits rotated and subsequently caused the palate to change its orientation as well due to integration between minor facial functional modules (Ross and Ravosa, 1993). The size and shape of the orbital complex cannot be compromised by the cranial base because of the importance placed on stereoscopic vision in primates. Reducing volume within the upper facial block has the potential to impinge upon the orbital cavity and its contents.

Hence, drastic changes would be detrimental to their survival, prior to cultural buffering.

Ripple effects would also be felt inferiorly through the masticatory complex.

The final explanation for increased flexion in the basicranium is the vocalization hypothesis. The dimensions of the external base influence proportions of the vocal tract

(Lieberman and McCarthy, 1999). Increased flexion effectively forces the larynx to descend to a position more inferior than the typical primate model. The respiratory complex would be affected by changes in the orbital complex because they share many of the same skeletal units. A decreased basicranial angle impinges on nasopharyngeal structures and directly impacts shape of the upper respiratory system (Laitman et al.,

1978). As the posterior cranial base encroaches on the face the nasopharynx becomes smaller, effectively decreasing airway size. Maintaining a patent airway may have acted as a major limiting factor in cranial base morphology (Jeffery, 2003).

3.4 ISSUES IN CURRENT RESEARCH

Although a better understanding of human evolution has been gained through an integrative, modular approach, there is room to improve current studies. One factor that plagues all of paleoanthropology, and paleontology for that matter, is the incompleteness of the fossil record. There is low probability that when an organism dies the environmental conditions will be just right to allow for fossilization. Despite the fact that cranial remains are the most abundant type of hominoid fossil, they are usually not complete specimens. Sample sizes are very small in many of the studies discussed previously. This cannot be rectified until more specimens have been discovered.

A more serious problem among current research has to do with the methods, specifically measurement or landmark selection. Studies in cranial variability fall into one of three categories with regards to measurement selection: 1) no explanation is provided (Cunha and van Vark, 1991; Song et al., 1992; Steyn and İşcan, 1998), 2) they are easy to record (Giles and Elliot, 1962, 1963; Wright, 1992), or 3) researchers want to describe a certain region of the cranium (Rightmire, 1976; Key 1983; Gill, 1984; Ross

AH et al., 2004). The same is true in studying modularization and integration. One example is measuring the length of the anterior cranial base. Classically, the length has been measured from nasion (the intersection of the frontal and nasal bones at the frontonasal suture) to sella. More recently, some have called for researchers to use foramen cecum instead of sella (Polanski and Franciscus, 2006). Using foramen cecum as the anterior extent of the cranial base keeps the measurement from extending into the nasofacial complex, as it does when nasion is used. There needs to be a call for standardization of measurement/landmark selection for the entire cranium.

Earlier studies of cranial variability employed interlandmark distances to record variability. This limits the analysis to only two dimensions of measurement and ignores the complex, three-dimensional shape of the cranium. One must be wary of using such interlandmark measurements because they overlap functional complexes (González-José et al., 2005; Sardi et al., 2006). Consequently, measurements may not accurately reflect phylogenetic relationships and are not efficient tools for studying adaptation or plasticity.

At the same time, landmarks used in landmark morphometric studies may overlap spatially contiguous modules. This study specifically addresses the second issue by

58 comparing three different sets of modules as defined by the functional matrix hypothesis, the functional-developmental, hypothesis, and proposed basicranial hypothesis. The traditional equipment used to measure crania (spreading and sliding calipers) also confines the researcher to lower levels of accuracy (Kolatorowicz, 2006).

A third issue that needs to be resolved is the lack of identifying specific adaptive pressures responsible for modularization and integration. Researchers can identify which major and minor structural modules are coupled together (Polanski and Franciscus,

2006). They provide speculation as to why a particular pattern of integration appears in an organism’s family tree, perhaps brain size, locomotor pattern, or a combination of both. No one has taken the next step and explicitly tested these hypotheses. Before this bold step can be taken, paleoanthropologists must be able to more closely tie a particular diet, locomotor pattern, etc. with a particular cranial form.

Finally, in order to more completely understand hominoid cranial evolution and changing integration patterns, one must understand how these phenotypes were being transmitted across generations. Some have suggested a research focus on past population structures (Sardi et al., 2005). Factors to consider are the time/mode of peopling, effective population sizes, population growth, and spatial/temporal isolation.

6.5 SUMMARY

Viewing hominoid cranial evolution from an integrative, modular perspective brings together ontogeny and phylogeny. It can be used as a theoretical framework for interpreting the fossil record. It synthesizes approaches in developmental biology,

59 evolutionary theory, and biological anthropology. This approach has potential contributions to the debate on identifying traits arising from natural selection and those that may be the most plastic or flexible in response to perturbations. Patterns of integration show how selection was acting at a particular evolutionary stage. All in all, cranial evolution is multifactorial with genetic and epigenetic factors to consider. Size and shape of the brain and cranial base are major determinants of the adult phenotype.

It is recognized that basicranial flexion and cerebral expansion are related developmentally and evolutionarily. A shift from quadrupedal to bipedal locomotion enabled cerebral expansion. A newly adopted orthograde posture in hominoid ancestors also caused cerebral expansion. Finally, a positive feedback loop began as natural selection favored bipedalism because of its strong relationship with expansion of the neocortex. The uniquely flexed hominin cranium is attributed is explained as a solution to spatial packing, postural behavior, facial retraction, and demands for vocalization.

CHAPTER 4: METHODS FOR DESCRIBING CRANIAL FORM

There are as many ways to measure biological form as there are questions to ask about biological form. Which aspects of biological form to focus on depends on the question to be asked. The questions inform the observer which measurement(s) to take.

This chapter presents the ways in which biologists measure form beginning with measurement theory. Non-metric and metric methods will be reviewed and applied to questions regarding skeletal variation. A distinction will be made among concepts that are commonly used synonymously, but deserve special, separate treatment: size, shape, and form. Next, landmark morphometrics, a subfield of biological morphometry that focuses on the studying covariation will be introduced along with the materials and methods to acquire, analyze, and interpret landmark data. This suite of unique instrumentation and maintenance of the relationships among data through all steps of falls under the specialized field of geometric morphometrics. Finally, landmark morphometric methodology will be applied to directly to questions related to hominin cranial variation and evolution.

4.1 NON-METRIC AND METRIC METHODS

Methods and standards for describing cranial morphology fall into one of two categories: non-metric and metric. Standards are listed in data collection reference

61 manuals ( rdlička, 1920; Martin, 1928; owells, 1937, 1973; Buikstra and Ubelaker,

1994; Moore-Jansen et al., 1994; Bass, 1995) with Buikstra and Ubelaker’s 1994 edited volume as the industry “standard” for human osteological data collection.

Non-metric methods treat phenotypic variation as discretely distributed. Discrete traits are scored on either ordinal or nominal scales, each having distinct categories with clear boundaries. Nominal scales have no inherent ranking between the different categories such as classifying a cranium as male or female. Ordinal scales do not have regular differences across categories and are not easy to measure. For example, the metopic suture may be scored as present or not present. A trait is placed into a discrete category by visual, subjective observation. The zygomaxillary suture may appear straight to one person and recurved to another. Assessing character states by non-metric methods require years of experience observing skeletal variation and is oftentimes population specific. Discrete trait frequency in different populations is not always reported. On the other hand, non-metric methods are advantageous in that they are very inexpensive (the only equipment required are a set of eyes) and may be performed rather quickly (examine a feature and note its state). All in all, non-metric methods are a verbal description of cranial morphology whereas metric methods are a numerical description.

Metric methods treat phenotypic variation as continuous, scored on a measuring scale with no distinct categories and can take any decimal value. It is measured on either ratio or semi-continuous ordinal scales. Ratio scales give rank to the variables and the differences between categories are regular. For example, mastoid height may be measured in one individual as 16 mm, which is twice as long as mastoid height measured at 8 mm in another individual. Semi-continuous ordinal scales place traits on a discrete 62 scale even though in reality they are continuous. In this case, continuous variation is divided into a number of discrete categories such as assigning the mastoid process a score from ‘1’ to ‘5’ (‘1’ being small and ‘5’ being large) (Buikstra and Ubelaker, 1994). A score of ‘4’ is not necessarily twice as great as a score of ‘2’. Frequently, metric methods employ the use of instrumentation such as sliding calipers, spreading calipers, radiometers, coordinate calipers, and digitizers to collect information from gross morphological features. These tools can range from hundreds to thousands of dollars; although, in the case of calipers, do not require as much experience to begin data collection. Observers are required to locate anatomically defined landmarks that may be difficult to locate. It should be noted that experience plays a role in reducing intraobserver measurement error. Numbers become abstractions of character states. One method is not superior to another; rather, the method one uses depends on the types of questions one asks, time, funding, and resources.

4.2 MEASURING SIZE, SHAPE, AND FORM

Anthropology has survived a sordid history of describing differences in shape among human groups beginning with simple description for purposes of categorization.

Fortunately, anthropologists have moved beyond description to testing hypotheses to explain variation in our species. In the conversation about morphology size must be eliminated because biologists are often most interested in shape differences. Size is viewed as less meaningful than shape in taxonomic studies (Corruccini, 1975). This is a generalization that characterized the viewpoint of many disciplines, including anthropology, but has since changed. A survey of the literature reveals variation in the 63 definition of size and shape. Human neurocranial size and shape will be used as an example to illustrate how one may estimate such parameters with a decided spotlight on morphometrics. Possibilities for size adjustment will also be introduced.

Intuitively, size describes if one object is bigger than another and shape refers to how an object looks. A feature on one individual will be smaller or larger than the same feature on another individual; therefore, single measurements describe the size of a feature. One may compare sizes of traits between two individuals or groups using simple, univariate statistical methods. For example, maximum cranial length is measured as 180 mm in one individual and 173 mm in another. Individual 1 has a cranium that is 7 mm longer than individual 2, a measure of size. If two measurements are taken of a feature, the second measurement is said to provide a description of its shape. Shape is some combination of the two or more measurements, which will make a feature on one cranium look different from the same feature on another. If more measurements are taken then one can be more detailed in one’s description of a cranium up to a certain point (van Vark and van der Sman, 1982; van Vark and Schaafsma, 1992). Analyzing multiple variables simultaneously requires multivariate statistical methods and subsequently advanced statistical computer software. These description or definitions are admittedly very rudimentary if only to introduce the topic.

A number of more precise definitions have been provided for size and shape all of which tells the reader their relative importance in the eyes of the researcher and possible methods to analyze. Size has been defined as “a component whose loadings are covariances with group held constant‒that is, a linear combination whose coefficients are its own pooled covariances within group” ( umphries et al., 1981:297), “an enlargement 64 without change of appearance” (Cheverud et al., 1983:155), “the magnitude of a vector of measurements of an organism” (Corruccini, 1987 in ietrusewsky, 2000:386), “an attribute of an organism” (Jungers et al., 1995:138), a “real scalar value” (Zelditch et al.,

2004:426), and “any positive, real-valued measure of an object that scales as a positive power of the geometric scale of the form” (Slice, 2005:4). Examples are length, area, volume, weight, or a linear combination of variables. In craniometrics terms such as wide, narrow, tall, short, and long are used to denote size differences. Shape has been more precisely defined as a “disturbance from (a) standard form” (Cheverud et al.,

1983:155), “a function of relative proportion normalized by size” (Corruccini, 1987 in

ietrusewsky, 2000:386), “relative size” (Jungers et al., 1995:138), and “all the geometric information that remains when location, scale and rotational effects are filtered out from an object” (Kendall, 1977 in Zelditch et al., 2004:73). Examples of a simple description of cranial shape may be round, square, sloping, or mesocephalic. All of these definitions range from statistically abstract to largely uninformative, in that interpretation may be subjective.

ata that contain information on size and shape are considered an organism’s form or its overall morphological appearance. Form is defined as “the configuration of a set of landmarks” (O’ iggins, 2000:103) and “all the geometric information not removed by rotation and translation” (Zelditch et al., 2004:416). In an extreme case form could also be considered the essential nature of an organism by capturing as many arbitrary measures as possible to describe its gestalt, which harkens back to typological thinking

(Rhoads, 1985). As perceived by the strictest of morphometricians size and shape are general factors, instead of measured variables, which report on distance measures 65 between groups or individuals (Humphries et al., 1981). These seemingly trivial differences in definition are a matter of semantics and are paramount to developing a mathematical theory of shape (Zelditch et al., 2004b).

The question now becomes whether or not it is necessary to make the distinction and if it really matters in the context of one’s research. The more one thinks about differences between size, shape, and form the less they make sense and the more insignificant the differences are, if any. These concepts can become an abstraction of multivariate statistical analyses in which organisms are reduced to a vector of measures or coordinates that cannot possibly capture all aspects of an organisms form. Emerging methods such as geometric morphometrics, considered somewhat of a revolution in the study of form (Adams et al., 2000; Richtsmeier et al., 2002; Slice, 2005), focus on centroid size as the size estimate for multivariate morphometrics. It is the square-root of the sum of squares of distances of all the points from their central location (Bookstein,

1989). This allows comparison between groups; however, it is not an actual representation of physical attributes per se. Instead centroid size is size in n-dimensional vector space.

Jungers et al. (1995) illustrate the importance making a distinction and removing the size component from form. They propose a hypothetical study in which one wishes to compare mouse lemurs and gorillas, the smallest and largest non-human primates, respectively. A series of measurements are taken to test hypotheses that the species are different in some manner. Without accounting for differences in scale the results of any analysis will relate to the observation that gorillas are bigger than mouse lemurs. It is obvious upon visual inspection they are different sizes, but maybe we are more interested 66 in their shapes. Is the mouse lemur a scaled-down version of the gorilla, an iso-gorilla, or vice versa? Are there evolutionary forces at work that produced these different forms?

Questions such as these demonstrate the necessity to discriminate between size, shape, and form as wells as to create operational definitions. These definitions do not have to be generalizable to all morphometric studies. Minimally, a definition should inform readers as to one’s research goals.

Authors fall into one of three camps when it comes to the issue of size and shape:

1) size and shape are not defined yet described as being very important to make the distinction (Howells, 1973, 1989; Corruccini, 1975; Campbell, 1978; van Vark and

Schaafsma, 1992; Richstmeier et al., 2002), 2) no distinction is made (Howells, 1969;

Lahr and Wright, 1996), or 3) size and shape are explicitly defined (Humphries et al.,

1981; Cheverud et al., 1983; Bookstein et al., 1985; Bookstein, 1989; Richstmeier et al.,

1992; Jungers et al., 1995). It seems as though no consensus on the relative importance of size and shape has been reached and no suggestion that it ever will be reconciled

(vanVark and Schaafsma, 1992).

Bookstein (1989) sets out to clear the air as he provides meanings for size and shape within five separate contexts: factor analysis, principal components analysis, geometric size, isometric size, and arbitrary size. His point of view is one of the most concise, explicit treatments of this topic and goes so far as to even question the meaning of the conjunction “and” as in “size and shape.” All in all, he wants research across observers to be comparable. Researchers uncritically use these terms interchangeably, according to Bookstein, when the analytical techniques to estimate these variables treat size and shape in very exacting fashions. 67 First, in factor analysis, size is the factor score that explains covariances of size variables and shape is patterns of covariance of the residuals. Size refers to allometric size. As one variable changes other variables will change at specific rates. Factor analysis searches for patterns of variation in shared underlying factors (Pietrusewsky,

2000). Second, principal components analysis, like factor analysis, seeks to reduce the original number of variables to a linear combination that accounts for the greatest amount of variation (Feldesman, 1997). Subsequent components account for less and less of the variation. The first principal component is treated as size, referring to allometric size, and is called general size. Shape, on the other hand, is found in an independent space orthogonal, or perpendicular, to the first component beginning with principal component

2. The meaning of components is derived by the weights for each variable. Third, geometric size is any combination of data scaled to a singular dimension. One may use a single measure as size, totals, geometric means, or root sum of squares. In this case size is arbitrarily defined by the particular combination of variables. Shape is described as all the ratios of each variable to the combination of variables. Fourth, isometric size is identified by hypothesizing which variable informs one about size before data collection.

Size is simply a function of the list of data and may not be a true size variable. Each variable is then divided by the combination of variables described above in geometric size to produce allometry-free shape variables. Fifth, arbitrary size may be defined by any of the previously mentioned methods or a biological measure like mass. A researcher defines size in the aforementioned terms and extracts shape by regressing size out of the data. Shape is viewed as the residuals, or the deviations of the observations from the fitted model, of that regression. 68 The provenance of shape, the provenance of size, the algebra of allometry, the covariance of size and shape, and the explanatory role of size all take on different meanings depending upon the method. In the end, Bookstein makes recommendations for alternative, more appropriate phrases. Size should be replaced with “the length XY,”

“the score on C1,” “the second centroid moment,” or “body weight” (1989:180). Shape may still be used as long as one specifies its relation to allometric factors whether the factors are linear space or ratios.

4.2.1 Estimating Size and Shape

Size and shape can be described by visual assessment alone noting where differences are observed, hence the emergence of terms dolicocephalic, mesocephalic, and brachycephalic. This assessment is based on individual subjective interpretation and prior experience examining skeletons in which there is no discrete dividing line from one shape to another upon visual inspection. However, such terms can be translated metrically as the cephalic index, a dated metric of cranial shape calculated by dividing maximum cranial width by maximum cranial length and multiplying by 100. Metric assessments of shape are only numerical representations of one’s visual interpretation

(Howells, 1969; van Vark and van der Sman, 1982). Most consider using multivariate statistical methods over univariate or visual methods because multivariate statistics maintain aspects of size and shape while comparisons of single measure decomposes individual to meaningless variables (vanVark and Schaafsma, 1992). Methods such as discriminant function analysis or principal components analysis show where in an individual’s vector of measurements differences may lie. 69 Deliberation must be made regarding variable selection and biological significance in eye of biologist (van Vark and van der Sman, 1982; Zelditch et al., 2004).

Some describe an “optimum measurement of complexity,” a phrase used to explain how the combination of the nature and number of variables affects group differences (van

Vark and van der Sman, 1982; van Vark and Schaafsma, 1992). The nature of the variables refers to the shape and size of the cranium that the measurements represent.

Although they discuss how the number of variables and the sample size balance out to reach an optimum level of differentiation, the authors do not give any exact numbers.

Van Vark (1976) describes an algorithm for choosing variables for multiple discriminant analysis, but here I apply these rules to data collection in general when time and fiscal constraints are imposed. First, make an informed decision as to which variables will best describe form differences. Second, order these variables from most important to least.

Third, decide which set of ordered variables should be included. This algorithm typically is followed on a base level when a research project is being formulated and in the middle of statistical analyses as well. Regardless of the number measured variables should be homologous between groups so that comparison is possible.

Howells (1969, 1973) championed uses of ratios before a time when multivariate statistical methods were practical and informed readers that ratios and indices reflect aspects of shape. More recently authors have noted that one should avoid their use as they are uninformative and confounding (Corruccini, 1975; Campbell, 1978; Bookstein,

1989). Specifically, the numerators and denominators can be highly correlated

(Humphries et al., 1981). Generalized or Mahalanobis distance (D2) is used to allocate based on size differences computed as the sum of squared distances between populations. 70 Some recognize that this distance does not inform one about differences in form, just how related two groups are based on given variables (van Vark and Schaafsma, 1992).

Another common method for estimating shape differences is by eliminating the size component by transforming raw measurements in to Z-scores. The deviation of each measurement from the general mean is divided by the general standard deviation

(Howells, 1989). This allows one to compare the shape of an individual to the entire group. However, two crania of the same shape and different size, i.e. scaled versions of each other, would receive the same z-score. Howells (1989) extracted the size component by summing all Z-scores for an individual and dividing by the number of measurements, the average Z-score, known as PENSIZE. C-scores are calculated to provide a better mathematical description of cranial form. PENSIZE is subtracted from each of an individual’s Z-scores effectively double-centering the original Z-score for a measurement. Now, raw measurements have been indexed relative to all others.

In order to test the effects of size correction Jungers and colleagues (1995) performed 11 size adjustments on a data set of interspecific guenons and intraspecific

Native American anthropometrics. Adjustments fall into one of three categories: ratios, regression, and factor analyses. Specific adjustments include residuals from the linear least squares regression, residuals from the linear log-log least squares regression, residuals from exponential least squares regression, C-scores, ratios of each variable to the geometric mean, logged ratios of each variable to the geometric mean, double mean- centering added to logged grand mean, removing first principal component, projecting logged data onto plane orthogonal to first principal component, shape distance matrix, and log-transformed shape distance matrix. The last two adjustments are based on 71 Euclidean distances between taxonomic units. The hypothesis that there would be no difference in shape (distance of ‘0’) among members of the same species was not supported except for variables scaled by group means. Yet, in the eyes of morphometrics size should not be removed (Bookstein et al., 1985).

In the end, researchers should provide a minimal definition of what they consider size and shape. Size should always be included in the discussion as it is an integral part of an organism’s form. istinctions should be made between allometric and isometric size.

4.3 LANDMARK MORPHOMETRICS

Landmark morphometrics is part of a revolution in analyzing form that aims to bring together geometric data, mathematical descriptions of how objects are deformed versions of each other, and biologically significant explanations of said form (Adams et al., 2002; Richtsmeier et al., 2002; Zelditch et al., 2004b; Slice, 2005). “Morphometrics is the study of covariances of biological form” and is grounded in four principals: landmark locations, shape coordinates, the form of questions, and the form of answers

(Bookstein, 1991:1). Landmarks are sets of discrete points represented by Cartesian coordinates (x, y, and z) and are given name. (Examples of landmarks as they are used in craniometric analysis of human variation will be described in the next section.) Their locations are essential in describing form. In order for a landmark to be useful in measuring variation, it must be homologous across individuals and biologically meaningful. Shape coordinates are the reduction of landmark locations from their arrangement. The form of questions refers to specific research questions one asks of the 72 landmark data to discover covariance among landmarks and the nature of that covariance.

Finally, the form of answers corresponds to the results of landmark methods. Instead of numerical tables as the only output, as is typical in traditional interlandmark distance studies, graphical representations of shape are provided to reduce mathematical abstraction and infuse biological significance. In other terms, morphometrics explores the relationship between extrinsic and intrinsic factors of form (O’ iggins, 2000).

Landmark morphometrics was born out of evolutionary studies of form and growth. Sir ’Arcy Thompson, a Scottish born mathematical biologist, is credited with planting the seed for this field to grow into a mature discipline (Bookstein, 1991; Zelditch et al., 2004b). He recognized, at the turn of the 20th century, that questions related to evolutionary significance of form were beyond the scope and capabilities of then current technology and analytical techniques. Statistical theory underlying the method, coupled with advancing computer technology capable of handling large datasets, was worked out in the 1980s and 1990s primarily by Fred Bookstein, James Rholf, and Leslie Marcus with contributions from Joan Richtsmeier, Dennis Slice, and James Cheverud, to name only a few scholars dedicated to this technique. The “landmark” texts considered foundational to the method and a starting point for anyone interested in such an approach have gained such notoriety that they are no longer referred to by their titles or authors.

As a substitute, researchers using landmark morphometrics speak of the “red book”

(Bookstein et al., 1985), “blue book” (Rholf and Bookstein, 1990), “orange book”

(Bookstein, 1991), “black book” (Marcus et al., 1993), “white book” (Marcus et al.,

1996), the “green book” (Zelditch et al., 2004b), and most recent “yellow book” (Cardini and Loy, 2013). 73 The red book, from Bookstein and colleagues (1985), outlined the new morphometry emerging in biology as defined by those in attendance of an international workshop on the subject. The blue book by Rholf and Bookstein (1990) is a summary of another special morphometrics workshop which brought together morpphometricians from around the world to present and solve issues faced by those employing landmark morphometric methods to answer questions related to biological form. The orange book is a treatise presented by Bookstein (1991) to formally define and outline landmark morphometric theory and methods. The black book by Marcus and colleagues (1993) served as a more general introduction to the field by introducing its history, methods for data acquisition, methods for data analysis, and example applications. The white book, again by Marcus and other (1996), is the result of a NATO Advanced Study Institute in which scholars from participating NATO countries were called to meet and present the latest updates in the fields of paleontology, systematics, geology, statistics and computer science as related to morphometrics. The green book was presented by Zelditch and colleagues (2004b) as an updated introduction to geometric morphometrics that would be accessible to any scholar interested in form. Finally, the yellow book, assembled by

Cardini and Loy (2013) is a special volume of the scientific journal Hystrix devoted to summarizing the field with updates over the previous decade and a focus on new software available for acquiring and analyzing data.

Today, landmark morphometrics has been applied to biological (and non- biological) shape analysis related to phylogeny, ontogeny, paleontology, forensic science, medicine, and orthodontics. It brings together several fields of inquiry to solve problems related to form and has spawned a related field called ‘virtual anthropology’ (Weber and 74 Bookstein, 2011). The evolution of geometric morphometrics to virtual anthropology was a natural one as researchers took advantage of the latest in hardware and software to collect and analyze three dimensional or four-dimensional representations of anatomical data primarily concerning the human species, including their relatives and ancestors

(Virtual Anthropology, 2009). Virtual anthropology is divided into six operational areas:

“1. Digitize–mapping the physical world, 2. Expose–looking inside, 3. Compare– quantitative evaluation, 4. Reconstruct–dealing with missing data, 5. Materialize–coming back to the real world, 6. Share–collaboration using electronic data transfer” (Weber,

2015:24). The current study employs all operational areas except 2. Expose. Technology to peer into internal structures will not be used. However, as will be described in the next chapter, specimens will be digitized using a portable coordinate measuring machine.

Next, geometric morphometric methods will be employed to compare cranial form.

Missing landmarks will be statistically reconstructed to maintain a large sample size.

Then results of the analysis will be explained within the context of evolutionary anatomy and its applications in clinical medicine and forensic science. Finally, all data will be made available on the World Wide Web for access by others.

4.4 USING LANDMARKS TO DESCRIBE CRANIAL FORM

Landmarks are categorized as one of three types (Bookstein, 1991) with human cranial landmarks given as examples. Type 1 landmarks are discrete juxtapositions of tissues. Intersections of sutures such as lambda, “the apex of the occipital bone at its junction with the parietals, in the midline” ( owells, 1973:168), is an example.

omology of landmarks is supported by local evidence (O’ iggins, 2000). Type 2 75 landmarks are maxima of curvature such as the tips of bony processes or the deepest part of depression. An example is basion: “On the anterior border of the foramen magnum, in the midline, at the position pointed to by the apex of the triangular surface at the base of either condyle” ( owells, 1973:166). In this case, homology is supported by geometric evidence (O’ iggins, 2000). Type 3 landmarks are extreme points. Subtenses are measured from chords to extreme points as in parietal subtense: “The maximum subtense, at the highest point on the convexity of the parietal bones in the mid-plane, to the bregma-lambda chord” ( owells, 1973:182). Type 1 and 2 landmarks can be identified independently whereas Type 3 landmarks must be identified relative to another point because they are deficient in one coordinate (O’ iggins, 2000). The minimum unit of morphometrics is a triangle defined by three landmarks.

One may play devil’s advocate and ask, “Why bother with quantification when I can see the difference between forms?” (Richtsmeier et al., 1992:302). It is not that landmark morphometrics are necessarily superior to other methods. As always, the methods one chooses depends on the hypotheses one wishes to test. Its primary advantage is that differences detected and interpreted by the observer upon visual inspection can be tested for statistical significance. One may respond to the previous statement by asking, “Still, why use landmark morphometrics when traditional interlandmark distances can be measured to discover morphological differences and explain variation?” The answer lies in the theoretical foundation of the method at hand: geometric relationships of the measurements are maintained throughout the analysis. Let us examine traditional interlandmark distances of the cranium as a case in point.

76 owells (1973), in a “landmark” survey of global human cranial variation, took

57 measurements on approximately 2,500 individuals comprising 17 populations. The measurements were subjected to factor and multiple discriminant function analyses to locate regions of the cranium that would best discriminate between populations and generalized distances were calculated between the groups. Measurements were chosen that would best capture information of cranial shape. Despite these best intentions, interlandmark distances contain no information of their location in space or their relationship with other distances (Slice, 2005). All such data are lost in the collection procedure whereby measurements become abstractions of cranial morphology. Analysts have to extract any sort of biological meaning from the variables that are weighted most heavily. This can be said of any statistical technique, but landmark morphometrics prides itself on its foundation in a mathematical theory of shape (Bookstein et al., 1985;

Bookstein, 1991). Collecting coordinate data ensures that spatial relationships are never lost because each coordinate is located in space relative to all other coordinates. They are not independent of one another. Techniques to collect coordinate landmark data allow researchers to locate landmarks in three or two-dimensional space as well as calculate

Euclidean (interlandmark) distances. There are, however, some disadvantages to this technique.

Landmark morphometrics is a relatively new method and was slowly introduced to Anthropology in the 1990s and has become an increasingly more popular method of choice in the 21st century (Adams et al., 2002). It uses the newest technologies in data collection and imaging as well as the latest computer software. The technology required to collect landmark data can be very expensive. Digitizing instrumentation to register 77 coordinate data cost thousands of dollars. Use of medical imaging equipment will incur hourly costs. The theoretical and practical mathematics behind morphometrics can be very complex and discourages researchers from using it. However, the mathematics are the same matrix algebra used in factor, principal components, cluster, or discriminant function analyses, the statistical methods used for traditional interlandmark distance studies. Also, although spatial relationships are maintained, all data that account for surface geometry or curvature between landmarks are lost (Richtsmeier et al., 1992).

Landmark morphometrics is not the solution to all methodological issues of shape analysis. All together, geometric morphometric methods allows the researcher to examine the organism or part of an organism as a whole.

4.5 ACQUIRING AND ANALYZING LANDMARK DATA

4.5.1 Acquisition of Landmark Data

Recording landmark coordinates falls under the broader field of coordinate metrology in which data sets are generated by probing an object with a coordinate measuring and then calculating geometric elements that specify the size, form, location, and orientation of the object (Ni and Wäldele, 1995). Landmark data can be collected from two or three dimensions. Two-dimensional (x,y) coordinates are recorded from images produced by photography or medical imaging. Plain film radiographs or film photographs are scanned as digital copies for manipulation using computer software.

Digital photography or medical imaging producing digital outputs is more commonly used. Either way, a researcher selects landmarks of interest on the image. The down side 78 to 2D landmark morphometrics is that all landmarks must be in the same plane. This plane must be parallel to the plane of the receptor collecting the image. If non-parallel, the object will be tilted about its centroid and distort any coordinates. In the same light, the image must be captured at an angle perpendicular to the object to avoid distortion from oblique angles. Two-dimensional data collection is best for shape analysis in the sagittal plane where magnetic resonance imaging, computed tomography, or radiography can be used to view deeper structures without specimen destruction.

A common tool used to record three-dimensional (x,y,z) data is a coordinate measuring machine which “give(s) physical representations of a three-dimensional rectilinear Cartesian coordinate system” (Ni and Wäldele, 1995:39). Machines are constructed as a digitizing arm that can range in size from a desktop unit that is portable

(to record cranial landmarks) to an arm as large as a room that moves about a gantry

(more common in industrial design applications). Here, a portable coordinate measuring machine typically used in osteometric research will be described. The arm is affixed to a base plate which, in turn, is fixed to the table. Arms typically have five degrees of movement, meaning that there are multiple segments in articulation, analogous to human upper limb joints, where the segments may pitch (lateral axis), roll (longitudinal axis), and yaw (vertical axis) as rotational movements about three dimensional space at multiple joints (Figure 4.1). The intention here is that the arm may be manipulated freely in the workspace around an object, a skull for example, and may locate an infinite number of coordinates with a resolution of tenths of a millimeter.

79 Pitch

Roll Yaw

Figure 4.1: Axes of rotation in coordinate measuring machine. Each axis is perpendicular to each other.

Ni and Wäldele (1995:41) outline five steps when performing measurement using a coordinate measuring machine:

1. Calibration of stylus with respect to reference point,

2. Metrological determination of workpiece position in the machine-related

coordinate system,

3. Measurement of surface points on the workpiece in the measuring machine-

related coordinate system,

4. Evaluation of geometric parameters of workpiece, and

5. Representation of the measurement results after coordinate transformation into the

workpiece-related coordinate system.

The stylus at the distal end of the arm is placed on a landmark, a foot pedal is depressed or a hand button is pressed, and the landmark’s x, y, and z coordinates are input to a computer with reference to a zero point (0,0,0) not on the skull. A major concern is that the skull cannot move while landmarks are being recorded. Three-dimensional medical 80 imaging or laser scanning are other options. Whether data is two-dimensional or three- dimensional, there are a number of options for statistical analysis, depending on the research question(s).

Using coordinate measuring machines offer two practical advantages over conventional metrology (Ni and Wäldele, 1995). First, one does not have to realign the object to the machine axis for each measurement. For example, using traditional calipers requires that the skull be repositioned between the recordings of interlandmark distances.

Using a digitizer allows the analyst to leave the object in the same position throughout a data recording session; thus, recording time is greatly reduced. Researchers may be able to record more data points from a single specimen and collect data from a larger sample with the extra time afforded. A second advantage is that all information can be recorded in one setup, with one instrument, and one reference system thereby eliminating sources of error from multiple measuring devices. In conventional craniometry, spreading calipers, sliding calipers, coordinate calipers, a radiometer, and a mandibulometer may be used to take a battery of measurements of a skull. Each instrument must be calibrated separately and each has unique possible errors associated with them. These instruments require two or three points to contact the skull in order to take a measurement whereas a coordinate measuring machine requires just one point to contact the object. The coordinate caliper and radiometer are some of the more unwieldy instruments to measure interlandmark distances proving difficult to use even with both hands. After the instrument is properly placed it is a matter of correctly reading the instrument, which can be problematic with analog devices. A digitizer mitigates transcription errors by digitally

81 recording the coordinates directly, as long as coordinates are registered in the correct order.

4.5.2 Analyzing Landmark Data

Statistical methods common in landmark morphometrics fall into categories of superimposition, linear distance, or deformation (Richtsmeier et al., 2002). Categories will only be briefly described with a few specific examples. The essential principal is that shape is invariant to the effects of scaling, rotation, or orientation and landmarks are treated as location surrounding a mean form (O’ iggins, 2000). A common problem in all analyses is that of registration. Objects must be measured in the same coordinate system or converted to the same system through registration for valid comparisons to be made and differences to be tested. Registration can be carried out by measuring to a common baseline, measuring so that most points fit well, or minimizing the sum of squared distances between homologous landmarks (O’ iggins, 2000). The last type of registration describes one kind of superimposition.

Generalized Procrustes analysis (GPA) translates, rotates, and scales the landmarks to a similar coordinate system and lays them on top of one another. GPA is named after the mythical Greek figure Procrustes, son of Poseiden, who offered his bed to passer-bys for rest and would either stretch or cut the legs off individuals so they would fit (Richtsmeier et al., 1992.) Coordinates are projected onto a tangent plane and subjected to principal components analysis where the eigenvalues represent shape variation (Rholf, 1999). Graphical outputs take the appearance of dots with vectors

82 indicating the magnitude and direction of change between two mean shapes or an individual and a mean.

Linear distance-based methods solve the problem of registration because registration is not required. Euclidean Distance Matrix Analysis uses interlandmark distances to create a matrix that expresses form (Richtsmeier et al., 2005). The form distance matrix indicates which landmarks have changed. This method is particularly attractive to those interested in form and not only shape or size because rotation and translation parameters cannot be accurately estimated (Richtsmeier, 2002).

Deformation methods describe differences in form by assigning a reference shape and target shape, akin to methods employed by Thompson in the early 1900s. The change in physical space of grids between reference and target indicates specific shape variation. These methods are also used to negate issues of registration by focusing on how shapes are deformed instead of absolute movements related to scale and orientation.

The thin plate spline is a concept borrowed from engineering and describes a theoretical, infinitely thin piece of metal sheeting that is minimally bent about a dimension perpendicular to its length and width (Bookstein, 1991). Bending relates to deformation of a reference shape to its target or an initial coordinate system is mapped to another.

Thin plate splines are typically used in combination with generalized Procrustes analysis to illustrate rotational differences between shapes. Finite-elements scaling analysis asks the observer to divide landmarks into sets of elements (Richtsmeier et al, 1992). A function maps all points of an element in a reference shape to homologous points on a target. Superimposition, linear distance, and deformation-based methods are powerful tools to investigate shape mathematically. 83 4.6 APPLYING LANDMARK MORPHOMETRICS TO HUMAN CRANIAL FORM

Geometric morphometrics has aided anthropologists in describing and explaining human variation in the past, present, and in relation to our closest living relatives. In particular, this method shows scientists what variation looks like through computer-aided visualization. The technique is most frequently used to record shape data from crania, as it is a structurally complex functional unit. It can be applied to a modern context to depict current levels of morphological variation, discover causes of secular change, or identify individuals in a medicolegal context.

Understanding morphological human variation and the processes that shape it begins with describing how much humans vary and which specific part(s) of their form varies. Hennessy and Stringer (2002) documented variability in cranial form of four modern regional groups. Their study characterized overall face shape and highlighted differences among groups from around the world. In a similar light, Wescott and Jantz

(2005) pinpointed specific cranial regions undergoing secular change in 20th century

Americans. They related these changes to proximate (growth, basicranial dimensions, stature) and ultimate (nutrition, biomechanical loading) causes.

Landmark data can be used to estimate ancestral group affiliation or sex for the purposes of creating a biological profile of an unknown individual for identification in medicolegal contexts (Hennessy and Stringer, 2002; Dirkmaat et al., 2008). Areas of the skeleton that were previously believed not to be variable using traditional methods are found to be variable and useful for discrimination. Differences in mandibular morphology in juveniles of different ancestral groups have been discovered (Buck and

Viðarsdóttir, 2004). The matter of gene flow in modern populations and its impact on 84 identifying skeletal remains are real issues forensic anthropologists must contend with.

These so-called “hybrid” individuals possess unique forms that can be distinguished from the parent populations when landmark morphometric methods are applied (Ross CF et al., 2004; Spradley et al., 2008). One can extend this to an historical context where admixture has occurred, as in the case of Spanish, Native American, and modern

Mexican populations (Martínez-Abadías et al., 2006).

Landmark morphometrics can be applied to an evolutionary context to discover the nature of change from one species to another by combining past and present variation within and between taxa across age. Ontogenetic trajectories have been examined to test hypotheses of the origin of cranial traits in humans by comparing modern humans and chimpanzees (Penin et al., 2002). Facial growth patterns of modern humans can be assessed and then applied to studying patterns of fossil hominins (Viðarsdóttir et al.,

2002). The practical concern with investigating growth is the need for both adult and juvenile specimens at multiple age stages, in lieu of a longitudinal study of live subjects.

Morphometrics can become a powerful tool when combined with investigations of phenotypic integration. It can be used to identify functional matrices and their level of integration or modularity (O’ iggins, 2000). Scientists can then postulate how constrained a species is against change.

Richtsmeier and colleagues (1992) propose some fascinating applications beyond the typical uses of landmark morphometrics related to evolutionary studies. First, it is possible to create hypothetical geometries if one knows the growth patterns of two different species (A and B). The growth pattern of species A can be applied to the juvenile form of species B. The application would result in a hypothetical adult geometry 85 of species B if it grew like species A. The hypothetical B adult could be compared to the

A adult form and test for differences. Taken even further, hypothetical selection pressures can be applied to forms and the predicted outcomes could be viewed in a “what if” scenario. Second, it has been suggested to reconstruct specimens with missing landmarks (Richsmeier et al. 1992). This would be extremely useful for fragmentary remains in order to maintain large sample sizes instead of excluding individuals due to incomplete landmark sets. As an alternative, the missing landmark is estimated based on the relative position of all other landmarks in the same individual. This is different from data imputation methods based on means of the entire sample.

4.7 SUMMARY

The hypotheses one tests influences the types of data collected, the methods for collecting said data, and the statistical techniques for analyzing the data. Each set of methods has its advantages and disadvantages, from time required, to relative ease of recording data, to cost of instrumentation, to computing power necessary to carry out analyses, to relative ease of interpreting results. The focus in this study is a collection of data gathering and analysis techniques that maintain spatial and biological relationships among data points throughout the entire process, geometric morphometrics, with the end goal of describing and explaining variation in form.

Landmark morphometrics allows one to examine biological variation from a different perspective compared to traditional methods such as interlandmark distances.

Generalized Procrustes analysis, Euclidean Distance Matrix Analysis, thin plate splines, and finite-element scaling analysis all provide viable options for morphometricians to 86 investigate form differences no matter what their theoretical stance is. Shape variation is maintained throughout the analysis and visual output is provided, making for a somewhat easier interpretation. One can visualize how shape changes from one form to the next as part of variation within a group or differences between groups, either synchronically or diachronically. The method is useful for understanding human variation across time and space and thus will be applied here in a more recent context.

CHAPTER 5: MATERIALS AND METHODS

This chapter introduces the research design including a description of the landmarks used in this study and a rationale for their selection. The high number of landmarks were chosen to provide a higher resolution for describing variation in cranial shape. Next, the provenance of the two main study samples is described along with a smaller error study sample used to refine measurement technique and data collection protocol prior to formal data collection. The instrumentation employed to collect data is introduced along with an explanation of the data collection protocol. Finally, the suite of statistical analyses is presented including a priori assumptions about the data, how missing data was handled, how outliers were identified and removed from subsequent analyses, and the specific methods used to test the hypotheses regarding variation within and among populations as well as evaluating patterns of modularity.

5.1 LANDMARKS

One-hundred and fifty two landmarks were selected to represent the eight functional cranial modules and seven functional-developmental cranial modules.

Appendix A lists the landmarks with their abbreviations used in this study and their definitions. Each landmark is identified as a Type 1, Type 2, or Type 3 landmark and to which functional or functional-developmental module it belongs. Nineteen are

88 endocranial and 133 are ectocranial. Of the 19 endocranial landmarks, six are bilateral and seven are unilateral. Nineteen ectocranial landmarks are unilateral and there are 57 bilateral ectocranial landmarks. The endocranial landmarks were added to more completely describe the shape of the cranial base, the most complex portion of the cranium. Table 5.1 provides the numbers of landmarks used to quantify each of the modules. Figures B.1-B.4 in Appendix B illustrate the locations of the landmarks on skull.

Table 5.1: Number of landmarks used to quantify shape of cranial modules.

No. of Unique No. of Unilateral and Modules Complex Landmarks Bilateral Landmarks Functional Neurocranium 51 85 Modules Anteroneural 7 9 Midneural 15 24 Postneural 21 32 Otic 10 20 Face 39 67 Optic 5 10 Respiratory 7 11 Masticatory 20 34 Alveolar 7 12 Functional- Dermatocranium 63 107 Developmental Vault 37 63 Modules Face 35 60 Chondrocranium 28 45 Nasal 6 10 Auditory 10 20 Orbit 3 6 Basicranial Basicranium 38 63 Module Vault 22 36 Face 30 53

89 5.2 SAMPLE

Landmark coordinate data (x, y, and z coordinates) were collected from 391 crania in the William M. Bass Donated Skeletal Collection at the University of Tennessee,

Knoxville and the Hamann-Todd Human Osteological Collection at the Cleveland

Museum of Natural History, Cleveland, Ohio. Two-hundred fifty seven males (65.7%) and 134 females (34.3%) were examined. Table 5.2 provides the demographic profile of the sample and Table 5.3 offers the age-at-death distribution. The Bass Collection is composed of approximately 1,000 individuals who have died within the last 25 years in the Tennessee region (“WM Bass” n.d.). It was established in 1981 to further the efforts of Dr. William Bass to understand the decompositional processes affecting human remains in central Tennessee. The skeletal remains have been donated to the collection by the individual prior to death, by their family, or from the medical examiner. The bodies are first used in decomposition studies and then processed for curation in the collection. Most of the individuals comprising the collection were born after 1940. Each individual has information on their ancestry, sex, date of birth, date of birth, registered age, education level, occupation, business/industry employed, habitual activities, military service, and childhood socioeconomic status.

The Hamann-Todd collection comprises over 3,100 individuals who lived in the

Cleveland, Ohio area during the late 19th and early 20th centuries (Jones-Kern and

Latimer, 1996). The collection began in 1893 by the dean of the Western Reserve

University’s School of Medicine, Carl August Hamann. At this time, anatomical specimens were difficult to acquire. Hamann amassed approximately 100 skeletons by

1912. Beginning at this time T. Wingate Todd, a professor of anatomy at Western 90 Reserve, increased efforts to build the anatomical collection by adding unclaimed bodies at the Cuyahoga County Morgue or city hospitals. Under a newly revised Ohio Code, authored by Hamann, Todd had to be notified of any unclaimed bodies. The bodies taken into custody by Todd were prepared for medical gross anatomy classes. After dissection by medical students the skeletons were prepared for further study and curation. No more individuals were added to the collection after 1938, by which time Hamann and Todd had passed away (Jones-Kern and Latimer, 1996). Each individual has information on their sex, race, age at death, and whether they were an autopsy specimen or cadaver specimen.

The Hamann-Todd collection will be considered a historic (specimens collected more than 50 years ago) and the Bass collection will be considered modern (specimens collected less than less than 25 years ago) groups to account for possible affects due to secular change (Wescott and Jantz, 2005; Little et al., 2006). Changes in cranial shape have accompanied generational differences in access to nutrition, processed foods, and detrimental environmental factors (Wescott and Jantz, 2005; Sutphin et al., 2014). The different ancestral and temporal origins of the subsamples allows for an evaluation of differences or similarities in cranial variability due to ancestry or secular trends.

91 Table 5.2: Sample demographics.

Collection Ancestry-Sex Group Count Hamann-Todd Black Males 65 White Males 65 Black Females 43 White Females 23 Bass Black Males 47 White Males 67 Black Females 11 White Females 55 Hispanic Male 9 White/Native American Male 4 White/Native American Female 1 Japanese Female 1 Total 391

Table 5.3: Sample age-at-death distributions.

Age-at-Death Collection Average Min Max Stdv Hamann-Todd 40.7 17.0 87.0 13.9 Bass 51.3 19.0 99.0 15.5

Only complete, adult crania with no major deformities were included in this study. Collection accession number, sex, ancestry, and age at death was recorded from the collections’ records, if available. If age at death was not known, adult status was determined by the presence of an erupted third molar and fusion of the sphenooccipital synchondrosis. Ten individuals in the Bass Collection and two individuals in the

Hamann-Todd Collection do not have known age-at-death. If the sex of a cranium was not known it was determined at the time of analysis based on gross morphological features, including size of mastoid process, supraorbital margin form, nuchal crests, 92 glabellar prominence, and overall cranial robusticity (Buikstra and Ubelaker, 1994). All crania have a sectioned callote to gain access to endocranial landmarks except for 49 individuals from the Bass collection.

There is a potential problem with the identification of individuals’ ancestral affiliation as given in the collections’ records. Both collections use racial groupings

‘Black’ and ‘White’ to describe the population affinity. Unclaimed bodies were identified by the morgue or hospital staff based upon superficial physical characteristics such as skin color, hair color, or hair type. Staff assigned each individual to a racial group based on the perceived association between phenotypic features and ethnic groups.

Donated individuals were self-identified, or identified by their family, as to their ancestral group affiliation. There was most likely a disconnect between self-identity and social identity of the individuals comprising the Bass and Hamann-Todd collections. For example, the medical examiner may have identified an individual as ‘White’ whereas the individual may have identified themselves as ‘Black’ during life. Thus, it is important to recognize that the ancestry component as identified in the collections may not accurately reflect the population histories. Additionally, the Bass collection includes individuals belonging to other ancestral groups such as Hispanic, Albanian, White/Native American,

White/Asian, and Japanese. Seventeen individuals from these groups were included in this sample.

In many large skeletal collections only a fraction of the total collection contains crania with a complete suite of landmarks present. Many landmarks are missing due to handling damage, trauma, or resorption. Also, the collections are mostly composed of older individuals, i.e. 50 years old or older. Seventy-two individuals across both samples 93 had antemortem tooth loss resulting in bone resorption of the maxillae. If an area surrounding a landmark was damaged or resorbed by approximately less than 2 mm, then the landmark’s coordinates was recorded and annotated in the database. If approximately more than 2 mm of bone is missing, the landmark was not registered at all.

5.3 EQUIPMENT

A MicroScribe® 3DX portable coordinate measuring machine with a foot pedal input tool was used to record coordinate landmarks (Figure 5.4). The 3DX has five axes of articulation, a position resolution of 0.13 mm, a position accuracy of 0.23 mm, and a

127 cm workspace reach. The digitizer was connected to a laptop computer for input in a

Microsoft Excel worksheet via MicroScribe® Utility Software v5.1. Additionally, a ring stand with support clamps and dental wax is used to suspend and secure a cranium while landmark coordinates are recorded (Figure 5.5).

94 stylus

digitizing arm

tombstone

base

Figure 5.1: MicroScribe® 3DX coordinate measuring machine shown in home position with measurement axes. (Adapted from Immersion, 2000.)

Figure 5.2: Data collection workspace setup. 95 5.4 DATA COLLECTION PROTOCOL

The digitizer is placed on a flat work surface and dental wax is pressed into the edge of the base where it contacts the table. This ensures that the digitizer does not move during data collection. If the digitizer base moves while digitizing a specimen all previous coordinates must be discarded and data recording restarted. The landmarks must first be registered to a reference grid by establishing an origin point in space next to the cranium. The zero point (0,0,0) is registered as the center of the digitizer base joint axis as it intersects with the bottom of the base plate as the digitizer is in the home position. A foot pedal is connected to the digitizer and placed on the floor to allow hands-free data input. For each digitizing session, the digitizer is placed in the home position and calibrated with the MicroScribe® Utility Software. Each axis of movement is evaluated for proper reading on the computer. Next, a ring stand is placed within the working radius of the digitizer and a support ring is attached. The support ring holds a cranium while digitization occurs. Three small pieces of dental wax are placed equidistantly around the support ring to protect the specimens from damage. The cranium is then placed on the support ring with the Frankfurt Horizontal positioned at an angle of approximately 45° upward from the work surface. This allows for access to all landmarks by the digitizing arm. Individual morphological variation required slight deviations from this typical position.

The cranium is secured on the ring stand with the calotte initially removed.

Endocranial landmarks are recorded first plus one ectocranial landmark then the callote is placed on the cranium with dental wax to approximate the thickness of the bone saw used to remove the callote. The same ectocranial landmark is recorded again to determine if 96 the cranium moved when the calotte was placed back on the cranium. If more than a 1 mm difference is detected previous landmarks must be discarded and the recording must start over. If less than a 1 mm difference is detected measurements can continue with the remaining ectocranial landmarks recorded. Movement of the specimen can be mitigated by firm placement on the support ring with dental wax. The order of landmark collection is given in Appendix C.

5.5 ERROR STUDY

An intraobserver error study was conducted prior to formal data collection to ensure data precision following the protocol of Ross and Williams (2008). Fifteen crania from the Department of Anatomy Skeletal Study Collection at The Ohio State University were measured four times each and the variance in landmark location was assessed. A minimum of three days separated each measurement round. The crania are unprovenienced, historic, anatomical specimens displaying a range of preservation.

Twelve out of 152 landmarks could not be recorded on some individuals due to preservation damage or resorption (Table 5.4). Ten of these landmarks are located on the alveolar and masticatory functional modules. The other two landmarks represent cranial base morphology. Two additional landmarks were excluded to retain the symmetry of the data set. A total of 14 landmarks were not included in the error study (Appendix D).

97 Table 5.4: Landmarks not recorded or included in error study.

Landmark Rationale C/P3 L absent alveolon L absent ectomolare L absent molars posterior L absent external palate length L absent external palate length R absent molars posterior R absent ectomolare R absent alveolon R absent C/P3R absent sphenozygomatic R absent petrosal L absent sphenozygomatic L excluded to retain symmetry petrosal R excluded to retain symmetry

Measurement error may arise from multiple sources including the measurement device, definition of the measure, quality of the measured material, the measurer, environment of the measurer, measurement protocol (Claude, 2008). Error due to the measurement device could not be altered. The digitizer has a fixed precision and accuracy of 0.13 mm and 0.23 mm, respectively, and was calibrated before each measuring session. Error due to the definition of the measure was reduced by performing repeated measures on the same sample crania to more precisely locate landmarks. Error due to the measurer was reduced by completing measuring rounds during the same time of day and with adequate time to complete all measurements. Error due to the environment was reduced by using a flat work surface with adequate lighting to locate all landmarks and in a relatively quiet location. To reduce error due to the measurement protocol the order of landmark data collection was created to go in logical order from

98 endocranial to ectocranial surfaces and following anatomical regions to more efficiently record landmark locations by moving the stylus the minimal distance between adjacent landmarks. The interaction between the observer and the digitizer is often overlooked when examining landmark configurations thereby exacerbating conditions that may produce error (von Cramon-Tuabedel et al., 2007). The efforts to reduce error in the error study were applied during data collection of the study samples. Statistical assessment of observer error is addressed in the next section.

5.6 DATA ANALYSIS

All statistical analyses were performed using MorphoJ v1.06d (Klingenberg,

2011), unless otherwise indicated. Geometric morphometric methods, a suite of statistical shape analyses, maintain the three-dimensional relationships among the landmarks and cranial modules through all steps of analysis (Bookstein, 1991). These methods will identify patterns of variation and modularity in cranial morphology through visualization of the three-dimensional data while maintaining relatedness of the coordinates. Parametric and/or non-parametric tests will be employed for many of the analyses. The parametric tests for differences between mean shape configuration uses the

T2 test and assumes equality of covariance matrices where the pooled within-group covariance matrix S is equal to

n 1 n 1 1 1 1 2 n1 n1 2 and the T2 statistic is calculated as

n n 1 2 T 1 n1 n1 99 where d is the vector of differences between matrices (Manly, 2005). The non- parametric tests use randomization rounds that examine the similarity of two distributions without making any a priori assumptions about the distributions. Here, the null hypothesis is that the populations have identical distributions. Permutation tests simulate repeated, random sampling of the distribution from all populations. A test statistic is calculated for each round indicating the deviation of the sample from the null hypothesis, in this case, no difference between samples (Manly, 2005). The proportion of repeated samplings that equals or exceeds the value observed in the original sample indicates the probability that one will observe no difference between samples.

5.6.1 Assumptions

Three assumptions are made prior to data analysis. First, all crania are correctly classified for ancestry and sex in the collections’ records. The demographic information will be compared to data from an anthroposcopic examine. Second, data are multivariate normally distributed. Most multivariate biological data are not normally distributed.

Normality will be evaluated by comparing the distribution of the sample Procrustes distances to a fitted multivariate normal distribution. Third, data distributions are uncontaminated. Data contamination can manifest itself in two ways: scale and location

(Lachenbruch and Goldstein, 1979). Scale contamination can occur if the instrument is not reading correctly. Instrument calibration will be performed and measurement accuracy will be checked prior to each data collection session. Location contamination can occur if the instrument is not placed at the correct landmark location by the observer.

100 This can be mitigated by assessing levels of intra-observer in a formal test prior to official data collection.

5.6.2 Missing Data

Landmark coordinates could not be collected from 155 crania with damaged or missing points. The number of missing landmarks on these specimens ranged from one to 31 (average ≈ 13), for a total of 1,691 landmarks (5,073 datum points; each landmark contains three data points: an x, y, and z coordinate), or 2.86% of the entire sample.

Considering a complete data set, 152 landmarks (456 datum points) would be collected from 391 crania each for a total of 59,432 landmarks (178,296 datum points). Specimens without a full complement of landmarks are not included in analyses using MorphoJ. To maintain the original sample size, missing landmark locations must be estimated.

A statistical reconstruction of the individual specimens with missing landmarks was carried out. The landmarks were input into the geomorph v2.1.3 package (Adams and Otarola-Castillo, 2013; Adams et al., 2014) for R v3.1.2 (R Core Team, 2013) and the “estimate.missing(A, method = c("TPS", "Reg"))” function was used. (See

Appendix E for R script used.) Each landmark with a missing value is regressed on all other landmarks for the complete specimens and then the missing values are predicted by the linear regression model (Gunz et al., 2009). A two-block partial least squares analysis wherein the missing data are one block and the non-missing data are another block is employed to complete the procedure. The multiple multivariate regression method was chosen over geometric construction using the thin plate spine because no semi-landmarks were collected close to all anatomical points. The thin plate spline 101 approach requires local landmarks in order to warp homologous landmarks from the reference to the target (Gunz et al., 2009). Additionally, a reference specimen must be chosen as the target which would not be appropriate within the context of this research examining broad morphological variation. Using thin plate splines would be better served in reconstructing individual specimens such as a damaged fossil.

5.6.3 Generalized Procrustes Analysis

Because each cranium is in a slightly different orientation during data collection, and to account for variation in size, a Generalized Procrustes Analysis was performed to scale landmarks to unit centroid size, shift the configuration to the coordinates (0,0,0), and then rotate the configuration until best fit is achieved. (Richtsmeier et al., 2005).

This happens in an iterative fashion by repeatedly fitting all configurations to a target configuration and then averaging the new configurations until a least-squares Procrustes fit is achieved. Coordinates are projected onto a tangent plane and subjected to principal components analysis where the eigenvalues represent shape variation (Rholf, 1999). The raw landmarks coordinates are transformed to Procrustes coordinates so that all specimens are on the same coordinate system. Both the error study sample and the main study sample must undergo a Procrustes fit before further analysis. The resulting shape space dimensionality is 3k – 7, where k is the number of landmarks (Kendall et al., 1999).

One degree of freedom was removed during scaling, three degrees of freedom were removed during translation, and three more degrees of freedom were removed during rotation. Generalized Procrustes analysis produces a new matrix of Procrustes coordinates that was used for all other analyses. 102 5.6.4 Observer Error

A Procrustes ANOVA (Klingenberg et al., 2002) based on Goodall’s F test

(Goodall, 1991) was carried out to assess intraobserver measurement error for shape and determine if there are significant differences in centroid size and mean shape of specimens in the four measurement rounds of the error study sample. Centroid size for a landmark configuration is defined as the square root of the sum of the squared distances of all landmarks from their centroid (Dryden and Mardia, 1998). After a Procrustes fit of the coordinate data, the relative degree of biological variation in landmark location is compared to measurement error. Visualization of landmark location will be used to select specific landmarks for removal. If the error due to measurement replication is less than error due to biological variation one can conclude that measurement error is negligible (Klingenberg, 2011). Caution must be taken here because the dimensionality of the data exceeds sample size, i.e. 138 landmarks were collected (407 dimensions) from a small sample of 15 individuals with four replications each (60 configurations).

Therefore, interpretations of the ANOVA model will be used to generally assess magnitude of observer error and does not account for magnitude or direction of error.

It is predicted that Type 3 landmarks will have the greatest error, Type 2 landmarks will have less error, and Type 1 landmarks will have the lowest error rates

(von Cramon-Taubadel et al., 2007; Ross and Williams, 2008). Type 1 landmarks represent discrete juxtapositions of tissues (Bookstein, 1991). For example, asterion is the point where the lambdoid, parietomastoid and occipitomastoid sutures intersect (von

Cramon-Taubadel, 2011). Type 2 landmarks are maxima of curvature such as the tips of bony processes or the deepest part of depression (Bookstein, 1991). Prosthion, the most 103 anterior point on the maxillary alveolar process between the two central incisors, is an example (von Cramon-Taubadel, 2011). Type 3 landmarks are extreme points that can only be defined with reference to another point (Bookstein, 1991). Metopion, the point where the frontal elevation above the chord from nasion to bregma is the greatest, or other subtense points are such landmarks (von Cramon-Taubadel, 2011).

5.6.5 Outliers

Outliers in the study sample were identified by examining the cumulative distribution of the Procrustes distances from the average. The cumulative frequency of

Procrustes distances is a measure of the absolute magnitude of the shape deviation and will be compared against the multivariate normal distribution fitted to the data

(Klingenberg and Monteiro, 2005). MorphoJ allows the user to identify error due to the measurer by locating landmarks that may have been mixed up in the digitizing process.

These landmarks can be swapped to their proper sequence for all further analyses. The shape of the curve will be assessed to determine if certain individuals should be removed from subsequent analyses. Individual specimens may be outliers due to unaccounted for random measurement error or natural biological variation.

104 5.6.6 Principal Components Analysis

Principal components analysis was used to identify the major areas of shape variation by using the newly created Procrustes coordinates to examine the overall data set (Klingenberg, 2009). Principal components analysis is a method to reduce a set of correlated variables into a new, smaller set of uncorrelated variables by aligning the coordinates to the axes of variation, called the principal components (Manly, 2005). As a method it does not consider group structure. Classifier variables were used to define groups based on sex, ancestry, and collection/time period. A covariance matrix was generated from which the analysis was carried out. A matrix correlation test for the equality of covariance matrices will be performed. Geometric morphometric methods do not use the correlation matrix to carry out principal component analysis because it would remove the scaling of axes in Procrustes distance units (Klingenberg and Zalkan, 2000).

Individual principal components will be plotted against one another to look for clusters of individuals based on sample subset groupings, i.e. sex, ancestry, time period.

A matrix correlation analysis was performed prior to calculating the principal components to evaluate the similarity of the different subsets within the sample.

Covariance matrices were calculated separately for the Bass collection, the Hamann-

Todd Collection, males, and females. Then, only the collections were compared and then the sexes were compared. The matrix correlation provides a general indication of the similarity between covariance matrices and a matrix permutation test evaluates the null hypothesis of complete dissimilarity between the matrices (Cheverud et al. 1989). This test was run using 10,000 random rounds.

105 5.6.7 Procrustes ANOVA

An analysis of variance with the classifier variables as main effects tested the null hypothesis that time period, sex, or ancestry have no effect on shape. It was performed in the same fashion as describe above for observer error.

5.6.8 Regression Analysis

Age-at-death of the individuals is a covariate that was treated separately from the categorical variables such as collection, sex, and ancestry. A multivariate regression analysis of the shape data was conducted to determine if there is a relationship between the dependent variable of shape and the independent variable of age. The permutation test using the null hypothesis that the dependent and independent variable are completely independent of one another was run for 10,000 rounds. Results can be used to predict skull shape from age-at-death and to determine the proportion of variance accounted for by the regression model. It is predicted that age will have little to no effect on cranial shape as all individuals in the sample are skeletally mature with growth having ceased.

5.6.9 Discriminant Function Analysis

Linear discriminant function analysis of the principal components scores will test the hypothesis that there are no differences in shape based on time period or sex.

(Ancestral groups, of which are several, will be reserved for a canonical variate analysis.)

If the resulting principal components do not adequately reduce the number of variables needed to describe the variation in the sample then the raw coordinates will be used instead. Discriminant function analysis is a predictive method for allocating specimens 106 into two or more groups by creating a linear function that maximizes the between group variation and minimizes the within group variation (Huberty, 1994). In a sample of k groups, k – 1 functions are created. Measures of statistical distance are then used to determine how close each case is to the group centroid. The farther away the case is from the group centroid the less likely they are a member. Procrustes distances (measures of the absolute difference in mean shape between groups) and Mahalanobis distances

(measure of distance relative to within-group variation) will be used.

One important assumption of this method is that all individuals are actually members of one of the groups and that they are correctly classified prior to constructing the functions. Discriminant analysis places an individual in a group regardless of whether or not they are actual members. Second, data must be multivariate normal. The frequency distribution of the squared Procrustes distances will be compared to a fitted normal multivariate distribution. A T2 test will be used to test the hypothesis that there is a difference between the group means. A non-parametric permutation test with 1,000 rounds will also be performed. This randomization tests the null hypothesis that the groups have identical distributions.

Results will be cross-validated to examine the performance of the discriminant functions (Lachenbruch, 1967). Cross-validation, or leave-one-out classification, is an iterative procedure to test the classificatory power of the functions without using an entirely new sample. This avoids testing the functions using the original sample that was used to create the functions, i.e. statistical incest, which inflates the functions’ classificatory power. Cross-validation also avoids having to collect data from a separate sample to test the functions. In step one, the first individual is removed from the sample 107 and discriminant functions are created using all other individuals. The functions are then used to classify the withheld individual and it is recorded if the individual was classified correctly. In step two, the first individual is reinserted in the sample, the second individual is withheld, and a new set of functions are calculated. The new functions are applied to the second individual and the classificatory results are recorded. These steps are repeated until all individuals in the sample have been withheld.

The classification results will be used to calculate the positive predictive value, negative predictive value, sensitivity, and specific of the test to further evaluate the functions’ precision. Consider a test in which one is attempting to discriminate between males and females wherein ‘male’ is identified as the positive state and ‘female’ is identified as the negative state. Positive predictive value (PPV) is the probability that individuals identified as belonging to a group actually belong to the group:

number of true positives

number of true positives number of false positives

Negative predictive value (NPV) is the probability that an individual does not belong to a group if they are classified into their true group:

number of true negatives N number of true negatives number of false negatives

Sensitivity is the proportion of positive cases that are correctly identified as such:

number of true positives Sensitivity number of true number of false negatives

Specificity is the proportion of negative cases that are correctly identified as such:

number of true negatives Specificity number of false positives number of true negatives

108 True positives are positive cases correctly identified as such (e.g. males classified as males), false positives are negative cases incorrectly classified as positive (e.g. females classified as males), true negatives are negative cases correctly classified as negative (e.g. females classified as females), and false negatives are positive cases incorrectly classified as negative (e.g. males classified as females.) Higher values for PPV, NPV, sensitivity, and specificity indicate a powerful test for discriminating groups. It is predicted that the discriminant functions will be able to separate the time period and sex subgroups.

5.6.10 Canonical Variate Analysis

A canonical variate analysis will next be carried out to visualize the variation among groups. This method assumes group structure within the data and will maximize the among-group variation relative to the within-group variation, much like discriminant function analysis (Manly, 2005). Canonical variate analysis produces a new set of variables, canonical variates, in the fewest number of dimensions (k − 1) that accounts for successive amounts of among-group variation relative to the within-group variation

(Albrecht, 1980). It is assumed that all groups share the same covariance matrix, which will be examined through matrix correlation permutation test. These new variables are uncorrelated among and within groups. Therefore, canonical variate analysis is also similar to principal components analysis.

The procedure for calculating canonical variates occurs in two steps. First, the multivariate space is scaled so that within-group variation becomes equal in all directions. Second, the coordinate system is rotated so that the new axes align with the

109 major axes of variation among the sample means. Mahalanobis and Procrustes distances are calculated between each group associated with 10,000 permutation rounds.

5.6.11 Covariation of Landmark Subsets

Four separate tests for modularity will be performed by creating four sets of partitions. The first set of partitions will divide the landmarks into subsets according to the Functional Matrix Hypothesis (Moss and Young, 1960) corresponding to two major

(face and neurocranium) and eight minor (anteroneural, midneural, postneural, otic, respiratory, masticatory, and alveolar) functional modules (Figure 5.7). The second set of partitions will divide the landmarks into subsets according to the Functional-

Developmental Hypothesis (von Cramon-Taubadel, 2011) corresponding to seven functional-developmental modules (chondrocranium, dermatocranium, vault, face, optic, auditory, and nasal. There is spatial overlap of the special sensory modules and the chondrocranial and dermatocranial modules; hence, they cannot be considered simultaneously. The nasal and orbital modules are part of the face while the auditory module is part of the chondrocranium. The chondrocranium, vault, and face will be considered as a separate group (Figure 5.8). The nasal, auditory, and orbital modules will be compared against the remainder of the cranium (Figure 5.9). The third set of partitions will divide the landmarks into basicranial, facial, and vault modules considering the cranial base as the keystone of the cranium (Figure 5.10). The definition of the basicranium here is different from the chondrocranial module in the functional- developmental hypothesis. Whereas the prior model defines the basicranium from a strictly developmental perspective base on embryological origins and endochondral 110 ossification the proposed model relies on a functional definition. As such, some landmarks that would be defined as part of the vault in the former model are considered part of the basicranium in the latter. The resulting levels of modularity will be compared across the three sets to determine which model best represents the interrelatedness of cranial components.

Anteroneural Midneural Postneural Otic Optic Respiratory Masticatory Alveolar

Figure 5.3: A priori hypothesis of modularity based on functional modules. 111 Face Vault Chondrocranium

Figure 5.4: A priori hypothesis of modularity based on developmental fields in functional-developmental modules.

112 Orbital Auditory Nasal Remainder

Figure 5.5: A priori hypothesis of modularity based on special sensory fields in functional-developmental modules.

113 Basicranium Vault Face

Figure 5.6: A priori hypothesis of modularity based on basicranial module.

114 Evaluating hypotheses of specific patterns of modularity involves comparing the degree of covariation among hypothesized modules to the range of covariation among all other possible alternate partitions (Figure 5.11). If the hypothesized subsets correspond to the true subsets then the covariation in this partition will be lower than alternate partitions (Klingenberg, 2009). The hypothesis of modularity will be rejected if covariation among subsets for the hypothesized modules is not weaker than most or all of the alternative modules. The hypothesized subsets will be compared to 10,000 random partitions that are spatially contiguous. Spatial contiguity is defined as all landmarks and the partitions of the entire configuration are connected (Klingenberg, 2009). Only modules that are connected to one another by adjacent landmarks are considered for comparison. Adjacency is determined by a Delaunay triangulation of all landmark points which divides the volume into a number of tetrahedrons that maximize the minimal angles of all tetrahedrons thus approximating the spatial contiguity of the modules

(Klingenberg, 2009) (Figure 5.12).

Figure 5.7: Evaluating hypothesis of modularity. The vertical dashed line represents the partition between two hypothesized modules (dotted boxes) and the oblique dashed line signifies an alternate partition. Arrows denote covariation between landmarks. (After Klingenberg, 2009.)

115

Figure 5.8: Delaunay triangulation of configuration to identify spatially contiguous landmarks and partitions.

The RV coefficient (Robert and Escoufier, 1976), analogous to the squared correlation coefficient, was calculated to describe relationships among functional modules and to test hypotheses of modularity (Klingenberg, 2008). The covariance matrix of the Procrustes coordinates is rearranged so the subsets are in different blocks such that

1 12 21 2 where S1 and S2 are the covariance matrices within the subsets and S12 and S21 are the covariance matrices between the subsets. The RV coefficient is then represented by

trace( ) 12 21 trace( 12 21)trace( 2 2) in which the numerator is the total squared covariation and the denominator is the squared covariation within the blocks. The trace of a matrix is the sum of the elements of

116 the main diagonal. For more than two sets of landmarks (k), the multi-set RV coefficient is used (Klingenberg, 2009):

1 2 ( , ) M ( 1) 1 1

An RV coefficient of zero indicates the sets of variables are completely unrelated and a value of one indicates the subsets of variables are completely interdependent.

5.7 SUMMARY

This chapter presented an outline of the research design for this study. One hundred and fifty two landmarks were used to describe endocranial and ectocranial form of modern and historic samples of human crania. Few studies combine both the features of the inside and outside of the cranium. These landmarks were selected to cover the modularity hypotheses as with foci on functional, functional-developmental, and basicranial modularity. The efforts to include endocranial anatomy limited this study to a sample size of 391 individuals housed at the William M. Bass Donated Skeletal

Collection at the University of Tennessee, Knoxville and the Hamann-Todd Human

Osteological Collection at the Cleveland Museum of Natural History. An intraobserver error study was conducted using a sample of 30 anatomical specimens housed in The

Ohio State University Division of Anatomy. A MicroScribe® 3DX portable coordinate measuring was used to register landmarks. Traditional multivariate statistical methods

(ANOVA, regression, principal components analysis, discriminant function analysis, canonical variate analysis) were applied in a geometric morphometric context

(Generalized Procrustes Analysis) to identify regions of the cranium displaying 117 variability within and between ancestry-sex groups across time. Last, the functional, functional-developmental, and basicranial hypotheses of cranial modularity were assessed by examining covariation of landmark subsets through the multiple set RV coefficient.

118

CHAPTER 6: RESULTS

This chapter presents the results of the statistical analyses of landmark data from both the error study and main study sample in relation to the main hypotheses. First, the level of observer error is assessed. Second, outliers are identified and removed from analysis. The results of the principal components analysis was used to identify regions in the cranium that vary the most within the sample and to determine if there are differences among ancestry-sex groups and time period (historical versus modern). Discriminant function analysis allows to further test whether classifying variables such as sex, ancestry, and time period can be used to identify individual group membership. Finally, covariation of landmarks subsets is inspected by comparing hypothesized partitions of landmarks to random, alternate partitions.

6.1 OBSERVER ERROR

Fourteen landmarks were not included in observer error analysis because they were missing from at least one specimen and therefore could not be compared across all specimens and measurement rounds or the landmarks were removed to maintain symmetry in the data set. This error study allowed for a refinement of the data protocol and the order in which the landmarks were recorded with the digitizer for the study sample. Figure 6.1 shows all 60 landmarks configurations. The numbers correspond to the 138 landmarks listed in Appendix D. Upon visual inspection it appears, as expected, 119 Type 3 landmarks have the greatest variation in location. A Procrustes ANOVA was performed to assess the level of variation in the landmark configurations due to biological variation or measurement error, considering both centroid size and shape.

Figure 6.1: Procrustes ANOVA landmark configuration for measurement error. Large blue dots represent landmark centroids. Small black dots represent individual landmark locations.

120 In the Procrustes ANOVA for centroid size (Table 6.1) the main effects are

‘individual’ (the 15 crania) and ‘digitizing’ (repeated rounds of measurement for each specimen, labeled as ‘residual’ in Table 6.1). The mean squares values, the expected deviation of each value around the next higher level, indicate that variance due to measurement error is approximately 1,407 times smaller than variance due to individual specimens. The effect of the individual is statistically significant and unsurprising in that there are differences in individual size among organisms.

Table 6.1: Observer error Procrustes ANOVA for centroid size.

Effect SS MS df F P Individual 27360.038268 1954.288448 14 1407.80 < 0.0001 Residual 62.468327 1.388185 45

In the Procrustes ANOVA for shape (Table 6.2) the main effects are ‘individual’ and ‘side’ (the symmetric component of shape) with an interaction term of individual-by- side (the random asymmetry component). The ‘residual’ represents error from digitizing during multiple rounds. Measurement error is approximately three times less than the smallest biological effect (random asymmetry). Since the digitizing error is small relative to biological variation it is concluded that intraobserver error is negligible and that repeated rounds of measurement are not necessary for subsequent data collection.

121 Table 6.2: Observer error Procrustes ANOVA for shape.

Effect SS MS df F P Individual 0.26021523 0.0000856535 3038 2.60 < 0.0001 Side 0.07392919 0.0003891010 190 11.82 < 0.0001 Ind*Side 0.08755326 0.0000329148 2660 3.07 < 0.0001 Residual 0.19636430 0.0000107215 18315

A second round of Procrustes ANOVAs was run to understand if removing landmarks that are difficult to locate might further decrease the effect of measurement error. A new data set was created that excluded all Type 3 landmarks (alare, metopion, parietal subtense point, opisthocranion, occipital subtense point, and zygion) and a couple

Type 2 landmarks (alveolon, orale). There was a minute difference from the initial

ANOVA. Thus, no landmarks were removed for further analyses. The fourteen landmarks removed from the initial list of 152 were added back into for all analyses hereafter. Ten of these contain information concerning the shape of the alveolar complex and four relate to basicranial shape. Leaving the alveolar landmarks out of the study would not allow for an evaluation of hypotheses concerning modularity related to the alveolar module as most of the upper jaw would be absent from analysis.

6.2 MISSING DATA

Landmarks that could not be collected due to damage or bone resorption were estimated using multiple linear regression resulting in new coordinates reflected on a shape subspace (Figure 6.2). Missing landmarks comprised 2.86% of the data set. All further analyses were conducted using the estimated values.

122

Figure 6.2: Multivariate regression of missing landmarks reflected on shape subspace.

6.3 GENERALIZED PROCRUSTES ANALYSIS

Each specimen’s landmark configuration was scaled, rotated, and translated to a common coordinate system in order to compare shape variation. Object symmetry is assumed and used for all analyses (Figure 6.3). Next, the mean landmark configuration was calculated. Upon the initial Procrustes fit and exploratory principal components analysis it was discovered that there were errors in data collection of landmarks for the zygomaticotemporal suture, carotid canal, jugular foramen, mastoid process, hypoglossal foramen, occipital condyle, and foramen magnum. These errors occurred while collecting data from a subset of individuals from the Hamann-Todd Collection. No systemic pattern was discovered in the raw data to fix theses errors. The errors were most likely due to collecting landmark locations in the wrong order. As such, 32 landmarks were removed from the data set (landmark #s 37, 38, 43, 45, 85-88, 91-94,

123 100-103, 105-108, 134-137, 139, 140, 143, 144, and 146-149 as listed in Appendix C) and all analyses were performed using the remaining 120 landmarks. A new Procrustes fit was performed with these landmarks (Figure 6.4). This configuration represents the average shape of all individuals in the sample.

Figure 6.3: Paired and median landmarks matching during Procrustes fit. (Transverse plane shown with anterior at top. Green dots are median landmarks, blue dots are paired landmarks, and lines indicate pairings.)

124

Figure 6.4: Landmark configuration after Procrustes fit. (Large blue dots represent landmark centroids. Small black dots represent individual landmark locations for all 391 crania. Median plane on top. Coronal plane on bottom.) 125 6.4 OUTLIERS

Before proceeding to formal statistical analyses the sample was examined for errors in data collection and to identify outliers. The landmark configuration for each individual was compared against mean shape to visualize deviation of individual landmark locations from the landmark centroids. Some landmarks had to be swapped because they were recorded in the wrong order. Also, several outliers were identified and removed from the sample.

The lollipop graph in Figure 6.5 shows the mean landmark configuration for the entire sample with the direction and magnitude of deviation for individual ‘HTH2512’ from the sample average. If a pair of landmarks have deviations that are of the same magnitude (squared Procrustes distance) and directed toward one another it is an indication that the landmarks were accidentally swapped in order during data collection

(landmarks 139 and 140 in the transverse plane view of Figure 6.5). MorphoJ allows the user to fix this error and automatically corrects the data set and creates a new Procrustes fit. This graph is also used as a visual representation of whether or not an individual specimen is considered an outlier. Outliers are identified by their squared Procrustes distance from the mean configuration. Examples of normal and non-normal individuals are given in Figure 6.6, with squared Procrustes distances of 0.00351 (closest to mean shape) and 0.13550 (farthest form mean shape) respectively. The normal individual has small deviations from the mean landmark configuration. The outlier has large deviations from the mean landmark configuration.

126

Figure 6.5: Checking for errors in landmark collection order. (Blue dots are mean landmark locations for sample. Red lines represent magnitude and direction of deviation for a single landmark for specimen HTH2512. Midsagittal plane on top, transverse plane on bottom.)

127

Figure 6.6: Examples of normal individual (left, UT94-07D) and extreme outlier (right, HTH2365).

Next, the cumulative distribution of squared Procrustes distances from the average was examined to identify outliers (Figure 6.7) The first three specimens with the highest squared Procrustes distances (0.13550, 0.12957, and 0.10736) were removed until the distribution of squared Procrustes distances no longer stretched to the top and right of the distribution curve. The next highest Procrustes distance was considerably lower at

0.06732. Such an asymptote would indicate that there is one or more specimens that deviate very strongly from the average shape (Klingenberg and Monteiro, 2005).

Overall, the distribution is non-normal and long-tailed, which is to be expected of

128 multivariate biological data. Subsequent analyses were carried out using the remaining

388 individuals in the sample. Non-parametric permutation tests will be carried out when possible.

Figure 6.7: Cumulative distribution of Procrustes distances from average. Red curve is actual data. Blue curve is a multivariate normal distribution fitted to the data.

6.5 PRINCIPAL COMPONENTS ANALYSIS

A principal components analysis was performed to explore the variation in shape within the sample by reducing the data into a smaller number of uncorrelated variables.

Prior to this analysis the covariance matrices between collections and sex were compared to evaluate their similarity. Separate matrix correlations were calculated and matrix 129 permutation tests were performed. The matrix correlation indicates the general similarity of the matrices while the matrix permutation test evaluates the null hypotheses of a complete lack of relatedness of the covariance matrices compared to 10,000 randomized rounds. The correlation between the two collections is moderately positive (r = 0.6738), the correlation between males and females is strongly positive (r = 0.7950), and both permutation tests indicate that the covariance matrices are similar (p < 0.0001) (Figures

6.8 and 6.9). Therefore, the requirement of similar covariance matrices for further analyses has been satisfied.

One hundred and ninety principal components (PCs) were created from the data set to account for all variation with the first 85 PCs accounting for approximately 95% of the variance (Table 6.3). The percent variance explained by subsequent components begins to drop off quickly after the second component, explaining only 19% of the cumulative variation; therefore, only the first two will be examined in detail. The percent of the variance explained by all principal components is illustrated in Figure 6.10.

130 Matrix correlation = 0.6738 Permutation test p < 0.0001

Figure 6.8: Covariance matrix correlation between collections.

Matrix correlation = 0.7950 Permutation test p < 0.0001

Figure 6.9: Covariance matrix correlation between sexes.

131 Table 6.3: Amount of variance explained by principal components.

PC Eigenvalues % Variance Cumulative % 1 0.00072255 11.82 11.82 2 0.00043691 7.15 18.96 3 0.00039677 6.49 25.45 4 0.00033548 5.49 30.94 5 0.00025271 4.13 35.07 6 0.00021139 3.46 38.53 7 0.00020594 3.37 41.90 8 0.00017107 2.80 44.70 9 0.00016050 2.62 47.32 10 0.00015182 2.48 49.81 … … … … 85 0.00000855 0.14 95.05 … … … … 190 0.0000023 <0.01 100.00

Figure 6.10: Percent variance explained by principal components.

132 The PC scores, the magnitude of shape change in Procrustes distance, were plotted against one another to identify patterns in the variation (Figure 6.11). It is important to be reminded that this analysis does not assume the data are structured in any particular way. On the other hand, lack of structure after analysis does not mean that the data are not structured. Further tests will explore the details of the samples variation. No clustering is apparent. A few isolated points indicates that outliers may still exist in the data set. The points were then identified by collection, sex, and ancestry to see if the any underlying structure could be identified (Figures 6.12-6.14).

In Figure 6.12, examining variance according to collection, PC1 and PC2 separates the collections. Individuals from the Bass collection have higher PC scores and individuals from the Hamann-Todd collection have lower PC scores. The ‘collection’ classifier here is taken as a proxy for time period with the Hamann-Todd collection being historic and the Bass collection representing a modern population. The date of death of individuals in each sample are separated by at least 50 years. Thus, a possible secular trend in cranial form is suggested. Or, there may be differences heretofore not realized between the northern and southern populations that may reflect environmental differences.

In Figure 6.12, examining variance according to sex, there is overlap in the distribution of scores of males and females in both PCs. Differences in males and females can primarily be attributed to size differences. Since size was removed during the generalized Procrustes analysis it is not surprising that it does not appear in the PCs.

Still, aside from size differences between the sexes, one would expect minor shape differences in areas such as the brow ridge, frontal curvature, and nuchal region. These 133 areas are not only typically larger in males, but take on a slightly different shape as a result of differing hormone levels related to growth (Scheuer and Black, 2000).

In Figure 6.13, examining variance according to ancestral group affiliation, there is overlap in the distribution of scores. White (lower PC scores) and Black (higher PC scores) populations cluster together with large amounts of overlap. Hispanic, Japanese, and White/Native American individuals are clustered toward the center of the scatterplot.

Figure 6.11: Scatterplots of PC1 and PC2 scores.

134

Figure 6.12: Scatterplots of PC1 and PC2 scores separated by collection.

135

Figure 6.13: Scatterplots of PC1 and PC2 scores separated by sex.

136

Figure 6.14: Scatterplots of PC1 and PC2 scores separated by ancestry.

137 Next, the shape changes for PCs 1 and 2 were examined by interpreting lollipop graphs (Figures 6.15 and 6.16). The graphs are scaled to a factor of 0.1 which corresponds to a change of PC score by 0.1 in the positive direction (Klingenberg, 2011).

It is the magnitude of shape change as a Procrustes distance. The dots represent the mean landmark configuration and the lines signify the magnitude and direction of shape variation for individual landmarks. The vectors have been scaled to exaggerate minor differences that might not be seen otherwise.

PC1 (Figure 6.15) explains 11.82% of the variance and represents variation in the vault, maxilla, face, and cranial base. Increase in PC score corresponds to an inferior shift of the midline vault above the nuchal plane. The vault inferior to the nuchal plane is displaced superiorly. The maxilla, including the dental arcade, is shifted anteriorly. The face becomes narrower across the cheeks with increased PC scores. The anterior and middle cranial fossae are more anteriorly placed, the middle cranial fossa is displaced inferiorly, and the posterior cranial fossa is shifted superiorly with higher PC scores. PC2

(Figure 6.16) explains an additional 7.15% of the variance and signifies changes in the vault, base, and face. Increased PC scores correspond to a heightening and slight widening of the vault with a concomitant narrowing of the midface and posterior shift of the maxillary alveolar region. The basicranium undergoes a posterior shift of the anterior cranial fossa and an anterior shift of the middle cranial fossa. Both fossae are also narrower with larger PC scores. No further PCs were examined closely as the amount of variance explained by each component begins to drop precipitously.

138 (a)

(b)

(c)

Figure 6.15: Shape changes for PC1. (a) Transverse plane, (b) Median plane, (c) Coronal plane. (Scaled to factor of 0.1.) 139 (a)

(b)

(c)

Figure 6.16: Shape changes for PC2. (a) Transverse plane, (b) Median plane, (c) Coronal plane. (Scaled to factor of 0.1.) 140 6.6 PROCRUSTES ANOVA

The effects of the classifier variables ‘collection’, ‘sex’, and ‘ancestry’ was determined through an analysis of variance of the shape data in a similar fashion as observer error (Table 6.4). Here, ‘sex’ is considered the error term and ‘ancestry’ is the residual. All variables have a significant effect on shape with ‘collection’ accounting for approximately seven times more variance than ‘sex’ and ‘sex’ accounting for approximately four times more variance than ‘ancestry’. The null hypothesis that there is no effect of temporal period, sex, or ancestry is rejected.

Table 6.4: Classifier effects, Procrustes ANOVA for shape.

Effect SS MS df F P Collection 0.11446289 0.0006024363 190 2.16 < 0.0001 Error 1 0.06126006 0.0000867706 706 4.17 < 0.0001 Residual 2.82185232 0.0000208175 135552

6.7 REGRESSION ANALYSIS

Ten individuals did not have known age-at-death and were excluded from the multiple regression analysis of age and shape. Some of these ages may have been estimated by the medical examiner if the person could not be identified. Interpretation of the results therefore must proceed cautiously. Age as an explanatory variable accounts for only 0.007744% of the variation in shape (10,000 round permutation, p < 0.001)

(Table 6.5 and Figure 6.17). Although there is a significant relationship between age and shape, the relationship is weak (R = 0.008776). This refutes the hypothesis that age-at- death has no effect on shape. 141 Table 6.5: Regression analysis of relationship between age-at-death and shape.

SS Total 2.30746428 Predicted 0.02024917 Residual 2.28721511 p < 0.0001

R = 0.008776 R2 = 0.00007744

Figure 6.17: Scatterplot of regression scores.

6.8 DISCRIMINANT FUNCTION ANALYSIS

Assuming that each individual actually belongs to the groups they are assigned, classification rules were established to separate groups based on collection and sex.

There are multiple ancestry groups in this sample and are evaluated later with a canonical variate analysis. Both the parametric T2 test and the non-parametric 1,000 round permutation test indicate that there is a statistically significant difference in mean group 142 shape based on collection and sex (p < 0.0001) (Table 6.6). When scaled to the within- group variation, the distance (Mahalanobis) between groups is far greater than the distance (Procrustes) based on the absolute shape difference. The distribution of the cross-validated discriminant function scores for shape differences have minimal overlap according to collection and significant overlap according to sex, as presented in Figures

6.18 and 6.19.

Table 6.6: Test for difference between mean shapes of groups.

Collection Sex Procrustes distance 0.0343 0.0170 Mahalanobis distance 6.8718 4.5403 T2 4580.3252 1781.9544 p < 0.0001 p < 0.0001 P-values for permutation tests Procrustes distance < 0.0001 < 0.0001 T2 < 0.0001 < 0.0001

143

Figure 6.18: Cross-validated discriminant function scores distribution for collections.

Figure 6.19: Cross-validated discriminant function scores distribution for females and males.

144

The classificatory power of the tests was evaluated by calculating the percent correct cross-validated classification and a number of indicators of the tests’ precision.

Since the discriminant functions were tested on the original sample, a cross-validation algorithm was employed to estimate the classificatory power in place of using a hold-out sample or an entirely new sample. These are close approximations to the real-world classificatory power, which would be slightly lower. Both tests perform well to correctly classify cases with rates of 94.58% by collection and 82.21% by sex. Modern specimens and females are proportionally misclassified at a higher rate than historic specimens and males. The complete results of the cross-validation procedure are detailed in Tables 6.7 and 6.8. Again, here it seems that there is less variation within groups according to time period allowing for a more complete separation of the modern and historic samples.

Table 6.7: Cross-validation classification results for collections. Bolded values are correctly classified cases.

True Allocated To Group Bass HT Total Bass 186 9 195 HT 12 181 193 (94.58% correct classification)

Table 6.8: Cross-validation classification results for females and males. Bolded values are correctly classified cases.

True Allocated To Group Female Male Total Female 101 29 130 Male 40 218 258 145 (82.21% correct classification) Positive predictive value (precision), negative predictive value, sensitivity (true positive rate), and specificity (true negative rate) are metrics for how well the test performs overall and were calculated using the cross-validation classification results

(Table 6.9). The closer the numbers are to 100% the more likely that the decision rules will correctly allocate a given case and that Type 1 (false positive) or Type 2 (false negative) errors will be avoided. ‘Bass’ and ‘Female’ are considered the positive state and ‘ T’ and ‘Male’ are considered the negative state under the gold standard for these tests. For the functions separating collections, these values are all greater than 93%. For the functions separating sex, just as in the classification rate, the values are no higher than

89%. In particular, the PPV for sex is only 71.63%, indicating the test is not as precise at correctly allocating females, but is much better at allocating males.

Table 6.9: Precision of decision rules for classification. All values given as %.

Metric Collection Sex Positive Predictive Value 93.94 71.63 Negative Predictive Value 95.26 88.26 Sensitivity 95.38 77.69 Specificity 93.78 84.50

Differences in mean shape can be visualized in wireframe graphs, scaled to

Procrustes distances, which connect contiguous landmarks (Figures 6.20 and 6.21). The reference shape (modern and female crania) is superimposed on a target shape representing an increase in discriminant function score (historic and male crania). When comparing historic individuals with modern individuals the biggest difference lies in the

146 height and width of the vault. Individuals from the Bass collection have narrower and taller vaults whereas individuals from the Hamann-Todd collection have slightly shorter and wider vaults. Related to differences in neurocranial shape is a related narrowing of the basicranium in modern individuals. Also, modern individuals have a higher palate while the historic individuals have a shallower palate. When considering sex as the discriminating variable, there are marked differences in the frontal bone, mastoid process, zygomatics, alveolar region of the maxillae, and vault shape. The frontal bone is more vertical with less prominent brow ridges in females. The zygomatics are not as flared in females as they are in males. The maxillae are displace inferiorly resulting in a proportionally larger midface in males. Finally, the vault in females is more rounded overall and more vertical along the coronal suture. These differences are typical of the sexually dimorphic features of humans (Buikstra and Ubelaker, 1994; White and Black,

2011). The discriminant function analyses support the hypothesis that there are differences in shape between time periods and the sexes.

147

Figure 6.20: Discriminant function shape differences between collections. (Light blue lines with open dots is the reference shape and dark blue lines with closed dots is mean shape of a twofold increase in discriminant score.)

148

Figure 6.21: Discriminant function shape differences between sexes. (Light blue lines with open dots is the reference shape and dark blue lines with closed dots represents a threefold increase in discriminant function score.)

149 6.9 CANONICAL VARIATE ANALYSIS

A canonical variate analysis was performed to further investigate data structure while considering all 12 collection-ancestry-sex groups (listed in Table 5.3) simultaneously. The resulting 11 canonical variates (CV) explain the variation that separates the groups, the first eight of which explain approximately 95% of the variation between groups (Table 6.10). The CV scores were plotted against each other to determine group structure (Figure 6.22). The unit of axes in the CV score plots is in

Mahalanobis units, or, the shape change per unit of within-group variation (Klingenberg,

2011). Only CVs 1-6 are presented here as they are the only CVs that provide distinct separation among groups. CV1 separates crania into three groups: HT Black males/females (CV score < −2), HT White males/females and Bass Black males/females and Hispanic (CV around 0), and all other groups (CV score > 0). CV2 separates the

Bass Black males/females with a lower CV score and the HT White males/females with a higher CV score from all other groups. Together, CV1 and CV2 separate individuals from the Bass collection from individuals from the Hamann-Todd collection. CV3 separates males (higher CV score) and females (lower CV score). CV4 separates the

White/Native American males with a higher CV score. CV5 separates the single

White/Native American female (CV score < −8) from all other individuals (CV score >

−4). CV 6 separates the Hispanic males from all other groups with a CV score less than

−4.

The Mahalanobis and Procrustes distances among groups are given in Tables 6.11 and 6.12, respectively, with the associated p-values form the permutation tests. Again,

Procrustes distances measure the absolute difference in mean shape between groups and 150 Mahalanobis distances measure the distance relative to within-group variation. The

Mahalanobis distances here are different from the Mahalanobis distances in the discriminant function analysis because the canonical variate analysis uses the pooled within-group covariance matrices of all groups at the same time (Klingenberg and

Monteiro, 2005). All Mahalanobis distances were statistically significant at α = 0.05 for all group-wise pairings except for Bass,J,F--Bass,B,M; Bass,J,F--Bass,H,M; Bass,W,F--

Bass,J,F; Bass,W,M--Bass,J,F; Bass,W/NA,F--Bass,H,M; Bass,W/NA,M--Bass,J,F;

Bass,W/NA,M--Bass,W/NA,F; and HT,B,F--Bass,J,F. All Procrustes distances were significant at α = 0.05 for all group-wise pairings except for Bass,J,F--Bass,B,F;

Bass,J,F-- Bass,B,M; Bass,J,F-- Bass,H,M; Bass,W,F--Bass,J,F; Bass,W,M--Bass,J,F;

Bass,W/NA,F-- Bass,B,F; Bass,W/NA,F-- Bass,B,M; Bass,W/NA,F-- Bass,H,M;

Bass,W/NA,F-- Bass,J,F; Bass,W/NA,F-- Bass,W,F; Bass,W/NA,F-- Bass,W,M

Bass,W/NA,M-- Bass,J,F; Bass,W/NA,M-- Bass,W,F; Bass,W/NA,M-- Bass,W,M;

Bass,W/NA,M-- Bass,W/NA,F; HT,B,F--Bass,J,F; HT,B,F--Bass,W/NA,F; HT,B,F--

Bass,W/NA,M; HT,B,M--Bass,J,F; HT,B,M--Bass,W/NA,F; HT,B,M--Bass,W/NA,M;

HT,W,F--Bass,J,F; HT,W,F--Bass,W/NA,F; HT,W,M--Bass,J,F; HT,W,M--

Bass,W/NA,F. This indicates there is no statistically significant difference in mean shape between the aforementioned groups. Still, one must consider that canonical variate analysis maximizes the within-group variation relative to the between-group variation.

Broad patterns emerge from the distance measures. Historic crania from all subgroups in the Hamann-Todd collection are statistically significantly different from the modern individuals in the Bass collection. Within the Hamann-Todd collection all subgroups are statistically significantly different from one another. Conversely, there is 151 considerable overlap in the CV scores of most Bass subgroups. Modern crania are more homogeneous, i.e. more overlap among distributions of CV scores, and historic crania are more heterogeneous. The sample sizes of individuals representing the Japanese (n = 1),

Hispanic (n = 9) and White/Native American (n = 5) groups are extremely small and most likely are not representative of variation in their respective populations. Also, males comprise almost two thirds of the total sample and may skew classification results.

Table 6.10: Amount of variation among groups explained by canonical variates.

CV Eigenvalues % Variance Cumulative % 1 15.5907 39.35 39.34 2 9.7633 24.64 63.99 3 4.6278 11.68 75.67 4 2.5359 6.41 82.07 5 1.5312 3.86 85.94 6 1.4115 3.56 89.50 7 1.2519 3.16 92.66 8 0.9795 2.47 95.13 9 0.7548 1.90 97.04 10 0.6841 1.73 98.764 11 0.4898 1.24 100.00

152

Figure 6.22: Scatterplots of canonical variates 1-6 scores.

153 Table 6.11: Mahalanobis distances among groups in canonical variate analysis. P-values for permutation tests are given below distances. Bolded values are not statistically significant at α = 0.05.

1. Bass,B,F 2 3 4 5 6 7 8 9 10 11 2. Bass,B,M 7.5240

<0.0001

3. Bass,H,M 10.3475 8.6537

<0.0001 <0.0001

4. Bass,J,F 15.7542 15.6265 15.6154

0.0013 0.0981 0.0528

5. Bass,W,F 8.4594 7.9770 9.1696 15.4951

154 <0.0001 <0.0001 <0.0001 0.1064

6. Bass,W,M 9.2488 6.6414 8.6150 16.0051 5.8144

<0.0001 <0.0001 <0.0001 0.1107 <0.0001

7. Bass,W/NA,F 20.4357 21.3316 21.2057 23.1734 19.7089 20.5704

0.0105 0.0008 0.0980 1.000 0.0172 0.0011

8. Bass,W/NA,M 13.7050 12.3472 11.9957 18.3254 12.5215 11.4590 22.4067

0.0008 <0.0001 0.0013 0.3166 0.0001 <0.0001 0.1920

9. HT,B,F 9.7495 8.7272 10.7769 17.5514 10.5987 10.5933 21.7693 15.8796

<0.0001 <0.0001 <0.0001 0.0930 <0.0001 <0.0001 0.0110 <0.0001

10. HT,B,M 11.1186 8.0853 10.7045 17.8336 11.5666 10.2116 22.0047 15.4124 5.8476

<0.0001 <0.0001 <0.0001 0.0389 <0.0001 <0.0001 0.0109 <0.0001 <0.0001

11. HT,W,F 10.6575 9.5412 10.1770 16.1064 8.5983 9.0262 20.7666 12.9492 8.5136 9.6050

<0.0001 <0.0001 <0.0001 0.0030 <0.0001 <0.0001 0.0081 <0.0001 <0.0001 <0.0001

12. HT,W,M 12.2128 10.0574 10.1345 16.6410 9.8765 8.5282 20.1088 12.6419 9.9784 9.3379 6.5335 <0.0001 <0.0001 <0.0001 0.0325 <0.0001 <0.0001 0.0128 <0.0001 <0.0001 <0.0001 <0.0001

154 Table 6.12: Procrustes distances among groups in canonical variate analysis. P-values for permutation tests are given below distances. Bolded values are not statistically significant at α = 0.05.

1. Bass,B,F 2 3 4 5 6 7 8 9 10 11 2. Bass,B,M 0.0345

0.0029

3. Bass,H,M 0.0456 0.0311

0.0001 0.0153

4. Bass,J,F 0.0773 0.0641 0.0673

0.2589 0.5423 0.2675

5. Bass,W,F 0.0458 0.0428 0.0417 0.0784

<0.0001 <0.0001 <0.0001 0.1640

6. Bass,W,M 0.0499 0.0356 0.0365 0.0744 0.0247 155 <0.0001 <0.0001 0.0020 0.2986 <0.0001

7. Bass,W/NA,F 0.0804 0.0743 0.0739 0.0872 0.0640 0.0648

0.1987 0.1829 0.1140 1.0000 0.5377 0.5611

8. Bass,W/NA,M 0.0535 0.0441 0.0443 0.0757 0.0411 0.0332 0.0729

0.0285 0.0455 0.0223 0.3020 0.1847 0.6685 0.3933

9. HT,B,F 0.0439 0.0296 0.0415 0.0706 0.0469 0.0447 0.0734 0.0533

<0.0001 <0.0001 0.0013 0.5578 <0.0001 <0.0001 0.4968 0.0964

10. HT,B,M 0.0461 0.0318 0.0419 0.0768 0.0495 0.0465 0.0760 0.0543 0.0208

<0.0001 <0.0001 0.0012 0.3103 <0.0001 <0.0001 0.3152 0.0641 0.0046

11. HT,W,F 0.0647 0.0575 0.0555 0.0812 0.0409 0.0461 0.0646 0.0514 0.0495 0.0480

<0.0001 <0.0001 <0.0001 0.0800 <0.0001 <0.0001 0.7541 0.0171 <0.0001 <0.0001

12. HT,W,M 0.0733 0.0632 0.0587 0.0888 0.0508 0.0520 0.0722 0.0585 0.0552 0.0501 0.0253 <0.0001 <0.0001 <0.0001 0.1105 <0.0001 <0.0001 0.5176 0.0007 <0.0001 <0.0001 0.0051

155 To visualize shape changes associated with the canonical variates, wireframe graphs for CVs 1-3, 5, and 8 (Figures 6.23 and 6.24) were produced, scaled to a factor of

10 Mahalanobis units, and are related to the scales in the CV scatterplots. The target shape represents a tenfold increase in CV score. For CV1 (39.35% variance), an increase in CV score corresponds to a taller vault with a less projecting maxilla and slightly narrower face. In CV2 (24.64% variance), an increased CV score matches a taller, wider vault with a wider anterior and middle cranial base and a flatter occipital. The face is slightly wider and more orthognathic. CV1 and CV2 separate the Bass and Hamann-

Todd collections. The historic individuals have a slightly taller, narrower vault, a narrower face along the zygomata, and a narrower posterior palate. The modern individuals have a more vaulted palate. (Palate vaulting refers to a superior displacement of the palate relative to the alveolar region taking on a more arched appearance.)

Females are the reference shape in CV3 (11.68% variance) while males having a higher

CV score are the target. The male shave a more projecting glabellar region with a posteriorly sloping forehead. Compared to females, the males have a slightly shorter vault that is narrower. For CV4 (6.41% variance), White/Native American males have a higher CV score. The higher score imparts a shape change with a less rounded vault, more superior nuchal plane, and wider nasal aperture. For CV5 (3.86% variance), the

White/Native American female has a lower CV score with a more vertical frontal, superiorly shifted palate, and a slightly wider and taller vault. The last CV examined in detail, CV6 (3.56% variance), places the Hispanic males with lower scores. These individuals have a more prognathic face and taller palate with a narrower vault.

156 There are two commonalities in shape change among the canonical variates.

First, there is a posterior shift of the posterior cranial fossa relative the rest of the cranium when CV scores increase. Second, there is a relationship between vault height and palate height. As the vault becomes relatively taller the palate takes on a more vaulted shape.

The canonical variates allowed for further ordination of the data and exploration of areas representing shape change in the sample. Combined with the discriminant function analysis the data are grouped based upon classifying variables, in order of amount of separation, time period, ancestry, and sex.

157

Figure 6.23: Shape changes associated with canonical variates 1-3. Light blue lines with open dots are the reference and dark blue lines with closed dots are the target, corresponding to a tenfold increase in CV score.

158

Figure 6.24: Shape changes associated with canonical variates 4-6. Light blue lines with open dots are the reference and dark blue lines with closed dots are the target, corresponding to a tenfold increase in CV score.

159 6.10 COVARIATION OF LANDMARK SUBSETS

The second major hypothesis of this study addresses the differences among theoretical patterns of morphological integration in the human cranium. It was predicted that a pattern recognizing the basicranium as the keystone of the cranium, from a functional and developmental perspective, will most accurately describe modularity.

Three patterns of covariation were quantified by calculating the multi-set RV coefficient for a hypothesized partition (Table 6.13). This value was compared to 10,000 random, spatially contiguous partitions of the landmark configuration. If the value is lower than all or most other random partitions we fail to reject the null hypothesis of modularity.

The proportion of partitions lower than the hypothesized partition is used as a proxy for the p-value. At the same time, RVM is examined in reference to a scale from ‘0’ to ‘1’ as an overall measure of modularity. A ‘0’ indicates the modules are completely independent from one another and a ‘1’ points to complete dependence of the partitions.

Table 6.13: Evaluation of modularity hypotheses. Bold value are statistically significant.

Functional Functional-Developmental Basicranial Modules Modules Module Development Sensory RVM coefficient 0.1831 0.3422 0.1868 0.3458 Partitions with RVM ≤ 0 36 994 1 a priori hypothesis Proportion (out of 10,000) 0.0000 0.0036 0.0994 0.0001 Partition with minimal RVM 0.1831 0.3165 0.1101 0.3398

160 The first pattern follows the functional matrix hypothesis of Moss and Young

(1960) that recognizes eight minor functional complexes based on protection of the brain, protection of special sensory organs, breathing, and chewing (Figure 5.7). The functional module hypothesis of modularity is accepted (RVM = 0.1831, p = 0.0000). The low RVM value suggests that the modules are strongly independent of one another. The hypothesized partition is considered the true partition because there are no random partitions with a lower RVM.

The second pattern follows the functional-developmental hypothesis of von

Cramon-Taubadel (2011) that reconciles the embryological origins and special functions of certain cranial modules. This pattern was subdivided into two other patterns: one based strictly on morphogenetic fields (chondrocranium, vault, and face) (Figure 5.8) and another based on special sensory modules (nasal, auditory, and optic) (Figure 5.9). The hypothesis of strictly developmentally based modules is accepted (RVM = 0.3422 >

0.3106, p = 0.0036). However, RVM is relatively low which signifies that these modules are not dependent on each other. That is, the likelihood of observing a set of partitions with an RVM lower than that observed is unlikely. The hypothesis of special sensory modularity is rejected (RVM = 0.1868 > 0.1101, p = 0.0994). The low RVM value indicates that the modules associated with olfaction, hearing and equilibrium, and vision are highly independent of one another. This is expected in that these modules are spatially separated from one another; although, the nasal, and optic module are in close proximity and a case could be made that they have contiguous borders, depending on how one defines module boundaries. Despite this modularity there are 994 random

161 landmark partitions that have an RVM value lower than the hypothesized partition. This indicates that the hypothesis is not in accordance with the true partition.

The third pattern of modularity follows the proposed hypothesis of basicranial centrality and brings together development and function by dividing the cranium into three complexes (basicranium, vault, and face) (Figure 5.10). Here, the basicranium is not defined on embryological origins alone, as it is in the functional-developmental hypothesis. The focus is on the basicranium as a functional structure that integrates the rest of the head. The hypothesis of basicranial modularity is accepted (RVM = 0.3458 >

0.3398, p = 0.0001). Much like the developmental model, this hypothesized modularity predicts more integration among modules and has a higher RVM coefficient. Thus, this particular functional-morphological definition suggests higher levels of integration between the vault, face, and base.

When comparing levels of modularity for each pattern, the sensory specific hypothesis has the highest level of modularity followed by the functional and basicranial modules. The developmental-only model is the most integrated. Distributions of the

RVM coefficient for random landmark partitions according to each modularity hypothesis are presented in Figure 6.24. While the value associated with each pattern is not lower than all other random partitions, they are lower than most. The hypothesis of functional model partitions was rejected; however, the value is close to a significance level of 0.05.

As a complex unit, the cranium is more tightly integrated along lines of development and more modular along lines of function.

162 (a) RVM = 0.1831 (b) RVM = 0.3422

5151asdad0.50.50

000.3255

163 (c) RVM = 0.1868 (d) RVM = 0.3458

(a) RV = 0.3255 M

Figure 6.25: RVM distributions of random landmark partitions for evaluating hypotheses of modularity. (a) Functional Modules, (b) Functional-Developmental Modules – Development, (c) Functional-Developmental Modules – Sensory, and (d) Basicranial Module. Arrow indicates RVM for hypothesized partitions.

163 6.11 SUMMARY

In summation, the error study conducted on an independent sample prior to formal data collection showed that measurement error is approximately three items less than error due to normal biological variation. No landmarks were removed from further analysis; however, twelve landmarks could not be collected in the error study due to the condition of the specimens. While the error level of these landmarks could not be evaluated, it is assumed that they would have similar level of low measurement error.

Next, landmarks that could not be recorded from the study collection were estimated using multiple linear regression. The resulting coordinate set was subjected to a generalized Procrustes analysis that scaled, rotated, and translated the landmarks to common coordinate system. Exploratory principal components analysis revealed an error in collecting data from the mastoids and several basicranial foramina. Thirty two landmarks were removed from the data set leaving a total of 120 landmarks for all further analyses. Then, the data set was evaluated for multivariate normality and outliers were identified. The data are not multivariate normal with long-tailed distributions. Three individuals were identified as extreme outliers that affected the data’s distribution.

Afterward, a matrix correlation analysis showed that the covariance matrices of sample subgroups are similar and could be pooled for further analysis.

Additional exploration of the data was carried out via a principal components analysis to reveal data structure and specific areas of variation on the crania. PC scores showed separation when considering time period only suggesting a secular trend in cranial form. Areas of statistically significant variation included the cranial base, alveolar region, vault and cheeks. Age-at-death was regressed on cranial shape to find 164 that age has a statistically significant effect on shape, but the relationship is extremely weak. Discriminant function analysis of time period groups and sex groups showed that there are statistically significant differences in shape according to these classifying variables. The resulting models are able to identify the collection of origin and sex of individual crania with high classificatory power. Canonical variate analysis was considered to explore the relationship among all ancestry-sex-time period groups through ordination of CVs. All in all, there is the greatest variation according to time period of origin. The first major hypothesis is rejected. Finally, three main hypothesized patterns of modularity were evaluated. The cranium is highly modular in view of functional demands of cranial components and more integrated from the perspective of more developmentally defined modules. Thus, the second major hypothesis is supported.

165

CHAPTER 7: DISCUSSION

The first hypothesis of this study addresses questions of variation in cranial form.

An argument will be made for reasons that explain the patterns of variation and specific areas of shape change observed in the study sample as related to groupings based on ancestry, sex, and time period. A separate, but closely related conversation regarding the concept of race and ancestry in human variation is presented. Afterward, the observed patterns of morphological integration are addressed in the context of three proposed hypotheses of modularity that are not mutually exclusive. The results are compared to other research that posited similar questions. A difficulty arises in making comparisons when studies define modules in different ways using dissimilar landmarks and different study populations. Next, the results are discussed in the framework of evolutionary forces that shaped modern human cranial diversity as it relates to morphological integration. Then, limitations of this study that concern sample composition, landmarks, and statistical methodology is addressed. Suggestions for future research will be made for including non-hominoid species and medical imaging modalities. Last, applications of this research to questions such as individual identification in medicolegal contexts and clinical medicine will further illustrate the significance of this work.

166 7.1 MORPHOLOGICAL VARIATION IN HUMAN CRANIA

7.1.1 Sex

The manifestation of sexual dimorphism in the study sample is consistent with indicators that are traditionally assigned to the sexes (Buikstra and Ubelaker, 1994;

Walker 2008; White and Black, 2011). It largely coincides with findings of previous research into levels of sexual dimorphism using geometric morphometric methods (Rosas and Bastir, 2002; Kimmerle et al., 2008; Green and Cunroe, 2010; Bigoni et al., 2010;

Humphries and Ross, 2011). As a classifying variable, sex can be used to separate crania into groups with an 83.11% cross-validated classification rate. There was overlap in shape between male and female distributions. The most significant shape changes associated with discriminant function and canonical variate scores were evident in the mastoid process, palate, vault, brow ridge, and forehead. These difference are attributed due to hormonal effects during growth and development as well as differential biomechanical loading (White and Black, 2011). The palate is more inferiorly placed and more vaulted in males. The more gracile female crania have slightly taller and narrower vaults. Shape changes in the palate and vault are related to masticatory stress placed on the neurocranium and alveolar region of maxilla. More stress as induced by the temporalis muscle produces a shorter, wider vault. Increased bite forces are normally attributed to a shorter palate. Here the opposite pattern is observed. The palate is wider in males, as would be expected with potentially more heavily muscled individuals. Yet, the palate is vaulted. The glabellar region is flatter in females. The frontal bone is more vertical in females with a more anteriorly positioned frontal subtense point relative to 167 bregma. The male frontal bone slopes backward with metopion positioned more posteriorly. A smaller difference was detected in the cheeks. Here, the maxillae and zygomatics are taller and more flaring in males. Muscle attachments influence the form of the cheeks during growth and development. Masseter muscle pulls on the inferior margin of the cheeks during elevation of the mandible for mastication.

By further increasing the scale factors for the discriminant function scores one can produce hypothetical hyper-male or hyper-female crania (Figure 7.1). These conjectural forms represent extreme individuals on the tails of the shape distribution. Such speculation is not possible using interlandmark distances. This is unique to geometric morphometric analysis. More than an academic exercise, these hypothetical forms can be used to predict possible extreme variants of male and female morphology.

Finally, it should be noted that different populations may display different levels of sexual dimorphism (Green and Cunroe, 2010; Bigoni et al., 2010; Humphries and

Ross, 2011). In one population, males and females may be more similar and in another population male and female cranial form may be more divergent. Therefore it is important to investigate population specific levels of sexual dimorphism. This study adds to that discussion and understanding.

168

Figure 7.1: Hypothetical changes associated with hyper-male and hyper-female shapes. Light blue lines with open dots represents hyper-female shape and dark blue line with closed dots represents hyper-male shape, or a tenfold increase in discriminant function score.

7.1.2 Ancestry

Ancestral group affiliation as a classifying variable was found to help describe shape variation. By proxy, cranial form is seen as a reflection of phylogenetic history.

Previous work has shown that the facial module is most influenced by climatic factors, especially populations living in colder climates (Harvati and Weaver, 2006; Hubbe et al.,

2009; Betti et al., 2010). Variation in the neurocranium is consistent with patterns of neutral genetic drift and thus is not phylogenetically informative (Hubbe et al., 2009;

Smith, 2011). In a similar light, others found that portions of the cranium predicted to be congruous with phylogenetic history, such as the basicranium, actually followed a neutral genetic model (von Cramon-Taubadel, 2011). Because the basicranium is early to develop it is perceived to be phylogenetically conservative and would represent ancestor- descendent relationships in more recent human populations. A principal components

169 analysis identified the basicranium along the anterior, middle, and posterior cranial fossa as being highly variable in this sample. The first principal component explained over one third of the total variation. It was predicted that the midline cranial base would be relatively stable and show low amounts of variation and that this variation would be unique to populations. A part of this component was a dramatic narrowing and inferior displacement of the fossae. This supports the findings of a more neutral chondrocranium, going against a developmental-first perspective to explain variation (von Cramon-

Taubadel, 2011). Yet, the narrowing of the basicranium may not be explained solely genetic drift. Narrowing of the vault can be caused by reduced masticatory loads placed on the vault and face. Although the vault is not linked to population history the canonical variate analysis of the sample identified areas of shape variation in portions of the vault that separated Black, Hispanic, Japanese, and White/Native American individuals.

Black individuals have a narrower vault along the coronal suture and a longer vault in the occipital region (CV2, 24.64% variance). The Hispanic crania were noticed to have a slightly shorter cranial length with marginally more vertical vaults on the lateral aspects along the coronal suture (CV6, 3.56% variance). This small variance was enough to separate the Hispanics from all other ancestral groups. There are only nine individuals that represent this sample and may not reflect the true level of variation in the larger population. The term ‘ ispanic’ in and of itself is a broadly defined group based primarily on cultural factors with imprecise genetic denotation (Spradley et al., 2008).

The single Japanese female along with the White/Native American individuals were distinguished by a vault that is narrower and shorter compared to all other groups (CV4

170 and 5, 10.27% variance). These groups are presumably more similar to one another because of their population history.

Although the vault has been show to align with neutral genetic evolution, the nature of variation in the vault can be used for identification of population affinity.

Examples of using this variation for such a purpose has been attempted for Hispanic

(Ross et al., 1999; Ross AH et al., 2004; Spradley et al., 2008), Iberian (Ross et al.,

2011), and South African (Stull et al., 2014) populations. The research investigating variability in Hispanic populations was stimulated by changes in population demographic composition in the United States and an increase in border deaths of individuals migrating to the US from Mexico and Cuba. When comparing craniometric variation,

Hispanic individuals are shown to be closest to populations of African ancestry followed by Spanish populations (Ross AH et al., 2004). Another study showed that a different

Hispanic sample was more similar to American White and Guatemalan Mayan samples

(Spradley et al., 2008). Admixture of groups is used to explain the similarity among certain populations (Ross et al., 2011).

Further discussion of issues surrounding ancestral group meaning and identification is warranted here. The collections from which data were collected in the present study use racial categories to classify individuals into subpopulations. However, this study considers these terms from the perspective of ancestry, the phylogenetic relationships of populations. The concept of race is one of the most controversial issues in anthropology because of the discipline’s history of attempting to classify human groups all the while inappropriately mixing biology and culture. The notion of human races is a Western, 18th century classification scheme based on few physical traits such as 171 skin/hair/eye color or skull shape which was used to place humans into categories such as

Caucasoid (White), Mongoloid (Yellow), or Negroid (Black) (Ousley et al., 2009). Race has persisted in Western society as a gross amalgam of culture and biology. There are problems with this scheme as well: not everyone defines race in the same way, racial categories change over time, classifications are based on imagined biological differences, and race provides justification for people’s prejudice. For these reasons, race is a social construct that humans have invented to categorize themselves.

The social use of race as a concept to describe and explain human biological variation and evolution is an outmoded term that implies discrete, typological, and thus artificial subdivisions of the human species based on their overt physical and behavioral characteristics (Nawrocki, 1993). Others suggest that racial groupings more appropriately should be viewed as folk taxonomies (Sauer, 1993). Race is a valid biological concept when describing subspecies. However, biological racial groupings cannot be made with modern human populations because there is only one human race that exists today. Ancestry, on the other hand, emphasizes ancestral-descendant relationships between groups and the microevolutionary selective or neutral forces that may create or redistribute human variation. Ancestral groups of human populations can be made based on the recent origins of a population. This concept is closest in line with cladistic races and is more appropriate to describe human populations. Cladistic populations are defined as ancestor-descendant breeding groups that share a common origin. They are populations located on the nodes of a phylogenetic tree (Andreasen,

1998). The cladistic race concept is not as popular as concepts because cladistics is traditionally used to define higher taxa and constructing trees at the lowest level proves to 172 be difficult. Andreasen (1998) posits that it is possible to define breeding populations based on genetic distances and recognizes monophyletic groups as separate taxa, extending this concept to human groups. Brace (1995) argues that geographic origins do not correlate with defined populations because of large amounts of variation within species, particularly modern humans. Cladistic groupings recognize the fluidity of the human species and the dynamic nature of our population structure. Humans are characterized as a polymorphic species, not to be confused with polytypic, or having multiple subgroups. Many of our genes have multiple alleles which can produce myriad phenotypes.

Other species concepts include biological, phonetic, and ecological. The biological species concept defines species as a group of interbreeding organisms that is reproductively isolated from other groups. It places taxonomies of natural species within the conceptual scheme of population genetics and explains why members of a species resemble each other. However, it can only be applied to one taxonomic level (i.e. species, genus, family, etc.), it may rely only on morphological characters, and it is strictly theoretical. The phenetic species concept defines species as a group of organisms that share the same morphological and physiological characters. An argument for this definition is that it is non-theoretical because it is based on observations and not estimates of character states. A potential problem with this definition is an arbitrary choice for methods to select defining characters. The ecological species concept defines a species as a group of organisms that exploit the same resources and occupy the same habitat.

This can explain extinction events and can be applied to all taxonomic levels. Still, niches may overlap to a large degree and this definition is theoretical. 173 Subspecies groups are identified by the same qualities that characterize each species concept. Additionally, groups assigned to subspecies may not be geographically reproductively isolated, share the same ecological niche, and have similar morphological/physiological traits. It can be said that subspecies are members of the same species that come in contact with one another and have the potential to interbreed, but do not for one reason or another. The distinguishing factor is that they may breed at different times or do not recognize each other due to bimodal trait distribution such as coat color, thus perceiving others as not of their same species (Andreasen, 1998).

In 1950, the United Nations Educational, Scientific, and Cultural Organization commissioned a report on the relationship between biology and culture as a response to the outcomes of World War II in Nazi Germany. Anthropologist Ashley Montagu was designated as the lead investigator of the ‘Race Question’ inquiry. Based upon the latest scientific understanding of variation it was determined that all ethnic groups have the same abilities, genetic differences do not determine cultural and social differences, and race mixture is tied to social factors (UNESCO, 1950). Later, Montagu (1956) summarized the latest research in human biological and cultural variation in “Race:

Man’s Most angerous Myth” to attempt an end to the race debate. Since then, scientists now know that there is no biological basis for the existence of human race, but the debate rages on in society. Several problems exist with biological racial classification in humans. First of all, human variation is clinal (Livingstone, 1962; Brace, 1964). There are no easily identifiable boundaries that separate modern human groups. Second, one cannot fit all populations into discrete categories. Third, groups of traits used to define races do not always appear together. For example, native Australians have dark skin and 174 blonde hair, two traits classically perceived as discordant. Fourth, the degree that environment affects phenotype is unclear. The human phenotype is so complex and mediated by many genes that is very difficult to predict phenotypic outcomes from environmental factors. Phenotypes may be the result of plasticity and/or heredity.

Finally, interbreeding is common between distant populations, which negates the biological species concept. These problems were solidified by Lewontin (1972) after presenting a study on blood group variation around the world. He discovered that 13% of diversity is among regions, 6% is among local populations, and 81% is within local populations. Thus emerged the mantra of biological anthropology: “There is more variation within human groups than between human groups.” This fact has not stopped anthropologists from using race as a classificatory tool. The field of forensic anthropology will be used as a case study to explain how anthropologists comprehend and use racial and ancestral groupings.

Forensic anthropologists correctly note that certain morphological traits are known to be present at higher frequencies in certain ancestral populations, and data suggest that clusters of traits may occur at higher frequencies in specific groups (Ousley et al., 2009). However, any specific trait may not occur exclusively in one particular group, and no trait or assemblage of traits can perfectly distinguish between all members of ancestral groups (Church, 1995). In other words, the presence of a trait or a cluster of traits may suggest, but not confirm, group affiliation. If one were to examine the global population one would find that many morphological traits are clinally distributed across the globe with no discrete boundaries between groups of people. In some ways the

175 population of the United States is an exception because of its unique structure and history in that socioeconomic boundaries limited gene flow among populations.

Groups from all over the world have recently migrated to the United States. In their native lands, morphological traits can be clinally distributed and grade gradually into neighboring groups (Brace and Hunt, 1990; Brace, 1995). Migrating subsets of these ancestral groups have largely tended to maintain their cultural and biological identities in

North America, creating non-clinal distributions of traits and increasing the appearance of biological discreteness relative to their native continents. This has been labeled as a form of positive assortative mating (Ousley et al., 2009). Ancestral categories as used in the

American medicolegal setting are descriptive tools used to identify unknown individuals and to communicate with law enforcement agencies (Sauer, 1992; Kennedy, 1995).

Forensic anthropologists predict facial, hair, and skin characteristics from skeletal morphology. This prediction of ancestral appearance can only be accomplished when the anthropologist is familiar with the variation that exists within populations that are likely to be found in the area that he/she is working (Komar and Buikstra, 2008). The variation in major genetic populations in the United States has been shown to be strongly related to self-identified social groups (Tang et al., 2005). Also, social race and morphological differences between American Whites and Black are highly concordant (Ousley et al.,

2009).

Nawrocki (1993) continues the argument and delves deeper into some of the more philosophical concepts surrounding human racial classification. He believes that the dismissal of racial categories of variation has been uncritically accepted and the problem anthropologists face concerning race is not based in biology; rather, it is based on 176 differences in definitions, approach, and outlook. The social meaning of the word race has become loaded with so much emotion that it has begun to affect scientific studies into modern human variation. This has unconsciously forced scholars into ignoring data that clearly point to separate breeding groups of humans.

Gill (1998) takes a somewhat different approach in his examination and explanation of human races. He puts forth the idea that races, whether human or non- human, are real biological entities which are produced as an adaptive response to the environment. The race formation process generates variation in a species which increases reproductive success and helps avoid extinction. Although Gill does not explicitly describe the human condition in this manner the connection can easily be made.

The success of humans as a species is due to the fact that we are an extremely diverse group of organisms with an incredible amount of potential variation. This has allowed us to expand to all parts of the globe and, with cultural innovation, become the dominant organism in any environment. Gill (1998) suggests that scientists should not use terms that have no biological meaning as labels for populations such as ‘Native American’,

‘European American’, and ‘African American’. Even though he does not suggest alternatives, Gill later uses the words ‘Black’, ‘White’, ‘American Indian’, and ‘East

Asian’ as labels for the respective groups. Gill continues with his discussion of race as he delves into phylogenetic relationships.

Even though the traits that Gill discusses for skeletal race attribution are different than the traits paleoanthropologists use for assigning specimens to branches of phylogenetic trees, both forensic anthropologists and paleoanthropologists sometimes make the mistake of underestimating the variability within the human species and even 177 within races. Gill proves this point in his discussion on craniofacial criteria for race determination. The traits that researchers examine such as skull shape, nose and mouth shape, the palate, palatine sutures, mastoid form, etc. are treated as discrete, discontinuous variables which only exist with limited variation. The problem with this approach to variation is that researchers are taking naturally continuous traits and artificially making them discrete. Each trait variation is then ascribed to one of several racial groups. This process creates unnatural boundaries between clusters of individuals that would be called races. Forensic anthropologists note that certain traits are known to be present more often in certain groups (Brues, 1992; Church, 1995; Scheuer, 2002) but oftentimes fail to inquire as to the frequency of occurrence or question the sample size of their observations. On some level, Gill’s remarks on the state of affairs of the race concept in forensic anthropology somewhat invalidates the work of anthropologists in the medico-legal system as being unscientific.

As a summary to the race debate Nawrocki (1993) expresses his concern for some then-current conceptions of race and states that it is appropriate that anthropologists have become more sensitive to pressing social issues, subsequently changing their approach to research. What he does not want everyone to forget is that there are important differences in the biology and history of our ancestors that produced the variation one may observe today. Ignoring the variation that exists within and among human subgroups will not increase our understanding of modern human biological variation. A factor hindering a path to that end is a difference in approach of the concept of race altogether. In doing so we must be explicit in its definition. Again, for the purposes of this study, ancestry refers to the phylogenetic relationships of populations. 178 7.1.3 Time Period

It was hypothesized that there would be a significant difference in shape between modern populations and historic populations. The collection from which crania originated had a significant effect on shape. It must be noted again that the samples used in this study cannot be used to make direct inferences about secular trends in cranial form. Instead, the historic and modern samples are used as proxy for changes in cranial shape over time. Both discriminant function (94.58% cross-validated correct classification) and canonical variate analysis illustrated this difference. Overall, there is more similarity between modern groups than the modern and historic groups and is consistent with observations in modern human crania whereby modern populations are more homogenous and historic populations are more heterogeneous (Wescott and Jantz,

2010). The difference is attributed to reproductive isolation due to cultural factors and the pattern of variation relates to environmental factors exposed to an individual during one’s lifetime; for instance, nutrition or masticatory stress. In particular, the vaults of historic individuals are considerably shorter, wider, and longer than modern individuals.

Also, the face is wider with more flaring zygomata and a shallower palate. Modern individuals in this sample are characterized by a taller, narrower, shorter vault. The face is narrower with an associated narrowing of the endobasicranium. The implications of these shape patterns will be discussed in the next section with comment on the study sample composition and origins of the individuals comprising the skeletal collections.

The dissimilarities seen between temporal groups in this study is consistent with patterns of variation observed across time and attributed to secular trends in cranial form of anatomically modern Homo sapiens. 179 The first such study that investigated change in cranial form was conducted by the father of American Anthropology, Franz Boas, who in his 1910 study documented trends in the skull shape of descendants of immigrants by measuring head width and length and face width. Study populations included East European Hebrews, Bohemians, Sicilians,

Neapolitans, Poles, Hungarians and Slovaks, and Scotch. There were significant difference between parental and offspring generations, but the pattern of change differed among immigrant groups: some experienced increased cranial length and a wider cranium while others experienced shortening and narrowing of the vault. The U.S. environment was used as the explanatory variable for this difference. Almost 100 years later others reevaluated Boas’ work and verified his findings that cranial form is plastic, attributable to environmental conditions (Gravlee et al., 2003). More recently Jantz and

Meadows Jantz (2000) and Jantz (2001) looked specifically at changes occurring in birth cohorts of American Blacks and Whites from the mid-19th century to the late 20th century.

They found general trends for increased vault height and decreased facial width that is related to environment changes over the last 125 years such as increased amount and quality of nutrition and decreased infant mortality (Jantz, 2001). Comparable changes were detected in cranial form of Eastern and Western bands of Cherokee populations in the form the late 18th to the late 19th centuries. Negative secular trends in cranial breadth and length attributed to detrimental environmental impacts as these groups and other

Native Americans experienced socioeconomic and nutritional hardships while subjected to systematic ethnocide by. There an overall decrease in cranial length over time in both bands and a sharp decrease in breadth in just the Western band. The trends observed in studies of historic Native American, American White, and American Black populations is 180 congruent with the findings in the current study. One limitation of these studies is that they all relied on interlandmark distance data.

To parse out variation related to the relative positing of landmarks, Wescott and

Jantz (2005) employed geometric morphometric methods of two-dimensional coordinate data by reconstructing owells’ (1973) worldwide craniometric data set. The trends occurring between 1850 and 1974 in American Black and American White cranial form along the midsagittal plane was attributed to a proximate cause of an inferior shift of the basicranium. Ultimately, Wescott and Jantz (2005) propose that this trend is typical of demographic transitions occurring in industrial societies. The historic sample in the present study has a similar pattern of change in vault shape; however, this is associated with a superior displacement and shortening of the anterior cranial base along the midline and laterally through the anterior cranial fossa. Here, the same ultimate causes of nutrition and functional demand on the vault are proposed to account for changes over time.

7.2 PATTERNS OF MORPHOLOGICAL INTEGRATION

The second purpose of this study was to compare theoretical patterns of morphological integration in the modern human cranium and test them using high resolution shape changes. The patterns of integration seen in the sample indicates that the cranial base is a module integrated with other cranial components. This is evident by its early formation in the embryo (Sperber et al. 2010), illustrating its importance in producing overall head form. This confirms previous work that investigated the integrative effects of the basicranium (Ackermann, 2005; Bruner and Ripani, 2008; 181 Lieberman et al., 200ab). Unique to this study is the more holistic view of the basicranium by considering both endocranial and ectocranial morphology instead of just one aspect or the other. Studies of basicranial morphology typically utilize only ectocranial landmarks (von Cramon-Tuabadel, 2011; Singh et al., 2012) or, more uncommonly, endocranial landmarks (Bruner and Ripani, 2008). This is due to the fact that accessing the endocranium is impractical or impossible given the nature of skeletal samples.

Frameworks that define modules based on functional demands produce patterns of modularity while frameworks that define modules based on developmental origins produce patterns of integration. This study is unique in that it considered multiple patterns using the same data set. Additionally, a larger number of three-dimensional landmarks was used to assess shape changes. The resulting patterns can be used to identify areas of morphological conservatism to predict shape changes and display a dichotomous relationship with overall cranial form.

On the one hand, parts of the skull that serve special sensory functions such as olfaction, vision, hearing, and equilibrium do not covary with each other to a significant degree. Theoretically, if one module were to change due to selective forces or functional demand within the lifetime of an organism the module would not induce a change in the other modules. Part of this modularity can be attributed to the physical distance between modules. Intuitively, if two modules share boundaries with one another it would be expected that they would share some level of change. If two modules are physically, completely separate from one another, as in the nasal and otic modules, and do not share boundaries then each module would be free to change over time. Or, the modules may 182 vary within a population without having any sort of effect, whether advantageous or detrimental, on the functional demands of the module. Following such logic, for example, a middle ear infection producing lytic lesions within the petrous portion of the temporal bone would alter the form of the internal petrous pyramid and potentially have long-term effects on audition. These effects would not be expected to create changes in the nasal complex. Granted, there is a secondary relationship between the modules via the auditory tube as it travels from the middle ear to the lateral wall of the nasopharynx.

On the other hand, parts of the skull that have unique embryological origins such as the chondrocranium, splanchnocranium, and dermatocranium influence one another to a greater degree. The head is canalized extremely early in development beginning with the cranial base at 28 days of gestation. From this point forward in growth and development, components of the skull that arise from related tissues or undergo similar ossification processes will stimulate each other to change because of their intimate connections. Embryological tissues all have their own relative ancestor-descendent relationships with each other. These tissue must be integrated with one another.

Together, developmental fields and functional matrices act independently of one another on two different spectra of variation: the first a spectrum of integration and the second a spectrum of modularity. This leads to a vital point that concepts of modularity and integration are not two ends of a single spectrum used to decode the relationships within complex systems. Figure 7.2 demonstrates two possible views of the relationship between modularity and integration. They are separate, but parallel factors influencing cranial form, function, variation, and evolution. The skull is integrated across the entire structure because of its overall function as well as its shared embryological and 183 evolutionary origins. Within the larger integrated structure are separate modules that may or may not be closely related to each other. When evaluating hypotheses of modularity based on covariation of landmarks subsets, high RVM coefficients are used as a substitute for integration. Low modularity indicates that the skull is integrated more tightly across the whole structure. Patterning of evolutionary changes influenced by levels of integration and modularity because the modules must maintain their functions while potentially being constrained by other modules and their roles.

Figure 7.2: Comparing concepts of modularity and integration. (a) Incorrect relationship. (b) Correct relationship of parallel patterning.

An issue plaguing investigations into modularity is the logic by which modules are defined from practical and theoretical perspectives (von Cramon-Tuabadel, 2011).

From a theoretical standpoint, defining modules based on a developmental origins may not be suitable for a study of function. From a practical standpoint, spatial contiguity poses some unique problems that must be resolved to identify neighboring modules. “A

184 set of landmarks is spatially contiguous if every landmark of the set is connected by the edges of the adjacency graph to every other landmark in the set either directly or indirectly through other landmarks that also belong to the set” (Klingenberg, 2009:413).

A Delaunay triangulation of the landmark configuration was used to determine adjacency

(Figure 5.12). Assigning landmarks to one particular module over another should be addressed because many landmarks and modules overlap according to physical space, function, and developmental origin. For instance, porion, “the most superior point on the margin of the external auditory meatus” (von Cramon-Taubadel , 2011:86), is used to define part of the auditory (otic) complex. At the same time, porion is used as a landmark to approximate overall basicranial width, or biporionic breadth (Lieberman,

2011). If the postneural complex and the otic complex are considered simultaneously there will be lower levels of modularity observed as they are spatially contiguous with each other and share embryological origins and endochondral ossification. In a similar fashion, the supraorbital region marks a large overlap between the facial and neurocranial complexes as the upper orbits and brow ridges contribute to defining these modules.

Portions of vault, correspond to functions of protecting the brain and serving as muscle attachment sites of masticatory muscles.

In the broader context of evolutionary forces, high levels of modularity indicate that a form is more flexible and is not as constrained as a more integrated system of modules. If a system is tightly integrated then a change in one subsystem will produce an effect on another subsystem. For example, change in the angulation of the cranial base is limited by the need to maintain a patent airway. If the modern human basicranial angle were more acute, i.e. more flexed, it would impinge on the soft tissues of the pharynx and 185 larynx limiting the space for air to travel through the naso-, oro-, laryngo-pharynx, and down through the voice box. Also, no food or liquid could traverse the oropharynx.

There would not be enough space to move into the esophagus. As such, a severely flexed cranial base would be deleterious to the fitness of an organism. In a most extreme case of basicranial flexion one would not be able to breathe or move food/water into the gut.

Thus, natural selection would limit the amount of flexion. Structures that are more modular, in one sense, may be considered more evolvable because they are not as constrained by other modules.

7.4 LIMITATIONS

Limitations in the current study are related to the quality of the sample, the composition of the sample, sample size, data collection, and statistical methods. There are opportunities for continued research despite these hindrances. As far as sample quality, the study was limited to crania with a sectioned calvarium to gain access to the endocranial surface of the basicranium. This reduced the pool of potential crania for analysis. It was not possible to locate all landmarks on some individuals. These missing landmarks were absent from either damage or resorption of the maxillary alveoli by way of antemortem tooth loss. In order to keep the sample size high and retain the full battery of landmarks some landmarks had to be reconstructed. Estimating missing landmarks via a statistical reconstruction, as was performed here, artificially makes the crania more similar to one another. The regression of the missing landmarks onto the non-missing landmarks may have resulted in an extrapolation of a missing location.

186 Some of the chosen landmarks were difficult to locate even after over 190 hours of data collection. Specifically, anterior frontal, alveolon, mastoidelae, stephanion, sphenobasion lateral, and points of maximum curvature proved to be the most challenging to locate on each specimen. Any landmark defined by intersections of sutures may not be discernable in individuals where sutures have begun to fuse or are obliterated. Although many landmark definitions are relatively easy to follow after practice there are still a number that are “eyeballed” by the observer. These are the Type

3 (e.g. zygion, opisthocrnaion) landmarks and some Type 2 (e.g. alveolare, coronale) landmarks. To more accurately and precisely locate these landmarks it would be best to locate them mathematically. This would require collecting curves or sliding, semi- landmarks over contours such as the curvature of the midline vault from nasion to opisthion. The maximum projection of the curvature from the frontal, parietal, and occipital cords could then be calculated to locate the precise subtense points that may be inaccurately found by the observer otherwise.

Observer error did not include 14 landmarks (Table 5.6); therefore, error in locating these landmarks could not be evaluated. However, because overall measurement error was three times less than error due to biological variation it is assumed that the missing landmarks would have similar, negligible amounts of error. Despite a non- significance of measurement error there is an issue with the generalized least-squares procedure that must be addressed. To bring all trials into the same coordinate system the variation in landmark location is spread across all landmarks, thus decreasing the error in imprecise landmarks and increasing the error in precise landmarks (von Cramon-

Taubadel et al., 2007). This Pinocchio effect (Chapman, 1990) averages out the total 187 error. For example, opisthocranion, “the most posterior midline point, which lies at the farthest chord length from glabella” (von Cramon-Taubadel, 2011:86), has higher error rates because it is instrumentally defined. When included in a data set it will raise the error associated with a landmark such as sphenion, “the most posterior extent of the sphenoparietal suture” (von Cramon-Taubadel, 2011:86) that can be identified with relative ease as an intersection of the sphenoparietal and squamosal sutures. Even if sphenion can be located with perfect accuracy the Procrustes fit of the repeated sampling rounds will lower its accuracy because opisthocranion is less accurate.

All conclusions drawn from this study are limited to the populations from which they are derived and reflect the patterns of variation unique to the United States. Most individuals in the sample were of low socioeconomic status. The Bass Collection provides information on this social variable for most, but not all individuals, that has biological consequences. The Hamann-Todd Collection does not provide this information; however, it is assumed that individuals that were unclaimed bodies were individuals of lower social status. These individuals may have experienced nutritional deficiencies during growth and development. They may have had access to less expensive foods, which are typically more processed and softer thereby reducing biomechanical load on the masticatory apparatus thus affecting adult shape of cranium

(Lieberman et al., 2004; Sardi et al., 2006), due to economic constraints. A diet of softer foods results in a higher, narrower vault with a taller, narrower face and gracile features on the whole. In addition, the palate tends to be more vaulted and narrower with the alveolar margins of the maxilla and mandible providing less space for the teeth to erupt, resulting in impaction, crowding, rotation, and overall malocclusion (Corruccini et al., 188 1985; Lieberman, 2011). The bones that serve as attachment sites for masticatory muscles (mandible, sphenoid, zygomatic, and temporal) and those that channel biomechanical forces related to mastication (mandible, maxilla, frontal, and zygomatic) are affected by a shift in diet (Larsen, 1997). This may create a slightly different pattern of modularity compared to a population with access to more nutritious, tougher food.

Related to the nutritional component of less expensive, softer foods is the higher carbohydrate content from refined sugars leading to a higher incidence of carious lesions, periodontitis, and dental calculus (Larsen, 1997). Individuals of lower socioeconomic status have more restricted access to dental and oral health care. As such, pathological conditions manifesting themselves in the oral and dental tissues may go untreated. A carious lesion may progress to a dental abscess which will eat away at the alveolar bone surrounding the tooth root and may travel into the neighboring bone within the maxilla or mandible. Or, it may be more inexpensive to remove an entire tooth or group of teeth instead of undergoing costly dental restoration. This antemortem tooth loss can have broad-sweeping effects on the size and shape of the masticatory modules. It was noted during data collection that many individuals had antemortem tooth loss of the entire maxillary dentition and could not be included in this study. Those individuals had a noticeably shorter midface from the reduced masticatory load of not having teeth.

Considering sample size, an ideal size for 152 landmarks would be 456 individuals, particularly when constructing statistical models that assume multivariate normality. There are other collections with similar population histories to further increase the sample size. The Robert J. Terry Anatomical Skeletal Collection, housed at the Smithsonian national Museum of Natural History, comprises approximately 1,700 189 individuals who were born in the latter half of the 19th century and the first half of the

20th century. These individuals were used as part of the Washington University Medical

School gross anatomy program through a body donated program or as unclaimed bodies form the St. Louis morgue. The Terry collection would complement the Hamann-Todd’s historical component of this study. The Maxwell Museum of Anthropology Documented

Skeletal Collection at the University of New Mexico contains over 280 individuals who died within the last 30 years (Documented Skeletal Collection, n.d.). These individuals donated their bodies to the collection or were not claimed by family members in the county morgue and thusly turned over to the collection for curation. The Maxwell collection would complement the modern component brought to the study by the Bass

Collection. Still, only the individuals with a sectioned calotte from either autopsy or anatomical dissection could be used.

7.5 FUTURE RESEARCH AND BROADER IMPACT

Concepts of modularization and integration in biological systems were first formally recognized and explicated more than fifty years ago (Olson and Miller, 1958).

There are many directions that research in the next fifty years could take based upon emerging technologies and analytical techniques as seen in the relatively new field of virtual anthropology (Weber and Bookstein, 2011). The first direction is using different samples. As of yet, only a small portion of the total number of primate species have been examined for integrative patterns. A step in this direction was taken by Singh and colleagues (2012) by comparing shape variation among humans, chimpanzees, gorillas, and orangutans. Although the forms of these species is quite different, the pattern of 190 integration was found to be very similar upon closer inspection of the vault, base, and facial modules. This implies that the specific pattern of integration in hominoids is conserved. There is an opportunity to continue this discussion by studying non-hominoid primates. Identifying unique patterns in other species may provide a link between adaptive strategies and integration and further explain the diversification of primates through time. It is not appropriate to use extant primates for models of evolutionary change in ancestral groups if patterns of integration are known to be different. Selective pressure may influence cranial form to travel down different trajectories if the baseline pattern of integration differs. Currently, patterning across primates and their ancestors is not fully known.

Examining fetal specimens has given researchers a great wealth of knowledge on ontogenetic processes (Ross and Ravosa, 1993; Lieberman and McCarthy, 1999;

Schilling and Thorogood, 2000; Jeffery and Spoor, 2002; Mooney et al., 2002; Zumpano and Richtsmeier, 2003; Jeffery and Spoor, 2004). Periods of growth and development studied in current research are determined by the sample structure. Expanded samples of pre-, peri-, and postnatal specimens are required to observe small changes in patterning.

Also, very few longitudinal data are available for study. Oftentimes, different individuals are used to represent typical growth in one individual. For example, a fetus naturally aborted at 25 weeks is used as an analog for the same stage in a fetus naturally aborted at

35 weeks. As anatomical collections grow, previous studies must be reevaluated using these data. Others have identified basicranial synchondroseal fusion as a promising avenue of research (Mooney et al., 2002). As discussed earlier, the cranial base is the first portion of the cranium to form, thereby directing growth of the remaining bones. 191 The timing of fusion of the basicranial fetal components has an effect on the resulting phenotype. Changing the timing of cranial base suture fusion may create adaptive or maladaptive traits. Using the visual, predictive qualities of geometric morphometric methods may help to produce hypothetical forms by tinkering with cranial forms.

Advances in medical imaging technology will push the threshold for the amount of detail that can be seen in fossilized specimens. This is especially true for structures that cannot be easily observed or measured on dry specimens. Conventional plain film radiography has given way to digital radiography. Computed tomography (CT) has given way to 3D-CT and micro-CT. Magnets used for magnetic resonance imaging (MRI) are becoming larger and larger resulting in higher resolution images (hrMRI). The transition to computer dependent imaging such as digital radiography, 3D-CT, micro-CT, and hrMRI has allowed paleoanthropologists to employ advanced multivariate statistical techniques not possible with plain film radiography used in the past. Three-dimensional medical imaging would allow access to a greater number of specimens in collections such as those used for the present study. The field of virtual anthropology harnesses the power of emerging technologies to explore fossil specimens too fragile or hidden within matrix to analyze with traditional methods. Increased computing power and advance mathematical models can now reconstruct damaged, plastically deformed, or missing portions of specimens.

The findings in the present study have broader implications for clinical medicine and individual identification in a medicolegal context. Documenting and understanding the patterns of interconnectedness within the skull can be used to predict surgical outcomes related to craniofacial disorders or traumatic injury. Theoretical shape changes 192 can be induced in a specific module through multiple regression to see how other modules may be affected. A surgeon conducting reconstructive surgery would know more precisely how their work may affect areas downstream of the area of interest area by way of integrative processes and statistical modeling of shape.

Documenting areas of population-specific variation in cranial modules using geometric morphometric methods has allowed for increased accuracy of identifying individuals in forensic contexts (McKeown et al., 1999). These methods can also be used to reevaluate older methods with a more fine-grained analysis. An example of using landmark morphometric for identification purposes is a computer program developed by

Slice and Ross (2009), called 3D-ID. This freely available program catered to the forensic science community allows user to input Cartesian coordinate data from an unknown specimen and compare it to a database of more than 1,000 individuals representing populations from around the world. Discriminant function analysis is used to assign the unknown to one of the groups while providing classificatory statistics so the user can use probabilities of group membership to inform their final decision as to the individual’s identity.

It may be that unknown skeletal remains are not of medicolegal significance. In most states, human remains that are determined to be more than 75 years old do not fall under the jurisdiction of medicolegal investigators; instead, the state archeologist has authority to investigate the provenience of the remains by placing them in an historic or pre-historic setting based upon biological features, cultural artifacts associated with the remains, and other contextual evidence. This study showed that there are significant differences in the shape of historic and modern crania. If contextual clues do not indicate 193 temporal context then comparing landmark data of an unknown specimen to individuals from historic and modern eras may help in the identification process. Recognizing a set of remains as being historic can reduce the number of unnecessary investigations and save time, money, and resources.

7.6 SUMMARY

This chapter related the findings of this study with previous investigations into cranial variation associated with sexual dimorphism, population affiliation, and secular trends. Patterns of variation with sex and temporal context as explanatory variables were consistent with previous work. Considering sex, the brow ridge, frontal bone, zygomatics, mastoid processes, and vault shape was different between males and females.

Shape changes were exaggerated to produce hypothetical forms of hyper-male and hyper- female conditions. Considering the historic or modern time period in which the sample individuals lived, vault shape and palate depth varied most significantly between the groups. Speaking to population affiliation, further consideration was given to the meaning of ancestral or racial groups in the United States. In this study patterns of variation distinguished Black, Hispanic, Japanese, and White/Native American subgroups. Afterward, the patterns of morphological integration discovered in this study was compared to patterns found in other studies and discussed within the broader context constraint, flexibility, and of evolutionary change. Of particular importance is how one defines cranial modules and to what ends the definitions will serve. The results here show that differing definitions can produce different patterns of perceived integration or modularity. 194 Next, limitations of this study were addressed. Landmark choice must be carefully considered for ease of locating and finding specimens that have these landmarks accessible. Different issues arise when estimating observer error in landmark configurations and estimating locations of missing landmarks via reconstructive methods.

These potentially limiting factors can pave new roads for future research. Namely, expanding the sample not only in size, but the number of species to understand broader patterns of morphological integration in the primates. Generally speaking, the findings of patterns of variation in this research can be applied to forensic investigations of unidentified human remains and findings related to integrative patterns can be used to help with surgical intervention of craniofacial disorders and injuries.

195

CHAPTER 8: CONCLUSIONS

The purpose of this study was to investigate the patterns of morphological variation in the modern human cranium through geometric morphometric methods. It uniquely contributes to the understanding of human biological variation because both ectocranial and endocranial surfaces were considered, bringing into focus the complex, three-dimensional structure of the head. Geometric morphometric treatment of landmark coordinate data provides an advantage over studies using interlandmark distances. The biological relationships among landmarks are maintained throughout all steps of study: beginning with data collection, continuing with analysis, and ending with final interpretation. It provides visual representations of shape data not possible with straight line distances and represents advancement in technology, statistical modeling, and computing science allowing researchers to test hypothesis in ways not possible through traditional methods. Still, it requires specialized, more expensive instrumentation, higher levels of computing power, and advance training in multivariate statistical analysis. The choice of geometric morphometric methods over others must be weighed by the hypotheses being tested.

This study comprised two sections: 1) an exploratory examination of regions within the craniofacial skeletal complex that vary in human populations and 2) comparing theoretical frameworks used to define cranial modules. A total of 152 landmarks were selected to describe cranial morphology. An intraobserver error study conducted on an 196 independent teaching collection of 15 crania with four replications showed that, via

Procrustes ANOVA, error due to measurement was three times less than error due to normal biological variation. It is concluded that landmarks can be repeatedly located with no significant error. Landmark data from 391 crania housed in the W. M. Bass

Donated Skeletal Collection at the University of Tennessee – Knoxville and the Hamann-

Todd Human Osteological Collection at the Cleveland Museum of Natural History were collected using a portable coordinate measurement machine. Missing landmarks were statically reconstructed by partial-least squares regression and the newly created coordinates were scaled, rotated, and oriented to a common coordinate system through generalized Procrustes analysis. Twelve outliers were identified and removed from the long-tailed multivariate sample distribution.

A principal components analysis reduced the variables to a set of 85 that explained 95% of the variation. Areas of greatest variation described by the first three principal components included the height and width of the vault, orientation and position of the foramen magnum, shape of the roof of the oral cavity, width of the endocranial base, and position of the zygomatics. The principal component scores were used in subsequent analyses for both parametric and non-parametric tests. An ANOVA of the

Procrustes coordinates revealed that time period, ancestral group affiliation, and sex have significant effects on cranial shape. It is not suggested that these variables are causative in nature. Detailed background information on individuals was not available for a more exhaustive exploration of these factors. Regression of age-at-death on shape revealed that this covariate accounted for a small amount of the total variance. Difference in mean shape between ancestry-sex-temporal groups were further explored through discriminant 197 function and canonical variate analysis. Subgroups were easily separated based on time period and sex with considerably more overlap in ancestral groups. Shape changes in the face are associated with evolutionary forces and shape changes in the vault are associated with neutral drift.

Theoretical frameworks for defining cranial modules influence the outcomes of research. Three separate theoretical frameworks were directly compared to one another with the same data, a comparison not made in other work. It was found that a functional approach suggests the cranium is highly modular whereas a developmental approach results in a seemingly more integrated system. Special sensory functions such as vision, audition, and olfaction are even more independent than general broader functions of support and protection found in the anteroneural, midneural, and postnerual complexes.

Hypotheses of modularity can be tested by evaluating the relationship among modules by calculating the multi-set RVM coefficient. This value quantifies the level of modularity on by comparing a hypothesized partition of landmarks (i.e. cranial modules) to random partitions of the same landmarks. This study rejects the hypothesis that the basicranium is a highly independent unit.

The field of forensic anthropology can benefit from the findings in this study by considering morphological variation of the endocranial base, a heretofore uninvestigated region of variation. The predictive modeling possible with geometric methods allows for some tinkering with shape that has applications for craniofacial reconstruction or repair.

In the broader context of evolutionary theory there are opportunities to extend our knowledge of integration patterns in other primate species to assess models used to describe evolutionary change. 198 The concept of morphological integration must be considered in any investigation into the variation and evolution of biological systems. It can be used at the developmental, genetic, or phenotypic levels, whatever the focus of study. Concepts of integration and modularity is a synthetic approach couched within the broader discipline of complex systems theory. The notion itself has an integrating function of bringing together multiple perspectives from differing fields all of which make contributions to our understanding of how a system develops, grows, varies, and changes. From its beginnings in 1958 by Olson and Miller, to the new morphometry of the 1990s, and the emergence of virtual anthropology in the 2010s, morphological integration is a powerful explanatory tool.

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APPENDIX A: LANDMARK DEFINITIONS

# Name Definition (Type) FM FDM BM

1 alare The most lateral point on the nasal F,R D,F,N F aperture taken perpendicular to the nasal height 3 2 alveolare The most anterior point on the alveolus F,A D,F F of the ﬁrst molar 2 3 alveolon† The point where the interpalatal suture F,A D,F F intersects the line joining the posterior margins of the alveolar process 1 4 anterior frontal* The most projecting point at the base of N,AN D,V B the third frontal circumvolution 2 5 asterion The point where the lambdoid, N,PN D,C,V V parietomastoid and occipitomastoid sutures meet 1 6 basion† The point where the anterior margin of N,PN C B the foramen magnum intersects the midsagittal plane 2 7 bregma† The point where the coronal and sagittal N,MN D,V V sutures intersect 1 8 C/P3 The most inferior external point F,A D,F F between the maxillary canine and the ﬁrst premolar 2 9 carotid canal The most lateral point on the carotid N,MN C,A B lateral canal 2 10 carotid canal The most medial point on the carotid N,MN C,A B medial canal 2 11 coronale The most lateral point on the coronal N,MN D,V V suture 2 12 dacryon The point of intersection of the F,Op D,F,O F frontolacrimal and lacrimomaxillary suture 1 13 ectoconchion The most lateral point on the orbital F,Op F,O F margin 2

216 14 ectomolare The most lateral point on the outer F,A D,F F surface of the alveolar borders of the maxilla 2 15 endobasion*† The most posterior point on the anterior N,PN C B border of the foramen magnum 2 16 endopisthion*† The most anterior point on the posterior N,PN C B border of the foramen magnum 2 17 external auditory The most anterior point on the margins N,Ot D,V,A V meatus anterior of the external auditory meatus 2 18 external auditory The most inferior point on the margins N,Ot D,V,A V meatus inferior of the external auditory meatus 2 19 external auditory The most posterior point on the margins N,Ot D,V,A V meatus posterior of the external auditory meatus 2 20 external palate The point on the inferior surface of the F,A D,F F length maxilla that denotes the most posterior point of the alveolar process 2 21 foramen cecum*† Depression at intersection of frontal N,AN D,V B crest and crista galli 1 22 foramen magnum The most lateral point on the margin of N,PN C B lateral the foramen magnum 2 23 foramen ovale The most anterior point on the foramen N,MN D,V B anterior ovale 2 24 foramen ovale The most posterior point on the foramen N,MN D,V B posterior ovale 2 25 foramen The point approaching the center of N,MN D,V B rotundum* aperture 1 26 foramen The point approaching the center of N,MN D,V B spinosum* aperture 1 27 frontomalare The most anterior point on the F,Op F,O F anterior zygomaticofrontal suture 2 28 frontomalare The most lateral point on the F,Op D,F F temporale zygomaticofrontal suture 2 29 frontotemporale The point on the frontal bone where the F,M D,F F temporal line reaches its most anteromedial position 2 30 glabella † Most anterior midline point on the F,AN D,F,V F frontal bone 3 31 hormion † The point of attachment of the vomer F,R C B and sphenoid bones 1 32 hypoglossal The most superior, anterior point on the N,PN C B foramen edge of the hypoglossal canal 2 33 incisivon † The most posterior inferior point on the F,M D,F F incisive fossa 2

217 34 infransion The point of intersection of the F,R D,F,N F nasofrontal, nasomaxillary, and maxillofrontal sutures 1 35 inion The point where the superior nuchal N,PN D,C,V F lines merge in the external occipital protuberance, at the base of the protuberance 1 36 internal occipital The most anteriorly projecting point at N,PN D,V V protuberance* the apex of the cruciform eminence 2 37 jugale The point in the notch between the N,M D,F V temporal and frontal process of the zygomatic bone 2 38 jugular lateral The most inferior, lateral point on the N,Ot C,A B margin of the jugular foramen 2 39 jugular medial The most inferior, medial point on the N,Ot C,A B margin of the jugular foramen 2 40 krotaphion The most posterior extent of the N,MN D,V V sphenoparietal suture 1 41 lambda † The point where the sagittal and N,PN D,V V lambdoid sutures intersect1 42 mandibular fossa The most lateral point on the mandibular N,MN D,V B lateral fossa 2 43 marginal process The most posterior and lateral point on F,M D,F B lateral the marginal process of the zygomatic 2 44 mastoideale The most inferior, lateral point on the N,PN C B mastoid process 2 45 mastoideale The anterior point of intersection of the N,PN C B anterior mastoid process and the external tympanic plate 2 46 mastoideale The posterior point of intersection of the N,PN C B posterior mastoid process and the digastric groove 2 47 mastoideale The most superior, lateral point on the N,PN D,C,V B superior mastoid process (on the Frankfort Horizontal) 2 48 maximum The point in the depth of the notch F,M D,F F maxillary between the zygomaxillary suture and curvature the alveolar process 2 49 metopion † The point where the frontal elevation N,AN D,V V above the chord from nasion-bregma is greatest 3 50 molars posterior The most inferior, posterior point on the F,A D,F F external maxillary alveolus (pos to 3rd molars) 2

218 51 nariale The most inferior point on the lower rim F,R D,F,N F of the nasal aperture 2 52 nasion † The point of intersection of the F,R D,F,N F nasofrontal suture and the midsagittal plane 1 53 nasomaxillare The most inferior point on the naso- F,R D,F,N F maxillary suture 1 54 occipital subtense The point where the occipital elevation N,PN D,V V above the chord from lambda-opisthion is greatest 3 55 occipitocondyle The most anterior, inferior point on the N,PN C B anterior occipital condyle 2 56 occipitocondyle The most lateral, inferior point on the N,PN C B lateral occipital condyle 2 57 opisthion† The point where the posterior margin of N,N C B the foramen magnum intersects the midsagittal plane 2 58 opisthocranion † The most posterior midline point, which N,PN D,V B lies at the farthest chord length from glabella 3 59 orale † The point of intersection on the palate F,M D,F F with a line tangent to the posterior margins of the central incisor alveoli 1 60 palatomaxillare † The point of intersection of the palatine F,M D,F F and the maxillary bones 1 61 palatomaxillare The most lateral point on the palate- F,M D,F F lateral maxillary suture 2 62 parietal subtense† The point where the sagittal elevation N,MN D,V V above the chord from bregma-lambda is greatest 3 63 petrosal The most anterior point of the petrous N,Ot C,A B element of the temporal bone 2 64 planum The most posterior point of lamina N,AN C B sphenoideum 1*† cribrosa 2 65 planum The posterior most point of anterior N,MN C B sphenoideum 2*† fossa, midline between bases of anterior clinoid processes 2 66 porion The most superior point on the margin N,Ot D,V,A V of the external auditory meatus 2 67 posterior frontal The point at which posterior border of N,AN D,V B anterior fossa fuses with endocranial lateral wall 2 68 prosthion† The most anterior point on the maxillary F,A D,F F alveolar process between the two central incisors 2 219 69 pyramidal apex* The most projecting point of temporal N,Ot C,A B pyramid contacting the sphenoid bone 1 70 pyramidal base* The point where posterior pyramidal N,Ot C,A B ridge meets temporo-occipital suture 1 71 radiculare The point of maximum inﬂection of the N,M D,V V zygomatic processes 2 72 sella*† The center point in sella turcica 2 N,MN C B 73 sphenion The most anterior extent of the N,MN D,V V sphenoparietal suture 1 74 sphenobasion † The midline point on the N,PN C B sphenooccipital suture 2 75 shpenobasion The most lateral, inferior point on the N,PN C B lateral sphenooccipital synchondrosis 2 76 sphenofrontale The point of intersection of the N,M D,V,F V frontozygomatic, zygomaticosphenoid, and sphenofrontal sutures 1 77 sphenomaxillare The most superior, lateral point of F,M D,V,F F superior contact between the maxilla and the lateral pterygoid plate of the sphenoid 2 78 sphenosquamosal The point of intersection of the F,M D,V B infratemporal crest and sphenosquamosal suture 1 79 sphenozygomatic The most posterior, inferior point on the F,M D,V,F V posterior sphenozygomatic suture 2 80 staphylion † The point where the interpalatal suture F,M D,F F intersects a line joining the deepest indentation of the posterior palate 2 81 stenion The most medial point on the N,Ot D,V,A B sphenosquamosal sutures 2 82 stephanion The point where the coronal suture F,M D,V V crosses the (inferior) temporal line 1 83 stylomastoid The most anterior, inferior point on the N,PN C B foramen stylomastoid foramen 2 84 subspinale † The point at which the inferior edge of F,R D,F,N F the nasal spine becomes the anterior edge of the maxilla 2 85 supraglabellare† The point at which the curve of the F,AN D,F,V V profile of the frontal bone changes to join the prominence of the glabellar region 2 86 zygion The most lateral point on the surface of F,M D,V F the zygomatic arch 3 87 zygomaxillare The most inferior, anterior point on the F,M D,F F zygomaticomaxillary suture 2

220 88 zygoorbitale The point where the F,Op D,F F zygomaticomaxillary suture intersects with the inferior orbital margin 1 89 zygotemporale The most inferior point on the F,M D,V,F F inferior zygomaticotemporal suture 2 90 zygotemporale The most superior point on the F,M D,V,F F superior zygomaticotemporal suture 2

* = endocranial † unilateral Functional Modules (FM): Major -- F = face, N = neurocranium; Minor -- AN = anteroneural, MN = midneural, PN = postneural, Ot = otic, Op = optic, R = respiratory, M = masticatory, A = alveolar Functional-Developmental Modules (FDM): D = dermatocranium, C = chondrocranium, V = vault, F= face, N = nasal, A = auditory, O = orbit) Basicranial Modules (BM): B = basicranium, V = vault, F = face

221

APPENDIX B: LANDMARK LOCATIONS

222

49 82

40 73 85

30 41 27 76

58 5 43 47 66 90 17 37 19 89 46 18 54 77 45 84 50 44 2 8 68

Figure B.1: Ectocranial landmarks, lateral. (Numbers correspond to landmark definitions in Appendix A.)

223

29 52 28 34

12 13

53 86 88

87 48 1

51 14

Figure B.2: Ectocranial landmarks, anterior. (Numbers correspond to landmark definitions in Appendix A.)

224

60 61

79 80

3 20 78 23 31

74 24 81 42 75 63 71 55 10 39 9

6 32 38 83 22 56 57

Figure B.3: Ectocranial landmarks, inferior. (Numbers correspond to landmark definitions in Appendix A.)

225

4 21

65 25 67 72

69 26

Figure B.4: Endocranial landmarks. (Numbers correspond to landmark definitions in Appendix A.)

226

APPENDIX C: LANDMARK COLLECTION ORDER FOR MAIN STUDY

1. foramen cecum * 37. zygotemporale inf R 2. planum sphenoideum 1 * 38. zygotemporale sup R 3. planum sphenoideum 2 * 39. zygomaxillare R 4. sella * 40. max maxillary curve R 5. endobasion * 41. max maxillary curve L 6. endopisthion * 42. zygomaxillare L 7. int occipital protuberance * 43. zygotemporale inf L 8. anterior frontal L * 44. zygion L 9. posterior frontal L * 45. zygotemporale sup L 10. foramen rotundum L * 46. jugale L 11. foramen spinosum L * 47. marginal process lat L 12. pyramidal apex L * 48. frontomalare temporale L 13. pyramidal base L * 49. frontomalare ant L 14. anterior frontal R * 50. frontotemporale L 15. posterior frontal R * 51. sphenofrontale L 16. foramen rotundum R * 52. sphenozygomatic pos L 17. foramen spinosum R * 53. sphenomaxillare sup L 18. pyramidal apex R * 54. sphenion L 19. pyramidal base R * 55. krotaphion L 20. prosthion 56. coronale L 21. subspinale 57. stephanion L 22. alare L 58. stephanion R 23. nariale L 59. coronale R 24. nariale R 60. krotaphion R 25. alare R 61. sphenion R 26. zygoorbitale L 62. sphenomaxillare sup R 27. ectoconchion L 63. sphenozygomatic pos R 28. dacryon L 64. sphenofrontale R 29. infranasion L 65. frontotemporale R 30. nasomaxillare L 66. frontomalare anterior R 31. nasomaxillare R 67. frontomalare temporale R 32. infranasion R 68. marginal process lateral R 33. dacryon R 69. jugale R 34. ectoconchion R 70. nasion 35. zygoorbitale R 71. glabella 36. zygion R 72. supraglabellare 227 73. metopion 115. staphylion 74. bregma 116. palatomaxillare 75. parietal subtense point 117. incisivon 76. lambda 118. orale 77. opisthocranion 119. palatomaxillare lat R 78. occipital subtense point 120. palatomaxillare lat L 79. inion 121. C/P3 L 80. asterion L 122. alveolare L 81. porion L 123. ectomolare L 82. ext aud meatus ant L 124. molars pos L 83. ext aud meatus inf L 125. external palate length L 84. ext aud meatus pos L 126. external palate length R 85. mastoideale ant L 127. molars pos R 86. mastoideale L 128. ectomolare R 87. mastoideale pos L 129. alveolare R 88. mastoideale sup L 130. C/P3 R 89. radiculare L 131. petrosal R 90. radiculare R 132. foramen ovale ant R 91. mastoideale sup R 133. foramen ovale pos R 92. mastoideale pos R 134. carotid canal lat R 93. mastoideale R 135. carotid canal med R 94. mastoideale ant R 136. jugular med R 95. ext aud meatus ant R 137. jugular lat R 96. ext aud meatus inf R 138. stylomastoid foramen R 97. ext aud meatus pos R 139. stenion R 98. porion R 140. sphenosquamosal R 99. asterion R 141. mandibular fossa lat R 100. hypoglossal foramen R 142. mandibular fossa lat L 101. occipitocondyle ant R 143. sphenosquamosal L 102. occipitocondyle lat R 144. stenion L 103. foramen magnum lat R 145. stylomastoid foramen L 104. opisthion 146. jugular lat L 105. foramen magnum lat L 147. jugular med L 106. occipitocondyle lat L 148. carotid canal med L 107. occipitocondyle ant L 149. carotid canal lat L 108. hypoglossal foramen L 150. foramen ovale pos L 109. basion 151. foramen ovale ant L 110. sphenobasion 152. petrosal L 111. sphenobasion lat L 112. sphenobasion lat R * endocranial 113. hormion 114. alveolon

228

APPENDIX D: LANDMARK COLLECTION ORDER FOR INTRAOBSERVER ERROR STUDY

1. foramen cecum * 36. zygion R 2. planum sphenoideum 1 * 37. zygotemporale inf R 3. planum sphenoideum 2 * 38. zygotemporale sup R 4. sella * 39. zygomaxillare R 5. endobasion * 40. max maxillary curve R 6. endopisthion * 41. max maxillary curve L 7. int occipital protuberance * 42. zygomaxillare L 8. anterior frontal L * 43. zygotemporale inf L 9. posterior frontal L * 44. zygion L 10. foramen rotundum L * 45. zygotemporale sup L 11. foramen spinosum L * 46. jugale L 12. pyramidal apex L * 47. marginal process lat L 13. pyramidal base L * 48. frontomalare temporale L 14. anterior frontal R * 49. frontomalare ant L 15. posterior frontal R * 50. frontotemporale L 16. foramen rotundum R * 51. sphenofrontale L 17. foramen spinosum R * 52. sphenomaxillare sup L 18. pyramidal apex R * 53. sphenion L 19. pyramidal base R * 54. krotaphion L 20. prosthion 55. coronale L 21. subspinale 56. stephanion L 22. alare L 57. stephanion R 23. nariale L 58. coronale R 24. nariale R 59. krotaphion R 25. alare R 60. sphenion R 26. zygoorbitale L 61. sphenomaxillare sup R 27. ectoconchion L 62. sphenofrontale R 28. dacryon L 63. frontotemporale R 29. infranasion L 64. frontomalare anterior R 30. nasomaxillare L 65. frontomalare temporale R 31. nasomaxillare R 66. marginal process lat R 32. infranasion R 67. jugale R 33. dacryon R 68. nasion 34. ectoconchion R 69. glabella 35. zygoorbitale R 70. supraglabellare

229 71. metopion 106. hypoglossal foramen L 72. bregma 107. basion 73. parietal subtense point 108. sphenobasion 74. lambda 109. sphenobasion lat L 75. opisthocranion 110. sphenobasion lat R 76. occipital subtense point 111. hormion 77. inion 112. alveolon 78. asterion L 113. staphylion 79. porion L 114. palatomaxillare 80. ext aud meatus ant L 115. incisivon 81. ext aud meatus inf L 116. orale 82. ext aud meatus pos L 117. palatomaxillare lat R 83. mastoideale ant L 118. palatomaxillare lat L 84. mastoideale L 119. foramen ovale ant R 85. mastoideale pos L 120. foramen ovale pos R 86. mastoideale sup L 121. carotid canal lat R 87. radiculare L 122. carotid canal med R 88. radiculare R 123. jugular med R 89. mastoideale sup R 124. jugular lat R 90. mastoideale pos R 125. stylomastoid foramen R 91. mastoideale R 126. stenion R 92. mastoideale ant R 127. sphenosquamosal R 93. ext aud meatus ant R 128. mandibular fossa lat R 94. ext aud meatus inf R 129. mandibular fossa lat L 95. ext aud meatus pos R 130. sphenosquamosal L 96. porion R 131. stenion L 97. asterion R 132. stylomastoid foramen L 98. hypoglossal foramen R 133. jugular lat L 99. occipitocondyle ant R 134. jugular med L 100. occipitocondyle lat R 135. carotid canal med L 101. foramen magnum lat R 136. carotid canal lat L 102. opisthion 137. foramen ovale pos L 103. foramen magnum lat L 138. foramen ovale ant L 104. occipitocondyle lat L 105. occipitocondyle ant L * endocranial

230

APPENDIX E: R SCRIPT FOR ESTIMATING MISSING LANDMARKS

library(geomorph) # loads geomorph package missingdata <- read.table("missingdata.txt", header=TRUE, row.names=1, stringsAsFactors=FALSE) # read in data from .txt file # header labels in first row # set the row names of the object to be the values in column 1 # data is a numeric 2d array with 391 rows and 456 columns missing3d <- arrayspecs(missing2d, 152, 3) # converts 2d array into 152 x 3 x 391 3d array, 391 sheets (individuals) each with a 152 row (landmarks) x 3 column (x,y,z coordinates) grid # 3d array required for function any(is.na(missing3d)) [1] FALSE # checks if there are any NAs in data, required for estimate.missing function missing3d[which(missing3d = = -999)] <- NA # replaces -999 with NA in data set any(is.na(missing3d)) [1] TRUE estimateReg <- estimate.missing(missing3d, method="Reg") # returns a 3d array with the missing landmarks estimated using multivariate regression estimate2d <- two.d.array(estimateReg) # converts 3d array to 2d array for analysis in MorphoJ write.table(estimate2d, "c:/Users/Adam/Documents/missing2d.txt", sep="\t") # export data to tab delimited file for analysis in MoprhoJ

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