Patterns of Craniometric Variation in Modern Thai Populations: Applications in Forensic Anthropology and Implications for Population History

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PATTERNS OF CRANIOMETRIC VARIATION IN MODERN THAI POPULATIONS: APPLICATIONS IN FORENSIC ANTHROPOLOGY AND IMPLICATIONS FOR POPULATION HISTORY

LAUREL ELIZABETH FREAS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2011

To my family, who have always believed and understood, even—and perhaps, most especially— when I have not

ACKNOWLEDGMENTS

Though often held as the embodiment of solitary scholarly endeavor, anyone who has ever written a doctoral dissertation knows that the bound and printed pages are but the ―tip of the iceberg,‖ beneath which lies a mountain of quiet, tireless labor and generous, yet largely silent, assistance on the parts of many, many people. For this dissertation, I extend my deepest thanks to the following:

My deepest debt of gratitude is owed to the members of my superlative advisory committee, Michael Warren (chair), David Daegling, John Krigbaum and Martha Burt, for providing a steadying hand, an endless supply of encouraging words, and the occasional motivating kick to the hindquarters, always right on time when I needed them most. If I am a better anthropologist today, it is for having learned at their elbows and stood on their shoulders.

I am additionally forever indebted to Mike for his outstanding mentorship, for being my biggest cheerleader and tireless advocate, for his generous support of my many, many trips to Thailand and, most of all, for his patient understanding of the peculiar workings of my brain, and for always knowing exactly what I needed to hear. It has been a very, very long road from first-year graduate student to this completed dissertation, and he has never once allowed me to falter along the path.

I am equally indebted to the people of Thailand, for the extraordinary generosity, hospitality, and friendship they have continuously extended to this farang in their midst. Chief among these are Dr. Porntip Rojanasunan and her staff at the Central Institute of Forensic

Sciences, for first suggesting this research project, and for opening the first doors; and Dr. Pasuk

Mahakkanukrauh and her faculty in the Department of Anatomy at Chiang Mai University, for her generous provision of unlimited access to her department‘s splendid skeletal collection, and for their constant support during my research trips. Additional special thanks are owed to Dr.

Pasuk for her enthusiasm for this project, for her indomitable spirit, for her kindness shown to a student far from home, and for her friendship. A huge goes also to her students, Aun,

Khempith and Nim, for the countless dinners, bowling games, daytime sightseeing trips, and nighttime motorbike rides around Chiang Mai that they shared with me. Thanks are also owed to

Dr. Sukkid Yasothornsrikul and Ajarn Chanasorn Poodendaen at Naresuan University, who, in addition to access to their university‘s skeletal collections also provided my first critical introductions into the Thai academic community. Dr. Panya Tuamsuk provided generous access to his collections at Khon Kaen University, allowing me to salvage the core question of this dissertation just when it seemed all was lost. And, finally, I owe an immeasurable debt of gratitude to Noon for being my indefatigable tour guide, translator, cultural emissary, language coach, dinner company, research assistant and, above all else, my friend. .

Gratitude in endless measure is owed to the gang from the Pound Lab—Carlos, Nico,

Katie, Traci and Ron—for tailgatoring, MarioKart (Diddy Kong for the win!), Red vs. Blue, and jamming out on the lab floor; for the countless hours at the maceration hoods, the inside jokes

(―Baby, stop!‖), and the secret agent names; for their boundless patience with me at those times when I was really, really cranky—which was a lot; and for always having my back. They have been my second family, always there to pick me up, dust me off, and push me on down the road when the going got tough. Other members of my second family, to whom similar thanks are owed, include Carrie, Joe, Foxy, Derek, and Owen, who know that when the going gets weird, the weird turn pro. If I have survived this with my sanity intact, it is through no strength of my own, but because of all of them. Many thanks go to Elizabeth Walters and Joe Hefner, for their tireless help with the minutiae of proper usage (both the grammatical and the statistical kinds),

for reading rough drafts and providing encouraging and thoughtful comments, and for at least having pretended to enjoy the chore!

Gracious thanks are owed to the many parties who provided generous financial support for this project. This research has been supported in part by the Ellis R. Kerley Forensic Sciences

Foundation, the University of Florida‘s Department of Anthropology. Additional travel support and access to the collections at Khon Kaen was provided by the Joint Prisoner of War/Missing In

Action (POW/MIA) Accounting Command‘s Central Identification Laboratory (JPAC-CIL).1

Last, but certainly not least, I own an immeasurable debt of gratitude to my family, for more things than I can possibly enumerate here. A partial list includes their unflagging support and unwavering confidence in me; their fearlessness in encouraging me to follow a path that has taken me far from home, and the sacrifices they have made that I might follow that path; and for their lessons that to do is to do whole-heartedly. Most of all, I thank them for giving me roots and wings.

1 The opinions contained herein are solely those of the author and do not reflect the opinions or official position of the Joint POW/MIA Accounting Command or the Department of Defense.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 11

ABSTRACT ...... 13

CHAPTER

1 INTRODUCTION AND STATEMENT OF QUESTION ...... 15

2 PREHISTORY AND POPULATION HISTORY IN THAILAND, PART I: THE BASIC CULTURAL OUTLINE ...... 24

The Pleistocene to the Paleolithic ...... 27 Holocene Hunter-Gatherers ...... 29 The Arrival of Agriculture ...... 43 The Metal Age ...... 48 The Bronze Age ...... 48 The Iron Age ...... 53 The Rise of the State ...... 56

3 PREHISTORY AND POPULATION HISTORY IN THAILAND, PART II: THE VARIED LINES OF EVIDENCE ...... 67

Archaeology ...... 77 Linguistics ...... 82 Population Genetics ...... 98 Physical Anthropology ...... 112

4 MATERIALS AND METHODS ...... 150

The Samples ...... 151 Statistical Methods ...... 152 Basic Assumptions ...... 153 Data transformations and non-parametric approaches ...... 156 Multivariate Statistical Analyses ...... 158 Analysis of variance ...... 163 Discriminant function analysis ...... 164 Final Considerations ...... 171

5 RESULTS AND DISCUSSION ...... 179

Discriminant Function Analysis – By Sex...... 179 Discriminant Function 1: All Variables ...... 179 Discriminant Function 2: 20 variables ...... 186 Discriminant Function 3: Forward-Selected Stepwise Analysis ...... 189 Discriminant Function Analysis – By Regional Subpopulation ...... 192

6 SUMMARY AND CONCLUSIONS ...... 227

APPENDIX: DEFINITIONS OF CRANIOMETRIC LANDMARKS AND MEASURMENTS ...... 241

Definitions of Cranial Landmarks ...... 241 Definitions of Cranial Measurements ...... 244

LIST OF REFERENCES ...... 246

BIOGRAPHICAL SKETCH ...... 265

LIST OF TABLES

Table page

4-1 Standard cranial measurements used in this study...... 172

4-2 Summary statistics of modern Thai population samples...... 176

4-3 Summary of Shapiro-Wilk tests of normality for individual craniometric variables...... 178

4-4 Summary of Levene‘s tests of normality for individual craniometric variables...... 178

5-1 Means, standard deviations, and number of missing cases, for 24 cranial measurements for the modern Thai sample, pooled by sex...... 208

5-2 Means and standard deviations for 24 cranial measurements for the modern Thai sample, by sex and population...... 209

5-3 Independent samples t-tests for equality of group means, by sex, for 24 craniometric variables...... 210

5-3 Continued...... 211

5-4 Rank transformed independent samples t-tests for equality of group means, by sex, for 14 craniometric variables...... 211

5-5 Wilks‘ Lambda test for equality of group means between males and females in Discriminant Function Analysis (DFA) 1...... 212

5-6 Structure matrix for Discriminant Function 1...... 212

5-7 Unstandardized canonical coefficients for Discriminant Function 1...... 213

5-8 Classification results for Discriminant Function 1...... 214

5-9 Wilks‘ Lambda test for equality of group means between males and females in Discriminant Function Analysis 2...... 214

5-10 Structure matrix for Discriminant Function 2...... 215

5-11 Unstandardized canonical coefficients for Discriminant Function 2...... 215

5-12 Classification results for Discriminant Function 2...... 215

5-13 Stepwise selection matrix for variables included in Discriminant Function 3...... 216

5-14 Unstandardized canonical coefficients for Discriminant Function 3...... 217

5-15 Classification results for Discriminant Function 3...... 217

5-16 One-way analysis of variance (ANOVA) results comparing from three regional Thai subpopulations, males only...... 219

5-16 Continued...... 220

5-17 Structure matrices for discriminant function analysis of regional subpopulations, by sex, based on 20 craniometric variables...... 221

5-18 Classification results for males, by regional subpopulation, from discriminant function analysis using 20 variables...... 223

5-19 Classification results for females, by regional subpopulation, from discriminant function analysis using 20 variables...... 224

5-20 Summary of stepwise discriminant function analyses of Culled Samples (CS) 1 through 4 (males only)...... 224

5-21 Unstandardized canonical coefficients for stepwise discriminant function analysis of Culled Sample 4...... 225

5-22 Cross-validated classification results for stepwise discriminant function analyses of Culled Samples 1 through 4...... 226

LIST OF FIGURES

Figure page

1-1 Political map of East and Southeast Asia and Near Oceania...... 23

2-1 Selected Paleolithic archaeological sites in Southeast Asia...... 63

2-2 Selected Neolithic archaeological sites in Southeast Asia...... 64

2-3 Selected Metal Age archaeological sites in Thailand...... 65

2-4 Selected state-level archaeological sites in Southeast Asia...... 66

3-1 Selected archaeological sites in China...... 148

3-2 Relationships among the various language families of Southeast Asia...... 149

4-1 Anterior views of the skull, detailing cranial measurements...... 173

4-2 Left lateral view of the skull, detailing cranial measurements...... 174

4-3 Inferior view of the skull, detailing cranial measurements...... 175

4-4 Vernacular sociopolitical regions of Thailand...... 177

5-1 Frequency distribution of male and female discriminant function scores for Discriminant Function 1...... 213

5-2 Frequency distribution of male and female discriminant function scores for Discriminant Function 2...... 216

5-3 Frequency distribution of male and female discriminant function scores for Discriminant Function 3...... 218

5-4 Scatter plot of discriminant function scores for males, by regional subpopulation, for discriminant function analysis using 20 craniometric variables...... 222

5-5 Scatter plot of discriminant function scores for females, by regional subpopulation, for discriminant function analysis using 20 craniometric variables...... 223

5-6 Scatter plot of discriminant function scores for males in Culled Sample 4, by regional subpopulation, for stepwise discriminant function analysis selecting 3 variables...... 225

6-1 Sign in front of the International Center Hostel at Chiang Mai University, with text in the Thai (top), Lanna (middle), and Roman scripts...... 239

6-2 Great Stupa of Wat Suandok, Chiang Mai; an example of Lanna-style religious architecture...... 240

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

PATTERNS OF CRANIOMETRIC VARIATION IN MODERN THAI POPULATIONS: APPLICATIONS IN FORENSIC ANTHROPOLOGY AND IMPICATIONS FOR POPULATION HISTORY

Laurel Elizabeth Freas

December 2011

Chair: Michael Warren Major: Anthropology

Craniometric analyses are a long-standing and well-developed part of physical anthropology‘s scientific framework, and have the potential to yield important insights on patterns of intra- and interpopulational human biological variation. Thus, craniometric analyses find overlapping application to forensic anthropology identifications and the exploration of biological relationships among temporally and geographically distinct human populations. Yet the validity of such analyses depends upon having population-specific reference datasets of sufficient robusticity to adequately characterize the true range of morphological variation within and among human populations. The 2004 Indian Ocean tsunami and other recent events have created the need for greater representation of Southeast Asian populations in modern forensic craniometric databases. Additionally, it is argued that resolution of the central question in

Southeast Asian prehistory—that of the essential biological continuity or discontinuity between the pre- and post-Neolithic populations of the region—will only be advanced through more focused sampling of the salient populations, to include consideration of patterns of regional and temporal variation within them.

To address these issues, 24 standard cranial measurements were collected on a large sample (n=429; 242 males/137 females) of modern Thai skeletons from three different regions within Thailand. These data were used to generate to generate linear discriminant functions for sex determination in unidentified remains from within the Thai population. These functions have cross-validated accuracy rates of 77-83%, similar to the performance of other population- specific sex determination standards, indicating their validity and broad utility to forensic anthropology. The Thai craniometric data were also used to explore patterns of regional within- population skeletal variation in juxtaposition to observed patterns of phenotypic variation within the living Thai population. Comparison of the three regional subpopulations found no significant craniometric differences among them, despite their apparent ethnic, linguistic and physical diversity, indicating the relative craniometric homogeneity of the modern Thai population. This finding is congruent with known historical events that have clearly shaped the biological structure of this population, but that are often overlooked by prehistorians. Given this greater understanding of craniometric variation within the modern Thai, it is hoped that commensurate gains in forensic anthropology applications and population history investigations will follow.

CHAPTER 1 INTRODUCTION AND STATEMENT OF QUESTION

A little neglect may breed mischief ... for want of a nail, the shoe was lost; for want of a shoe the horse was lost; and for want of a horse the rider was lost. -Benjamin Franklin Poor Richard's Almanac (1758)

Metric approaches to the analysis of human variation (i.e., osteometry and anthropometry) are among the oldest scientific traditions in the field of physical anthropology. Despite an ignoble intellectual adolescence, which saw metric analyses in general—and craniometrics in specific—applied tacitly and overtly in support of racist sociopolitical and pseudoscientific agendas (Gould 1996), these methods have matured into a robust and respected part of modern physical anthropology‘s analytical engine (Hrdlička 1939; Martin and Saller 1956; Giles and

Elliot 1962, 1963; Howells 1973, 1989, 1995; Buikstra and Ubelaker 1994; Moore-Jansen et al.

1994; Pietrusewsky 2000, 2006; Jantz and Ousley 2005; Buikstra and Komar 2008; SWGANTH

2011). Craniometrics now sees regular application to questions of human growth and development (Baughan and Demirjian 1978; Humphrey 1998), bioarchaeological analyses

(Buikstra and Ubelaker 1994; Jantz and Owsley 2001; Pietrusewsky and Douglas 2002 Oxenham and Tayles 2006), human paleontology and fossil taxonomy (Kramer 1993; Matsumura and

Pookajorn 2005), regional and global geographic and temporal patterns of human morphological variation and genetic diversity (Relethford 1994, 2002; Relethford and Harpending 1994; Jantz and Jantz 2000; Jantz and Owsley 2001; Roseman 2004; Roseman and Weaver 2004), population history and relationships among temporally and geographically distinct populations (Howells

1973, 1989; 1995; Pietrusewsky et al. 1992; Hanihara 1993; Nakbunlung 1994; Brace and Tracer

1995; Jantz 2004; Spradley 2006), and forensic identification (Moore-Jansen et al. 1994; Jantz and Ousley 2005; Slice and Ross 2004). The dissertation research presented herein embraces the

last two of these uses of craniometric analyses, by considering the forensic applications and the population history implications of patterns of craniometric variation within the modern population of Thailand.

Between these two uses of craniometric analyses, it is the matter of forensic analysis that has provided the immediate impetus for this research. Most of the modern analytical standards used by forensic anthropologists for determining biological profiles (sex, ancestry, age at death and stature) were developed using data collected from populations originating wholly within the

United States: primarily, the Terry and Hamann-Todd skeletal collections, the remains of U.S. military personnel killed in action, and forensic cases from urban medical examiners‘ offices

(Buikstra and Komar 2008). However, as forensic anthropology has become increasingly active in the global arena—largely through forensic anthropologists‘ participation in efforts to identify the victims of natural disasters, wars and human rights violations—and as our understanding of the true nature of human population variation has grown, so too has our realization that these

American forensic anthropology standards are largely inadequate for application to other populations around the world (Rankin and Moore 2004; Slice and Ross 2004; Ubelaker 2004;

Schaefer and Black 2005; Steadman and Haglund 2005; Djuric et al. 2007; Buikstra and Komar

2008).

As is true of all scientific reasoning, our ability to make predictions of sex, ancestry, age and stature using forensic anthropology methods is constrained by the representativeness of the skeletal samples which underpin these methods. Because the current forensic anthropology standards are based on a relatively small, restricted subsample of the total global human population, they often rapidly diminish in precision, accuracy and reliability the farther one moves from this population. This is especially true of discriminant function analyses of cranial

measurements (the preferred metric method for purposes of sex and ancestry determination), in which both the classification of an unknown individual and the statistical certainty of that classification (in terms of posterior and typicality probabilities) is driven entirely by patterns of variation within and among the reference populations from which the discriminant functions have been calculated (Howells 1973, 1989, 1995; Pietrusewsky 2000; Jantz and Owsley 2001;

Buikstra and Komar 2008). Reliable sex determination standards that are appropriate for the population(s) under consideration are of the utmost importance to forensic anthropology, as the determination of all other parameters of the biological profile is predicated on an accurate assessment of an unknown individual‘s sex. Similarly, reliable standards for ancestry determination are critical to identification efforts, as ancestry or race is the second-most dominant criterion, after biological sex, by which individuals are classified and identified.

The 2004 Indian Ocean tsunami, which killed nearly 250,000 people in South and

Southeast Asia, including approximately 8200 in Thailand, is but one of a series of natural disasters and human rights violations which have laid bare this need for regionally based, population-specific forensic anthropology standards (Ubelaker 2004; Schaefer and Black 2005;

Steadman and Haglund 2005; Djuric et al. 2007; Buikstra and Komar 2008; Kenyhercz 2010).

Although genetic and dental comparisons are being applied with considerable success to the still- ongoing identification efforts in Thailand, the lack of and/or loss of antemortem DNA

(deoxyribonucleic acid) exemplars and dental records means that these methods are not applicable in all cases. This dilemma has led the Thai government to look to other areas of forensic science, including forensic anthropology, for alternative means of identifying the dead.

To date, however, only three papers discussing the need for Thai-specific forensic standards have been published in the international forensic literature (İşcan et al. 1998; King et al. 1998; King

1999). Importantly, these studies clearly demonstrate that the available American standards are inaccurate when applied to the Thai population and in such applications will lead to systematic misclassifications of Thai males as females, due to population differences in patterns of sexual dimorphism and overall skeletal robusticity. Nevertheless, these studies are an insufficient remedy to lack of appropriate population-specific standards for the Thai, as they only address sex determination from postcranial skeletal elements, which are generally regarded as less informative and reliable than the more robust craniometric analyses (but see Spradley and Jantz

2011 for evidence to the contrary).

The impact of the analytical constraints imposed by this lack of representative datasets is not limited to forensic anthropologists working in foreign contexts. These constraints also have significant influence on forensic anthropologists working to identify unknown individuals within the United States. This is particularly true if an unknown individual belongs to an ancestry group not represented in the FORDISC 3.0 reference database (Jantz and Ousley 2005). For example, the total Asian sample in the current FORDISC 3.0 reference database comprises only

100 Japanese males and females, 79 Chinese males and 51 Vietnamese males—leaving a great many Asian populations completely undocumented, as well as grossly underrepresenting the total range of morphological variation within those populations that are documented. In such a diverse population as exists within the United States, with immigrant populations from nearly every nation and ethnic group on Earth, the potential for misidentifications simply for want of representative datasets looms large in much forensic anthropology case work. In light of these international and domestic considerations, there is a clear need for expanded representation of

Asian populations in general, and the Thai population in specific, in the craniometric reference databases and analytical standards used by forensic anthropologists.

Although this research was initiated at the request of Thailand‘s Central Institute of

Forensic Science, explicitly for the purposes of developing a Thai-specific craniometric database for use both in Thailand and the United States, it also presents valuable opportunities for the investigation of broader physical anthropological questions pertaining to population history of the modern Thai people. There is little that is conclusively known about the origins of the modern Thai and other Southeast Asian populations; prevailing theories hold that these peoples either 1) evolved in situ from the indigenous prehistoric hunter-gatherer populations; or 2) are descended from a wave of rice agriculturalists who expanded southward from the Yangzi Valley around 4500 years ago; and 3) that the Thai may be uniquely derived from peoples who fled into

Thailand from southern China in advance of the Mongol expansion in the thirteenth century AD, replacing or absorbing other existing populations (Briggs 1945; Rinehart 1981; Nakbunlung

1994; Higham and Thosarat 1998; Baker and Phongpaichit 2005; Lertrit et al. 2008). However, there is considerable debate and little agreement on this matter among archaeological, linguistic and historical lines of evidence. Anthropological and scientific common sense would suggest that this is reflective of a much more complex pattern of population origins and history.

Because of Thailand‘s strategic location within Southeast Asia1 (Figure 1-1), it has known cultural and linguistic, and therefore likely biological, ties with diverse peoples from throughout

Asia. Among these are:

 the peoples of India, who, in addition to trade goods, brought with them the religious traditions of Hinduism and Buddhism and the linguistic traditions of Sanskrit and Pali, all of which are still evident in Thailand today;

1 Following most authorities working in this region, Southeast Asia is defined here as the area encompassing modern-day Cambodia, Laos, Myanmar (Burma), Thailand, Vietnam (i.e., mainland southeast Asia), Malaysia, Indonesia, and the Philippines (i.e., island Southeast Asia) (Bellwood 1997; Higham 2002; Pietrusewsky 2006).

 the peoples of China, who exerted their influence through historically and archaeologically documented trade networks and population movements, and also though hypothesized linguistic influences.

 the Khmer civilization of the Mekong Valley—itself heavily influenced by India—which contributed documented and hypothesized political, architectural, religious and linguistic influences (Briggs 1945; Kaplan 1981; Rinehart 1981; Nakbunlung 1994; Bellwood 1997; Stark and Allen 1998; Higham and Thosarat 1998; Higham 2002; Baker and Phongpaichit 2005).

Today, the modern Thai population displays a surprising amount of phenotypic variation, much of which is said to follow broad regional patterns within the country. Thus, Thais often draw fairly reliable colloquial inferences about a person‘s place of birth and place of family origin within Thailand on the stated basis of their phenotypic appearance (skin tone, eye shape, facial features and build), in much the same way Americans use regional speech accents to ascertain where a person was raised.

As noted above, craniometric analyses are often used to examine the biological relationships between geographically and temporally distinct populations via biological distance statistics. Such analyses draw broad methodological and theoretical support from research that has demonstrated the high precision and repeatability of cranial measurements; the moderate to strong heritability of craniometric dimensions; and the generally strong correlation between patterns of phenotypic and genetic variance when examined in adequately large samples

(Howells 1973; Susanne 1977; Sjøvold 1984; Cheverud 1988; Buikstra et al. 1990; Relethford and Harpending 1994; Konigsberg and Ousley 1995; Pietrusewsky 2000, 2006; Relethford 2002;

Roseman 2004; Carson 2006). Thus, biological distances based on craniometrics are frequently used and widely accepted as a proxy for genetic relationships among populations when dealing with groups ―for whom true genetic distances are usually unobtainable‖ (Buikstra et al. 1990:1;

Howells 1973; Pietrusewsky 2000, 2005; Relethford 2002; Roseman 2004). Previous studies have attempted to place archaeological and modern Thai skeletal series in relation to other Asian

populations using biological distance statistics, typically either Euclidean or Mahalanobis‘ D2 distances (Brace and Tracer 1992; Pietrusewsky et al. 1992; Hanihara 1992, 1993, 1996, 2006;

Nakbunlung 1994; Pietrusewsky 2000, 2005, 2006; Matsumura and Hudson 2005; Matsumura

2006; among many others). The results of these studies, not surprisingly, generally group the

Thai with other mainland Southeast Asian populations and place them close to East Asian and island Southeast Asian populations, yet most differ considerably in the fine details of the clustering patterns among the populations.

However, these studies frequently consider the archaeological or modern populations of

Thailand as something of a monolith, using a single (often small) archaeological or modern sample from a single locale as a proxy for the entire population and paying no attention to the possibility of regional variation within the population, or to that variation‘s potential impact on the reconstruction of population relationships. In only few studies are multiple contemporary populations from Thailand considered (Matsumura and Hudson 2005; Matsumura 2006;

Pietrusewsky 2006), with multiple discriminant function and biological distance analyses revealing considerable differences among the three best-represented archaeological skeletal series from Thailand (from the sites of Ban Chiang, Non Nok Tha and Khok Phanom Di). These results also suggest several different relationships between these individual archaeological samples and geographically proximate non-Thai populations, further suggesting that there may be significant intrapopulation diversity and regional geographic patterning within the modern

Thai population.

In light of the foregoing discussion, this dissertation will seek to explore patterns of craniometric variation in the modern Thai population, including patterns of sexual dimorphism, as well as variation among regional subpopulations within the modern population of Thailand.

This exploration will address the proximate, practical need for Thai-specific forensic anthropology standards and normative databases for sex and ancestry determination, which will find use within the forensic science community not only in Thailand but in the rest of Southeast

Asia and the United States, as well as hopefully provide some insight into physical anthropology questions of broader academic interest regarding the population history of the modern Thai.

Figure 1-1. Political map of East and Southeast Asia and Near Oceania; Thailand is highlighted in red (Map source: Central Intelligence Agency (CIA) World FactBook 2010; image used with permission).

CHAPTER 2 PREHISTORY AND POPULATION HISTORY IN THAILAND, PART I: THE BASIC CULTURAL OUTLINE

As for what concerns the origine of the Siameses, it would be difficult to judge whether they are a single people, directly descended from the first men that inhabited the countrey of Siam, or whether in the process of time some other nation has not also settled there, notwithstanding the first inhabitants. -S. de la Loubère A New Historical Relation of the Kingdom of Siam (1693:14) (Quoted in Higham 2002:18)

Scholarly questions about the origins of the modern population of Thailand date back at least as far as the earliest European contact with the peoples of the Kingdom of Siam1, in the sixteenth and early seventeenth centuries (Higham 1996, 2002; Baker and Phongpaichit 2005).

As is broadly true of all questions of population origins, recurrent among inquiries into the origins of the Thai is the consideration of whether the peoples of this country evolved in situ from long-standing prehistoric populations, or whether they are the result, wholly or partly, of population migrations from other nearby regions (Nakbunlung 1994; Higham and Thosarat 1998;

Higham 2002; Baker and Phongpaichit 2005; Matsumura and Hudson 2005; Pietrusewsky 2006).

For example, F. Marcello de Ribadeneyra, a seventeenth-century Portuguese missionary to the

Siamese capital of Ayutthaya, remarked, ―We suppose that the founders of the kingdom of Siam came from the great city which is situated in the middle of a desert in the kingdom of

Cambodia,‖—i.e., Angkor (de Ribadeneyra 1601; quoted in Higham 2002:17). Indeed, the question of Thai population origins is firmly embedded within the larger discussion of similar indigenous versus migratory (or, endogenous versus exogenous) influences in the population history of Southeast Asia as a whole (Pietrusewsky 1981, 1990, 2006; Turner 1987, 1990;

1 The nation was not known as ―Thailand‖ until 1939, when the toponym was deliberately and formally changed from ―Siam‖ as a political maneuver (Baker and Phongpaichit 2005). For the sake of clarity and consistency, I will use ―Thailand‖ and ―Thai‖ throughout this dissertation to refer to the land and its peoples, except when making explicit references to specific historical eras for which the use of the toponym ―Siam‖ and the demonym ―Siamese‖ is more appropriate and accurate, as it is here.

Bellwood 1997; Matsumura and Hudson 2005; Hanihara 2006; Matsumura 2006). In turn, the examination of population history in Southeast Asia is particularly interesting because, in many ways, it is an intense distillation of the larger question that pervades the study of modern human origins in general: does the biological (i.e., fossils, modern skeletons, genetics) and artifactual evidence provide a picture of essential discontinuity (i.e., ―replacement‖), or one of essential continuity among populations through time? Instead of ―Out of Africa,‖ the question as applied to Southeast Asia is one of ―Out of China.‖ This parallelism with the meta-debate between the

Recent African Origins model and the Multiregional Evolution model of modern human origins structures much of the discourse surrounding the origins of the modern populations of Southeast

Asia (Pietrusewsky 1990, 2006; Bellwood 1997; Howells 1997; Matsumura and Hudson 2005,

Matsumura 2006).

It would be folly, however, to assume that questions of the origins and population history of the modern Thai are solely the province of Western scholars, or are questions of purely academic interest. Because Thailand, alone among the countries of Southeast Asia, was never subjected to colonial rule by a European nation, Thai scholars were able to take leading roles in historical and archaeological investigations of their own people‘s history and prehistory throughout the nineteenth and early twentieth centuries. At a time when academic enterprise in neighboring countries was monopolized by the occupying European colonials and manipulated in the service of their imperial agendas, historic and archaeological research in Thailand was promulgated from within—and most importantly, from above via royal patronage—as a means of preserving and celebrating the cultural heritage of the Siamese/Thai people in the face of increasing foreign pressures (Nakbunlung 1994; Higham and Thosarat 1998; Higham 2002;

Baker and Phongpaichit 2005). At the same time, the output of this home-grown scholarship

was nevertheless enlisted variously, first by the monarchy and later by subsequent military regimes and elected governments, in support of prevailing and/or desired notions of

Siamese/Thai nationalism and ethnic identity, and in the furtherance of specific internal political agendas (Baker and Phongpaichit 2005). In this regard, the work of these Thai scholars is as colored by the zeitgeist—albeit from their own unique reflexive and internal perspective—as is that of their Western contemporaries. These potential fluctuating biases, driven by changing perceptions of the past and its relationship to the present, must be always borne in mind when considering the insights that both Western and Thai scholars contribute to the understanding of the origins of the modern Thai (Wood 1989; Stark and Allen 1998; Baker and Phongpaichit

2005).

In order to proceed with an examination of the origins and history of the modern Thai population, it is necessary first to lay out the cultural baselines of Thailand‘s prehistory and its early historic period, with some reference to contemporaneous developments in adjacent areas of

Southeast Asia. This framework can then be juxtaposed against what is known and what is hypothesized of the broader pattern of population history in Southeast Asia. While an exhaustive survey of the relevant literature on the prehistory of Southeast Asia is beyond the scope of this chapter, every attempt is made to highlight the salient discoveries throughout the region that inform our understanding of the prehistory of Thailand itself. The authoritative summary of the prehistory of Thailand is provided by Higham and Thosarat‘s volume Prehistoric Thailand:

From Early Settlement to Sukhothai (1998); the following discussion draws heavily on information presented in this source and in Higham‘s Early Cultures of Mainland Southeast Asia

(2002).

The Pleistocene to the Paleolithic

Despite the fact that expanding Homo erectus populations must have passed through

Thailand in order to have reached Indonesia as early as 1.8 to 1.6 million years ago (Rinehart

1981; Swisher et al. 1994; Higham and Thosarat 1998), no fossils of this hominid species have been recovered from Thailand. Rather, the earliest traces of human occupation in Thailand are the putative pebble-core chopper tools, ostensibly produced by H. erectus, which were discovered by Pope and colleagues at the riverine sites of Ban Mae Tha and Ban Don Mun and the limestone karst rock shelter site of Khao Pah Nam, all in Lampang Province in northern

Thailand (Figure 2-1) (Pope 1985; Pope et al. 1978, 1986; Higham and Thosarat 1998). These tools, which are dated to between 800,000 and 600,000 years ago (Pope et al. 1986; Nakbunlung

1994), considerably antedate the earliest known H. erectus fossils from mainland Southeast Asia, the fossilized teeth found in Tham Khuyen Cave in Lang Son Province, northern Vietnam, and dated to ca. 475,000 years ago (Ciochon et al. 1996).

Even if one prefers to accept the more conservative age estimates of 1 million to 800,000 years ago for the earliest Indonesian H. erectus fossils (Bellwood 1997; Klein 1999), the scant evidence of contemporary H. erectus populations in Southeast Asia has long been puzzling.

Surely the rock shelters and karstic caves that would shelter later groups of hunter-gatherers throughout Southeast Asia, and that have yielded so much of Southeast Asia‘s prehistoric archaeological record, would have appealed equally to the earlier populations of H. erectus inhabiting this region, and would have proven equally favorable for the preservation of their remains. As with all gaps in the fossil record, the possible causes of this lacuna are manifold and speculative. Perhaps the populations of H. erectus moved through the region too quickly to leave a substantial fossil record behind; or maybe the relevant fossils have been lost to the constant fluctuations of the Southeast Asian shoreline. Regardless of the circumstances, the lack

of H. erectus fossils in mainland Southeast Asia will likely continue to play a role in the debate over the true antiquity of the migrations of this most contentious of fossil hominids. Indeed, it is frequently used as a line of evidence in support of a middle Pleistocene date, rather than terminal

Pliocene/early Pleistocene date for the Indonesian erectines (Klein 1999; Higham and Thosarat

1998).

Following this indirect appearance of H. erectus in Thailand, the archaeological record is silent for nearly three-quarters of a million years, until the arrival of anatomically modern Homo sapiens populations in the late Pleistocene. The first evidence of these groups of hunter- gatherers in Thailand comes from the Lang Rongrien rock shelter, in western peninsular

Thailand. Here, charcoal hearths dated to ca. 38,000 to 27,000 years before present (BP) are found in three basal occupation layers, accompanied by faunal remains and a basic flake-and- pebble tool industry (scrapers, knives and some possible pebble-core choppers) created from local stone (Anderson 1990; Bellwood 1997; Ha Van Tan 1997; Higham and Thosarat 1998;

Higham 2002). These cultural deposits likely represent repetitive cycles of transitory occupation and abandonment of the rock shelter by small, mobile hunter-gatherer groups over the course of many millennia, in tune with diachronic fluctuations in environmental conditions and resource availability (Higham and Thosarat 1998:24-25).

A flake-and-pebble tool industry similar to that at Long Rongrien has been reported from the deepest cultural deposits at the nearby rock shelter site of Moh Khiew (Bellwood 1997:160;

Pookajorn 1991, 1994; Nakbunlung 1994; Matsumura and Pookajorn 2005). A single extended burial was found at the interface between the two deepest cultural levels; accelerator mass spectrometry (AMS) dates of ca. 26,000 BP for charcoal samples from the grave make this individual one of the earliest-known modern humans from mainland Southeast Asia (Matsumura

and Pookajorn 2005). The biological affinities of this individual will be discussed in Chapter 3.

Other inland sites throughout Southeast Asia have also yielded analogous, roughly contemporary flake-and-pebble tool complexes that have been ascribed to various regional traditions. These include the Nguom and perhaps the early Sonviian industries of Vietnam, dated to slightly before, and slightly after 23,000 BP, respectively (Ha Van Tan 1997; Nakbunlung 1994; Higham

2002:31); the lithic assemblages found in peninsular Malaysia at the sites of Bukit Bunuh, dated to ca. 40,000 BP (Saidin 2006); and possibly the assemblage from Kota Tampan, which may date to anywhere between 74,000 and 30,000 BP, depending on whether the tools‘ association with volcanic ash from the eruption of Toba is accepted (Bellwood 1997; Saidin 2006).

Holocene Hunter-Gatherers

During the terminal Pleistocene, these regionally varying lithic traditions gave way to a more uniform and widely distributed stone tool industry now known as the ―Hoabinhian,‖ after

Hoa Binh province in northern Vietnam where the type specimens were originally discovered in the 1920s (Colani 1927; Barth 1952; Solheim 1972; Higham 2002). The Hoabinhian industry, which is broadly dated to between 18,000 and 3000 BP, is known from inland rock shelter sites spread from Burma to Vietnam and from southern China to peninsular Malaysia and northeastern

Sumatra. Its occurrence throughout Southeast Asia is thought to represent the expansion of hunter-gatherer groups into unoccupied or previously abandoned locations during the period of climatic amelioration following the Last Glacial Maximum (ca. 21,000 to 18,000 years ago)

(Bellwood 1997:158-161; Bellwood 1993; Ha Van Tan 1997; Higham and Thosarat 1998;

Higham 2002). Hoabinhian lithic assemblages are characterized by a predominance of pebble- core tools (rather than flake tools), particularly the distinctive unifacially worked river cobbles known as ―sumatraliths‖ or ―unifacial discoids.‖ The assemblages also include choppers, axes and so-called ―short axes,‖ which may simply be worn or broken and resharpened axes and

sumatraliths (Bellwood 1997:158; Ha Van Tan 1997; Higham 2002:33; Higham and Thosarat

1998). Late Hoabinhian assemblages typically include an increasing proportion of edge-ground stone tools—a late-phase variant known as the ―Bacsonian‖ industry (Bellwood 1997:161)—and the possible presence of early pottery forms, although the latter is somewhat debated (Bellwood

1997; Higham 2002).

The relationship between the Hoabinhian and preceding lithic traditions is complex, and appears to vary regionally: in Vietnam, the Hoabinhian is thought to have emerged directly from the preceding Sonviian tradition ca. 18,000 BP (Ha Van Tan 1997:37); similar local continuities between the Hoabinhian and earlier lithic assemblages are also suggested for southern China and northern and central Thailand (Bellwood 1997; Higham 1996; Ha Van Tan 1997). In contrast, in peninsular Malaysia and southern/peninsular Thailand, Bellwood (1997:160) describes a

―technological discontinuity‖ between the earlier flake tool assemblages found at Kota Tampan,

Lang Rongrien and Moh Khiew, and the Hoabinhian complex, which does not appear at these sites until at least 13,000 BP. Nevertheless, Higham and Thosarat (1998:63-65) see the

Hoabinhian in Thailand as a more or less direct continuation of the earlier hunter-gatherer traditions such as those known from Long Rongrien.

In Thailand, the principal Hoabinhian sites are clustered throughout the Chao Phraya River catchment in the northern and central part of the country. These include the cave sites of Sai

Yok, Tham Ongbah, Ment Cave, Khao Talu and Heap Cave in the southern reaches of the Chao

Phraya and the Kwae Noi River valleys (see Figure 2-1) (van Heekeren and Knuth 1967;

Sørensen 1979; Higham and Thosarat 1998; Higham 2002). To the north, in Mae Hongson province, the rock shelter sites of Spirit Cave (dated to ca. 9000 to 5500 BC), Banyan Valley

Cave (3500 to 2000 BC) and Steep Cliff Cave (5500 to 3500 BC) have all yielded extensive

Hoabinhian deposits from their lowest occupational layers (Gorman 1969, 1971, 1972; Reynolds

1992; Higham and Thosarat 1998; Higham 2002). Spirit Cave is a particularly important site, not only because of its time depth, but also because a number of cord-marked and incised pottery sherds, polished quadrangular stone adzes and ground slate knives were recovered from the interface between the two uppermost Hoabinhian layers here. At the time of their discovery, these were believed to be the earliest such tools from mainland Southeast Asia, and the earliest known pottery in the world (Solheim 1972:37). Gorman (1969:671, 673; 1971) initially interpreted these artifacts as evidence of ―a culture contact situation‖ between the local

Hoabinhian hunter-gatherers and an incipient agricultural group around the end of the seventh millennium BC.

Additionally, Gorman‘s meticulous excavation of the cultural layers at Spirit Cave, and his innovative decisions to use flotation analysis and to screen the excavated deposits through a fine wire mesh, resulted in the recovery of an unprecedented quantity and diversity of faunal and botanical remains, yielding crucial insights on the subsistence patterns of the cave‘s inhabitants

(Gorman 1969; Solheim 1972; Higham and Thosarat 1998; Higham 2002). Among the recovered plant and animal species from Spirit Cave are almonds, betel nuts, butternut, candle nut, beans, peas, bottle gourds, water chestnuts, pepper and cucumbers, as well as the remains of fish, crustaceans, mollusks, deer, pigs, monkeys and an array of small- to medium-sized arboreal and terrestrial mammals (Gorman 1969, 1971; Higham 2002). Gorman (1969:672) saw the contents of this assemblage—particularly the beans, peas, cucumber, gourd and chestnut—as ―a group of food plants which suggests economic development beyond simple food gathering.‖ Yet despite his original characterization of this evidence of agriculture, especially the pottery and ground stone tools, as intrusive elements in the Hoabinhian deposits, Gorman (1969:673) was

convinced that the nascent agricultural tradition was of Southeast Asian origin. Therefore, the region was demonstrably not ―a culturally backward cul-de-sac‖—the passive recipient of advancing culture via diffusion from elsewhere (i.e., China)—but rather was ―a progressive emanating center of early cultural development.‖

Solheim (1972) took this interpretation a step beyond and proclaimed the artifacts and biological remains from Spirit Cave to be proxy evidence that Southeast Asia was the location of the world‘s earliest transition to agriculture, one that had antedated the development of agriculture in the Levant by several millennia (Higham and Thosarat 1998). This transition,

Solheim argued (1972:39), had begun with incipient horticulture ―probably about 13,000 BC somewhere in the northern reaches of Southeast Asia,‖ and had developed into the full agricultural complex of domesticated cultivars and livestock plus pottery during the time of the occupation of Spirit Cave. Interestingly, though, Solheim (1972:39) acknowledges that the earliest inhabitants of Spirit Cave possessed only portions of this agricultural suite, and would not acquire pottery ―until an entire spectrum of sophisticated wares arrived at Spirit Cave from elsewhere in Southeast Asia‖ much later (ca. 6000 BC) during the final occupation phase.

In Solheim and Gorman‘s assessments of the archaeological evidence from Spirit Cave, we can clearly detect a facet of the continuity versus discontinuity debate in Southeast Asia. Despite the obvious and (by their own pens) admittedly intrusive nature of the pottery and polished stone tools in the uppermost cultural layers at Spirit Cave, Solheim and Gorman circumlocute their way into proffering this material as evidence for the local development and advancement of agriculture. Gorman‘s (1969) contradictory presentation and interpretation of this evidence has already been mentioned above. Solheim (1972:39-40), mere paragraphs after stating that the pottery and ground stone tools at Spirit Cave were introduced from elsewhere in Southeast Asia,

asserts that ―that complex, however, is not eligible for status as an independent culture; one cannot separate its elements from their late Hoabinhian associations.‖ Solheim then continues on to draw parallels between the Spirit Cave material and similar artifacts from Taiwan—Chang‘s

Ta-p‘en-k‘eng culture, of which more will be said later (Chang 1969, 1970, 1986; Bellwood

1997) and a similarity that Gorman (1969) observed as well—and notes that both the material from Spirit Cave and the Ta-p‘en-k‘eng culture date to a prehistoric age he denotes as ―the

Extensionistic Period,‖ between 8000 BC and AD 0.

As is evident from the name, the defining characteristic of the Extensionistic period is the movement and expansion of populations; a perhaps overly broad age range notwithstanding, this name is actually a fairly apt acknowledgement of a dominant agent in middle Holocene

Southeast Asia. Unfortunately, Solheim limited his posited population movements to those in which people moved out of the mountains (where the majority of prehistoric hunter-gatherer sites in Southeast Asia are found) and into the plains and coastal regions where agriculture would be favored and where later Neolithic, Bronze and Iron age sites would be discovered. He did not allow for the possibility of a real biological and cultural connection between the populations of

Taiwan and mainland Southeast Asia, noting both the large geographic distance separating the two, and the contrast between Hoabinhian rock shelters and the open-air Ta-p‘en-k‘eng sites

(Solheim 1972).

My intention, however, is not to make straw men out of Gorman and Solheim, to whom we owe much of our understanding of Southeast Asian prehistory, and who were writing without the benefit of the illumination provided by nearly 40 years of further research and field work that have occurred since these articles were published. Rather, my point here is to demonstrate how the undercurrent of endogenous versus exogenous origins and influences pervades scientific

inquiry into population history in Southeast Asia, even when not dealing directly with the human populations themselves. First impressions are telling, and Gorman and Solheim‘s oscillating and incongruent presentations and initial interpretations of the evidence from Spirit Cave—the recognition of clearly intrusive, likely exogenous elements in the archaeological record, and the seemingly irresistible inclination to shoehorn these elements into a locally continuous, endogenous evolutionary sequence that establishes Southeast Asia as a region of special cultural and historical significance—demonstrates exactly how powerful this undercurrent is.

As it stands now, the pottery and ground stone tools from the upper layers at Spirit Cave have been determined to be of a much younger age than their archaeological context would suggest. Higham (2002) reports that these artifacts are now dated to ca. 2000 to 1000 BC—well after the Hoabinhian period—on the basis of AMS dating of organic residues on one of the sherds. Discarded too is the notion that the Hoabinhians of Spirit Cave were practicing any form of well-developed agriculture: the densely forested and steep terrain and thin acidic soils in the area are poorly suited to plant cultivation. Additionally, there are no traces of rice, taro or dogs—all hallmarks of the Southeast Asian agricultural complex—in the botanical and faunal remains recovered from Spirit Cave. Rather, the suite of plant and animal remains recovered from the cave are indicative of a sophisticated broad-spectrum foraging subsistence strategy operating in a resource-rich subtropical montaine/riverine environment (Gorman 1971:315;

Hutterer 1983, 1998:67; Glover and Higham 1996; Higham and Thosarat 1998; Higham 2002).

On the other hand, the possibility that the Hoabinhians were selectively fostering the growth of some types of plants, such as beans, within the native environment cannot yet be definitively excluded (Bellwood 1997; Hutterer 1998; Higham 2002). In any case, sites such as Heap Cave and Khao Talu in the Kwae Noi River valley speak to the success of this hunting-and-gathering

subsistence strategy, as the Hoabinhian tradition persisted there until as late as 2500 BC— practically to the eve of the advent of agriculture in Thailand (Rinehart 1981; Pookajorn 1990;

Bellwood 1997).

Other Hoabinhian sites in Thailand have yielded similar evidence for small, mobile groups of broad-spectrum hunter-gatherer populations. These include the previously mentioned Steep

Cliff Cave and Banyan Valley Cave, both of which were excavated by Gorman to the same exacting standards as Spirit Cave. Banyan Valley Cave generated significant interest when it was reported that carbonized rice husks were recovered from the uppermost cultural levels and in association with edge-ground adzes, slate knives and cord-marked pottery similar to that from the upper layer at Spirit Cave (Reynolds 1992; Higham and Thosarat 1998; Hutterer 1998;

Higham 2002). It was subsequently established by Yen (1977) that this rice is of a wild type and probably dates to 900 BC to AD 1000, as does the uppermost cultural layer in general. These dates are only slightly younger than the AMS dates of the pottery from Spirit Cave and lend weight to the interpretation that the pottery and polished stone tools from Spirit Cave were introduced into the Hoabinhian deposits from a much later occupation (Reynolds 1992).

In the far south of Thailand, Hoabinhian artifacts are also found in the upper cultural layers at Lang Rongrien, dated to ca. 8000 to 4000 BP, and at Moh Khiew (Anderson 1990; Pookajorn

1991, 1994; Bellwood 1997; Higham and Thosarat 1998; Hutterer 1998; Higham 2002;

Matsumura and Pookajorn 2005). A single, crouched burial excavated at Moh Khiew, found in association with Hoabinhian tools and dated to between 26,000 and 6000 BP, is similar to burials from Hoabinhian contexts at the sites of Hang Dang and Moc Long in Vietnam (Pookajorn 1992;

Higham 2002; Matsumura and Pookajorn 2005). Interestingly, this burial posture differs from the Hoabinhian period flexed burials from the much closer peninsular Malaysian sites of Gua

Cha (10,000 to 3000 BP) and Gua Gunung Runtuh (11,000 to 7500 BP) (Bellwood 1993, 1997;

Zuraina 1994; Matsumura and Zuraina 1999). Clearly, despite the long-standing overall technological uniformity of the Hoabinhian tool industry throughout mainland Southeast Asia, such regional variations in other aspects of the Hoabinhian culture indicate that these hunter- gatherer populations were neither static nor monolithic.

Changing environmental conditions towards the end of the Hoabinhian period have permitted us further insights into patterns of cultural variation among hunter-gatherer groups in

Thailand. During the Last Glacial Maximum, ca. 21,000 to 18,000 BP, mean sea levels were as much as 100 to 150 meters lower than their current level, exposing the shallow continental shelf around much of mainland and island Southeast Asia (Chappell and Thom 1977; Chappell 1994;

Bellwood 1997). During this period, the mainland of Southeast Asia was joined with the islands of eastern Indonesia to create the landmass known as ―Sundaland.‖ As the glaciers retreated over the next 12,000 years, the sea levels rose, progressively inundating the lowest-lying areas and obscuring any traces of early human habitation along the ever-changing coastline. Around

6000 BP, the rising sea reached its maximum and then retreated several meters to the present-day shorelines of Southeast Asia (Higham and Thosarat 1998:38; Bellwood 1997; Higham 2002). It is only from this point on that evidence of hunter-gatherer populations living along the coast is accessible to archaeologists, though as Higham and Thosarat (1998:64) point out, ―There must have been a long tradition of maritime hunting and gathering before then.‖

Excavations of coastal sites from this period reveal important contrasts with the

Hoabinhian hunter-gatherer groups from the inland uplands of Thailand. Here, the biotic richness of the tidal swamps, mangroves and estuaries supported larger, predominantly sedentary groups of fisher-hunter-gatherers, who left behind a more expansive suite of cultural remains

than their more migratory late Hoabinhian contemporaries. At the important sites of Nong Nor

(ca. 2500 BC) and Khok Phanom Di (2000 to 1500 BC), edge-ground stone tools, worked bone and cord-marked, burnished and incised pottery are common, and were likely produced locally, as suggested by intrasite patterning in the distribution of these artifacts (Higham and Thosarat

1998:43; O‘Reilly 1995; Bellwood 1993, 1997; Higham 2002). These items may have been traded with the inland hunter-gatherer groups, as suggested by the presence of marine faunal remains at the inland sites and of stones obtained from inland sources at the coastal sites. A single, crouched burial from Nong Nor echoes those found at Moh Khiew and at Hoabinhian sites in Vietnam, but with the added element of accompanying pottery vessels (Higham and

Thosarat 1998:43; Higham 2002).

The cultural richness of these costal populations is exemplified by the many burials recovered from the mound site of Khok Phanom Di, near the head of the Gulf of Thailand

(Higham and Bannanurag 1990, 1991; Higham and Thosarat 1994, 1998; Nakbunlung 1994;

Bellwood 1993, 1997; Higham 2002). Within the three mortuary phases (MP) attributed to the early hunter-gatherer occupation of the site (out of seven total mortuary phases), people were interred in a supine, extended position, often in clusters believed to represent family units. A wealth of grave goods heretofore unknown for the prehistoric inhabitants of Thailand included pottery vessels and pottery-making tools, edge-ground stone tools, bone fishhooks and harpoons, shell knives, bark cloth and asbestos fabric, red ocher, shell beads and other bone and shell ornaments, and selected faunal remains. The asymmetrical distribution of some of these items, particularly within the third mortuary phase, hints at the nascent expression of individual rank and social stratification, as well as the emergence of sex-related differences in mortuary practices

(Higham and Thosarat 1998:55-56; Higham and Thosarat 1994; Nakbunlung 1994; Bellwood

1997; Higham 2002).

The apparent sedentism and cultural richness (especially in terms of the volume and variety of pottery forms) of the population of Khok Phanom Di has led some to suggest that these people were among the earliest people to practice agriculture in Thailand (Higham and

Thosarat 1998; Higham 2002). This suggestion has been lent additional weight by the recovery of domesticated rice chaff from coprolites and the abdominal regions of burials belonging to the third mortuary phase (Higham and Thosarat 1998:56; Higham 2002:63; Thompson 1992; Glover and Higham 1996). The presence of dog bones in the second and third mortuary phases also hints at agriculture, since the dog is not native to Southeast Asia and is almost universally found accompanying agricultural communities elsewhere in the world (Higham and Thosarat 1998:54;

Higham 2002:59, 63; Higham 1996). Sauer (1952) suggested that resource-rich marine coastal environments such as that at Khok Phanom Di could have fostered experimentation with plant cultivation and horticulture by providing both 1) an abundance of useful plant species with which to experiment; and 2) an abundance of resources in general, providing the inhabitants of these areas with sufficient free time away from their hunting and gathering activities to permit such experimentation (Bellwood 1997). As Bellwood (1997:203-204) notes, however, Sauer‘s core hypothesis has been discounted on several grounds. Not least among these is the unlikelihood that hunter-gatherer populations living in resource-rich environments, with seemingly endlessly renewable food sources, would ever feel the need to experiment with plant cultivation in order increase the yields of already abundant plants. Rather, Bellwood (1997:204) and others

(McCorriston and Hole 1991; Higham and Thosarat 1998) favor a model of agricultural development wherein increasing plant cultivation must have been induced—indeed, was

probably ultimately necessitated—by environmental or demographic stresses on existing food sources.

Such stresses do not appear to have affected the early inhabitants of Khok Phanom Di.

Although their skeletal remains indicate that they suffered the short life expectancy, high infant mortality, and parasitic and infectious disease burdens common to all hunter-gatherer populations, the people appear to have been well-nourished and in overall good health (Bellwood

1997; Higham and Thosarat 1998; Tayles 1999; Higham 2002). This further supports the hypothesis that the rich marine coastal environment provided adequate nutritional resources for this population, without the need for active cultivation of additional food sources. Nor do the climate or environment appear to have been contributing factors to any nascent agricultural development here. Rather, as Higham and others (Higham and Thosarat 1998:53; Thompson

1996; Higham 2002) note, the soil and groundwater at Khok Phanom Di during the period of the first three mortuary phases were likely too saline to support rice cultivation, or even the growth of wild rice. It is more likely, they surmise, that the domesticated rice consumed at Khok

Phanom Di during this period was obtained from elsewhere in Thailand or Southeast Asia via trade networks (Higham 2002:77; Hutterer 1983; Higham and Thosarat 1994, 1998; Glover and

Higham 1996; Higham 1996). This contact with other groups may be evinced by the appearance of new styles of pottery, including new types of clay and temper and new patterns of decoration and shape, which occurred during the second mortuary phase and again during the third (Higham

1996; Higham 2002; Vincent 2003, cited in Higham 2002).

Whatever the nature of this early agricultural influence, both it and the cultural richness of

Khok Phanom Di appear to have reached an apogee during the time when burials of the fourth and fifth mortuary phases (often referred to as the middle cultural layers of the site; Bellwood

1993, 1997) were laid down. During this period, it is believed that a climatic or other environmental shift resulted in the area around Khok Phanom Di becoming more of a freshwater riverine or estuarine environment. This shift is evidenced by changes in the composition of the faunal assemblages from these phases, to include more freshwater fish and shellfish species

(Mason 1991, 1996; Higham and Thosarat 1994, 1998; Bellwood 1997; Higham 2002). The first appearance of the remains of water buffalo, an increasing prevalence of granite hoes, and a dramatic increase in shell knives (believed to have been used for harvesting rice) during this period have led many to conclude that the people of Khok Phanom Di had by this time begun to actively cultivate domesticated rice in the more agriculturally favorable freshwater swamps and floodplains which surrounded the site (Higham and Thosarat 1998:62-63; Higham and Thosarat

1994; Glover and Higham 1996; Higham 1996; Bellwood 1997; Higham 2002).

This period witnessed changes in mortuary practices as well: some of these, such as the differentiation between the graves of males and females by the inclusion of turtle carapaces and clay anvils, respectively, were intensifications of practices established in earlier mortuary phases.

Others, including a relaxation of the tight clustering of graves by family group and an increase in individual graves, to the point where clustering ceased altogether during the fifth mortuary phase, mark a departure from previous mortuary practices (Higham and Thosarat 1998; Higham

2002). These shifts are indicative of a dynamic and malleable sociocultural environment at

Khok Phanom Di, one that was responding to fluctuating climatic, geophysical, social and economic conditions, and they may be further evidence of contact with other cultural groups. A distinct hiatus in interments between the third and fourth mortuary phases, during which over a third of a meter of non-mortuary cultural deposits accumulated, gives some sense of the upheaval

experienced by the inhabitants of the site during this time period (Higham and Thosarat 1998;

Higham 2002).

The greatest changes in mortuary practice, suggesting the greatest changes in the lives of the people, are observed in the fifth mortuary phase, which also contains perhaps the most famous prehistoric burial in all of Thailand: the so-called ―Princess of Khok Phanom Di.‖ A middle-aged female—believed to be an accomplished potter based on the amount of pottery, raw clay cylinders and potter‘s tools included in her grave—she was interred with an unprecedented wealth of shell bangles, ear ornaments, discs and other jewelry of novel design, including a necklace containing over 120,000 shell beads. All of these were made from the shells of marine mollusks that by this time were exotic to the freshwater environs of Khok Phanom Di, further heightening our perception of this woman‘s special standing within her community. Adjacent to her, two infants thought to be her daughters were interred in similarly rich graves, one of which appeared to mimic in miniature the Princess‘s grave, with the inclusion of an array of child-sized potter‘s tools and jewelry. Beyond the wealth of their graves, the location of their burials in an area that had never before been used for interments, and that was set apart from the primary mortuary zone, provides additional emphasis of the high status accorded these individuals

(Higham and Thosarat 1998:61-63; Bellwood 1997:256; Higham 2002:68-73). Although we do not know the true nature of the Princess‘s role within her community in life—revered artisan; matriarch of a prominent family; tribal leader; head of a prosperous and influential immigrant agricultural community recently arrived at Khok Phanom Di; or something else altogether—her exaltation in death foreshadows the emergence of increasing social stratification during the

Neolithic and Metal Ages.

The provision of special burial rites for selected individuals continued and was elaborated in the sixth mortuary phase, during which a few rich burials were enclosed within a unique clay structure adjacent to a wooden structure containing more pedestrian burials. But such practices were abandoned during the seventh and final mortuary phase of Khok Phanom Di, the overall depauperate nature of which indicates a site in decline. Paleoenvironmental reconstructions indicate that beginning late in the fifth mortuary phase, conditions at Khok Phanom Di had returned to a more marine, coastal environment, the salinity of which forced the inhabitants to abandon rice cultivation and return to procuring this staple via trade contacts (Higham and

Thosarat 1998:61-63; Higham 2002:74-75). The people may have struggled to return to their pre-agricultural lifeways of fishing and gathering coastal food resources, and when the environment shifted again, this time moving the seacoast far from the site, Khok Phanom Di was abandoned (Bellwood 1997:258; Higham and Thosarat 1998; Higham 2002).

Although the remains of domesticated rice are present throughout nearly the entire occupation of Khok Phanom Di, it appears that it was grown locally only during a short interval in the middle of the site‘s lifespan, when local groundwater and soil conditions became less saline and thus more suitable for rice cultivation. As was noted above, it is most likely that at all other times, the inhabitants of Khok Phanom Di obtained their rice via trade with other groups, who must have been practicing agriculture elsewhere in Thailand and the surrounding areas of

Southeast Asia. The identity and origins of these agricultural groups, however, as well as the means by which they came to possess their knowledge and techniques of rice cultivation, compose what Higham and Thosarat (1998:65) have described as ―one of the most important questions in Thailand‘s past.‖ Unfortunately, the emergence of agriculture is one of the murkiest periods in Thai prehistory, and our understanding of the Neolithic period in Thailand suffers at

the hands of a host of archaeological misfortunes, including the destruction (by both natural and artificial processes) of critical sites, inaccurate or otherwise unreliable dating estimates, and, remarkably, the absence of direct evidence for rice agriculture from key sites that otherwise undoubtedly belong to the Neolithic. Small wonder then, that this most important of questions has dominated the modern era of archaeological research in Thailand. Excavations of Neolithic sites elsewhere in Southeast Asia have helped clarify the picture in Thailand, but the debate remains active today and it is central to the understanding of the origins of the modern Thai population. The broader archaeological, linguistic, genetic and skeletal data that inform and drive this debate will be considered in the following chapter; for now, let us continue our focus on what we have learned from the salient archaeological sites in Thailand.

The Arrival of Agriculture

The secure dating of Khok Phanom Di and several other sites allows us to determine that rice agriculturalists were present in Thailand by at least 2300 BC, and most authorities concur that these groups probably inhabited sites situated on inland river flood plains favorable for rice agriculture (Glover and Higham 1996). Among the most important of these inland sites is the

Neolithic cemetery of Ban Kao, in the Kwae Noi River valley northwest of Bangkok, which was occupied between 2300 and 1500 BC (Figure 2-2) (Sørensen and Hatting 1967; Bellwood 1997;

Higham and Thosarat 1998; Higham 2002). Here, Sørensen and colleagues found 44 single and paired extended burials interred with quantities of pottery; polished adzes and other stone tool forms; bone tools and projectile points; stone and shell sickles and knives used for harvesting rice; and stone, animal bone, antler and shell worked into bangles, ornaments and beads

(Sørensen and Hatting 1967; Sørensen 1972; Bellwood 1997; Higham and Thosarat 1998;

Higham 2002).

Although ―no direct evidence for rice‖ has been found at Ban Kao, ―its presence must be assumed given its importance at [the contemporary site of] Khok Phanom Di‖ (Bellwood

1997:258). The presence of rice agriculture at Ban Kao has also been inferred from the quantity of stone and shell sickles and reaping knives recovered from the site. But with regards to the questions of the origins of agriculture in Thailand and in Southeast Asia, the most important materials recovered from Ban Kao are the ceramics. Sørensen (1972) described twelve distinct pottery forms from the graves there, divided into early and late phases and including carinated jars with tripod bases, goblets, ring-pedestalled jars, and flat-bottomed jars and bowls (Bellwood

1997; Higham and Thosarat 1998; Higham 2002). Most of the pottery forms are red or black wares that are burnished or cord-marked on their external surfaces, but some bear a distinctive motif of sharply delineated sinuous bands of incised or impressed dentate surface textures

(Sørensen 1972; Bellwood 1993, 1997; Higham 1996, 2002; Higham and Thosarat 1998).

This impressed dentate motif and the carinated tripod vessels—the defining features of

Sørensen‘s ―Ban Kao Culture‖—have become something of a hallmark of expanding agricultural populations in Southeast Asia (Sørensen 1972; Glover and Higham 1996; Higham 1996, 2002;

Bellwood 1993, 1997; Rispoli 1997; Higham and Thosarat 1998). For example, such ceramic wares appear as novel elements in the grave-good assemblages from the second and third mortuary phases at Khok Phanom Di (Higham 1996, 2002; Bellwood 1997; Higham and

Thosarat 1998). As noted above, these finds prompted Vincent (2003, cited in Higham 2002) to suggest that the new pottery forms were indicative, at a minimum, of contact and trade with outside groups—i.e., the agriculturalists from whom the hunter-gatherers of Khok Phanom Di are believed to have obtained their domesticated rice—but they may also indicate an influx of peoples to Khok Phanom Di from other areas (Higham 2002:58-65).

Elsewhere in the Chao Phraya basin, similar impressed dentate motif pottery, along with a broadly similar material culture in general, has been found at the Neolithic sites of Ban Tha Kae

(poorly dated but probably ca. 2500 to 1500 BC), Non Pa Wai (2300 to 1500 BC) and Khok

Charoen (1400 to 800 BC, and thus a late Neolithic/early Metal Age site), as well as in the upper layers overlying the Hoabinhian deposits at Heap Cave and Khao Talu (Higham 1996, 2002;

Loofs-Wissowa 1997; Pigott et al. 1997; Bellwood 1997; Higham and Thosarat 1998). The presence at these inland sites of beads and other ornaments made from marine shells lends further weight to the interpretation that these agriculturalists were engaged in exchange relationships with the coastal hunter-gatherers from sites such as Nong Nor and Khok Phanom

Di (Higham and Thosarat 1998:81-82). On the Khorat Plateau, this same pottery motif has been found in conjunction with definitive evidence of rice cultivation in the Neolithic levels at the key sites of Non Nok Tha (ca. 2000 to 1500 BC) and Ban Chiang (2300 to 1500 BC), (Higham and

Thosarat 1998:83; Higham 2002:91-92; Bayard 1972; Gorman and Charoenwongsa 1976;

Hutterer 1988, 1993; Higham 1996). As at the Neolithic sites in the Chao Phraya basin, the deceased at Non Nok Tha and Ban Chiang were interred in a supine, extended position, along with grave-good assemblages composed of pottery vessels, polished stone tools, shell beads and faunal remains. The bones of domesticated pigs, dogs and cattle recovered during excavations help round out the picture of these sites as full-fledged agricultural settlements.

This uniformity of practice in pottery styles and mortuary ritual throughout Neolithic

Thailand suggests a sole source, whether exogenous or endogenous, for the advent of agriculture in the area. Additionally, these common pottery forms and mortuary practices are not unique to agricultural settlements in Thailand. Rather, they have a broad distribution throughout Southeast

Asia and speak to the spread of agriculture throughout the region (Leong 1991; Glover and

Higham 1996; Higham 1996, 2002; Bellwood 1997; Higham and Thosarat 1998). In Vietnam, the earliest agricultural settlements are found on flood plains of the lower reaches of the Red

River valley, and date, as in Thailand, to the late third to mid-second millennium BC (Higham

2002:86; Hutterer 1983; Higham 1996; Higham and Thosarat 1998). At the sites of Phung

Nguyen, Co Loa, Trang Kenh and Lung Hoa, the suite of dentate stamped and incised pottery forms—known locally as the Phung Nguyen culture—is found in conjunction with mortuary material culture assemblages similar to those from Neolithic Thailand. Significantly, the Phung

Nguyen culture also features the added, distinctive elements of bangles, beads and other ornaments carved of nephrite and jadeite and reminiscent of similar artifacts from Neolithic

Chinese sites (Ha Van Tan 1985; Higham 1996, 2002; Higham and Thosarat 1998; Hoang Xuan

Chinh and Nguyen Ngoc Bich 1978, Ha Van Tan 1980, Lai Van Toi 1999, cited in Higham

2002).

Similar incised and dentate stamped ceramic wares and/or ring-pedestalled and tripod vessels have also been found in the Mekong Valley at Samrong Sen, in Cambodia (Higham

1996, 2002), and in peninsular Thailand and Malaysia at Lang Rongrien, Gua Cha (in association with extended burials at both sites), Gua Kecil and Jenderam Hilir, among others (Sieveking

1954, 1987; Anderson 1990; Leong 1991; Bellwood 1997; Higham and Thosarat 1998). It is worth noting that the Neolithic ―Ban Kao Culture‖ ceramic tradition appears several centuries to a millennium later in the Malay peninsula than it does in Thailand, Vietnam and Cambodia, suggesting both a direction and a time frame for the spread of agriculture throughout Southeast

Asian mainland (Bellwood 1993, 1997). Bellwood (1997:265) also notes that there is typically a sharp discontinuity between the earlier Hoabinhian and later Neolithic deposits at the peninsular sites, suggesting, as at Khok Phanom Di, that agriculture was carried to these sites by an influx

of people from elsewhere. Both of these points, and their impact on our understanding of population history in Southeast Asia, will be considered further in Chapter 3.

With the establishment of agriculture in the resource-rich Southeast Asian mainland, the eventual emergence of metallurgy there would seem to be a foregone conclusion. Indeed, in a number of ways, the Bronze Age in Thailand appears to be an intensification and elaboration of many cultural trends begun during the Neolithic. Rice agriculture expanded and was made more efficient by the use of metal tools to clear the land, till the soil and reap the harvest. Growing populations fed by the increased yields continued to settle on low mounds along fertile river flood plains and tributaries. Many of the key Neolithic sites from the Chao Phraya basin and

Khorat Plateau were also thriving Bronze Age settlements, indicating successful adaptation to technological and social innovations by longstanding communities. Material culture expanded to include new forms and styles of both utilitarian and ornamental items. Exchange relationships among groups, for items both required and desired, became more complex and expanded in economic scale and geographic scope to include a broader range of ordinary and exotic goods and materials. In some places, the differential distribution of these items in grave-good assemblages indicates that the nascent social stratification observed in late hunter-gather and early agricultural sites began to crystallize during the Bronze Age, as individuals and lineages began to distinguish themselves, perhaps through auspicious descent, leadership ability, finesse as craftsmen, or prowess as traders within an increasingly complex society. Everywhere, the pace of social and cultural change quickened (Bellwood 1997; Higham and Thosarat 1998;

Higham 2002; Baker and Phongpaichit 2005).

The Metal Age

The Bronze Age

Many of the same research questions that pertain to earlier prehistoric eras in Thailand have also been applied to the Bronze Age: did the practice of metallurgy have an independent, in situ origin in mainland Southeast Asia (Nakbunlung 1994), or was it imported from elsewhere in

Asia (i.e., from China), and if so, via what means—the diffusion of ideas, the trade of artifacts, or the movement of peoples (Bellwood 1997; Higham and Thosarat 1998; Higham 2002; Baker and Phongpaichit 2005)? As with the origins of agriculture in Southeast Asia, early researchers

(Solheim 1968; Gorman and Charoenwongsa 1976; inter alia) were quick to suggest that the advent of metallurgy in Southeast Asia occurred not only independent of, but also prior to, its emergence in China and the Near East (Higham and Thosarat 1998:92-93; Nakbunlung 1994).

As was the case with the debate over the origins of agriculture, the excavation of additional sites and the acquisition of better radiocarbon dates have resolved some, but not all, of the questions surrounding the emergence of metallurgy in Southeast Asia.

Although initially thought to date as far back as the fourth millennium BC (Solheim 1968;

Gorman and Charoenwongsa 1976; Rinehart 1981; White 1982), the Bronze Age in Thailand and most of mainland Southeast Asia is now considered to have begun no earlier than 1500 BC

(Higham and Thosarat 1998; Higham 2002). This makes Southeast Asian metallurgy much younger than the Bronze Age in China, which likely dates back to the early third millennium BC

(Renfrew and Bahn 1996; Higham and Thosarat 1998; Diamond 1999; Higham 2002).

Additionally, similarities in smelting and casting technologies and artifact forms between sites in southern China and Southeast Asia suggest that the idea, if not the actual physical practice, of bronze metallurgy spread throughout the region from an ultimate source in China, via increasingly expansive and important trade networks. Further evidence for such trade networks

can be found in the Chinese-style jades that have been recovered from late Neolithic and Bronze

Age contexts in Thailand (Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

In general, two types of Bronze Age sites are encountered in Thailand: those where copper ores were mined, smelted and then cast into ingots for trade; and those where the ingots were alloyed with tin or lead to create bronze that was then worked into tools and artifacts. The majority of bronze artifacts are known from mortuary contexts at both types of sites. Important mining and smelting sites have been found near copper ore sources along the Mekong River in northeastern Thailand, and in the Chao Phraya basin in central Thailand (Figure 2-3). In the latter area, the smelting slag, clay smelting furnaces, casting crucibles and ingot molds found at

Non Pa Wai and dated to 1500 to 1000 BC provide the earliest evidence for bronze metallurgy in

Thailand (Natapintu 1991; Higham and Thosarat 1998). The nature of the cultural deposits suggests that while ore-processing sites such as Non Pa Wai, Nil Kham Haeng and Phu Lon were in use for many centuries, they were only occupied seasonally, as smelting fires would have been easier to maintain during the annual dry season hiatus in monsoon rains. The raw copper and bronze ingots produced at these sites would then have been traded with other settlements clustered along the rivers that, by this time in Thailand‘s prehistory, were flourishing avenues of commerce and exchange. And it is these sites, where bronze was cast into tools and ornaments for work and for wear, and included in grave-good assemblages, that provide the greatest insight into Thailand‘s Bronze Age.

At Ban Na Di (1400 to 400 BC), on the Khorat Plateau, excavation in two separate areas of the site (designated Areas A and B) revealed not only extensive evidence for bronze casting— including an alloying furnace, crucibles and fragments of bivalve casting molds—but also a rich and diverse material culture. Interestingly, there appears to have been a significant disparity in

the distribution of this material wealth among the population of Ban Na Di, as evidenced by a stark contrast in the provisioning of grave goods between the groups of burials in the two excavation areas. Despite a shared basic mortuary assemblage of pottery vessels, shell beads and faunal remains, throughout three Bronze Age mortuary phases, the graves in Area A are distinctly more modest than those at Area B, containing hundreds, rather than thousands, of shell beads, fewer ritual offerings of cattle and pig forelimbs, and almost no bronze artifacts. By contrast, in Area B, individuals were buried with miniature clay figurines of cattle, deer, elephants and people; bangles cast in bronze and carved from shell, marble and other exotic stones; and the first iron artifacts known from the site. A few individuals, including a child, were buried with ornaments created from the skin and bone of crocodiles. Higham and colleagues

(Higham and Kijngam 1984; Higham and Thosarat 1998; Higham 2002), however, have argued that while this striking difference in the allocation of grave goods certainly indicates a higher social status for the people buried in Area B, the asymmetry is of a magnitude insufficient to suggest that these people belonged to a distinct ruling class. Rather, they suggest that the two groups were members of different, and differently favored, lineal groups within the same general social class.

Elsewhere in the Khorat Plateau, similar evidence of bronze casting—including furnaces, crucibles and bivalve molds—has been found at the sites of Ban Chiang and Non Nok Tha (both

1500 to 1000 BC or later). Likewise, the burials found at these sites share many of the same mortuary traditions with Ban Na Di, such as the inclusion of pottery, shell beads and bangles, axes, projectile points and bangles cast in bronze, miniature cattle figurines made of clay, and ritual offerings and ornaments derived from both domesticated and wild animals, as well as the alignment of graves in clusters or rows (Bayard 1972; Gorman and Charoenwongsa 1976;

Higham and Thosarat 1998; Higham 2002; Pietrusewsky and Douglas 2002). Somewhat surprisingly, though, neither of these sites appear to display the type of patterned asymmetry in the distribution of grave goods within the population as was observed at Ban Na Di (Higham and

Thosarat 1998; Higham 2002; Pietrusewsky and Douglas 2002).

There are several possible explanations for this apparent difference in social and mortuary practice among these contemporary and geographically proximate settlements. The simplest of these is that the perceived patterning in grave good distribution at Ban Na Di is due to sampling error. Had more of the burials from a greater area of the site been excavated, the sharp distinctions in mortuary assemblages between Area A and Area B might been subsumed within a broader continuum of relative grave wealth, and thus not seem so sharp after all. This explanation is largely unsatisfying, though, not least because the burials at Ban Chiang were also excavated from two widely separated areas within the site, and therefore might reasonably be expected to display a similar dichotomy in the provisioning of grave goods. A much more likely and appealing hypothesis is that these intersite contrasts in mortuary practice reflect intersite differences in social structure and/or the means of reification of social structure through cultural practice. White (1995:105) and Pietrusewsky and Douglas (2002:188), for example, note that it may be more accurate to regard the burgeoning social complexity of Bronze Age societies in

Southeast Asia as heterarchical, rather than hierarchical, in nature. In a heterarchical society,

―complexity need not be ranked in a vertical relationship,‖ and status relationships within a community may be defined along fluid lines that cross-cut the society from a variety of social and cultural angles (Pietrusewsky and Douglas 2002:188; Crumley 1995). Thus, the status relationships signified by the asymmetrical distribution of grave goods at Ban Na Di may not

have existed in the same form within the communities at Non Nok Tha or Ban Chiang, or may have been expressed through some other facet of their cultural traditions.

While the Bronze Age saw the continued growth of successful Neolithic communities in the Chao Phraya basin and the northern Khorat Plateau, it also witnessed the first large-scale settlement of the Mun River valley, in the southern half of the plateau. These new settlements, such as Ban Lum Khao (1400 to 500 BC), followed very much the same cultural pattern as their long-established counterparts in the northern region of the plateau: settlements located on low rises near river floodplains made favorable for rice agriculture by annual flooding; burials laid out in rows and including grave-good assemblages composed of pottery, faunal remains, a few bronze tools and jewelry items, and beads, bangles and other ornaments made of exotic shell and stone; and the presence of furnaces, crucibles and molds for alloying and casting bronze. As in the north, there is also some local differentiation in cultural traditions in the southern Khorat, as evidenced by the presence at these sites of a distinctive red-slipped, trumpet-rimmed pottery tradition. While a few individuals at Ban Lum Khao were buried with particularly rich grave good assemblages, as at Ban Chang and Non Nok Tha, ―the dominant impression of this assemblage is uniformity of treatment…on the basis of sex, age, or location.‖ There is no suggestion of the type of broad intrasite dichotomy in the allotment of grave goods as observed at Ban Na Di (Higham 2002:146; Higham and Thosarat 1998; O‘Reilly 1999).

One other locale that does appear to show significant intrasite and intrapopulation pattering in the distribution of grave goods is the Bronze Age cemetery at the important pre-Neolithic coastal site of Nong Nor (1100 to 600 BC). Here, small groups of individuals with distinctly richer grave-good assemblages have been found in both the earlier, western sector and the later, eastern sector of the cemetery. The eastern sector of the cemetery is of particular interest, for

grave-good assemblages from this area also include ornaments carved from jade, serpentine and talc, and the earliest known carnelian artifacts from Thailand. As noted above, the jades were most likely obtained via trade with Bronze Age peoples in southern China. The carnelian, on the other hand, is tantalizing evidence of early, albeit likely remote and indirect, trade contacts with

India (Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

During the Iron Age and beyond, economic and social contact with emergent Indian civilizations would only intensify, and would exert far-reaching and long-lasting influences on the people and cultures of Southeast Asia in general, and of Thailand in specific (Higham and

Thosarat 1998; Stark and Allen 1998). Although little is known about the precise nature and magnitude of the earliest contact with Indian civilizations, it is no surprise that the first evidence for this contact comes from a coastal site such as Nong Nor, which is separated from the Bay of

Bengal only by the narrow Kra Isthmus of the Malay Peninsula. Carnelian artifacts are also known from terminal Bronze Age contexts at Ob Luang and Nil Kham Haeng, located in the northern and central reaches of the Chao Phraya basin, respectively, and directly upstream from

Nong Nor. Just as the Chao Phraya and Mekong rivers and their tributaries had served as critical internal avenues of trade within Southeast Asia during the Neolithic and Bronze Ages, so would they also prove to be important thoroughfares for the spread of truly external and exotic ideas and goods during both the Iron Age and the period of the rise and rule of early state-level civilizations in Southeast Asia (Bellwood 1997; Higham and Thosarat 1998; Higham 2002;

Baker and Phongpaichit 2005).

The Iron Age

Most authorities agree that the Iron Age (500 BC to ca. AD 500) in Thailand and the rest of Southeast Asia was a period of great social and cultural upheaval and change, due in no small part to burgeoning contacts with the truly foreign cultures of India and the Han and Qin dynasties

of China (Stark and Allen 1998). While, as noted above, it is likely that the first agriculture and, later, the first bronze metallurgy were introduced from southern China, it can be (and has been) argued that, in terms of climate, physical geography and prehistoric culture, southern China is more properly considered a part of greater Southeast Asia, particularly during the Neolithic and early Metal Age (Barth 1952; Bellwood 1993, 1997; Matsumura and Hudson 2005; Pietrusewsky

2006). The same cannot be said for the cultures of the Han and Qin dynasties, who expanded from their heartland in central China to colonize most of southern China and even the northern parts of Vietnam in the last centuries BC (Bellwood 1993, 1997; Higham and Thosarat 1998;

Higham 2002; Baker and Phongpaichit 2005). Whether iron forging was introduced to Thailand via these expanding cultural and economic ties is, again, both unknown and the subject of much debate. What is certain is that along with new types of material goods, Southeast Asian populations would also have been exposed to new languages, belief systems and religious practices (specifically those of Buddhism and Hinduism), sociocultural norms and political structures. Other social changes were surely wrought by expanding population sizes—fueled by ever more efficient and elaborate agricultural practices—that taxed supplies of natural resources and strained cultural mechanisms for resolving conflict and mitigating distress within society.

New economic, spiritual and political apparatuses would have provided additional, more robust means for exceptional individuals and lineages to reinforce the incipient societal segmentation begun in the Bronze Age, and to begin to convert heterarchy into hierarchy (Nakbunlung 1994;

Bellwood 1997; Higham and Thosarat 1998; Stark and Allen 1998; Higham 2002; Baker and

Phongpaichit 2005).

As with previous ages in Thailand‘s prehistory, our best understanding of the Iron Age comes from mortuary contexts, which have yielded the bulk of the evidence for foreign trade

during this period, and which, as a whole, are richer than their Bronze Age precursors. At Ban

Don Ta Phet, in the southern Chao Phraya basin (see Figure 2-3), mortuary contexts have yielded cotton textiles, etched jades and a carnelian lion figurine, all of undoubted Indian origin or influence. This last item is of particular significance, as the lion was an early avatar of the

Buddha, and thus it represents a concrete manifestation of the exchange of not just material items, but also of ideas between the peoples of Thailand and India (Glover 1996; Bellwood 1997;

Higham and Thosarat 1998; Higham 2002). With the advent of forged iron tools, bronze became predominantly a luxury material and was cast into a variety of new ornamental forms, including bangles, earrings, torcs, beads, bells, rings, figurines and decorated bowls, all of which were included in the grave assemblages. Along with these bronzes, grave-good assemblages at Ban

Don Ta Phet and other Iron Age sites include novel glass, jade and agate beads and pendants, iron tools and weapons, and the ever-present marine shell jewelry, faunal offerings and ornaments, and locally distinctive ceramics (Higham and Thosarat 1998; Higham 2002). For some individuals, there was also an elaboration of the mortuary ritual itself during the Iron Age, as Sørensen (1973, 1988; Higham and Thosarat 1998; Higham 2002) uncovered at Tham Ongbah a group of burials contained within boat-shaped coffins featuring carved bird heads at the prow and stern. Several of these graves also included rare, elaborate bronze drums from the Iron Age

Dong Son culture of northern Vietnam. At other sites, the dead were placed in plaster- or clay- lined, rice-filled grave chambers stocked with grave good assemblages of extraordinary richness.

At every Iron Age site, archaeologists have found some small group of individuals singled out by some unique facet of the mortuary practice, and have concluded that these individuals must be the emergent elites (Higham and Thosarat 1998; Higham 2002).

The settlements themselves also underwent changes and elaboration in size, configuration and location: mounded sites near waterways were still preferred, but during the Iron Age they came to be ringed with canals or moats, perhaps for defense or to ensure a supply of water to the growing populations. Many of the main sites are believed to have had populations of many hundreds or even a few thousand people—far larger than could have been supported by the agricultural yields of the surrounding lands. Thus, the Iron Age saw the first establishment of settlements situated at higher elevations in the terraces above the floodplains, at sites that were smaller and less favorable for rice agriculture. These sites were likely subordinate to the main settlements around which they clustered, and if so, represent the first emergence of settlement hierarchies in Thailand. The appearance of numerous local pottery traditions such as Phimai

Black, Ban Chiang Red-on-Buff and Roi Et cord-marked red-banded wares, further reinforces this notion of peripheral settlements organized around long-standing local centers. This wealth of localized mortuary and material traditions contributes to the picture of Iron Age Thailand as a period of flourishing cultural diversity and social complexity, building inexorably to the appearance of the first state-level civilizations (Nakbunlung 1994; Higham and Thosarat 1998;

Higham 2002; Baker and Phongpaichit 2005).

The Rise of the State

The first state-level civilization to emerge in Thailand proper was the kingdom of

Dvaravati, comprising numerous settlements along the Chao Phraya basin (Figure 2-4), and probably initially established during the late prehistoric period, around the sixth century AD

(Briggs 1945; Rinehart 1981; Nakbunlung 1994; Higham and Thosarat 1998; Higham 2002;

Baker and Phongpaichit 2005). A less-well known sister state—or perhaps, rather, a colony—of

Dvaravati, with which the latter appears to have had favorable economic, political and cultural relations, developed at Hariphunchai in the northern reaches of the Chao Phraya basin beginning

in the late seventh century AD (Briggs 1945; Rinehart 1981; Higham and Thosarat 1998). The rise of Dvaravati and Hariphunchai in Thailand followed the waning of the civilization of Funan

(AD 100 to 550), whose influence extended from its center on the Mekong Delta in southern

Vietnam, across Cambodia and into the area encompassed by modern-day Thailand. The first definitive state-level society in Southeast Asia, Funan laid the foundations of extensive maritime and overland trade with China and India and across Southeast Asia; the enthronement of hereditary rulers; the further development of populous urban centers supported by an agricultural hinterland; the adoption of Indic religious practices and the construction of monumental public architecture tied closely to the practice of Buddhism and Hinduism that would come to characterize the varied civilizations of Thailand right up through the modern era (Rinehart 1981;

Bellwood 1997; Higham and Thosarat 1998; Stark and Allen 1998; Higham 2002; Baker and

Phongpaichit 2005).

The society of Dvaravati was likewise heavily influenced by Indian culture. By as early as the seventh century AD, its rulers are known from inscriptions—the earliest in Thailand, written in Sanskrit, Pali and Mon, and using scripts borrowed from southern India—to have adopted

Sanskrit names, the veneration of the Buddha and of the Hindu pantheon, the conventions of

Buddhist and Hindu religious and monumental architecture, and the material and ritual trappings and symbology of the Indic elite. Theravada Buddhism, the predominant form of Buddhism in modern-day Thailand, was possibly introduced from Sri Lanka during this period. Also present at Dvaravati-period sites are a number of artifacts, including items of Greco-Roman, Byzantine,

Persian and Semitic origin that illustrate the influence of India and China‘s far-reaching trade networks in the Old World. Dvaravati-period sites also frequently continued the Iron Age pattern of constructing defensive moats around the settlements, and artwork from this period

suggests increasing levels of conflict among, and possibly within, the emerging states of

Southeast Asia (Briggs 1945; Rinehart 1981; Bechert 1984; Bunnag 1984; Higham and Thosarat

1998; Stark and Allen 1998; Higham 2002; Baker and Phongpaichit 2005).

One of the states with which Dvaravati may have clashed2 was the early Khmer civilization of Chenla (or Zhenla), which emerged in the seventh century AD in the middle Mekong Valley, also following the collapse of Funan. Initially centered in northeastern Cambodia and central

Vietnam, the Chenla civilization was at first a collective of semi-autonomous local polities, each organized around a central settlement that was surrounded and supported by smaller agricultural communities, much like Dvaravati. However, by the early ninth century, these individual polities were increasingly consolidated into a true empire under the rule of the dynamic and aggressive kings of the Jayavarman dynasty, who in the early tenth century established a new capital at Angkor, in northwestern Cambodia. The growing early political and economic influence of Chenla also extended northward, into the Mun River valley of northeastern

Thailand, for at several sites here, Khmer cultural deposits are found overlying layers containing

Dvaravati-style artifacts. This influence intensified during the Angkor Period (tenth to fifteenth centuries AD), when the ruling dynasty was supplanted by Jayavarman VI, who, despite his promulgation of the dynastic name, had ancestral ties not to Chenla, but to the Iron Age settlement of Phimai, in the Mun Valley of the Khorat Plateau. He and his successors would develop northeastern Thailand into an important and prosperous province of the Angkor state, one whose political and economic support was central to the empire‘s ongoing survival. Indeed, many of the most important Angkor-period temples and archaeological sites are found not in

2 Given the presence of Khmer-influenced artifacts at Dvaravati-period sites, the two civilizations probably enjoyed favorable political and economic relations at times, but they appear to have come increasingly into conflict with one another as each state grew in power and size.

Cambodia, but in northeastern Thailand. Angkor further extended its reach into Thailand during the tenth and eleventh centuries, when it came to completely dominate the Dvaravati heartland in the Chao Phraya basin (Briggs 1945; Rinehart 1981; Higham and Thosarat 1998; Higham 2002;

Baker and Phongpaichit 2005). The majority of Thailand would thus remain under the control of the Angkor until the fall of that city in AD 1431.

Remaining beyond the sway of Angkor, however, were northern Thailand and the adjacent

Laos uplands north of the Mekong River. In this area, just as in the rest of Thailand during the late Iron Age, small polities called ―muang‖ were formed by coalitions of neighboring villages clustered along the waterways. As these polities grew in size and influence over the course of the first millennium AD, so their leaders grew in stature and ambition. By the second half of the thirteenth century, two independent but allied kingdoms, each composed of numerous muang, had emerged in the region and begun to stand in resistance to the expanding Khmer empire. The first of these was Lanna, the ―Land of One Million Rice Fields,‖ which had its capital at Chiang

Mai, just north of the old Dvaravati-period capital of Hariphunchai. The second was Sukhothai, ruled by the legendary King Ramakhamhaeng and situated in the northern-central part of the

Chao Phraya basin near the modern city of Phitsanulok. It was Ramakhamhaeng who reaffirmed

Theravada Buddhism as the predominant religion of the state, and who is also believed to be the author of the oldest known inscription (dated to AD 1292) written in the Thai language and script, the latter of which was derived from the Khmer script3. The art and architecture at

Sukhothai incorporated Indian, Chinese, Khmer and Dvaravati influences, and in so doing would presage the cultural diversity of modern Thailand (Briggs 1945; Rinehart 1981; Bunnag 1984;

Higham and Thosarat 1998; Baker and Phongpaichit 2005). Indeed, today the modern Thai

3 The Khmer script was itself adapted from scripts borrowed from the languages of southern India (Higham 2002; Baker and Phongpaichit 2005)

people look to the founding of Sukhothai in 1238 as the birth of the ur-Thailand: the first nation that was truly Thai, and the first from which the modern Thai nation can claim direct descent

(Rinehart 1981; Higham and Thosarat 1998; Baker and Phongpaichit 2005).

As the kingdoms of Lanna and Sukhothai expanded during the thirteenth and fourteenth centuries, so did Angkor‘s dominion over the Chao Phraya basin begin to ebb. The populace at the far-flung rural fringes of the Khmer empire had suffered under the heavy taxation required to support the empire‘s seemingly boundless growth, but they had received few of the benefits of citizenship in the flourishing and rapidly advancing civilization. The emergent prosperity of

Sukhothai and Lanna must have been a tempting alternative for communities that were of perhaps less than wholehearted and steadfast allegiance to distant Angkor (Rinehart 1981;

Higham 2002; Baker and Phongpaichit 2005). By the middle of the fourteenth century,

Sukhothai and Lanna had come to control most of the northern and central Chao Phraya catchment, while the new kingdom of Ayutthaya had risen in the Chao Phraya delta and southern portion of the basin, leaving only northeastern Thailand under Khmer hegemony.

Ayutthaya, founded in AD 1350, soon outstripped its northern neighbors in economic and demographic strength, owing largely to a strategic location in the Chao Phraya delta that allowed this kingdom to control the bulk of maritime trade with China and India (and later, Europe)

(Rinehart 1981; Stark and Allen 1998; Baker and Phongpaichit 2005). In AD 1378, Ayutthaya conquered Sukhothai, and then turned its attentions towards Angkor and Lanna. Over the next century, many wars and rebellions would rage back and forth between these three kingdoms, with Ayutthaya, bolstered by its near-monopoly on access to foreign trade, mercenaries and superior foreign weaponry, ultimately emerging victorious. By the sixteenth century, the

kingdom of Ayutthaya (known to the West as the ―Kingdom of Siam4‖ following the first

European contact in 1511) controlled a territory encompassing the area of modern Thailand as well as parts of Cambodia and Laos, and was ―marked by the Portuguese as one of the three great powers of Asia, along with China and…India‖ (Baker and Phongpaichit 2005:10; Rinehart

1981).

Like Angkor before it, the society and culture of the kingdom of Ayutthaya flourished on the wealth generated by the kingdom‘s command both of the vast natural resources to be found within its borders and of the vast commercial opportunities to be found in foreign trade with empires beyond them. And like Sukhothai before it, Ayutthaya would draw its cultural richness from both without and within. Its kings looked to India, the powerful gods of the Hindu pantheon, the righteous benevolence of Buddhist philosophy and practice, and the ritual and spiritual grandeur of the Brahmans as sources for their power, authority, legitimacy and apotheosis. Much of architecture in the city, particularly the temples and royal buildings, invoked the Khmer styles of Angkor Wat, and the Khmer empire as a whole was reimagined as a gilded and exalted ancestor of Ayutthayan civilization. Trade with China and Europe brought new styles of politics, art, literature, philosophy, music and dress to the kingdom, and the foreign traders and merchants who settled in Ayutthaya enriched the life of the city (Rinehart 1981;

Stark and Allen 1998; Baker and Phongpaichit 2005). Ayutthaya blossomed into a mature, sophisticated and cosmopolitan society which attracted both admiration and envy from all corners of the world.

Ayutthaya‘s peace and prosperity were not beyond threat, however. In 1559 and again in

1767, the city was sacked by the Burmese, and its wealth, cultural treasures, and even its people

4 It is the Chinese name for Ayutthaya, ―Xian,‖ from whence the historic name of ―Siam‖ is derived, via transliteration into Portuguese (Baker and Phongpaichit 2005).

were hauled away en masse by the conquering army. After the first attack, the city was left to stand as a vassal to the Burmese empire at Pegu until the reign of Naresuan the Great (1590-

1605), who overthrew the Burmese suzerainty and restored Ayutthaya‘s sovereignty. The latter siege left Ayutthaya in ruins and was the prelude to nearly a half-century of relentless aggression by the Burmese, intended to utterly destroy the kingdom of Ayutthaya and its people. During this time, nearly every major settlement between Chiang Mai and the Isthmus of Kra was destroyed and depopulated; many cities and towns would not recover to their prewar condition until the mid- to late nineteenth century. With characteristic Thai resilience, however, the capital—and along with it, the first shreds of renewed Siamese independence—would be restored much more quickly, and in 1782, both the modern capital of Bangkok and the ruling

Chakri dynasty were founded (Rinehart 1981; Baker and Phongpaichit 2005). The Kingdom of

Siam thus established would continue to grow and evolve into the modern Kingdom of Thailand, a land and a people now, as seemingly at all times throughout their history and prehistory, very much at the center of events in Southeast Asia, and deeply interconnected to the wider networks of commerce and culture in the East and beyond.

A. Ban Mae Tha, Ban Don Mun, Khao Pah Nam B. Tham Khuyen C. Lang Rongrien, Moh Khiew D. Hoabinhian type- sites E. Sai Yok, Tham Ongbah F. Heap Cave, Khao Talu, Ment Cave G. Spirit Cave, Steep Cliff Cave, Banyan Valley Cave H. Gua Cha I. Gua Gunung Runtuh J. Nong Nor K. Khok Phanom Di

Figure 2-1. Selected Paleolithic archaeological sites in Southeast Asia. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Site locations following Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

A. Ban Chiang B. Ban Kao C. Ban Tha Kae, Non Pa Wai, Khok Charoen D. Gua Cha E. Heap Cave, Khao Talu F. Khok Phanom Di G. Lang Rongrien H. Non Nok Tha I. Phung Nguyen sites

Figure 2-2. Selected Neolithic archaeological sites in Southeast Asia. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Site locations following Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

A. Ban Chiang B. Ban Don Ta Phet C. Ban Lum Khao D. Ban Na Di E. Non Nok Tha F. Non Pa Wai, Nil Kham Haeng G. Nong Nor H. Ob Luang I. Tham Ongbah

Figure 2-3. Selected Metal Age archaeological sites in Thailand. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Site locations following Higham and Thosarat 1998; Higham 2002).

A. Angkor B. Ayutthaya C. Core Dvaravati sites D. Funan sites E. Haripunchai F. Isanapura (Chenla) G. Phimai H. Sukhothai

Figure 2-4. Selected state-level archaeological sites in Southeast Asia. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Site locations following Higham and Thosarat 1998; Higham 2002).

CHAPTER 3 PREHISTORY AND POPULATION HISTORY IN THAILAND, PART II: THE VARIED LINES OF EVIDENCE

In truth we know nothing, for truth lies in the depth. -Democritus

The discussion of the foregoing chapter has, hopefully, provided the reader with a useful précis of the history of human occupation and endeavor in Thailand. But while an appreciation of when and how the prehistoric communities of Thailand attained the critical milestones of cultural evolution provides us with important landmarks for understanding the origins of the modern Thai nation and its people, it tells us little about who these people are, who their ancestors were, and whence these forebears came. Archaeological investigations can, of course, provide some insight into these questions, as the spread of artifacts of a given type can also be used to chart the spread of peoples. But these are only indirect insights: artifacts can be traded widely among groups with little in common culturally, historically or genetically. More direct evidence comes from the spread of languages, which requires closer and more extended contact, and from the spread of genes, which requires closer contact still.

Such data are not easily obtained, though, particularly for prehistoric populations.

Researchers may find themselves hard-pressed to reconstruct the basic features of an ancient language, or to convincingly connect that language to a given ancient population absent written texts (which appear comparatively late in Southeast Asia). Genetic evidence from ancient populations is similarly difficult to come by, as curators and site directors are understandably reluctant to allow destructive sampling of scarce and precious ancient skeletal remains. Even if such permission is granted, the financial expense and technological difficulties of extracting and sequencing ancient DNA samples may prove equally prohibitive.

On the other hand, however, physical anthropology analyses of craniometric and dental morphology have long been recognized as valuable proxy measures of biological relationships among geographically and temporally disparate populations, in circumstances where appropriate genetic data are unavailable (Howells 1973, 1989; 1995; Spielman and Smouse 1976; Van Vark and Howells 1984; Turner 1987, 1990; Cheverud 1988; Buikstra et al. 1990; Li et al. 1991;

Pietrusewsky et al. 1992; Hanihara 1993; Nakbunlung 1994; Brace and Tracer 1995;

Pietrusewsky 2000, 2005, 2006; Relethford 2002; Jantz 2004; Roeseman 2004; Matsumura and

Hudson 2005; Matsumura 2006; Spradley 2006). As was noted in the introduction, these physical anthropology analyses have the additional multiple advantages of being 1) non- destructive; 2) inexpensive and technologically easy to execute; and 3) founded upon traits known to be of high heritability, and therefore reasonable surrogates for genetic markers of population affinities (Howells 1973; Spielman and Smouse 1976; Susanne 1977; Sjøvold 1984;

Turner 1987, 1990; Cheverud 1988; Buikstra et al. 1990; Li et al. 1991; Relethford and

Harpending 1994; Konigsberg and Ousley 1995; Bellwood 1997; Pietrusewsky 1994, 2000,

2005, 2006; Relethford 2002; Roseman 2004; Carson 2006). Although obviously not as direct a measure of population relationships as genetic analyses are, reconstructions of population history based on studies of craniometric and dental morphology are nevertheless considered superior to those based on linguistic and archaeological data (Hudson 1999; Matsumura and Hudson 2005).

They are not without their limitations, however, as the multivariate statistical methods underpinning these analyses derive their power from the consideration of moderately large numbers of variables from moderately large sample sizes, both of which can be difficult to come by in small, typically fragmentary and incomplete archaeological skeletal populations.

Reconstructions of population relationships may be further complicated by the frequent lack of

secure dates for archaeological skeletal populations included in the analyses (Bellwood 1997;

Pietrusewsky 2000; Matsumura 2006).

There is thus no single analytical panacea for resolving questions of population history and the relationships among ancient and modern peoples. Indeed, Cavalli-Sforza and colleagues

(1988:6005; 1991), as well as other researchers (Bellwood 1993, 1997; Diamond and Bellwood

2003; Matsumura and Hudson 2005), have suggested that the only ―truly satisfactory‖ approach is to consider archaeological, linguistic, and genetic (and by extension, biological) lines of evidence in conjunction, and that the most powerful and reliable conclusions will be drawn when these varied lines of evidence are all in accord. Although the available studies of archaeology, linguistics, genetics and craniometrics in the myriad ancient and modern populations of

Southeast Asia do not provide full coverage of the relevant geographic regions, time periods, ethnic groups or skeletal collections, there are substantial areas of overlap. These are sufficient to provide meaningful insights into the questions of population history within the region and to inform the debate over population continuity versus discontinuity and endogenous versus exogenous origins.

Given the scant evidence for Homo erectus in Thailand, and this author‘s preference for the Recent African Origins model of modern human origins, it is held here that H. erectus‘s role in the origins of the modern populations of Southeast Asia in general is negligible at best. We will thus forgo any further consideration of this fossil species, and the voluminous debate over the nature of its relationship to H. sapiens, and instead turn our attention to the first anatomically modern humans in Thailand. As noted in Chapter 2, these populations are primarily known from sites in peninsular Thailand, such as Moh Khiew and Lang Rongrien, and are dated to ca. 40,000 to 25,000 years BP (before present) (Anderson 1990; Pookajorn 1991, 1994; Nakbunlung 1994;

Bellwood 1997; Ha Van Tan 1997; Higham and Thosarat 1998; Higham 2002; Matsumura and

Pookajorn 2005). Skeletal evidence for these early inhabitants is scarce in the utmost, and genetic and linguistic data are, of course, all but nonexistent. The ages for the few early H. sapiens that have been found in mainland Southeast Asia make them roughly contemporary with—or perhaps a little younger than—the majority of the earliest-known H. sapiens fossils from island Southeast Asia and Australia (Kline 1999; Bellwood 1997; Howells 1997;

Matsumura and Pookajorn 2005). Since, as nearly all authorities agree, the expanding populations of anatomically modern H. sapiens would have had to pass through Thailand to reach island Southeast Asia and ultimately Australia1 and Melanesia, it has been hypothesized that early H. sapiens from each of these regions should display mutual biological affinities.

Bellwood (1997), Howells (1997), Matsumura (2006) and others (Brace and Hunt 1990;

Matsumura and Hudson 2005; Matsumura and Pookajorn 2005) have argued that such affinities are demonstrated in a shared pattern of skeletal morphology, termed the ―australoid‖ (Coon

1966) or ―Australo-Melanesian‖ (Bellwood 1997) phenotype, which persists today in the modern aboriginal populations of Australia and Melanesia.

Skeletally, the australoid phenotype is composed of a dolichocranic skull with ―thick walls, a low vault, and heavy brows;‖ a prominent development of glabella; and large teeth set in prognathic, heavily built jaws, set atop a ―medium-slender,‖ long-limbed body build. In living populations, the skeletal phenotype is typically accompanied by dark skin and straight-to-wavy hair (Howells 1997:171; Matsumura and Hudson 2005:182-183; Matsumura 2006:38; Comas

1960). Outside of the earliest fossil material from Australia, such as the Lake Mungo, Willandra

1 Most likely colonized ca. 40,000 to 30,000 years ago, but possibly as early as 60,000 years ago (Bellwood 1997; Howells 1997; Higham and Thosarat 1998; Kline 1999; Thorne et al. 1999; Bowler and Magee 2000; Brown 2000; Gillespie and Roberts 2000).

Lakes, Keilor and Coobool Creek specimens, the australoid phenotype has also been attributed to the early fossil H. sapiens from Niah Cave in Borneo (the ―Deep Skull;‖ at ca. 40,000 years BP, the oldest H. sapiens in Southeast Asia) and from Tabon Cave on the Philippine island of

Palawan (ca. 23,000 to 16,500 years BP) (Brothwell 1960; Macintosh 1978; Bellwood 1997;

Howells 1997; Krigbaum and Datan 1999; Dizon et al. 2002; Matsumura and Hudson 2005;

Matsumura and Pookajorn 2005; Matsumura 2006). An extensive analysis of the Moh Khiew skeleton from peninsular Thailand establishes the concurrent presence of the australoid phenotype in mainland Southeast Asia populations during the late Pleistocene. This conclusion is based on findings of significant cranio- and odontometric, as well as general morphological, similarities between the Moh Khiew skeleton and both the Coobool Creek fossils and skeletal samples from modern Australian Aborigines (Matsumura and Pookajorn 2005; Matsumura

2006). Although Lang Rongrien has yielded no skeletal remains for analysis, it is broadly assumed that the stone tool assemblage there, similar as it is to that from Moh Khiew, also represents ―traces of occupation‖ by the expanding Australo-Melanesians of the late Pleistocene

(Higham and Thosarat 1998:24).

Other studies have established the australoid affinities of a number of Hoabinhian (i.e., terminal Pleistocene to early to mid-Holocene) skeletal samples as well, including those from

Gua Cha, Gua Gunung Runtuh and other sites in peninsular Malaysia, Ma Da Dieu and Mai Da

Nuoc in Vietnam, and the early Holocene sites of Tam Hang and Tam Pong in Laos (Barth

1952; Trevor and Brothwell 1962; Cuong 1986; Bellwood 1993, 1997; Matsumura and Zuraina

1995, 1999; Matsumura and Hudson 2005; Matsumura and Pookajorn 2005; Matsumura 2006).

As part of their broader analyses of the Moh Khiew skeleton, Matsumura and Pookajorn (2005) demonstrated that several skeletons from early to middle Holocene contexts in Laos and Vietnam

also cluster closely with both Moh Khiew and Coobool Creek. Even the contentious Wadjak fossils from Java (dated to ca. 6500 years ago) have been argued to be of generalized australoid affinities (Dubois 1922; Weidenreich 1945; Bellwood 1997; Howells 1997; Matsumura and

Pookajorn 2005; Matsumura 2006). These morphological conclusions are also somewhat reinforced by the work of Oota et al. (2001), if the results of their analyses of ancient DNA

(aDNA) from the Moh Khiew skeleton can be accepted as valid (always a dicey consideration where aDNA sequence data from fossilized remains is concerned). Their analyses demonstrated that the mitochondrial DNA (mtDNA) sequence of the late Pleistocene Moh Khiew skeleton and that of another specimen from later Hoabinhian contexts within the cave (dated to ca. 12,000 years ago; early Holocene) both cluster predominantly with modern Australian, Papua New

Guinean, and Semang Negrito samples, all of which are considered populations of australoid affinities.

The persistence of the australoid phenotype in mainland Southeast Asia until at least the early Holocene suggests that these skeletons are representative of a well-established and long- surviving population within the region, one that must be accounted for in any consideration of the development of the modern populations of Southeast Asia (Bellwood 1997; Howells 1997;

Matsumura 2006). Most authorities consider the Holocene australoid populations in Southeast

Asia to be a ―cousin‖ to those in Australia, Papua New Guinea and Melanesia, joined by common descent from a shared late Pleistocene ancestor (Bellwood 1997:91; Howells 1997;

Matsumura and Hudson 2005; Matsumura 2006). Further, it is generally believed that the varied modern hunter-gatherer populations of Southeast Asia, including the Semang and other orang asli, or aboriginal peoples, of peninsular Malaysia, the Andamanese, and similar populations in the Philippines—despite being universally referred to in the literature as ―Negritos‖—actually

constitute relict populations of Australo-Melanesians in Southeast Asia descended, like the modern Australians and Melanesians themselves, from the late Pleistocene parent population

(Barth 1952; Brace et al. 1991; Bellwood 1993; 1997; Howells 1997; Matsumura and Hudson

2005; Matsumura 2006; but see Hanihara (1992,1993) for a widely divergent interpretation of the phenotypic affinities and population history of these groups).

Juxtaposed against the ancient and modern Australo-Melanesians of Southeast Asia are the current predominant inhabitants of the region, who possess a phenotype frequently termed

―southern mongoloid‖2 (Barth 1952; Comas 1960; Bellwood 1993, 1997; Howells 1997;

Matsumura and Hudson 2005; Matsumura 2006). This phenotype, distinctly different from that of the australoids, is characterized skeletally by a short, light to medium build and gracile, brachicephalic crania, and is accompanied in living individuals by a medium complexion, straight dark hair, and low to moderate frequencies of the epicanthic fold (Comas 1960; Coon

1966; Bellwood 1997; Howells 1997). In a narrow sense, the southern mongoloid phenotype can be regarded as a ―mild‖ mongoloid phenotype (Howells 1997:204), as these populations typically have lower frequencies and less pronounced expressions of the morphological features

2 The term ―mongoloid‖ will undoubtedly give some readers pause, as it has been widely, and with good reason, discarded in favor of ―Asian‖ for classifying and describing individuals and populations hailing from the Far East (Brace 1995:171; Li et al. 1991; Brace and Tracer 1992; Sauer 1992; Kennedy 1995). However, in the main, and for better or worse, ―southern mongoloid‖ is the term used throughout the pertinent literature to describe the skeletal (and particularly cranial) morphology of the populations of Southeast Asia. This understandably objectionable terminology is necessitated both by the fact that the southern mongoloid phenotype is a distinct sub-type within the broad Asian or ―mongoloid‖ phenotype, and that ―south Asian‖ or ―southern Asian‖ are generally taken as referring to the peoples of the Indian subcontinent. To use any of these to refer to the physical characteristics of the modern and archaeological populations of Southeast Asia and neighboring areas would therefore be both misleading and confusing. In the same vein, ―southern mongoloid‖ is also more generally used as a collective term for the various populations characterized by this phenotype, since the distribution of these populations—both in archaeological and modern contexts—does not neatly correspond to a specific geographic region of origin that could provide a convenient descriptive term. For example, many populations long resident in southern China possess the southern mongoloid phenotype, but southern China is not generally considered a part of Southeast Asia (but more on that later.) For the sake of clarity and consistency with the extant literature, the same terminology of ―mongoloid‖ and ―australoid‖ will be used herein to refer to the phenotypes characteristic of certain populations—the suffix ―-oid‖ meaning ―shape,‖ and occasionally to the populations themselves, particularly in archaeological contexts. The modern populations, where permitted by concerns of clarity and accuracy, will generally be referred to by the more acceptable, if not wholly precise, geographic terms ―Southeast Asian‖ and ―Australo-Melanesian.‖

considered stereotypical of the Asian phenotype: these include the prominent zygomatic bones and accompanying ―flat‖ faces, the epicanthic fold, and certain dental traits such as shovel- shaped and winged incisors, which are often collectively described as the ―northern mongoloid

―phenotype (Comas 1960; Bellwood 1997; Howells 1997). So different are the australoid and mongoloid phenotypes that repeated craniometric analyses have arrived at the conclusion that the

Pleistocene inhabitants of Asia are ―unlikely [direct] ancestors‖ for modern Asian populations

(Jantz and Owsley 2001:153; Kamminga and Wright 1988; Van Vark and Dijkema 1988;

Bellwood 1997; Sarich 1997; Matsumura and Zuraina 1999).

Thus, the physical anthropological distinction between the australoids and southern mongoloids is widely recognized as an ancient and fundamental division of humanity, one that separates ―two of the major geographical races of mankind‖ (Bellwood 1997:70; Cavalli-Sforza et al. 1988; Brace and Hunt 1990; Li et al. 1991; Pietrusewsky et al. 1992; Howells 1989, 1997;

Pietrusewsky 1990, 2000, 2005, 2006; Jantz and Owsley 2001; Matsumura and Hudson 2005;

Matsumura 2006; Turner 1990, 2006). However, at this point it must be said, of course, that the overall pattern of human variation over the vast expanse encompassing mainland and island

Southeast Asia, Australia, New Guinea and Melanesia must be clinal in nature—just as it is across broad swaths of geography the world over (Bellwood 1997; Howells 1997). Indeed,

Bellwood (1997:75) singles out the modern population of Indonesia as singularly demonstrative of the continuum of variation between the southern mongoloid phenotype, which ―predominates in the west and north‖ of the archipelago, and the australoid phenotype, which prevails in the south and east. It is only when the populations at either end of this continuum are brought into apposition that the contrast between the australoid and southern mongoloid phenotypes is thrown into sharp relief—as it is in modern Southeast Asia. This contrast leads inescapably to the

question of how it is that the entirety of Southeast Asia, once home to a long-resident population of clear australoid affinities, came to be dominated by the markedly different southern mongoloids. When and where and by what means did this population transition take place?

What is the relationship between the various ancient populations of Southeast Asia and the modern inhabitants of the region? It is here that the great debate of Southeast Asian population history—the debate of exogenous versus endogenous origins, of essential discontinuity versus essential continuity—is centered.

The debate is traditionally framed in terms of two competing hypotheses: the ―Two-

Layer‖ model and the ―Regional Continuity‖ model (Matsumura 2006:33-34; Jacob 19967;

Nakbunlung 1994; Bellwood 1997; Pietrusewsky 2006). Both models acknowledge the presence of an indigenous late Paleolithic (i.e., terminal Pleistocene and early to middle Holocene) australoid population, resident in mainland Southeast Asia from at least 26,000 years ago. Both models also agree that the Neolithic revolution and the advent of wet-rice agriculture in

Southeast Asia is the nexus around which the entire debate revolves (Bellwood 1993, 1997;

Matsumura and Hudson 2005; Pietrusewsky 2005, 2006). But beyond this small common ground, the two models diverge widely. In brief, the former holds that this original population of australoids was subsequently displaced in some fashion—whether by direct replacement or indirectly via admixture and gene flow—by expanding populations of southern mongoloid

Neolithic rice agriculturalists from southern China, following the development of rice agriculture in the central and lower Yangzi River valley approximately 8000 years ago. These Neolithic populations are believed to have spread from southern China south through mainland and island

Southeast Asia approximately 5000 years ago, and eventually on to Micronesia, Polynesia and

Remote Oceania during the following millennia (Brace et al. 1991; Brace and Tracer 1992;

Bellwood 1993, 1997; Glover and Higham 1996; Higham 1996; Howells 1997; Higham and

Thosarat 1998; Matsumura and Hudson 2005; Matsumura 2006). Thus, the character of the modern populations of Southeast Asia is the result of the ―superimposition‖ of these intrusive southern mongoloid agricultural populations on the existing australoid hunter-gatherer population (Barth 1952; Coon 1966; Bellwood 1997; Matsumura and Hudson 2005; Matsumura

2006). Thus, under the Two-Layer model, Southeast Asian populations are locally continuous only back to the time of the Neolithic expansion.

In contrast, the Regional Continuity model holds that there was no such Neolithic expansion of southern mongoloid populations into Southeast Asia. Rather, the modern populations of the region evolved in situ in conjunction with the localized, endemic development of rice agriculture, independent of the advent of rice agriculture in China. The modern populations of Southeast Asia are therefore directly regionally continuous not only with the their immediate Neolithic forebears, but also with all the populations who have inhabited Southeast

Asia since the terminal Pleistocene (Turner 1987, 1990; Nakbunlung 1994; Hanihara 1992, 1993,

2006; Pietrusewsky 2006).

The Two-Layer model is the elder of the two hypotheses, having been the sole model of population origins in Southeast Asia for much of the twentieth century (Matsumura and Hudson

2005:182). As such, it is underpinned by a considerably larger body of research, stemming from a greater diversity of scientific fields, than is the Regional Continuity model (Bellwood 1997).

Lines of evidence in support of the Two-Layer model come from archaeology (Bellwood 1993,

1997; Glover and Higham 1996; Higham 1996, 2002; Higham and Thosarat 1998), linguistics

(Benedict 1942; Higham 1996,2002), physical anthropology (Mijsberg 1940; Barth 1952; von

Koenigswald 1952; Coon 1966; Brace et al. 1991; Brace and Tracer 1992; Matsumura and

Hudson 2005; Matsumura 2006), and genetics/biochemistry (Ballinger et al. 1992; Nei and

Roychoudhury 1993; Melton et al. 1995; Omoto and Saitou 1997; Oota et al. 2001; Lertrit et al.

2008). While a comprehensive summary of all the relevant literature would require several volumes, the following discussion will attempt to summarize the most salient research from each of these fields.

Archaeology

While it may have been interactions with the living peoples of Southeast Asia during the

Age of Exploration and the Colonial Era that prompted the first questions among Western observers about their origins, it was the excavations of the sites inhabited by their ancestors that has propelled these questions to the forefront of modern scientific inquiry in the region (Higham and Thosarat 1998; Baker and Phongpaichit 2005). The archaeological excavations have also provided the raw materials—including the human remains, the artifacts and the earliest inscriptions—through which those questions are addressed. Although these are often presented separately, and merit consideration as independent lines of evidence (Bellwood 1993), it must be remembered that these fields nevertheless represent different facets of the same broad phenomenon of human agency, and therefore are necessarily overlapping and mutually reticulate in nature (Bellwood 1996:882; Cavalli-Sforza et al. 1988; Higham 1996; Diamond and Bellwood

2003).

As was noted previously, it is the search for the origins of rice agriculture in Southeast

Asia that has galvanized much of the debate surrounding the origins of the populations of

Southeast Asia. It is an understatement in the utmost to say that the inception of agriculture is a revolutionary event—regardless of when and how it occurs within a given region—and one that always engenders much excitement and debate among researchers. It has long been acknowledged that China was one of the world‘s great epicenters of agricultural development,

but Southeast Asia‘s own fertile rice paddies have led many to wonder whether agriculture did not also have an independent origin here (Bellwood 1997; Higham and Thosarat 1998). On the other hand, if rice agriculture came to Southeast Asia from elsewhere, then perhaps its people did, too. Many of the earliest serious archaeological excavations in Thailand, such as those at

Spirit Cave and Banyan Valley Cave, led to exuberant proclamations of an early and independent origin of rice agriculture in Thailand, and much of the effort in these excavations was focused on recovering any trace of domesticated rice (Gorman 1969; Solheim 1972; Higham and Thosarat

1998; Higham 2002). But at each turn, the rice recovered from these early to mid-Holocene sites has been shown to be either a wild type or an intrusive deposit from later Neolithic occupations

(Higham and Thosarat 1998; Higham 2002). It is not until the mid-third millennium BC that we find unequivocal evidence of rice agriculture in Southeast Asia.

Today, as Higham and Thosarat (1998:76) emphatically state,

No excavated site in [Thailand] has any evidence for agricultural settlement before about 2300 BC, nor have concentrations of pottery which might be earlier than this date ever been found during site surveys. Nor do we know of an archaeological sequence suggesting a local transition from hunting and gathering to the Neolithic in Thailand.

The pottery concentrations to which they are referring, of course, are the large, distinctive ceramics assemblages from the contemporary sites of Khok Phanom Di, Ban Tha Kae, Non Pa

Wai, Non Nok Tha, Ban Chiang, and, most importantly, Ban Kao. It nearly goes without saying that pottery is an important hallmark of a burgeoning agricultural tradition, in which people have a newfound need to store surpluses of food and stockpiles of seed for the future. Styles of pottery manufacture and decoration frequently differ from site to site or region to region, and are often used by archaeologists as a ―trail of breadcrumbs‖ with which to track trade networks and the movements of people. The Ban Kao pottery tradition identified by Sørensen (1963; Sørensen and Hatting 1967) is particularly provocative because it appears across nearly the entire extent of

Southeast Asia at precisely the same time and in the same locations as much of the earliest undisputed evidence for domesticated rice and rice agriculture. Upon his analysis of the Ban

Kao pottery tradition, with its signature motif of curvilinear bands infilled with impressed or stamped dentate textures and distinctive carinated tripod vessels, Sørensen (1963; Sørensen and

Hatting 1967) was among the first researchers to ―suggest that inhabitants of Ban Kao were part of an expansionary movement of farmers from southern China‖ (Higham and Thosarat 1998:70).

Although this view was unpopular at the time, as were most theories of cultural change via demic expansion, Sørensen‘s hypothesis has gradually gained steam as Ban Kao-style pottery vessels have been found in conjunction with the remains of domesticated rice at sites throughout

Thailand and the rest of Southeast Asia (Higham and Thosarat 1998:70; Sieveking 1954, 1987;

Bayard 1972; Gorman and Charoenwongsa 1976; Ha Van Tan 1985; Hutterer 1988, 1993;

Anderson 1990; Leong 1991; Bellwood 1993, 1997; Glover and Higham 1996; Higham 1996,

2002; Loofs-Wissowa 1997; Pigott et al. 1997; Rispoli 1997; Vincent 2003). At the majority of these sites, the appearance of Ban Kao-style ceramics typically marks a sharp discontinuity with the preceding cultural deposits, whether they be the aceramic assemblages of inland Hoabinhian sites or the locally produced pottery forms found at thriving late hunter-gatherer estuarine and coastal settlements such as Khok Phanom Di and Nong Nor (Bellwood 1997; Higham and

Thosarat 1998; Vincent 2003). At other sites, such as Non Nok Tha and Ban Chiang, Ban Kao- style pottery accompanied the first settlers of these newly established communities. Yet nowhere in Southeast Asia is there a ceramic culture that can be considered a direct and immediate precursor to the Ban Kao style, as would be expected in a locally evolving pottery tradition.

Additionally, it was realized that there is a significant correlation between the temporal and geographic distributions of the Ban Kao pottery forms within Southeast Asia: they arrive first in

the northern part of the region, appearing as the Phung Nguyen culture of the Red River valley in northern Vietnam, around 2500 to 2000 BC. A few centuries later, the pottery appears farther to the south at Ban Kao itself, as well at Ban Chiang (both dated to 2300 to 1500 BC) and other sites throughout the Khorat Plateau and Chao Phraya River valley (Bellwood 1993, 1997;

Higham 1996, 2002; Higham and Thosarat 1998). Somewhat later and farther south still, as

Bellwood (1997:259) notes, ―by 2000 BC, assemblages related to those from Khok Phanom Di and Ban Kao begin to appear in sites all down the Malay peninsula, southward for 1500 km, into central peninsular Malaysia.‖

Collectively, these observations strongly suggest that the Ban Kao pottery forms represent an invasive element throughout the region, one with distinctly different origins than the foregoing home-grown material culture of the Paleolithic and Mesolithic hunter-gatherer populations of Southeast Asia. One of the most striking features of the Ban Kao culture, which greatly reinforces the interpretation of a single, external origin and subsequent widespread dispersal throughout the region, is the overall homogeneity of the culture (Bellwood 1993, 1997).

As Bellwood (1993:48) notes, ―it seems unlikely, given the widespread archaeological similarities right down the peninsula, that agriculture and the distinctive associated artifact forms were simply adopted wholesale by preexisting Hoabinhian foragers, without considerable pressure from immigrant farmers.‖

Where, then, is the ultimate source of the ur-Ban Kao pottery tradition, and along with it, rice agriculture? Phung Nguyen and other contemporary sites along the Red River valley in

Vietnam have provided tantalizing clues to this question, in the form of nephrite and jadeite bangles, beads and other ornaments that have clear stylistic and technological links with the material culture of the Chinese Neolithic (Higham 2002). The presence of Chinese-influenced

artifacts at sites along the Red River valley is significant, because the importance of rivers as avenues of commerce and population movement is a recurrent thread in Southeast Asian prehistory. Many researchers have noted that the major rivers of South and Southeast Asia, including the Red, Mekong, Chao Phraya, Salween, Irrawaddy and Brahmaputra Rivers, are arrayed ―like the spokes of a wheel‖ radiating from a ―hub‖ in Yunnan Province in central southern China, along the upper reaches of the Yangzi River (Figure 3-1) (Higham 1996:115;

Chang 1976; Watanabe 1984; Blust 1993a, cited in Higham 1996; Blust 1996; Higham and

Thosarat 1994, 1998; Glover and Higham 1996; Higham 2002). Following these spokes back to the hub leads to a number of sites in Yunnan, such as Baiyankun, that are also dated to the mid- third millennium BC, and that have yielded both domesticated rice and Ban Kao-style dentate stamped pottery (Higham 1996, 2002).

From these sites in the upper Yangzi, one needs only to travel to the middle and lower

Yangzi Valley to reach the sites of Pengtoushan and Fenshanbao in Hunan province and Hemudu in Zhejiang province, where domesticated rice was unquestionably cultivated as early as 8000

BC (Glover and Higham 1996; Bellwood 1997; Higham 2002; Matsumura and Hudson 2005)

Working forward, then, it is easy to see how rice agriculture could have spread from its place of origin in the lower Yangzi, first upstream along the great river to Yunnan province and beyond, and then from this hub downstream along the major rivers of Southeast Asia to the rich, fertile lowlands of the Red, Mekong and Chao Phraya basins and deltas, the Khorat Plateau, and the great flood plains of central Cambodia (Chang1976; Watanabe 1984; Spriggs 1989; Blust 1993a, cited in Higham 1996; Glover and Higham 1996; Higham 1996; 2002; Matsumura and Hudson

2005).

Linguistics

It is one thing to use an archaeologically durable pottery tradition as an index for tracking the spread of the more archaeologically ephemeral phenomenon of rice agriculture. It is something quite different to infer the movement of populations solely from the spread of a ceramic typology and/or subsistence strategy. The Ban Kao pottery tradition and wet-rice agriculture are memes (Dawkins 1976) that require no large-scale, permanent movement of peoples to account for their spread across Southeast Asia. Their distribution within the region can be and has been explained simply, if somewhat unsatisfactorily, by mechanisms of passive cultural exchange and diffusion, whereby the patterns and processes spread but the people stay put. Instead, if we are making the argument of the Two-Layer Model, that agriculture was brought to Southeast Asia by southern mongoloid populations who displaced or replaced the indigenous australoid populations, then we are in need of lines of evidence involving

―replicators‖(sensu Dawkins 1976) that are more implicitly reliant on population migrations to carry them into new areas.

One such system of replicators is language. Cavalli-Sforza and colleagues (Cavalli-Sforza

1991; Cavalli-Sforza et al. 1988) have established that cluster analyses of linguistic and population genetic data tend to produce strikingly similar phylogenetic trees, indicating a strong correlation between the flow of genes and the flow of languages among populations (Cavalli-

Sforza 1991; Cavalli-Sforza et al. 1988). This correlation is attributed to the fact that languages behave in much the same way as gene pools do (Cavalli-Sforza 1991). When a single parent population is divided into two separate daughter populations via allopatric mechanisms such as migration and geographic isolation, their gene pools become increasingly divergent due to the actions of genetic drift, mutation and natural selection of existing variation. Their languages will diverge in similar fashion, due to similar mechanisms: a sort of linguistic drift, characterized by

distinct initial dialects in each daughter population (including differences in accent, lexicon and idiom); the generation of novel variation via neologisms; and ongoing evolution of the dialect/protolanguage in response to changing social and environmental contexts (Bryson 1990).

As Cavalli-Sforza (1991:109) writes, ―Each fragment [i.e., daughter population] evolves linguistic and genetic patterns that bear the marks of shared branching points. Hence, some correlation is inevitable.‖ Like genetic divergence, amounts of linguistic divergence also can be measured and used to reconstruct a timeline for these branching events—a linguistic clock akin to the molecular clock (Cavalli-Sforza et al. 1988; Cavalli-Sforza 1991; Bellwood 1997).

Of course, languages can spread to new areas through diffusion without accompanying large-scale population movements. However, this phenomenon is most common in the areas of interface between different linguistic populations, where the speakers of different languages are already in frequent contact with one another, and therefore have an economic or social need to bridge their mutual communication gap. This mode of language transfer seldom results in the wholesale replacement of one language with another; rather, it more commonly leads to the creation of new languages, beginning as pidgins and creoles, which draw on elements of both languages for their structure, syntax and vocabulary (Bryson 1990). Correspondingly, gene flow between populations at these interfaces may be quite common and result in some ―blending‖ of the respective gene pools, but will not amount to a total replacement of one gene pool with another. True in situ language replacement generally occurs only in conjunction with significant population migrations, via two mechanisms: elite dominance, where the language of an invasive ruling class is imposed upon the subjugated populace; and numerical superiority, where some technological or economic advantage allows the immigrant population and their language to outstrip and eventually absorb or otherwise overwhelm the indigenous population and their

language (Renfrew and Bahn 1996:447; Renfrew 1987; Cavalli-Sforza et al. 1988; Cavalli-

Sforza 1991). Both mechanisms are likely at play in the linguistic history of Thailand, but for now, we are primarily concerned with the latter.

Today, the primary languages of Southeast Asia fall into four families:

 Austroasiatic: includes Vietnamese; the Khmer language of Cambodia; the Munda language of northeastern India; and Mon, which is currently spoken only by small populations on the Andamanese coast and in the hills between Burma and Thailand, but which once had a much wider distribution across much of Thailand.

 Austronesian: includes Formosan, Malay, Filipino, and the languages of island Southeast Asia, coastal Papua New Guinea, Polynesia and Micronesia; also includes Cham, which is currently spoken only along the central Vietnamese coast and in a small area of the adjacent uplands, and in isolated pockets in southeastern Cambodia. Like Mon, Cham once had a much wider distribution throughout Vietnam. This family also includes Malagasy, the language of Madagascar.

 Daic (or Tai-Kadai): includes Thai, Laotian, Kadai (small linguistic isolates spoken primarily in Hainan and Southern China) and the languages of some of the Hill Tribes of northern Thailand and Laos (Hmong-Mien) (Benedict 1942; Briggs 1945; Diffloth 1991; Bellwood 1997; Howells 1997; Higham 1996, 2002; Baker and Phongpaichit 2005).

 Sino-Tibetan: includes Chinese as well Burmese, Karen and the languages of other Hill Tribes living in the mountainous region between Burma and Thailand), and, with the exception of Chinese, is perhaps the least well-understood of the four groups. It will be of little further concern here.

Not surprisingly, the interrelationships of these languages are rather complex, and are neither fully understood nor agreed upon. We can gain some sense of their time depth within

Southeast Asia from the various inscriptions that have been recovered from archaeological sites in the region. We know that in the Chao Phraya basin, the inhabitants of Dvaravati and

Hariphunchai, the earliest state-level societies in Thailand (sixth to twelfth century AD), spoke

Mon (Briggs 1945; Rinehart 1981; Bechert 1984; Bunnag 1984; Higham 1996, 2002; Higham and Thosarat 1998). To the east, in the lower Mekong Valley, the people of Angkor (ninth to fifteenth century AD) and its predecessor, Chenla (seventh to ninth century AD), spoke Khmer.

Central and southern coastal Vietnam were dominated by the Cham speakers of the kingdom of

Champa (fifth to fifteenth century), while the Red River valley to the north forms the core of the area inhabited by Vietnamese speakers (Higham 1996; 2002). Over the intervening centuries, as these various states waxed and waned, the speakers of these languages ebbed and flowed across

Southeast Asia before settling into their modern geographic distributions. The Thai language itself is a relative latecomer to the area, known certainly only from the late thirteenth century AD onwards, and superimposed upon the linguistic landscape of mainland Southeast Asia by the rapid ascendancy and expansion of kingdoms of Sukhothai, Lanna and Ayutthaya (Barth 1952;

Bayard 1979; Briggs 1945; Rinehart 1981; Higham and Thosarat 1998; Higham 2002; Baker and

Phongpaichit 2005).

But what of the prehistoric populations of Southeast Asia—what languages did they speak? Since we know that the scripts in which Mon, Cham, Khmer and Thai were written were derived from Sanskrit (itself most likely introduced along with Buddhism during the late Iron

Age), we can reasonably assume that these languages had been resident in Southeast Asia for some time. Less well-established languages would likely have been easily and thoroughly overwhelmed by a linguistic tradition as robust and sophisticated as Sanskrit (Bellwood 1997).

Instead, as far as we can tell, the populations of Southeast Asia co-opted the script and numerous loan words, but retained the central character of their respective indigenous languages. This resilience is a common occurrence in the evolution of languages: a given language will rarely absorb all of the influence of an unrelated language, regardless of how intensive and wide- ranging that influence may be. Similarly, divergent languages descended from a common predecessor will, despite profound changes in structure and vocabulary over the course of millennia, nevertheless retain seemingly immutable traces of their common ancestry. This being the case, we can look to the lexical content of individual languages and broader phylogenetic

relationships between these languages and language families to understand when and where they are likely to have emerged and how they have spread (Cavalli-Sforza 1991; Higham 1996;

Bellwood 1997).

To begin with, it has been suggested that the Daic and Austronesian families can be united in a superfamily known as Austro-Tai (Figure 3-2) (Benedict 1942, 1975; Higham 1996;

Bellwood 1997; Howells. 1997). This grouping is based on the existence of similarities in grammar and syntax between Thai and the Austronesian languages of Indonesia, as well as extensive cognates for a number of fundamental objects and concepts, including the sun, moon and stars; water, fire, ice and other environmental elements; parts of the body and bodily functions; the words for eat, die, this, I and grandfather; and, tellingly, the word for rice fields

(Benedict 1942:591-592). Via additional research, Benedict (1975) has argued that proto-

Austro-Tai, the common linguistic ancestor of the Daic and Austronesian languages, contained numerous terms related to the wet-rice agricultural complex, including words for ―wet field, garden, plow, rice…cattle, water buffalo‖ (Bellwood 1997:111) as well as ―to sow, [to] winnow, the pestle and mortar, to cultivate, seed, grain…‖ (Higham 1996:114). This reconstruction of the proto-Austro-Tai vocabulary leads, almost ineluctably, to the conclusion that this language has close ties to the emergent southern Chinese Neolithic contexts of the middle and lower

Yangzi Valley, and that the descendent languages must have thus spread to their current distributions from this ancient homeland (Benedict 1942, 1975; Higham 1996; Bellwood 1997).

Additionally, it is estimated that the Austronesian and Austro-Tai language families may have begun to diverge as much as 7000 years ago, which corresponds well with the best dates for rice agriculture in China (Glover and Higham 1996; Higham 1996).

As the Daic language family is somewhat less than well-studied, owing to the minor status of its member languages (except for Thai), the much more completely reckoned Austronesian language family provides critical insights into the possible developmental and migratory paths of agriculture and language in prehistoric Southeast Asia (Higham 1996:112; Bellwood 1997;

Matsumura and Hudson 2005). By comparing the points of divergence among the various languages that constitute the Austronesian family, linguists can reconstruct a language

―cladogram‖ of sorts, and they have computed that the most recent branching took place between the Oceanic language group (the languages of Polynesia and Micronesia) and the languages of eastern Indonesia (Bellwood 1997:103). Because the coastal and archipelagic geography of the western Pacific acts to channel and constrain human migration routes (even during periods of lowered sea levels), it is much easier here to trace the spread of languages and speakers back to their place of origin (Higham 1996; Diamond and Bellwood 2003). Thus, the aboriginal home of the Austronesian language family has been traced back to the island of Taiwan, from whence it spread and diversified: first southward to the Philippines, then arcing simultaneously westward, through Borneo, Java and Sumatra to the Malay peninsula and coastal Vietnam (the Cham), and eastward, to the Lesser Sunda islands of eastern Indonesia, the coastal regions of Papua New

Guinea and the islands of western Melanesia, and then onward across the whole of Oceania

(Bellwood 1997:105; Benedict 1942; Blust 1984-1985; Higham 1996; Howells 1997). Based on these linguistic reconstructions, Bellwood (1997:117) places the initial Austronesian colonization of Taiwan around 4000 to 3000 BC.

If we are to entertain the argument that the spread of agriculture is commensurate with the expansion of the Austronesians, then the archaeological record of Taiwan should provide some corroborating evidence. The early Neolithic culture of Taiwan is known as the Ta-p‘en-k‘eng

(or TPK) culture, and is dated to 4500 to 2500 BC (Chang 1969, 1970; Spriggs 1989; Bellwood

1997). However, no rice remains have been recovered from TPK contexts; it does not conclusively appear in Taiwan until the following Suo-kang period. Instead, the presence of agriculture in general during the TPK period has been suggested both by environmental data

(i.e., palynology cores) and by the nature of the material culture itself. TPK assemblages feature incised and cordmarked pottery, quadrangular stepped stone adzes, bark cloth beaters and ground stone projectiles. These artifacts contrast sharply with the Hoabinhian-like flaked pebble tool assemblages of the preceding Ch‘angpinian. As Bellwood (1997:212) notes, ―The whole [TPK] culture gives the appearance of having been introduced into the island fully formed.‖

In addition to some broad similarities in artifact type and form between TPK and the

Chinese Neolithic site of Hemudu, which unquestionably had rice agriculture as early as 5200

BC, it has been observed that some elements of the TPK pottery assemblage bear a striking resemblance to pottery from other Neolithic sites in coastal southern China. These sites are dated to ca. 5200 to 4200 BC and feature pottery assemblages characterized by decorative bands of dentate stamping (Bellwood 1997:208-213; Chang 1977). By now, this motif should be familiar to the reader as the hallmark of the Ban Kao pottery culture and its posited correlation with the spread of rice agriculture from southern China into mainland Southeast Asia (Higham

1996). These coastal sites are tantalizing candidates for the jumping-off point of the initial

Austronesian colonization of Taiwan, based both on their dating, which places them as immediate antecedents of the TPK sites, and on their locations in Fujian and Guangdong provinces, south of the lower Yangzi Valley and directly opposite Taiwan across the Taiwan

Strait (see Figure 3-1)

It will be recalled from the previous chapter that Solheim (1972) also saw parallels among the TPK culture, pottery he had previously observed in the Philippines, and the pottery he recovered from Spirit Cave. Solheim saw this connection as supporting his hypothesis of an

―Extensionistic Period‖ marked by widespread population migrations and expansions, tied to a growing reliance on agriculture, between 8000 BC and AD 0. Although Solheim‘s dates were off by some millennia—the Spirit Cave pottery is now dated to 2000 to 1000 BC and is clearly intrusive to the Hoabinhian contexts from which it was recovered (Higham 2002)—there is a great unrecognized kernel of truth in his assessment of the relationship between the movements of agricultural populations and the distribution of this pottery style throughout Southeast Asia.

Unfortunately, Solheim misinterpreted this pottery distribution as evidence of a once-widespread population of agriculturalists fragmented into separate island and mainland populations by rising sea levels, rather than as evidence of precisely the type of radiating population expansion implicit in his framing of the Extensionistic Period and currently hypothesized for the

Austronesians (Solheim 1972; Higham 1996).

At this point, the hypothesized link between the expansion of rice agriculture and the expansion of the Austronesians may seem perilously close to a circular argument— archaeological evidence offered in support of linguistic hypotheses, and linguistic reconstructions used to explain the distribution of the archaeological evidence. But this is not exactly the case. The summary presented here has been necessarily brief, and cannot adequately convey the full subtlety and depth of the pertinent linguistic and archaeological evidence. But

hopefully, what emerges from the foregoing discussion is a realization of the parallel distributions of a distinctive Neolithic material culture on one hand, and the Austronesian languages on the other. Where we find one, we also find evidence for the other. This is the

purest and simplest definition of a correlation. What is compelling in the argument is that these distributions of language and material culture have been established using analytical methods that proceed in opposite directions from their fundamental data to their primary conclusions.

Historical linguists work from the structure and range of present-day languages backwards through time to recreate the origins and dispersal of the proto-languages of prehistory.

Archaeologists, although looking backwards at past cultures, nevertheless work forward through time, charting cultural innovation and evolution and the growth of social complexity over time.

That these lines of evidence are thus so convergent in their ultimate conclusions speaks to the scientific merit of the hypothesis they underpin.

There is also a great deal more to the argument of the connections between the spread of languages and agriculture in Southeast Asia, as we have not yet considered the role of the

Austroasiatic language family in the cultural and biological history of the region. As was noted above, the Austroasiatic family includes the Khmer, Vietnamese and Mon languages, as well as the Munda language of eastern India. As there are no Austronesian languages on the Southeast

Asian mainland other than Cham (which most authorities agree was introduced to the area by more recent immigrants from island Southeast Asia, probably during the first millennium BC), our understanding of the spread of the Austro-Tai languages sheds little light on the population history in this core area of Southeast Asia (Blust 1993b, cited in Higham 1996). However, Blust

(1993a, 1993b, 1996), Reid (1994), Higham (1996), and others (Bellwood 1997) have taken up the hypothesis first put forth by Schmidt (1906) that the Austroasiatic and Austro-Tai language families are both descended from an even higher-order superfamily called Austric. As Higham

(1996:112) explains,

The relationship between the Austroasiatic and Austro-Tai languages is a matter of considerable relevance to the prehistorians. If, for example, it could be shown that

they are related, then we could seek a common origin and early population expansion in association with rice cultivation in Southeast Asia and eastern India.

Conversely,

The assumption than Austroasiatic and Austronesian are separate would support more than one transition to rice cultivation and Neolithic expansion, perhaps one in the Yangzi Valley and the other in tropical Southeast Asia (Higham 1996:110).

The explication of the Austric hypothesis proceeds in much the same fashion as the argument for the Austronesian expansion, but it is necessarily focused on mainland, rather than island, Southeast Asia. The Austroasiatic languages are divided into two main subfamilies,

Mon-Khmer (which includes Vietnamese) and Munda. The presence of numerous cognates among these languages, for things such as ―uncooked husked rice…bamboo and bamboo shoots, pestle and mortar, husking rice…the dog, cow, and chicken,‖ suggests shared origins in the proto-language of people living in a Neolithic rice agricultural context. Based on this reconstruction, it is likely that the various Austroasiatic languages diverged from this ancestral proto-language beginning around 4000 years ago (Zide and Zide 1976; Diffloth 1991; Higham

1996; Higham and Thosarat 1998). The homeland of proto-Austroasiatic has, like that of proto-

Austro-Tai, been placed in southern China, owing to the presence of Austroasiatic loanwords in the historic and modern dialects of southern China. These loanwords includes words for various animals, including tiger, dog, fish and crab, as well as words for shaman, child, son, ivory, to die and river (Higham 1996:115; Benedict 1942; Hashimoto 1972; Norman and Mei 1976; Norman

1985). Interestingly, some of the dialects in which these loanwords appear are themselves centered in Fujian and Guangdong provinces, noted above as the probable jumping-off point for the Austronesian colonization of Taiwan.

Like Austro-Tai, the existence of the Austric language superfamily is supported by linguistic evidence of structural and lexical similarities between the Austroasiatic and

Austronesian languages. These include a number of the seemingly omnipresent cognates for various facets of rice agriculture (Reid 1994; Higham and Thosarat 1998; Higham 1996, 2002).

And as with the Austronesian expansion, it is hypothesized that the incised-banded, dentate- stamped Ban Kao-style pottery found at Neolithic archaeological sites throughout mainland

Southeast Asia is evidence of populations of rice agriculturalists expanding from southern China.

But here on the mainland, these migrating agriculturalists carried with them the Austroasiatic languages of Mon, Khmer and Vietnamese, such that when the Austronesian-speaking agriculturalists spreading westward across Indonesia reached the Malay peninsula and the

Vietnamese coast, they found these areas already inhabited by Austroasiatic-speaking peoples

(Higham 1996, 2002; Bellwood 1993, 1997). Lastly, just as the Austronesian expansion was constrained by the geography of island Southeast Asia, so too was the Austroasiatic expansion constrained by the geography of mainland Southeast Asia. These peoples appear to have settled preferentially along the inland valley floors and flood plains of the many rivers of the mainland, including the Chao Phraya, Mun, Chi, Mekong and Red Rivers. As was noted in the previous chapter, this represents a largely novel settlement pattern among the peoples of this region and was probably driven by the ecological and environmental demands of wet-rice agriculture

(Higham 1996).

Thus, we are confronted with archaeological and linguistic evidence of the separate dispersals of three groups of early Neolithic peoples, from a posited common ancestral homeland in southern China, to various regions of South and Southeast Asia. All of these peoples possessed knowledge of rice agriculture, and the majority of them shared a distinctive dentate- stamped pottery tradition. We have clear ideas of the paths of their migrations: the Munda, down the Brahmaputra river to eastern India; the Mon-Khmer, down the Chao Phraya, Mekong

and Red Rivers to the heart of mainland Southeast Asia; and the Austronesians, across the islands of Taiwan, the Philippines and Indonesia to the whole of the Pacific Basin. The most parsimonious interpretation of this body of evidence is a single origin for these peoples, their languages, and their agricultural economy—a single origin in southern China, followed by radiating migrations down the spoke-like rivers of mainland Southeast Asia and across the stepping-stone archipelagoes of island Southeast Asia and the Pacific (Blust 1993a, 1996;

Bellwood 1993, 1997; Howells 1997; Higham 1996, 2002; Higham and Thosarat 1998; Diamond and Bellwood 2003; Matsumura and Hudson 2005).

This connection between the spread of agriculture and the spread of language is more than mere correlation: there is a clear causal relationship between the advent of agriculture and the initiation of vast population migrations. This relationship has been documented in association with the rise of agricultural populations all over the world and is grounded in mechanisms that are familiar and pervasive throughout the whole of anthropology. Quite simply, this causal relationship is attributed to the population growth that is an inevitable result of the adoption of a successful agricultural subsistence strategy (Barth 1952; Renfrew 1987; Renfew and Bahn 1996;

Higham 1996; Bellwood 1997; Diamond 1999; Diamond and Bellwood 2003; Matsumura and

Hudson 2005). Hunter-gatherer populations, by their very nature, maintain a state of long-term equilibrium with their environment wherein population densities are held in check by the distribution and density of available food resources. The tightness and immediacy of this balancing feedback loop is such that hunter-gatherer populations will rarely approach, much less exceed, the carrying capacity of their territories, all else being equal. However, once the difficulties of the transition to agriculture are surmounted, fully fledged agricultural societies universally exhibit rapid population growth, owing to the increases in birthrate and life

expectancy and decreases in infant and child mortality afforded by food surpluses and a sedentary lifestyle (Renfrew 1987; Renfew and Bahn 1996; Matsumura and Hudson 2005). Such populations will quickly reach and then outstrip the carrying capacity of their lands, necessitating the expansion of agricultural settlements into new territories.

The magnitude of such population expansions is generally sufficient to completely overwhelm any hunter-gatherer populations already resident in the area of expansion. In addition to the advantages of sheer numbers, expanding agricultural populations also carry with them the additional assets of the more advanced material culture and greater social complexity permitted and encouraged by a sedentary agrarian lifestyle (Barth 1952; Diamond 1999;

Diamond and Bellwood 2003). Thus, the Austronesian- and Austroasiatic-speaking, southern mongoloid agriculturalists who spread from southern China into Southeast Asia, with their polished stone tools and sophisticated pottery tradition, easily and readily displaced the resident inland populations of australoid Hoabinhian hunter-gatherers, and converted the closed, forested inlands of Southeast Asia into wide-open wet-rice paddies. We have seen that coastal hunter- gatherer populations, such as that at Khok Phanom Di, who themselves had developed a considerable measure of material wealth and social complexity, were not so easily displaced

(Bellwood 1997; Higham and Thosarat 1998; Higham 2002). Instead, the people of this site likely interacted with the immigrant agriculturalists for several centuries, even briefly adopting agriculture for a time while environmental conditions were favorable. This circumstance highlights an important consideration when reconstructing population migrations: from an archaeological perspective, the demographic shift from one population to another associated with such migrations often appears to have occurred instantaneously, heralded by the sudden, dramatic appearance of new artifact types and new patterns of behavior. In fact, the process is

quite gradual, subtle and accumulative (Renfrew 1987; Matsumura and Hudson 2005). Cavalli-

Sforza (Cavalli-Sforza et al. 1988:6006) has drawn an analogy with punctuational events in evolution (Eldredge and Gould 1972), wherein an abrupt, radical change—in this case, the advent of agriculture and ensuing rapid population expansions—sets in motion a lengthy period of gradual yet profound transformation that establishes a new equilibrium among the affected populations(s).

And what of the Thai? As it happens, their early history provides a suitable example of this type of apparent punctuational population change. As was noted above and in the previous chapter, inscriptions in Thai do not appear in Southeast Asia until the famous stela of King

Ramakhamhaeng of Sukhothai, dated to AD 1292 and written in an adapted Khmer script

(Higham and Thosarat 1998). By this time, however, the ur-Thai kingdoms of Sukhothai and

Lanna were already well-enough established in northern Thailand and the Chao Phraya basin to have begun gnawing away at the western fringes of the Khmer empire; Ayutthaya was soon to follow. These literate and well-organized polities did not just burst forth over night, and so the

Thai must have been resident in the general area of northern Thailand for some time prior to this efflorescence. However, since the Mon kingdom of Dvaravati dominated the Chao Phraya basin as far north as modern Chiang Mai from at least the sixth century AD until its subjugation by

Angkor in the tenth to eleventh centuries AD, we can also reasonably surmise that the Thai did not arrive in the basin until the last centuries of the first millennium AD, at the earliest (Benedict

1942; Higham and Thosarat 1998; Higham 1996). As speakers of a Daic language, the Thai must also have spread to the region from the ancestral homeland of Austro-Tai in southern

China.

Today, the greatest concentration and diversity of Daic languages other than Thai remains centered in Yunnan and adjacent provinces in southern China. This suggests that the majority of the Daic peoples (also collectively known as the Tai) stayed ―close to home‖ while their

Austroasiatic- and Austronesian-speaking cousins fanned out across Southeast Asia and Oceania

(Benedict 1942; Rinehart 1981; Nakbunlung 1994; Bellwood 1997; Higham and Thosarat 1998;

Baker and Phongpaichit 2005). This is not to imply cultural stagnation amongst the Daic speakers, however, as archaeological evidence suggests that a state level society emerged in

Yunnan as early as the first century AD, and eventually came to extend its influence over most of southern China between the coast and the foothills of the Himalaya (Rinehart 1981; Baker and

Phongpaichit 2005). It is now generally believed that this Tai civilization, known as the kingdom of Nanchao, was under constant pressure from the relentless imperial expansion of the northern Han Chinese. Over the course of several centuries, this pressure, along with the open hostilities it likely precipitated, led to a gradual southward movement of the Daic peoples, including the Thai, overland and along the river valleys into Southeast Asia. Here, they encountered the Mon-, Khmer- and Cham-speaking populations resident in the area since at least the mid-third millennium BC. The southward drift of Daic peoples was doubtlessly further hastened by the Mongol conquest of southern China in AD 1276 (Briggs 1945; Rinehart 1981;

Nakbunlung 1994; Higham 1996; Baker and Phongpaichit 2005). This likely lead to a rapid secondary influx of large numbers of people into the nascent Thai kingdoms of Sukhothai and

Lanna, providing leaders there with the manpower they would need to eventually stand and challenge Angkor (Rinehart 1981).

Unlike the Austronesian and Austroasiatic expansions, there is little to suggest that the emerging dominance of the Thai people and language in the Chao Phraya basin can be attributed

merely to numerical or technological superiority. The Mon and Khmer had been resident in

Southeast Asia for several millennia prior to the arrival of the Thai, and had given rise to sweeping, populous imperial civilizations of surpassing complexity and robusticity. The Thai shared with them a common economy centered around rice agriculture and bronze and iron metallurgy, but as recent immigrants to the area, they lacked the type of economic infrastructure and political machinery with which Angkor asserted and maintained its dominance over the region. Thus, the Thai initially would have had neither an advantage of men nor of matériel that would have allowed them to overrun the Mon and Khmer. Instead, by all accounts, the Thai integrated themselves into the resident populations, and for a time dwelt as subjects of Angkor.

This is reflected in the high proportion of Khmer and Mon loanwords in Thai, but the comparatively low proportion of Thai loanwords in Khmer (Kaplan 1981; Baker and

Phongpaichit 2005).

Nevertheless, the Thai retained their distinct linguistic and cultural identity, which Bayard

(1979) believes included a social standing as something of a warrior-elite, their martial skills honed over centuries of conflict with the Han Chinese and the Mongols. If this is true, then the

Thai may have been regarded by the dispossessed and overtaxed populace of the outlying western fringe of the Khmer empire as an attractive alternative for their allegiance. By aligning themselves with the Thai, adopting their language and culture, and strengthening the power base of Sukhothai and Lanna, these converts hastened both the downfall of Angkor and the spread of the Thai language across Southeast Asia (Briggs 1945; Rinehart 1981; Higham and Thosarat

1998; Higham 2002; Baker and Phongpaichit 2005). Thus, the initial spread of the Thai language can be attributed to the elite-dominance model, in contrast to the initial spread of

Austronesian and Austroasiatic languages, which is attributed to the numerical and technological superiority of these agricultural populations.

Population Genetics

As the forgoing discussion has hopefully made clear, the archaeological and linguistic— i.e., the cultural—evidence in support of the Two-Layer model of Southeast Asian population history is considerable. It would be an easy thing, however, to get caught up in the voluminous minutiae of linguistic reconstructions and archaeological excavations, and forget that at its core, the Two-Layer model is a hypothesis regarding the biological origins of the modern populations of this region. It is a frustrating truth that linguistic cognates and pottery sherds will always be more abundant than ancient DNA samples and intact skeletal remains, and so the closer we get to the peoples in question, the more scant the evidence of their biological origins becomes. To date, only a handful of genetic studies3 have been brought to bear directly on the question of population history in Southeast Asia, although an additional number have some indirect relevance.

On a large scale, Cavalli-Sforza and colleagues (Cavalli-Sforza et al. 1988; Cavalli-Sforza

1991) have found that a linkage analysis of 120 allele frequencies for 42 classical genetic markers4 in 42 worldwide populations generates a phylogenetic tree that closely corresponds to a reconstructed tree of linguistic phyla. In the genetic tree, Southeast Asians form the first major division within the Non-African branch, opposite of North Eurasians (including Europeans and

3 Xing et al. (2010:199) specifically site the populations of Southeast Asia as being particularly under-represented, in terms both of previous genetic studies and of inclusion in the major genetic diversity databases, such as HapMap.

4 Classical genetic markers are derived from polymorphic loci that are expressed as serological and cytological characters, such as blood group antigens, serum antibodies, proteins, and enzymes. These markers include such familiar systems as ABO, Rh, and Diego blood types, the HLA complexes, PTC-tasting ability, and the G6PD enzyme. Because of their ease of detection and analysis from blood samples, such markers were used to study genetic relationships among populations prior to the direct genetic analyses permitted by the mapping of the mitochondrial and nuclear genomes.

Northeast Asian populations such as the Koreans, Japanese and Mongolians). Within the

Southeast Asian division, separate groupings exist for Papua New Guineans/Australians, Pacific

Islanders and mainland/island Southeast Asians. Interestingly, this mainland/island Southeast

Asian cluster includes southern Chinese populations. These genetic groupings are mirrored to a remarkable degree in the linguistic tree, with the Australian and Papuan languages as outliers to the Austronesian languages of island Southeast Asia and the Pacific and the Austroasiatic and

Daic languages of the mainland, all of which are united under the Austric superfamily (Cavalli-

Sforza et al. 1988:6003-6004). The southern Chinese populations are also something of linguistic outliers to the Austric cluster, as speakers of various dialects of Chinese, a Sino-

Tibetan language. However, this is likely a more recent linguistic tradition imposed upon these peoples by the expanding Han Chinese of the last millennium BC and first millennium AD

(Benedict 1942; Higham 1996; Howells 1997).

At the time of its publication, Cavalli-Sforza‘s research was revolutionary in its empirical demonstration of the correlation between genetic and linguistic divergence times and the accord between these times and well-dated events in the archaeological record. Since then, a number of other studies have looked to genetic data as a means of confirming hypotheses originally derived from archaeological and linguistic evidence. In a study similar in scope (121 alleles at 29 polymorphic loci) to that of Cavalli-Sforza and colleagues (1988), but with different methodological approaches and analytical assumptions, Nei and Roychoudhury (1993) generated a phylogenetic tree which also groups together Southeast Asian and southern Chinese (Fujian province) populations, to the exclusion of Australian/Papuan populations on one hand and East

Asian populations (Japanese, Tibetan, Korean) on the other. In a significant divergence from

Cavalli-Sforza‘s results, however, these East Asian populations are grouped in a ―Greater Asian‖

supercluster along with the Southeast Asians, Oceanians and Australo-Melanesians, rather than with the European populations in a Eurasian cluster. Curiously, this tree grouped Polynesian and

Micronesian populations into the Australo-Melanesian branch, in direct opposition to the prevailing archaeological and linguistic reconstructions, which align them with southern mongoloid, Austronesian-speaking populations (Bellwood 1997; Howells 1997). However, examination of the genetic distances (DA) between the Poly/Micronesian populations and the

Australo-Melanesian populations (DA = 5.3 to 7.9) reveals them to be more than twice as great as the distances between the Poly/Micronesians and the Southeast Asians (DA = 2.1 to 3.8). This suggests that the grouping of Oceanic and Australo-Melanesian populations may be an artifact of the clustering algorithm, rather than a reflection of true population relationships (Nei and

Roychoudhury 1993:931, 934). Indeed, using different clustering parameters, Nei and

Roychoudhury (1993) find the Southeast Asian-Oceanic grouping is restored.

Other studies of classical genetic markers have repeatedly underscored both the fundamental dissimilarity between the populations of Southeast Asia and Australia/Papua New

Guinea, and the fundamental similarities among Southeast Asians, Polynesians and Southern

Chinese (Barth 1952; Kirk 1982, 1986; Pietrusewsky 1994; Bellwood 1997; Omoto and Saitou

1997; Matsumura and Hudson 2005; Matsumura 2006). On a smaller scale, several studies of classical genetic markers in Thai populations have also demonstrated similarities to populations from southern China and island Southeast Asia (Simmons et al. 1954; Fongsatikul 1997). In recent years, however, such studies of classical genetic markers have given way to population genetic studies based on direct sequencing of the mitochondrial and nuclear genomes. These studies permit the exploration and reconstruction of population relationships with much greater precision and resolution. As with the classical genetic studies, they vary greatly in scope, from

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broad considerations of populations world-wide to targeted studies of specific polymorphic loci within circumscribed geographic or populational boundaries. In these studies too, mainland and island Southeast Asians usually group most closely with each other and with Oceanians, before they are joined in the general Asian cluster by East and Northeast Asian populations (Melton et al. 1995; Bellwood 1997; Matsumura and Hudson 2005; Lertrit et al. 2008; Xing et al. 2010). In a study of more than 250,000 single-nucleotide polymorphism (SNP) loci in 850 people from 40 worldwide populations, Xing and colleagues (2010), for example, found that this precise clustering pattern is repeated in 90 to 100% of 1000 bootstrapped iterations of the clustering algorithm. This very high replication rate speaks to the confidence with which this clustering pattern can be viewed as reflective of the true genetic relationships among these populations.

In the same study, Xing et al. (2010:204-206) use the ADMIXTURE algorithm (Alexander et al. 2009) to estimate the major ancestry components of the individuals in their study sample, based on ―the probability of the observed [individual] genotypes using ancestry proportions and population allele frequencies‖ (Alexander et al. 2009:1655). Their analysis reveals that the ancestry of the individuals in their Thai sample is composed substantially of Indian/South Asian,

East Asian and Austronesian5 components. This ancestry pattern is echoed as well in the ancestry of the Cambodian and Vietnamese samples, but these populations have a far smaller

Indian/South Asian component to their ancestry than do the Thai. Such patterns of ancestry are precisely what would be expected for these populations based on reconstructions of their population history derived from archaeological and linguistic evidence. The Austronesian component, which accounts for approximately two-thirds of the ancestry of the Thai and

5 Though left unnamed by Xing and colleagues, this component dominates the ancestry of the Iban individuals in their study sample, to the exclusion of all other ancestry components. The Iban, or ―Sea Dayak,‖ are an indigenous people of northern Borneo whose language belongs to the Austronesian family. Based on this, I believe it is justified to refer here to this ancestry component as ―Austronesian,‖ and to consider it representative of southern mongoloid populations in general.

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Cambodian samples and one-half of the ancestry of the Vietnamese sample, is reflective of these peoples‘ ancient roots in the expanding southern mongoloid agriculturalists of the mid-Holocene.

The Indian and East Asian component in the ancestry of the Thai speaks to the historical position of Thailand as the crossroads of Southeast Asia, through which the religious, political and mercantile emissaries of the great Indian and Chinese civilizations must have passed in order to spread their influence throughout Southeast Asia (Briggs 1945; Rinehart 1981; Higham and

Thosarat 1998; Baker and Phongpaichit 2005; Matsumura and Hudson 2005). The same is also true, albeit to a lesser degree, of the Cambodians, who during the Angkor period were the direct recipients of much of the cultural and material wealth brought to Southeast Asia by these Indian and Chinese scholars and merchants. The Vietnamese, on the other hand, though likewise descended from an ancestral southern mongoloid population, were much more heavily influenced by the Han Chinese, who conquered the Red River valley early in the first century

AD and who would maintain their hegemony over this Vietnamese heartland for the next millennium (Higham and Thosarat 1998; Stark and Allen 1998; Higham 2002).

Unfortunately, Xing and colleagues (2010) did not include any populations from Australia or Melanesia in their study, so their results shed no light on the potential contributions of the preceding australoid Hoabinhian populations to the ancestry of modern Southeast Asians. If the immigrant southern mongoloid agriculturalists assimilated and intermixed with, rather than completely replacing, the resident hunter-gatherer populations, as has been argued by many researchers (Bellwood 1997; Matsumura and Hudson 2005; Matsumura 2006), we should also see some traces of Australo-Melanesian ancestry in modern Southeast Asians. Fortunately, some insight into this question is provided by several other studies. Firstly, analysis of Southeast

Asian mtDNA sequences has demonstrated that the oldest haplogroup in Southeast Asia,

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haplogroup M, has a probable antiquity of 45-65,000 years (Schurr and Wallace 2002:437). This age is commensurate with the initial peopling of Southeast Asia by australoid populations, as reconstructed from the fossil record and from the likely dates of colonization of Australia by anatomically modern H. sapiens. That this genetic signature was carried by the initial australoid colonizers of Southeast Asia is further supported by the observation that it is the dominant haplogroup in the highland populations of Papua New Guinea. These are australoid populations who are rather genetically isolated due to the ruggedness and remoteness of their territory.

Haplogroup M is also found at moderate frequencies (25 to 45%) in all Southeast Asian populations, suggesting that there is a near-uniform, region-wide substratum of australoid mtDNA sequences throughout the populations of Southeast Asia (Bellwood 1997; Schurr and

Wallace 2002:437; Lertrit et al. 2008:435). Additionally, haplogroup M attains its greatest frequency in the aboriginal Negrito populations of Malaysia, who as noted above, are believed to be the direct descendants of the australoid Hoabinhian populations of Southeast Asia, but with varying degrees of genetic admixture with neighboring agricultural populations (Ballinger et al.

1992; Bellwood 1997; Schurr and Wallace 2002).

Secondly, the mtDNA study (Oota et al. 2001) which revealed the Moh Khiew skeleton to be genetically closest to individuals from Papua New Guinea, in concordance with its posited australoid phenotypic characteristics (Matsumura and Pookajorn 2005; Matsumura 2006), also found that four skeletons from early Neolithic deposits (ca. 7500 to 8000 BP) at Sakai Cave in peninsular Thailand show nearly equal degrees of genetic similarity to populations from Papua

New Guinea (i.e., Australo-Melanesians), Indonesia (i.e., Austronesians) and the Senoi agriculturalists of Malaysia. Although the modern Senoi are generally regarded in morphological terms as a Negrito (and therefore australoid) population, they have clear genetic

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links with Austroasiatic (Mon and Khmer)-speaking populations (Saha et al. 1995; Bellwood

1993, 1997; Oota et al. 2001). It has been argued that the Senoi are the descendant population arising from a large degree of admixture between an ancestral Hoabinhian (australoid) hunter- gatherer population and immigrant Neolithic (southern mongoloid) agricultural populations during the mid-Holocene (Bellwood 1993, 1997). Again, if we are willing to accept as valid the results of these ancient DNA analyses, then the genetic similarity between the Sakai Cave specimens and the Senoi argues for a similar degree of admixture between the Hoabinhians and the southern mongoloid immigrants in at least one other early Neolithic population of Southeast

Asia.

Other genetic studies have shed light on specific aspects of the southern mongoloid expansion. It has long been recognized by geneticists that Southeast Asian and Oceanic populations possesses a 9-base pair (bp) deletion between the Cytochrome Oxidase II (COII) and

Lysine tRNA (tRNALys) mitochondrial gene loci (Cann and Wilson 1983; Ballinger et al. 1992;

Schurr and Wallace 2002). The frequency of this deletion within these populations ranges from approximately 20% in mainland Southeast Asia and eastern Indonesia, to approximately 40% in the Philippines and western Indonesia, to near-fixation in Polynesians. The COII/ tRNALys 9-bp deletion occurs at much lower frequencies (7 to 15%) in East Asian populations, and is nearly absent (0 to 3%) in Australians and the highlanders of Papua New Guinea (Ballinger et al.

1992:144; Melton et al. 1995:407; Schurr and Wallace 2002). It has been estimated that this mutation arose as far back as approximately 60,000 years ago in southern China (Redd et al.

1995; Ballinger et al. 1992; Melton et al. 1995; Fucharoen et al. 2001), and several researchers

(Ballinger et al. 1992; Melton et al. 1995) have argued that both its near-absence in Australo-

Melanesian populations and near-fixation in Polynesian populations are due to founder effects in

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the colonizing populations of these regions. This would support the interpretation that the populations of Polynesia and Australo-Melanesia are derived from separate lineages that arose some time after the origin of the mutation. The COII/ tRNALys 9-bp deletion also occurs at high frequencies (40 to 47%) in the aboriginal populations of Taiwan and the Philippines. As Taiwan is generally accepted as the aboriginal home of Austronesian speakers, and the Philippines likewise as the first stage of their subsequent radiation throughout island Southeast Asia, it has been argued that the distribution of this gene throughout island Southeast Asia and Oceania is an index for the expansion of Austronesian-speakers beginning in the middle Holocene (Ballinger

1992; Melton et al. 1995; Schurr and Wallace 2002).

Further support for this interpretation comes from the evolution of three additional point mutations within the mtDNA control region that occur only in the presence of the COII/ tRNALys

9-bp deletion. These mutations, which consist of transitions at bases 16217 (Thymine 

Cytosine, or T  C), 16247 (Adenine  Guanine, or A  G), and 16261 (C  T) (shorthanded

―CGT‖) and which have collectively been termed the ―Polynesian motif‖ (Melton et al.

1995:404), are found almost exclusively in Polynesian populations. This motif does, however, occur occasionally in eastern Indonesian populations, reinforcing the likelihood that this area served as the jumping-off point for the settlement of Oceania (Hagelberg and Clegg 1993;

Melton et al. 1995). Although the full Polynesian ―CGT‖ motif is not seen in the more western populations of island and mainland Southeast Asia, precursor states of the motif are. The immediate precursor, which lacks the transition at the 16247 locus and therefore has the character state ―CAT,‖ is found in nearly 50% of Taiwanese aboriginals, and is also found in low to moderate frequencies (10 to 25%) in southern Chinese, western Indonesians (i.e., Borneans,

Javans), Malaysians and Filipinos. Even more intriguing, the deep ancestral character state

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―CAC‖ (i.e., lacking the transition mutations at both the 16247 and 16261 loci) is found in moderate to high frequencies throughout Southeast Asia. Of particular note, this character state is found in nearly 50% of southern Chinese but is completely absent both in Polynesian and in

Australo-Melanesian populations (Melton et al. 1995:410-411). The distribution of these three genetic mutation motifs gives the clear impression of an evolving character state being carried throughout Southeast Asia and the Pacific by the radiating migrations of an actively expanding population. In this case, that population is very likely the southern mongoloid, Austronesian- speaking populations of rice agriculturalists who, archaeological and linguistic evidence suggest, moved first from southern China into Taiwan and the Philippines, before fragmenting into populations that would colonize nearly every island between Madagascar and Hawaii.

The genetic traces of the island-hopping Austronesian expansion thus established, other studies have turned their attention to the genetic history of the mainland Austroasiatic and Tai-

Kadai populations. Unfortunately, the reconstructions of the genetic history of these peoples, like the corresponding linguistic reconstructions, are complicated by the ebb and flow of the great civilizations of mainland Southeast Asia (Bellwood 1997; Matsumura 2006; Lertrit et al.

2008). As Funan, Champa, Chenla, Dvaravati, Angkor, Sukhothai and Ayutthaya each waxed and waned in turn, their populations spread and contracted and intermingled with the resident populations of newly conquered territories, to a far greater extent than was possible among the inhabitants of the far-flung islands of Oceania. Adding to the complexity are the unknown genetic contributions of the unnumbered Indian and Han Chinese travelers and traders who came to Southeast Asia to ply their spiritual and material wares (Briggs 1945; Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

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With regards to the Thai specifically, the question of biological descent from the ancient populations within their borders is a particularly charged one. The Thai can, and do, clearly trace their direct descent back to the Kingdom of Sukhothai, whence came the first inscriptions in Thai, and which is widely considered the first ―true‖ Thai kingdom. But as has been repeatedly observed, the founding of Sukhothai is a relatively late development in the population history of Southeast Asia. There is a tremendous amount of history and prehistory within the borders of modern Thailand prior to the Sukhothai period—not least the civilizations of

Dvaravati and Angkor—that many Thai consider dear to their ethnic and cultural heritage. And this is to say nothing of the rich prehistoric cultures of Khok Phanom Di, Ban Chiang, Ban Kao and many others, of which the Thai are also, quite rightly, rather proud (Rinehart 1981;

Nakbunlung 1994; Higham and Thosarat 1998; Baker and Phongpaichit 2005).

In an effort to begin to address these questions, Lertrit and colleagues (2008) have undertaken what is perhaps the most comprehensive examination to date of the genetic relationships among the prehistoric and modern populations of Thailand and other modern populations of East and Southeast Asia. Their study sample includes mtDNA sequence data from the Bronze-Iron Age (1200 BC – AD 400) skeletal population of Ban Lum Khao and the

Iron Age (400 BC – AD 500) population from Noen U-Loke6—the first-ever ancient DNA to be obtained from any archaeological population in Thailand—as well as samples from modern populations of Austroasiatic (n = 3 populations), Sino-Tibetan (Han Chinese populations from both northern and southern China; n = 9,) and Tai-Kadai (including northern, northeastern and central Thai; n = 7) speaking peoples. This study is revealing in numerous ways. Firstly, moderate frequencies (20 to 61%) of haplogroup M were observed throughout all the populations

6 Both sites are located in northeastern Thailand, in the upper reaches of the Mun River valley, a key settlement area during the Bronze and Iron Ages.

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in the study (Lertrit et al. 2008:435). This finding echoes the results of the genetic studies of island Southeast Asian and Polynesian—that is, Austronesian—populations (Schurr and Wallace

2002) and reinforces the interpretation that this haplogroup represents an australoid genetic substratum shared by all post-Paleolithic Southeast Asian populations.

Additionally with regards to haplogroup diversity, the Noen U-Loke and Ban Lum Khao populations, along with the Tai-Kadai, Austroasiatic and southern Han Chinese (from Yunnan and Guangdong provinces) populations share absent to low frequencies (0 to 12%) of haplogroup

A (Lertrit et al. 2008:435). In contrast, northern Han Chinese and Tibetan populations display moderate frequencies (21 to 34%) of this haplogroup. Such a pattern indicates mutual genetic affinities among these four population groups, as would be expected under the hypothesis that both the Tai-Kadai and Austroasiatic populations have their origins in southern China.

Furthermore, it suggests broad southern mongoloid affinities for the prehistoric inhabitants of

Ban Lum Khao and Noen U-Loke—and perhaps by extension for the other Bronze and Iron Age inhabitants of Southeast Asia as well. These results clearly align this set of biological data with both the Austric linguistic hypothesis and the archaeological reconstructions of Neolithic southern mongoloid population migration patterns. In this light, it is intriguing that the southern

Han Chinese populations should show such strong genetic similarities to the Austroasiatic and

Tai-Kadai populations—clearly, the genetic traces of the ancient southern mongoloid populations of this region persist in the modern inhabitants, despite the seemingly overwhelming cultural, linguistic and, by inference, biological influences of the historic Han Chinese. Yet the fact that haplogroup A is anywhere from two to ten times more common in northern Chinese populations than in southern Chinese, Austroasiatic or Tai-Kadai populations speaks to the distinctness of these genetic lineages (Lertrit et al. 2008).

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This same pattern of shared genetic affinities among the Ban Lum Khao, Noen U-Loke,

Tai-Kadai, Austroasiatic and southern Chinese populations, to the exclusion of northern Han

Chinese populations, is also seen with regards to haplogroup B, which is nearly twice as common in the former populations as it is the northern Chinese. The relative frequency of haplogroup B in these populations is of particular interest since other researchers have already linked its distribution with the Neolithic expansion of Austronesian populations, and both the 9-bp COII/ tRNALys intergenic deletion and the ―CGT‖ Polynesian motif and its precursors appear on the background of this haplogroup (Ballinger et al. 1992; Melton et al. 1995; Fucharoen et al. 2001;

Schurr and Wallace 2002). However, the age of this haplogroup has been estimated at anywhere between 25,000 and 60,000 years (Schurr and Wallace 2002), making it of sufficient antiquity to be shared among the divergent Austronesian, Austroasiatic and Tai-Kadai lineages. Fucharoen et al. (2001:122) have found the 9-bp deletion in 18 to 32% of Tai-Kadai-speaking populations in Thailand (including both the Thai and several ethnic minority groups); the majority of individuals with this mutation in their sample also possess the intermediate ―CAT‖ stage of the

Polynesian motif. As was noted above, the distribution of this distinctive suite of the COII/ tRNALys and CAC-CAT-CGT genetic mutations associated with haplotype B gives the clear impression of an evolving character state being carried throughout Southeast Asia by the radiating migrations of an actively expanding population. Although initially proposed to explain only the distribution of Austronesian-speaking populations, based on the foregoing genetic evidence, there is no reason not to expand the scope of this phenomenon to include the

Austroasiatic and Tai-Kadai speakers of mainland Southeast Asia as well. In short, then, this genetic signature is not merely the hallmark of the Austronesians, but possibly of all southern

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mongoloid populations radiating throughout island and mainland Southeast Asia from the

Neolithic onward.

Further insights into the genetic history and relationships of Tai-Kadai and Austroasiatic populations are provided via examination of patterns of inter- and intragroup haplotype diversity and genetic distances provided by Lertrit and colleagues (2008:435-437). Firstly, haplotype diversity (i.e., genetic variation) was found to be greater among the separate linguistic groups

(Sino-Tibetan, Austroasiatic and Tai-Kadai) than within them, indicating that these populations are significantly distinct from one another. This distinctness is confirmed by cluster analysis, which places the Sino-Tibetan populations on a separate branch from the clusters of

Austroasiatic and Tai-Kadai populations. This genetic distinctness of the Sino-Tibetan group, on one hand, from the Austroasiatic and Tai-Kadai groups on the other is unsurprising given the linguistic distinctness—and indeed, the clearly separate linguistic origins—of these populations

(Benedict 1942; Higham 1996, 2002; Bellwood 1997). In contrast, one might reasonably expect the Austroasiatic and Tai-Kadai groups to show less separation, given their hypothesized shared

Austric origins (Benedict 1942; Higham 1996, 2002; Bellwood 1997). However, it must be remembered that despite this shared ancestry, these groups constitute separate waves of immigrant populations spreading into Southeast Asia; their genetic patterns of intragroup homogeneity and intergroup diversity are therefore reflective of the founder effects that would have acted on each of these populations separately (Lertrit et al. 2008:438; Higham 1996, 2002;

Bellwood 1997; Higham and Thosarat 1998; Baker and Phongpaichit 2005).

The separate trajectories of the Austroasiatic and Tai-Kadai populations are further revealed in the observations that 1) the Thai populations in the study have fewer haplotypes in common with Austroasiatic populations than they do with southern Chinese Sino-Tibetan

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populations; and 2) genetic distances between Austroasiatic populations are relatively greater than distances between the Tai-Kadai populations in the study (Lertrit et al. 2008:435). Both of these observations reflect the earlier migration of Austroasiatic populations into Southeast Asia, which allowed them to accumulate in situ a greater degree of intrapopulational genetic diversity, as compared to later-arriving Thai populations, who are less-far removed from the founder effects induced by their more recent migration into Southeast Asia (Lertrit et al. 2008:438;

Higham 1996, 2002; Bellwood 1997; Higham and Thosarat 1998; Baker and Phongpaichit

2005). Thus geographically separated for the better part of four millennia, the Austroasiatic populations diverged from their Tai-Kadai cousins, who gained or maintained closer genetic ties

(i.e., more shared haplotypes and smaller genetic distances) to neighboring East Asian Sino-

Tibetan populations by virtue of their longer residence in southern China (Lertrit et al. 2008).

Closer examination of the genetic relationships at the level of individual populations, rather than at the level of linguistic groups, is additionally revealing. Not surprisingly, the geographically and temporally proximate prehistoric skeletal populations from Ban Lum Khao and Noen U-Loke are closest to each other in terms of genetic distance; they also show relatively close genetic distances to geographically proximate modern Thai and Austroasiatic populations.

However, under more robust principle components and cluster analyses, these prehistoric populations display much tighter associations with Austroasiatic populations, and are clearly separated from Thai populations (Lertrit et al. 2008:437). Such Austroasiatic, rather than Tai-

Kadai, genetic affinities in the Bronze and Iron Age populations of Thailand are precisely what would be expected under the hypothesis, based on archaeological and linguistic reconstructions, that mainland Southeast Asia was colonized during the Neolithic by intrusive populations of

Austroasiatic-speaking agricultural populations (Higham 1996, 2002; Bellwood 1997; Higham

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and Thosarat 1998; Baker and Phongpaichit 2005; Lertrit et al. 2008). Similarly, genetic distances for the modern Thai populations in the study sample are closest first among the Thai populations themselves and secondly between the Thai and the southern Chinese populations from Yunnan and Guangdong provinces. As with all the other genetic relationships elucidated by Lertrit et al. (2008), these results are unsurprising and follow logically from the reconstructed population history of the Thai as derived from linguistic, archaeological and historic lines of evidence. They also reinforce the perceived distinctness of the Tai-Kadai populations moving into Thailand from the late first or early second millennium AD on, compared to the

Austroasiatic populations already resident there, despite their common Austric ancestry (Higham

1996, 2002; Bellwood 1997; Higham and Thosarat 1998; Baker and Phongpaichit 2005; Lertrit et al. 2008).

In summary, then, it is clear that a considerable amount of genetic research into the biological affinities of the populations of Southeast Asia is in close agreement with the hypothesized population histories suggested by both the archaeological record and paleolinguistic reconstructions. Further, the genetic analyses, like the archaeological and linguist analyses, clearly lend strong support to the Two-Layer model of Southeast Asian population origins. However, as illuminating as these genetic studies are, for the time being they remain altogether too limited in scope to stand alone as the definitive line of biological evidence with which to fully resolve the driving questions of essential continuity versus discontinuity among the prehistoric and historic populations of Southeast Asia.

Physical Anthropology

In the absence of the requisite genotypic data, physical anthropologists have turned to various forms of phenotypic data that can stand as reasonable surrogates. Thus, the population history of Southeast Asia has primarily been addressed via craniometric and odontometric

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studies and analyses of non-metric dental traits. Outside the archaeological literature, such studies represent the most voluminous body of knowledge that has been brought to bear on the debate between the Two-Layer and Regional Continuity hypotheses. But other, more general observations pertaining to patterns of human phenotypic variation have also been offered, particularly in support of the Two-Layer model. For example, although frequently discussed in a manner which implies that they are physically homogenous, the modern populations of Southeast

Asia are, in fact, tremendously varied in terms of broad phenotypic characters such as skin color, hair color and form, eye shape, overall skeletal build and so forth. It has long been recognized that this variation has a clinal structure, with a gradient between mongoloid and australoid phenotypes extending from the northwest to the southeast across the region. This has led various researchers all to the same general conclusion, that the most parsimonious explanation for this pattern is ―a model of Mongoloid expansion into an Australo-Melanesian sphere‖ (Bellwood

1997:74-75; Barth 1952; Coon 1966; Howells 1997; Matsumura 2006; Pietrusewsky 2006). In the same vein, it has been noted that the degree of skin pigmentation in most Southeast Asian populations is much lighter than that of indigenous populations living at similar tropical latitudes

(21°N to 11°S) elsewhere in the world (Bellwood 1997:75-76; Brace 2005; Matsumura and

Hudson 2005). Although skin color is generally assumed to be under strong environmental selective pressure, it is also thought to be a slowly evolving character that has taken tens of millennia to attain its present range of variation in populations worldwide (Molnar 2002; Mielke et al. 2006). Reasoning from the latter to the former, Bellwood (1997:76) ―find[s] it hard to escape the conclusion‖ that the lighter-than-expected skin color of Southeast Asian populations indicates a more recent expansion into the area from more temperate latitudes, likely in the north.

Conversely, if Southeast Asian populations had evolved largely in situ since the terminal

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Pleistocene, as argued by the Regional Continuity model, they should reasonably be expected to have darker skin tones more comparable to the adjacent populations of indigenous Australians,

Papuans and Melanesians (who are known for certain to have evolved in situ since the terminal

Pleistocene).

These observations, although intriguing and contributing to our understanding of population variation in Southeast Asia, are nonetheless somewhat anecdotal, and thus not fully persuasive. The same can be said of early craniometric studies, as these tend to be largely descriptive rather than analytical in nature, owing to the lack, at the time, of relevant comparative datasets and of the computational power necessary to execute sophisticated multivariate statistical analyses (Howells 1973; Nakbunlung 1994; Pietrusewsky 2000). Yet such studies can still be informative. For example, Sangvichien (1966a, 1996b, et al. 1969) described the proportions and morphology of skeletal remains from Ban Kao as similar to the modern southern mongoloid populations of Thailand (Bellwood 1993, 1997; Nakbunlung 1994; Pietrusewsky and

Douglas 2002; Matsumura and Hudson 2005). Although Sangvichien (1966a) interpreted this similarity as evidence that the Thai had not migrated to their present territory from elsewhere in

Asia (i.e., a determination of local population continuity), this conclusion was drawn without the benefit of much of the major archaeological fieldwork in the region. In light of our present understanding of Southeast Asian prehistory, one would expect the Neolithic inhabitants of Ban

Kao to have southern mongoloid phenotypes, as this population (and its distinctive pottery tradition) has been tied so closely to the Austronesian and Austroasiatic expansions (Bellwood

1993, 1997; Higham 1996). Under the Two-Layer model, the modern populations of Southeast

Asia should be locally continuous with archaeological populations extending back to the

Neolithic, such as that from Ban Kao; that is, we should expect the discontinuity to occur further

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back in time, between the respective australoid and southern mongoloid populations of the pre- and post-Neolithic epochs (Pietrusewsky and Douglas 2002; Matsumura and Hudson 2005;

Matsumura 2006).

Modern multivariate studies of cranial and dental morphology directed at resolving the question of Southeast Asian population origins have taken a range of approaches to the problem.

The more general approaches have tended to focus broadly on samples of modern and recent historic populations from various locations within Asia, Australia and the Pacific, and have yielded correspondingly broad conclusions. A major contribution of such studies is that their results have tended to be in agreement with, and in some cases have foreshadowed, the results of similar genetic studies, thus affirming the validity of craniometric and dental data for illustrating population relationships in the absence of genetic data. The results have also tended to correspond directly with expected population relationships predicted by archaeological and linguistic data. Indeed, these studies have more or less consistently demonstrated:

 the fundamental division between Asian and Australo-Melanesian populations, driven by the stark contrast between the mongoloid and australoid phenotypes;

 the inclusion of Polynesian populations within the general Asian cluster, and their tendency to cluster most closely with populations from Southeast Asia7 in general and island Southeast Asia in specific;

 the tendency of East and Southeast Asian populations to form separate subclusters within the general Asian cluster, and the frequent (but not exclusive) alignment of populations from southern China with the populations of Southeast Asia;

 the general Australo-Melanesian affinities of Southeast Asian aboriginal populations, such as the Andaman Islanders (Howells 1989; Brace and Hunt 1990; Brace et al. 1991; Li et al. 1991; Brace and Tracer 1992; Bellwood 1997; Pietrusewsky 2000; Matsumura and Hudson 2005).

7 Brace‘s (Brace and Hunt 1990; Brace et al. 1991; Li et al. 1991) consistent, idiosyncratic findings of closer relationships between Polynesians and the Ainu and Jomon of Japan notwithstanding. Although Brace‘s craniometric analyses are compelling, there is little archaeological, linguistic or genetic evidence to support a link between the prehistoric populations of Japan and the ancient or modern Polynesians.

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A few more-detailed studies have also included the occasional archaeological population, and although the sample sizes are usually small, the results of these studies have been additionally illuminating. For example, Li and colleagues (1991) found that a Chinese Neolithic sample from the Yangshao culture of the central Yellow River valley routinely fell in an intermediate location between the East and Southeast Asian subcluster. The authors reject the subsequent interpretation that this population possibly represents a common ancestor to the East and Southeast Asian populations, solely on the grounds of the sample size being too small to permit this population to be reliably placed by the clustering algorithm (Li et al. 1991:272).

However, there is a kernel of archaeological truth attached to the craniometric classification of this population. Archaeological evidence suggests that the Yangshao culture was centered around rice agriculture brought to the Yellow River valley in the fourth millennium BC (Glover and Higham 1996). As such, the Yangshao sample likely represents the northward expansion of a southern mongoloid population similar to those that spread southward into mainland and island

Southeast Asia. Although the Yangshao population would therefore not be ancestral to modern

Southeast Asians, it nevertheless bears a distinct ―familial resemblance‖ to Southeast Asian populations owed to their common descent from the Neolithic populations of the Yangzi Valley.

This interpretation garners further support from an expanded analysis that included more populations but considered fewer craniometric variables, in which the Yangshao sample is joined in its intermediate position by Neolithic populations from the sites of Ban Chiang and Ban Na Di in Thailand (Li et al. 1991:275). In a similar study, Brace and colleagues (1991:254) found that

Neolithic populations from both China and Thailand cluster most closely with modern Southeast

Asian populations, affirming the southern mongoloid phenotypic affinities of these prehistoric populations.

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As intriguing as these results are, such craniometric studies do not include archaeological population samples of sufficient time depth or antiquity to provide much more than general insights into the relationships between the prehistoric and modern populations of Southeast Asia.

As was noted above, it is expected under the Two-Layer model that the Neolithic populations of southern China and Southeast Asia should be highly similar to the modern populations of this region, because one half of the core hypothesis of this model is that the latter are the direct descendants of the former. If the Two-Layer model is indeed true, this evidence of population continuity from the Neolithic onwards should be juxtaposed against a distinct discontinuity between the Neolithic, historic and modern southern mongoloid populations of Southeast Asia on one hand and the australoid populations of the Paleolithic on the other (Pietrusewsky and

Douglas 2002; Matsumura and Hudson 2005; Matsumura 2006). The most persuasive multivariate studies of cranial and dental morphology directed at resolving the question of

Southeast Asian population origins have sought to probe the available skeletal material specifically for evidence of just such a population discontinuity (Pietrusewsky and Douglas

2002; Matsumura and Hudson 2005; Pietrusewsky 2005; Matsumura 2006).

Thus, the case for the Two-Layer model is made most convincingly by Matsumura and

Hudson (2005; Matsumura 2006) who have presented a methodologically robust multivariate analysis of craniometric, odontometric and non-metric datasets. In addition to the standard principle components, factor, cluster and discriminant function analyses presented in most craniometric studies, these researchers have used a variety of multidimensional scaling methods to further distill and explore the relationships of various skeletal populations in multivariate space. Their argument gains additional strength from the breadth and depth of their datasets, which, in addition to containing a wide spectrum of modern East and Southeast Asian,

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Australian, Melanesian and Oceanic populations, also include a broad sampling of late

Paleolithic (i.e., early and middle Holocene) and Neolithic skeletal populations from a great diversity of sites in mainland and island Southeast Asia. This representation of archaeological skeletal populations is far greater in breadth and depth than has been presented in any other studies reviewed in the course of this dissertation. In each of their analyses, regardless of the particular statistical approach and whether based on cranial or dental data, the same patterns of population relationships were revealed, superimposed upon the ever-present first-order separation of Australo-Melanesian and Asian population clusters, and the subdivision of this latter cluster into East and Southeast Asian subclusters.

First, the Paleolithic archaeological samples from Southeast Asia consistently clustered most closely with modern and archaeological Australians and Melanesians. This includes not only the late Pleistocene remains from Moh Khiew (Thailand) and the Hoabinhian-period skeletons from Gua Gunung Runtuh, Gua Cha, Guar Kepah, (Malaysia), Mai Da Nuoc and Mai

Da Dieu (Vietnam) as discussed above, but also the early to middle Holocene hunter-gatherer populations from Indonesia, Malaysia, Laos and Vietnam (including samples from the late/post-

Hoabinhian cultures of Bac Son and Da But). Matsumura and Hudson (2005; Matsumura 2006) interpret these results as clear confirmation of the australoid morphological character of these early (i.e., pre-Neolithic) indigenous populations of Southeast Asia.

Curiously, different analytical iterations also repeatedly place the early Neolithic skeletal samples from the sites of Ban Kao, Ban Chiang and Non Nok Tha—perhaps the three most important Neolithic sites in Thailand—into this Australo-Melanesian grouping. At first blush, this would see to run contrary to the pattern of population relationships expected under the Two-

Layer model. However, it must be remembered that the cultural influences of expanding

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agricultural populations would have arrived earlier and had a greater initial impact on the resident populations of the region than did the genetic influence of these immigrant populations

(Matsumura 2006:52). Thus, although the earliest Neolithic populations of Southeast Asia had taken on the material and social trappings of an agricultural lifestyle, a number of generations may have passed before they also began to manifest the southern mongoloid phenotype carried by the intrusive agriculturalists and currently characteristic of the region (Matsumura and

Hudson 2005:205). The possibility of a slight phenotypic shift (corresponding to an underlying genetic shift) towards the southern mongoloid pattern among the early Neolithic populations of

Thailand is suggested by multidimensional scaling analysis, which places these samples closer than any other australoid population to the zone of overlap between the Asian and Australo-

Melanesian spheres (Matsumura 2006:48).

These analytical observations and subsequent interpretations strike a blow against critics of the Two-Layer model who interpret the model as insisting upon full replacement of the resident australoid hunter-gatherer populations by immigrant southern mongoloid agriculturalists (Turner

1990; Hanihara 1992, 1993). Indeed, the major proponents of the Two-Layer model (Bellwood

1997; Matsumura and Hudson 2005; Matsumura 2006) argue for a much more complicated and subtle process of change, one which

must allow for intermarriage, local evolution, and also for the important concept that expansion [of agricultural populations] involved more a change in the structure of a Mongoloid-Australoid cline than a migration of uniform and distinct peoples from a remote area such as China (Bellwood 1997:82).

The complexity of this process of demic change is underscored by the fact that in one of

Matsumura and Hudson‘s (2005) most significant analyses of odontometric variation, the sample from Non Nok Tha again takes a position within the Australo-Melanesian cluster, while the sample from Ban Kao falls within the Asian cluster. This suggests varying levels of genetic

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influence from East Asian populations among the early Neolithic peoples of Thailand, and presumably the rest of Southeast Asia as well (Matsumura and Hudson 2005:203; Matsumura

2006).

Secondly, the positioning of other archaeological populations under cluster and scaling analyses merits consideration as well. As in other analyses, the Neolithic population sample from southern China falls clearly within the Southeast Asian, rather than East Asian, subcluster, reaffirming the phenotypic similarities between the modern inhabitants of Southeast Asia and their hypothesized Neolithic ancestors. Although Matsumura and Hudson (2005; Matsumura

2006) do not include any Bronze Age populations from Southeast Asia in their studies—a serious shortcoming—they do include the early Iron Age population of Dong Son, in the Red

River valley of northern Vietnam, which dates to the early first millennium BC (Higham 2002).

This population falls clearly within the Southeast Asian subcluster, aligning most closely with the modern population of Vietnam, but also showing affinities for the modern population of

Thailand as well as the Anyang and Yayoi Bronze Age populations of central China and Japan, respectively.

This placement is significant because it suggests a terminus ante quem for the firm establishment of the southern mongoloid phenotype in the prehistoric populations of Southeast

Asia. The rapidity of this phenotypical shift from australoid to mongoloid, occurring over the course of, at most, two millennia, is far too dramatic to be reasonably attributed to the in situ evolution of local populations in the absence of any external influence. Nor can it be ascribed to mere secular change or to a hypothesized trend of cranial ―modernization‖—i.e., reductions in tooth size, facial prognathism and overall cranial robusticity, as well as a trend towards increasing brachicephalization—as proposed by Bulbeck (1982; cf. Bellwood 1997; Matsumura

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and Hudson 2005). Such microevolutionary processes, although powerful in the overall development of modern human races since the Pleistocene, are inadequate to account for such a radical morphological change in so brief a period of time (Bellwood 1997; Jantz and Jantz 2000).

As Bellwood (1997:81) emphasizes, microevolutionary forces, acting ―over a period of perhaps

40,000 years across a range of temperate and tropical environments in Australia and New

Guinea, [have] been insufficient to produce major phenotypic differences equal to those we see in the major Australo-Melanesian and Mongoloid divisions today.‖ Such observations make arguments for in situ population evolution and continuity extending back to the late Pleistocene in Southeast Asia very difficult to entertain.

Lastly, and perhaps most significantly for resolving questions of population origins in

Southeast Asia, is the behavior of the modern Southeast Asian population samples in Matsumura and Hudson‘s (2005; Matsumura 2006) analyses. As was noted above, under cluster analysis of odontometric data, the modern populations of Southeast Asia cluster more closely with the populations of East Asia than with Australo-Melanesians, yet form their own distinct subcluster within the larger Asian grouping. However, under multidimensional scaling analyses of both metric and non-metric dental data, modern Southeast Asians tend to fall in intermediate positions between the East Asian populations on one hand and the Australo-Melanesian populations on the other. This potential for revealing intermediate populations is cited by Matsumura and Hudson

(2005:198; Matsumura 2006:49) as a major analytical advantage of multidimensional scaling analyses over cluster analyses, as the latter will always force intermediate populations to join one cluster or another, creating the perception of discrete populations groupings where a continuous spectrum of variation is present.

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The intermediate positioning of Southeast Asian populations revealed under multivariate analyses can also be seen in the simple univariate population frequencies of certain non-metric dental traits. For example, the frequency of incisor shoveling ranges from 75 to 95% in modern

East Asians (Japanese, Northern Chinese, Mongolians, Okinawans), 40 to 55% in modern

Australo-Melanesians (Australians, New Britain and Loyalty Islanders, Andaman Islanders), and

50 to 75% in modern Southeast Asians (Thai, Lao, Vietnamese). Similar patterns obtain for traits such as De Terra‘s tubercle and double-rooted premolars (Matsumura and Hudson

2005:196-20l; Matsumura 2006:50). Interestingly, however, this intermediate position is not wholly uniform across analyses: in terms of metric dental traits and some non-metric traits, the modern populations of Southeast Asia shift slightly closer to East Asians, while in terms of other non-metric dental traits they are more closely aligned with Australo-Melanesian populations.

This intermediate positioning of modern Southeast Asian populations, along with their fluctuating affinities for the populations of East Asia and Australo-Melanesia, highlights two important aspects of the singular pattern of human variation among these populations. First, it reinforces our understanding of the clinal nature of this variation, with the populations of

Southeast Asia positioned intermediately both geographically and morphologically between the northern, mongoloid and southern, australoid extremes of the continuum. Secondly, it underpins the perception of the general southern mongoloid phenotype of the modern Southeast Asian populations as a hybrid between that of the broad mongoloid and australoid phenotypes—a perception that has long been held by many researchers (Bellwood 1997:74-75; Barth 1952;

Coon 1966; Howells 1997; Matsumura 2006; Pietrusewsky 2006). As was noted previously, the clinal nature of population variation within the region is most simply explained by a model of demic expansion of the southern mongoloid Austroasiatic and Austronesian agricultural

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populations into an area already inhabited by australoid hunter-gatherer populations, followed by varying degrees of admixture between these populations. If we accept that the expanding southern mongoloid populations intermixed with, rather than simply replaced, these resident australoid populations, then the resulting morphological pattern of the descendant populations should reflect this superimposition of a mongoloid phenotype upon the australoid phenotype—in other words, we should expect an intermediate, ―hybrid‖ phenotype, with affinities for both populations. And indeed, this is what Matsumura and Hudson (2005; Matsumura 2006), along with many others (Barth 1952; Coon 1966; Bellwood 1997; Howells 1997), argue is the central phenotypic character of modern, southern mongoloid Southeast Asians: a unique morphological pattern that shows the strongest affinities to the East Asian populations that share the dominant southern mongoloid phenotype, but that also maintains traces of the underlying indigenous australoid phenotype.

In summary, then, the results of Matsumura and Hudson‘s (2005) morphological analyses of dental and cranial remains are in full congruence with the conclusions drawn from the substantial bodies of research in Southeast Asian archaeology, linguistics and population genetics. Collectively, these independent, yet intertwined, lines of evidence make a compelling argument for the Two-Layer model of population history in Southeast Asia, and this is whence the model draws its strength. The convergent interpretations and conclusions derived from these varied fields, requiring few, if any, ad hoc justifications to explain inconsistencies in the data, speak to the near-certain biological and historical veracity of Southeast Asian population history as reconstructed under the Two-Layer model.

And yet. The question of population history in Southeast Asia would be a lesser area of scientific enquiry if all were consensus. Thus, in the latter half of the twentieth century, an

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alternative hypothesis of regional continuity among the modern, historic and prehistoric populations of Southeast Asia was proposed expressly to challenge the dominant theoretical paradigm of expansion, migration and admixture among these populations (Bulbeck 1982;

Turner 1987, 1990, 2006; Nakbunlung 1994; Matsumura and Hudson 2005; Pietrusewsky 2005,

2006; Matsumura 2006). The emergence of the Regional Continuity model of modern Southeast

Asian population origins was but one facet of a larger reactionary movement in anthropology at the time, in which theoretical models were shifted away from invoking population migration— and its implied correlate, population replacement—as a means of demic change (Adams et al.

1978; Hutterer 1983; Cavalli-Sforza et al. 1988; Wolpoff and Caspari 1997; Turner 2006). In some cases, this was a general rejection of migration as an anthropological deus ex machina for explaining quantum shifts in social complexity, technological advancement or biological characteristics within an archaeological or cultural sequence, without giving due consideration to other mechanisms of change. In the case of Southeast Asia, however, this shift was intended as a more specific repudiation of China as ―the all-important center of cultural diffusion from which new innovations reached Southeast Asia‖ (Barth 1952:5; Hutterer 1983; Turner 1990;

Nakbunlung 1994; Stark and Allen 1998; Pietrusewsky and Douglas 2002; Matsumura and

Hudson 2005).

Surrounded and overshadowed by the enormous cultural complexes of India and especially

China, Southeast Asia was long viewed merely as a passive recipient of, rather than an active participant in, the broad advances in human cultural, political, economic and biological development that have shaped the modern populations of Asia and the Pacific. In the post- colonial era, however, researchers sought to distance themselves from the inherent Indo-centric and Sino-centric biases of the previous eras, and to view the cultures of Southeast Asia as

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independent and dynamic agents in the region. This change in perception was accelerated by the groundswell of archeological and historical research in Southeast Asia in the middle to late twentieth century, which clearly illuminated both the richness and depth of the historic and prehistoric civilizations here, as well as the breadth of their contributions to the development of the modern cultures and peoples of eastern Asia and the Pacific (Hutterer 1983; Nakbunlung

1994; Bellwood 1997; Stark and Allen 1998; Higham 2002; Pietrusewsky and Douglas 2002;

Matsumura and Hudson 2005). Additional momentum in this era was provided by the emergent nationalisms of the modern states of Southeast Asia, which structured their identities and legitimated their rights to sovereign rule and territorial control via their perceived descent from the great ancient civilizations of the region (Rinehart 1981; Baker and Phongpaichit 2005). In light of these epistemological shifts, both scientific and political agendas were driven, implicitly and explicitly, by a desire to demonstrate direct, lineal continuity between the modern peoples of

Southeast Asia and their historic and prehistoric predecessors.

The primary proponent of the Regional Continuity model is Turner (1987, 1990, 2006), who has argued for local population continuity in Southeast Asia extending as far back as the late

Pleistocene. Turner‘s read on the continuity model is based centrally on cluster analyses and univariate distribution frequencies of 28 non-metric dental traits in numerous modern and archaeological Asian and Pacific populations, in which he has repeatedly observed two discrete population groupings. These population groupings correspond to two distinct suites of dental morphology, which Turner has designated Sundadonty and Sinodonty. The former grouping, which is characterized by what Turner (1987, 1990, 2006) asserts is a simpler and therefore more primitive dental morphological pattern, contains all the modern and archaeological Southeast

Asian populations in the analyses, including:

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 late to terminal Pleistocene (including Hoabinhian) specimens from Niah, Tabon, Gua Cha and Guar Kepah, along with other contemporary specimens from Malaysia and the Indonesian archipelago;

 Mesolithic-Neolithic specimens from Laos and Vietnam;

 Neolithic to Iron Age samples from Ban Chiang, Non Nok Tha and Don Khlong in Thailand;

 ―Prehistoric‖ specimens from Taiwan, dated to ca. 2000 BC – AD 500;

 Modern samples from mainland and island Southeast Asia, the Philippines and Polynesia (Turner 1987, 1990, 2006).

Conversely, the second grouping contains the more northerly populations of East Asia (i.e., the northern and southern Chinese, Japanese, Koreans, Mongolians and Asiatic Siberians, among others). This group is characterized by the Sinodont dental pattern: a more complex and therefore more derived morphology containing many of the features, such as small relative tooth size, and high frequencies of incisor winging and shovel-shaped incisors, which we today tend to think of as the ―classic‖ mongoloid dental complex (Turner 1987, 1990, 2006). These basic clustering patterns are reiterated in additional analyses comprising different selections of populations, reaffirming for Turner the biological and evolutionary validity of the Sinodont and

Sundadont dichotomy.

Turner (1987, 1990, 2006) derives several primary conclusions from these groupings.

First, the tight clustering of modern and archaeological samples from Southeast Asia (i.e., the

Sundadonts), to the exclusion of all populations from East Asia, indicates long-term, deep-time population continuity in both regions. This conclusion is emphasized by the observation that the sample from southern China clusters not with the Southeast Asian populations as expected under the Two-Layer model, but with the other East Asian Sinodont populations. Additionally, as was alluded to above, Turner (1987, 1990, 2006) argues that the southern Sundadont pattern is the more primitive dental pattern, based on both its overall morphological simplicity compared to the

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northern Sinodont pattern, and its similarity to late Paleolithic fossil dentitions from northeastern

Asia. It follows from this argument that the more ―specialized‖ Sinodont pattern is necessarily derived from the ancestral, ―generalized‖ Sundadont pattern. Accordingly, Turner argues, this morphological dichotomy could have evolved only if the Sundadont and Sinodont populations had diverged a long time ago and progressed along separate population history trajectories with minimal to no gene flow between them. This divergence and subsequent isolation would have been the only means of preserving the regional continuity of the distinct East and Southeast

Asian populations, thus allowing the development of the derived Sinodont complex in East Asia and the retention of the Sundadont complex in Southeast Asia (Turner 1987, 1990, 2006;

Bellwood 1997). Based on the fossil and modern skeletal data, Turner (2006:455; Turner 1990;

Turner et al. 2000) argues that the Sinodont complex may have emerged as early as 30,000 years ago and was in place ―certainly before 13,000 years ago,‖ when it was carried to the Americas by populations migrating across the Bering land bridge. The precise evolutionary mechanism by which Sinodonty emerged is unclear, but Turner (1987:311, 315; Turner 1990) posits the action of genetic drift or founder effects as the most likely cause, while not completely ruling out selective pressures favoring more elaborate dental architecture in northern populations.

Secondly, two subclusters are discernible within the Sundadont populations: the first containing the modern populations of island Southeast Asia and the Philippines, plus Burma and

Nepal; the second containing all the archaeological populations, along with the modern Thai and other mainland Southeast Asians. Turner (1987:308) admits that this clustering pattern does not follow as logically from geography as does the southern Sundadont/northern Sinodont division, but he argues that this split within the Sundadont cluster possibly reflects the greater historic influence of India and the Middle East (correlated with the spread of Buddhism and Islam) on the

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modern populations of the first subcluster, while the archaeological populations of the second subcluster would be free of such influence. Alternately, it is suggested that the populations of the first cluster reflect greater degrees of admixture with northern Sinodont populations.

Lastly, Turner (1990, 2006) argues that there is a certain degree of similarity between

Sundadonty and the dental morphology of modern Australo-Melanesians, indicating that these populations shared a common ―proto-Sundadont‖ ancestor in Southeast Asia prior to ca. 50,000 years ago, and providing additional evidence of the antiquity and long-term continuity of the

Sundadont complex among the populations of Southeast Asia. If Sundadonty has its origins in the late Pleistocene, following the separation of the Southeast Asian and Australo-Melanesian populations, Turner (1990, 2006) reasons, then there is no possible way that the Sundadont phenotype observed in the modern Southeast Asians can be attributed to admixture between resident australoid and immigrant southern mongoloid populations dating only from the

Neolithic onward.

While Turner‘s continuity model is intriguing, there is little in his analyses or conclusions that cannot be explained with an equal or greater degree of parsimony by the Two-Layer model.

To begin with, the tight grouping of modern and archaeological Southeast Asian populations within the Sundadont cluster, and the lack of diachronic morphological change between archaeological and modern Southeast Asian populations—observations that form the linchpin in

Turner‘s Regional Continuity argument—are neither incompatible with, nor unexpected under, the Two-Layer model. The archaeological samples in Turner‘s (1987, 1990) dataset consist primarily of Neolithic and Metal age populations from mainland Southeast Asia—populations that under the Two-Layer model should be morphologically continuous with the modern populations of Southeast Asia, as they all postdate the hypothesized Neolithic southern

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mongoloid expansion. As was noted earlier, under the Two-Layer model, the expected population discontinuity instead should occur between the pre- and post-Neolithic populations of

Southeast Asia, which should be characterized respectively by australoid and southern mongoloid phenotypic patterns (Bellwood 1997; Matsumura and Hudson 2005; Matsumura

2006).

It is noteworthy that the few pre-Neolithic populations included in Turner‘s (1987, 1990) analyses are treated in a uniformly unsatisfactory analytical manner, one that seems certain to confound the reality of these populations‘ true biological affinities. For example, the Mesolithic-

Neolithic specimens from Laos and Vietnam are lumped together with the far more numerous and far more populous post-Neolithic samples from Thailand. Whereas on its own, this

Mesolithic-Neolithic sample might be quite revealing of population affinities at the critical hunter-gatherer-to-agriculturalist transition, instead it is subsumed within a temporally, culturally, archaeologically, and therefore biologically meaningless amalgam that Turner

(1987:308) calls ―Mixed Early Mainland SE Asia.‖ As a result, any unique morphological patterning in this transitional population, along with any potential insights into population history that this patterning might reveal, is effectively statistically ―drowned out‖ by the more numerous post-Neolithic populations.

Similarly, Turner (1987:306-308) points to the inclusion of the terminal Pleistocene specimens from Malaysia and Indonesia in the Sundadont cluster as evidence of the great temporal depth of the Sundadont complex and, therefore, of population continuity in Southeast

Asia. Yet he fails to acknowledge that this ―Early Malay Archipelago‖ sample is positioned as something of an outlier within his Sundadont cluster. This is a critical oversight, because this sample‘s peripheral positioning likely reflects the fact that its constituent skeletal remains have

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long been argued to display the australoid, rather than the southern mongoloid, phenotype (Barth

1952; Trevor and Brothwell 1962; Cuong 1986; Bellwood 1993, 1997; Matsumura and Zuraina

1995, 1999; Matsumura and Hudson 2005; Matsumura and Pookajorn 2005; Matsumura 2006).

Indeed, Matsumura and Hudson (2005; Matsumura 2006) found that in terms of dental morphology, these same specimens are much closer to modern Australo-Melanesian populations than to modern Southeast Asians. Thus, the inclusion of this ―Early Malay Archipelago‖ sample in the Sundadont cluster could just as easily be explained within the Two-Layer model as a function of the greater similarity between these australoid remains and the intermediate/hybrid phenotype of the modern Southeast Asians than either group shares with Turner‘s Sinodont populations. It also bears mentioning at this point that Turner‘s Sinodont populations are very heavily biased towards the northern extremes of Eastern Asia, including four separate northern

Chinese and eastern Siberian populations. As such, the Sinodont cluster is likewise heavily biased towards the morphological extremes of the northern mongoloid phenotype, which

Howells (1997:205) argues is a highly specialized morphological suite that arose as an

―evolutionary response in relatively recent times to the intense cold of the latest Pleistocene.‖

Small wonder, then, that the early specimens from Malaysia and Indonesia would cluster with other tropical populations, regardless of which population history model is correct.

In other facets of his analysis, Turner‘s (1987, 1990, 2006) interpretations give the impression that he is struggling mightily to fit square data into a round theoretical hole. For example, the two subclusters of populations Turner observed within the Sundadont group—one comprising the modern populations of island Southeast Asia, the Philippines, Burma and Nepal, and the other containing modern and archaeological populations from mainland Southeast

Asia—correspond almost perfectly to the Austronesian/Austroasiatic population dichotomy

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posited from archaeological and linguistic reconstructions. But rather than acknowledging this correspondence, which would seem to bring much of his analysis in line with the Two-Layer model, Turner (1987, 1990) resorts to convoluted justifications involving historical population influences that are patently incorrect. It was noted previously that Turner ascribes the separation of these two subclusters to the disproportionate influence of either 1) India and the Middle East, or 2) northern Sinodont populations, on the populations of island Southeast Asia, the Philippines,

Burma and Nepal. Yet both of these suggestions are highly nonsensical. There is absolutely no archaeological or historical support for a greater degree of Indian influence in the early civilizations of island Southeast Asia compared to those of mainland Southeast Asia—in fact, quite the opposite is true (Bellwood 1997; Higham and Thosarat 1998; Higham 2002).

Similarly, although Islam has had a profound effect on the modern and historic populations of Indonesia and Malaysia going as far back as the thirteenth century, this early influence never penetrated in any meaningful way into the Philippines or the islands of eastern Indonesia that constitute the bulk of Turner‘s island Southeast Asia population samples (Bellwood 1997). Even in western Indonesia, where Islam gained its greatest footholds in Sumatra and Java, dating back to the fifteenth century AD, a large degree of this influence dates only to the nineteenth century and is attributable throughout more to the flow of ideologies and commerce, rather than of people or genes, to the archipelago (Bellwood 1997; Winchester 2003). It therefore is likely of little bearing on the biological affinities of these modern populations, or on the question of regional continuity. It is equally absurd to suggest that the far-flung populations of island

Southeast Asia would show greater evidence of admixture with northern mongoloid populations than would the more geographically proximate populations of mainland Southeast Asia. It is likewise meaningless for Turner‘s refutation of the Two-Layer model that his southern Chinese

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population sample is grouped with the Sinodonts rather than the Sundadonts, as this sample is composed substantially of modern and historic individuals from Canton, who likely are predominantly of Han Chinese descent and therefore should be expected to display a northern mongoloid/Sinodont phenotype.

Collectively, these critiques indicate that Turner‘s continuity hypothesis is, at best, seriously undermined by a flawed rendering of Southeast Asian prehistory, and of the archaeological, historical and linguistic evidence germane to the debate of population origins in this region. Turner (1987, 1990) also shows a paradoxical regard for the influence of geography on the population origins debate that weakens his position, as he alternately emphasizes geographical proximity over known historic population relationships, or he disregards geography entirely in order to somehow explain away incongruent data and interpretations. Indeed, the whole issue of population continuity versus discontinuity within Southeast Asia is largely resolved if southern China is appropriately regarded as biogeographically continuous with, and therefore properly a part of, mainland Southeast Asia, rather than separated from it by China‘s modern political borders (Barth 1952; Bellwood 1993, 1997; Matsumura and Hudson 2005;

Pietrusewsky 2006). The subtle distinction between the modern and historic populations of this who are ethnically Chinese (i.e., Han Chinese) and the archaeological populations who inhabited sites that today are located in China is a critical point that often seems to be lost on the advocates of the Regional Continuity model. Turner (2006:456; 1987, 1990) exacerbates this error by incorrectly framing the Two-Layer model‘s interpretation of the Southeast Asian southern mongoloid phenotype as the result of ―admixture between unmigrated Australmelanesians [sic] and Northeast Asian peoples.‖ In statements such as this, it is clear that Turner is conflating the modern populations of China, who are universally and correctly regarded as Northeast Asians

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(i.e., northern mongoloids), and the archaeological populations of regions that would eventually become part of China, who are more appropriately regarded as Southeast Asians (i.e., southern mongoloids) (Howells 1997; Bellwood 1993, 1997; Matsumura and Hudson 2005). Such misinterpretations, whether accidental or intentional, inject interference into the scientific dialogue between the competing theoretical models and impede progress towards an objective, scientifically valid resolution of the underlying debate.

At worst, Turner (1987, 1990, 2006) foregoes evolutionary parsimony to construct an alternative interpretation of raw data and analytical results already in agreement with the dominant theoretical model of Southeast Asian population origins. Turner (1987, 1990) reasonably concludes that the Sinodont complex likely originated due to genetic drift or a founder effect among populations spreading northward from the Sundadont homeland in

Southeast Asia. But then, in order to account for the long-term continuity of the distinct

Sundadont and Sinodont lineages, he relies on a thoroughly unsatisfactory scenario of tens of millennia of near-absolute genetic isolation between ostensibly culturally similar populations, spread across an area that lacks any significant geographical or climatic barriers to population movement and admixture, in a region that has been a thoroughfare for large-scale population movements since the time of Homo erectus (Bellwood 1997; Higham and Thosarat 1998;

Higham 2002; Baker and Phongpaichit 2005). And yet in nearly the same breath, Turner (1990,

2006) argues that the Australo-Melanesian and Southeast Asian populations who have been separated by thousands of miles for as long as 60,000 years retain sufficient similarities in dental morphology to be grouped together to the exclusion of the Sinodonts, without suggesting a suitable evolutionary mechanism by which this similarity was maintained.

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Turner‘s (1987, 1987, 2006) assertion that both Sinodonty and the Australo-Melanesian dental complex are descended from an ancestral population in Southeast Asia, and his position that the defining features of Sinodonty and the Australo-Melanesian complex were established by genetic drift and founder effects, are both probably true (Howells 1997). Yet the evolutionary scenario he has constructed to explain this pattern of descent, relying on a sole mechanism of divergence and subsequent continuity in isolation, is most certainly flawed. Matsumura and

Hudson (2005, 2006) have argued that these dental complexes are neither as discrete nor as static over time as Turner contends and as the continuity model demands. Indeed, despite the clarity with which Turner‘s (1987, 1990) cluster analyses reveal the Sinodont and Sundadont population groupings, his presentation of individual trait frequencies demonstrates a continuous, rather than bi-modal, pattern of dental morphological variation among these populations. Although analysis of univariate trait frequencies is not the most powerful or elegant statistical approach to exploring population relationships, such analyses do suggest intermediate morphological patterns that may be obscured by the rigid inclusion/exclusion parameters of most cluster analyses

(Pietrusewsky 2000; Matsumura and Hudson 2005, 2006; Matsumura 2006). Within this continuous spectrum of variation, Turner (1987, 1990; Matsumura and Hudson 2006) acknowledges that his Sundadont populations often tend to fall in intermediate positions between the complex Sinodont morphological pattern and the simple Australo-Melanesian morphological pattern. Turner (1990:315) also acknowledges that there is evidence of some diachronic change in trait frequencies between prehistoric and modern Southeast Asians, with the later populations displaying a subtle shift towards the Sinodont pattern.

These observations, admitted only in passing by Turner, are not in keeping with an evolutionary pattern of divergence followed by isolation, but are fully consistent with a pattern

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of divergence followed by admixture—in short, with the patterns of population history posited by the Two-Layer model. Thus, despite Turner‘s (1987, 1990, 2006) best efforts to the contrary, the biological evidence delivers us once again to the conclusion that the unique morphological pattern of the modern Southeast Asians, intermediate between the phenotypic and geographic extremes of East Asia and Australo-Melanesia, is most parsimoniously explained by the recombination of these divergent mongoloid and australoid populations in a region where they overlap. From archaeological and linguistic evidence, we can be increasingly confident that this overlap was brought about by the southward migration of agricultural populations from southern

China into the midst of the resident hunter-gatherer populations of Southeast Asia (Bellwood

1997, Howells 1997, Higham and Thosarat 1998; Higham 1996, 2002; Matsumura and Hudson

2005, 2006; Matsumura 2006).

Despite the flaws in Turner‘s reconstructions, however, other researchers have taken up the torch of the Regional Continuity model using craniometric evidence as well. Nakbunlung‘s

(1994) dissertation is noteworthy as one of the few studies in existence that specifically address the question of the origins of the modern Thai people. In this study, modern and prehistoric Thai and Chinese samples are compared using a somewhat unconventional statistical approach based on path analysis and Tukey‘s multiple comparison test. These tests demonstrate that the modern

Thai are unlike both the archaeological Thai and archaeological Chinese samples, leading to the conclusion that neither group is a likely ancestor for the modern Thai. However, Nakbunlung

(1994:72, 106) notes that the modern Thai are less unlike the prehistoric Thai than they are unlike the Neolithic Chinese, and that therefore her data are generally in agreement with the

Regional Continuity model.

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Although Nakbunlung‘s study is commendable for its focus and its thoroughness, her interpretations, unfortunately, are undermined by several flawed assumptions. First, given the historical and linguistic evidence that the ethnic Thai did not arrive in the Chao Phraya basin until the late first millennium AD at the earliest, it should come as no surprise that the modern

Thai are unlike Nakbunlung‘s prehistoric sample, which consists of pooled skeletal data from

Khok Phanom Di (late hunter-gatherers), Ban Kao (Neolithic), Non Nok Tha (Neolithic/Bronze

Age), Ban Na Di (Bronze Age) and Tham Ongbah (early Iron Age) (Nakbunlung 1994:43).

Likewise, Nakbunlung‘s Neolithic Chinese sample consists of pooled data from five populations: four from the Yellow River basin and only one small population (n = 9) from southern China.

As such, this sample is hardly representative of the agricultural southern Chinese populations whence the Austroasiatic and Thai populations of post-Neolithic mainland Southeast Asia are thought to be derived. These two archaeological samples are thus of little comparative value for exploring the population history of the modern Thai or testing the relative merits of the Two-

Layer or Regional Continuity models with regards to this modern population. It must also be stated that this critique is perhaps rather unfair, as these analytical shortcomings in Nakbunlung‘s study underscore a challenge acknowledged by all craniometric researchers: that it is exceedingly difficult to obtain sufficient craniometric data from comparative populations that are geographically and temporally appropriate to the question at hand. Still, researchers must be cognizant of the interpretive limitations imposed by such incomplete or inadequate datasets, regardless of how unavoidable they may be.

Most notable among the supporters of the Regional Continuity model, however, is

Pietrusewsky (1981, 1984, 1990, 1992, 1994, 1996, 2000, 2005, 2006, 2008a,b; Pietrusewsky and Douglas 2002; Pietrusewsky et al. 1992, among many others), who, although less vociferous

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than Turner, has been no less stalwart in his adherence to this theoretical framework over the last three decades. Through numerous methodologically and statistically robust craniometric studies,

Pietrusewsky has concluded, like Turner, that the biological distances and resultant clustering patterns demonstrated among Asian, Australo-Melanesian and Pacific populations are most consistent with the Regional Continuity model. Although Pietrusewsky‘s body of literature is voluminous, the majority of his studies differ from one another only in terms of the number of populations he has included in his analyses. While some studies are rather narrowly focused on specific countries or time periods—looking, for instance, at the modern, historic and prehistoric populations of Japan (Pietrusewsky 1992, 1996), or at early Metal Age populations in Thailand

(Pietrusewsky 1981, 1984; Pietrusewsky and Douglas 2002)—other studies are remarkable in their breadth, incorporating as many as 70 modern and archaeological populations from a vast swath of Asia, Australia and Oceania. Through this collective body of work, Pietrusewsky has made two critical accomplishments above and beyond making his case for the Regional

Continuity model: 1) he has compiled a comprehensive array of craniometric datasets from nearly every modern and archaeological population in Asia and the Pacific that is relevant to the question of population origins in this region (although his samples are primarily only males); and

2) his repeated analyses of similar sets of population samples, with their overwhelmingly similar outcomes, have established the precision and reliability of the clustering patterns detailed in these studies.

In his larger craniometric studies, Pietrusewsky‘s (1990, 1992, 1994, 1996, 2000, 2005,

2006, 2008a,b; Pietrusewsky et al. 1992) cluster analyses of Mahalanobis (D2) distances repeatedly demonstrate that these modern and archaeological populations fall first into two large, now-familiar superclusters, one for Asia and Polynesia and one for Australia and Melanesia.

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Within the Asia/Polynesia supercluster, Pietrusewsky invariably finds a primary division between Polynesians and East/Southeast Asian populations. Within this latter cluster, he likewise consistently finds distinct subclusters of Japanese, Chinese and Southeast Asian populations, although, interestingly, the specific interpopulational relationships are rather fluid within each these subclusters. These clustering patterns are also confirmed by stepwise multiple discriminant

(canonical) function analyses, which generally show three discrete, well-separated constellations of populations along the canonical axes: Australo-Melanesians, Polynesians/Micronesians and

East/Southeast Asians (this last grouping frequently resolves into separate East and Southeast

Asian constellations). Almost without exception, Pietrusewsky interprets these patterns as indicative of separate population origins and long-term continuity within each of these regions, congruent with the Regional Continuity model (Pietrusewsky 1990, 1992, 1994, 2000, 2005,

2006, 2008a,b; Pietrusewsky et al. 1992).

On a finer scale, Pietrusewsky‘s (1992, 1994, 2000, 2005, 2008a,b; Pietrusewsky et al.

1992) craniometric analyses have revealed a host of population relationships that are clearly in line with conclusions from archaeological, linguistic, historic and genetic evidence, including:

 Connections between the modern populations of island Southeast Asia and Polynesia, consistent with the theory that the Polynesians have their ultimate origins among the Neolithic Austronesian populations of the Indonesian archipelago, rather than among the australoid populations of Melanesia (Benedict 1942; Ballinger et al. 1992; Melton et al. 1995; Higham 1996; Bellwood 1997; Howells 1997).

 Close relationships between the populations of mainland and island Southeast Asia, indicating that these populations have a common origin at some point in prehistory (Higham 1996, 2002; Higham and Thosarat 1998; Bellwood 1993, 1997; Howells 1997).

 A clinal pattern of morphological variation within island Southeast Asia, ranging from populations of greater mongoloid affinities in the north and west to populations with more australoid affinities in the south and east, reflecting the respective spheres of influence of these two phenotypic complexes (Barth 1952; Coon 1966; Bellwood 1997; Howells 1997; Matsumura and Hudson 2005; Matsumura 2006).

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Of greatest interest for addressing the endogenous versus exogenous debate surrounding

Southeast Asian population history, however, are the clustering patterns among the archaeological and modern populations of China and mainland Southeast Asia. Here,

Pietrusewsky points to two recurring observations as definitive evidence of the direct, locally continuous, in situ development of the modern populations from their prehistoric antecedents

(Pietrusewsky 1981, 1992, 1996, 2000, 2005, 2006, 2008a,b). First, the archaeological populations from China and mainland Southeast Asia generally group most closely with geographically proximate modern populations. Thus, the Bronze Age (ca. fourteenth to eleventh century BC) Chinese sample from Anyang, in the eastern Yellow River valley, clusters with modern Chinese and other East/Northeast Asian populations, including Koreans and Taiwanese;

Similarly, samples from Khok Phanom Di, Ban Chiang and Non Nok Tha in Thailand group closely with one another, with Hoabinhian/early Neolithic samples from Laos and Vietnam, and with modern Southeast Asian populations (Pietrusewsky 1981, 1984, 1992, 1994; 1996, 2006;

Pietrusewsky and Douglas 2002). Second, and from Pietrusewsky‘s perspective more importantly, the modern populations of East Asia (particularly those from China) and Southeast

Asia always form separate clusters with little to no overlap, clearly indicating the separate origins of these regional populations with little to no admixture between them (Pietrusewsky

2005, 2006, 2008a,b; Pietrusewsky et al. 1992).

The sheer scope and statistical robusticity of Pietrusewsky‘s analyses, along with their demonstrated high level of replicability, lend a considerable amount of weight to his arguments in support of the Regional Continuity model, which have rarely been challenged. As with much of science, however, what constitutes the most persuasive lines of evidence is generally a matter of one‘s perspective. Because of his theoretical position, Pietrusewsky places his emphasis on

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the separation between the East and Southeast Asia clusters. But it bears noting that this separation is not incompatible with the Two-Layer model, which accepts that the post-Neolithic populations of both China and Southeast Asia each followed separate developmental trajectories along the path towards literate state-level civilizations. As such, we should expect to see some separation between the modern populations of these two areas, despite their common roots in the early agricultural populations of China.

Matsumura and Hudson (2005, Matsumura 2006), on the other hand, take a different view of the clustering patterns among the populations in Pietrusewsky‘s studies, emphasizing instead the similarities between the Chinese and Southeast Asian subclusters, which consistently join with one another before either group clusters with any other Asian population group. These repeated demonstrations of strong affinities between the Chinese and Southeast Asian subclusters argue just as strongly for a significant historical pattern of gene flow and biological relatedness between them, as would be suggested under the Two-Layer model. Indeed, in light of the widely acknowledged close biological and cultural ties between Polynesia and (especially island) Southeast Asia—the separation between which only dates back ca. 4000 years (Bellwood

1997; Howells 1997)—we might reasonably expect the Polynesian and Southeast Asian subclusters to link up first. The fact that they do not argues for the great strength of the biological ties between East and Southeast Asians dating back over the same time period. It hardly needs mentioning at this point that this interval of four millennia also corresponds almost precisely to the post-Neolithic era in Southeast Asia and the hypothesized timeline for the migrations of agricultural populations from southern China into Southeast Asia. As such, the clustering patterns between East and Southeast Asian populations demonstrated in

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Pietrusewsky‘s studies arguably can be taken in support of the Two-Layer model as easily as they can be said to support the Regional continuity model (Matsumura and Hudson 2005).

Granted, the more distant clustering relationships between Polynesians and Southeast

Asians could possibly be dismissed as evidence of a sharp divergence between these populations created by profound genetic drift in Polynesians, as a result of the severity of the repeated founder effects imposed during their colonization of the Pacific. However, the strong genetic and linguistic similarities which exist between modern Polynesians and Southeast Asians despite these founder effects (Balinger et al. 1992; Melton et al. 1995; Bellwood 1997) would seem to argue against this interpretation. Likewise, the close relationships between Chinese and

Southeast Asian populations could be attributed to recent admixture among the modern populations of these areas, which certainly has occurred but is unlikely to be sufficiently uniform in its extent across all of Southeast Asia to fully account for this clustering pattern. Accordance with the Two-Layer model is a far more parsimonious and a far less ad hoc justification for the observed patterns of population relationships.

Curiously, additional support for the Two-Layer model in Pietrusewsky‘s analyses can be found if, instead of stepping back from the data and examining clustering patterns at a regional level, we close in and look more narrowly at the clustering behavior of individual populations, rather than temporal or geographic groups of populations. Conveniently, a number of prehistoric populations from Thailand are given particular attention in several of Pietrusewsky‘s analyses

(Pietrusewsky 1981, 1984, 1994, 2006; Pietrusewsky et al. 1992; Pietrusewsky and Douglas

2002), allowing us to evaluate the population relationships between archaeological and modern populations that are central to the debate of Southeast Asian origins. In some of the earliest studies (Pietrusewsky 1981, 1984), cluster analyses of Mahalanobis (D2) distances positioned a

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small sample (n = 10) from the Neolithic/Bronze Age site of Ban Chiang as outgroup to a general Asian cluster containing modern mainland and island Southeast Asian and East Asian populations. In the same analyses, ―Neolithic‖ population samples from Vietnam and Laos are positioned as the outgroup to all populations, including Ban Chiang. Direct examination of the distance matrix as well as the canonical plots from stepwise discriminant function analysis also reveals somewhat close affinities between the Ban Chiang population and the populations of island Southeast Asia. Although Pietrusewsky (1981:23) interprets these observations as indicative of population continuity back to the late Pleistocene, this clustering pattern also lends itself to several conclusions consistent with the Two-Layer.

First, while the distance matrix does align the Ban Chiang population with island Southeast

Asia, the distances here are relatively large (5.75 to 13.8, with most between 7.0 to 9.0) compared to distances among the modern populations of East and Southeast Asia (which range widely, but are typically on the order of 1.5 to 7.0) (Pietrusewsky 1981:11-12). This general dissimilarity is reflected in the ―peripheral positioning‖ of the Ban Chiang sample relative to modern Asian populations (Pietrusewsky 1981:8). Collectively, these observations suggest that the Ban Chiang population is, to use a uniquely Southeast Asian turn of phrase, the ―same same, but different‖ from the modern populations of Southeast Asia. Under the Two-Layer model, we would expect Neolithic populations such as Ban Chiang to display a unique phenotype reflecting the recent initiation of admixture between intrusive southern mongoloid and resident australoid populations. This would result in variable Neolithic populations that are similar to, yet noticeably distinct from, the modern populations in which morphological variation has been somewhat homogenized via continued admixture over the course of millennia.

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Secondly, the positioning of the ―Neolithic‖ Lao and Vietnamese in Pietrusewsky‘s (1981,

1984) analyses is fully in agreement with the Two-Layer model, as these populations in fact probably date to the Hoabinhian period and therefore are likely to display the australoid phenotype (Matsumura and Hudson 2005; Pietrusewsky 2006). Their extreme separation from modern Asian populations, as reflected in their relatively large Mahalanobis distances and their position as the ultimate outgroup, underscores the morphological distinction between these australoid Paleolithic populations and the modern mongoloid populations of Asia. Furthermore, their morphological separation from the Ban Chiang population is a demonstration of precisely the type of discontinuity between pre- and post-Neolithic populations predicted by the Two-

Layer model.

Similar clustering patterns, with Neolithic/Bronze Age populations from Thailand positioned as an outgroup to all modern Asian populations, are observed in several other studies as well (Pietrusewsky 1994, 2006). In each case, the archaeological populations join the Asian cluster immediately before the Polynesian cluster joins to create the Asian/Oceanic supercluster.

Such a clustering pattern further indicates a degree of close morphological, and by extension genetic, similarity among these populations that is at odds with the scenario of separation and divergence at the core of the Regional Continuity model. But even the rare exceptions to this pattern are illuminating: in one study (Pietrusewsky et al. 1992), a pooled sample of skeletal specimens from Ban Chiang, Ban Na Di (Bronze/Iron Age) and Non Pa Kluay (Bronze Age/Iron

Age) clustered not with Asian populations but with populations from Tasmania and Melanesia.

Although it is unwise to draw too broad a conclusion from a single, apparently idiosyncratic analytical result, this clustering pattern provides a tantalizing suggestion of some remnant degree of australoid morphology among the Metal Age populations of Thailand.

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That Pietrusewsky‘s analyses can be read in support of either of two competing hypotheses speaks to the intricacy of the debate over Southeast Asian origins, as well as the subtlety and complexity of the population relationships driving that debate. None of the forgoing discussion is to say that Pietrusewsky is mistaken in his interpretations of his own data, which are uniformly scientifically and logically sound. My point here is only to demonstrate that his data do not provide the iron-clad support for the Regional Continuity model that he repeatedly argues they do. But this leaves us no closer to resolving the debate in any sort of satisfactory fashion. So how do we pursue such a resolution?

The archaeological and linguistic lines of evidence bearing on the question of Southeast

Asian population origins are so well established and so widely accepted—so unlikely to undergo significant revision—that they provide few avenues for potential scientific advances in our understanding of this debate. Others will surely argue that genetic studies are the way forward.

As demonstrated above, it is true that populations genetics research has the potential to yield great insight into the population history of Southeast Asia. However, just as craniometric and odontological analyses have failed to resolve the matter on their own, it is equally unlikely that population genetics would be able to singlehandedly put the matter to rest. The first problem is that it is unlikely, although certainly not impossible, that we would be able to obtain sufficient

DNA sequences from the salient archaeological populations in Southeast Asia to allow a truly definitive assessment of the affinities of these populations. The time, expense, curatorial objections to, and sheer difficulty of collecting, extracting and sequencing ancient DNA argues against this possibility. Although the possibility of reconstructing the time depth of specific mutations and other sequence data using molecular clocks and DNA sequences from modern populations does help the situation somewhat, it is less ideal than being able to obtain samples

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directly from the ancient populations themselves. Therefore, genetic analyses will likely contribute only a part, not the whole, of the answers we are seeking with regards to population history in Southeast Asia.

At this point, it seems inevitable that a good deal of the remaining portion of those answers will have to come from craniometric studies, which have none of the temporal, financial, or technical difficulties of DNA, and only a very few of the social and philosophical difficulties.

But repeated analyses of the same sets of populations, with only a few new populations added here and there, is unlikely to yield any new insights or to get us any closer to the answers we seek. A significant flaw of the existing craniometric studies is that they frequently consider archaeological and modern populations somewhat monolithic, using a single, inappropriately small sample from a single locale as a proxy for the entire population. Thus, in many of the craniometric studies reviewed here, the ―modern Thai‖ sample consists of only about 50 individuals obtained from a single medical school in Bangkok. We have no idea how well this small sample reflects the true extent of craniometric variation in the Thai population as a whole, much less the extent of variation in the 80% of the population that does not live in Bangkok.

Likewise, the Ban Chiang sample that figures so prominently in so many of Pietrusewsky‘s

(1981, 1984, 1994, 2006; Pietrusewsky et al. 1992; Pietrusewsky and Douglas 2002) analyses consists of only 10 to 12 individuals, selected from among of 142 burials recovered from nearly two millennia of cultural deposits. How can such a sample be truly representative of this ancient population, which spans the interval from the Neolithic to very near the advent of literate civilization in Thailand?

Little to no attention has been paid to the possibility of significant variation within temporally or geographically bounded populations, or the potential impact of such variation on

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the reconstruction of population relationships. For example, is there significant morphological variation among the skeletal series from various Neolithic sites in Thailand? Any influx of agricultural populations from southern China at the dawn of the Southeast Asian Neolithic was unlikely to be uniform in its distribution across mainland Southeast Asia, so there is no reason to expect the Neolithic populations of that region to be completely homogenous in terms of morphology. The various analyses of skeletal series from Ban Chiang, Non Nok Tha and Khok

Phanom Di (Pietrusewsky 1981, 1984, 1994, 2006; Pietrusewsky et al. 1992; Pietrusewsky and

Douglas 2002; Matsumura and Hudson 2005; Matsumura 2006) indicate different close relationships between each of the individual Thai samples and various geographically proximate, non-Thai populations, further suggesting that there may be significant intrapopulation diversity and regional geographic patterning within the ancient and modern Thai populations.

Matsumura (2006) has argued that the way forward in resolving the debate between the

Two-Layer and Regional Continuity models of Southeast Asian population history does not lie in broader geographic sampling of populations. Rather, future sampling efforts must have greater temporal depth, yet be more geographically focused, because the nature of prehistoric and historic population interactions likely varied substantially from one time period to another and from location to location within the larger geographic contexts of Southeast Asia. This is particularly true for Thailand, because superimposed on the complex patterns of population interactions (i.e., locally varying degrees of admixture and replacement between indigenous and immigrant populations) posited by the Two-Layer model, is an equally complex pattern of recent and historical population movements and interactions (Rinehart 1981; Baker and Phongpaichit

2005). As has been repeatedly emphasized, Thailand‘s unique geographic location places it at a crossroads of cultural and biological influences from prehistoric, historic and modern

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populations all across central, eastern and southeastern Asia (Briggs 1945; Rinehart 1981;

Bellwood 1997; Higham and Thosarat 1998; Higham 2002; Baker and Phongpaichit 2005). The only way to truly grasp the nature and intensity of these varying influences on the modern Thai population, and our only hope of even beginning to unravel the complex, deep-time population history of the Thai and the other peoples of Southeast Asia, is to sample these modern populations as thoroughly as we can at the local level, to build up our understanding of local patterns of intra- and interpopulation morphological variation, and then to compare any such patterns to similar local patterns of variation in the salient archaeological populations.

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A. Anyang B. Baiyankun C. Fenshanbao D. Hemudu E. Pengtoushan

Figure 3-1. Selected archaeological sites in China. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Site locations following Glover and Higham 1996; Higham 1996, 2002).

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Figure 3-2. Relationships among the various language families of Southeast Asia.

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CHAPTER 4 MATERIALS AND METHODS

The human features and countenance, although composed of but some ten parts or little more, are so fashioned that among so many thousands of men there are no two in existence who cannot be distinguished from one another. -Pliny the Elder Natural History, VII, 8 (AD 77-79)

Resolution of the debate over Southeast Asian population origins cannot be accomplished within the space of a single dissertation—nor, indeed, within the space of a single career. What follows instead is an attempt to begin to address several persistent issues in craniometric studies of Southeast Asian populations, all of which have hampered our understanding of these populations in the contexts of both forensic anthropology and population history. The first of these issues is the exceedingly inadequate representation of Southeast Asian populations in craniometric databases used by forensic anthropologists in the identification of unknown skeletal remains. As such, most practitioners have only a vague sense of the scope and pattern of craniometric variation within these populations. Such deficiencies have a serious negative impact on forensic identification efforts in Thailand, in cities and regions of the United States with significant Southeast Asian communities, and by agencies such as the Joint Prisoner of

War/Missing in Action Accounting Command‘s Central Identification Lab (JPAC-CIL) that frequently encounter the remains of Southeast Asians in the course of their casework.

Fortunately, this problem is easily remedied through more extensive sampling of modern populations; in the case of Thailand, robust skeletal collections are available at any of several medical schools throughout the country.

In addition to advancing the cause of forensic identification, more extensive sampling of the modern Thai population allows us to explore patterns of intrapopulation craniometric variation on a much finer scale, and to consider the possible impact of such variation on the

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reconstruction of population history through craniometric comparisons. It also allows us to address the question of whether a sole, relatively small sample from a single anatomical or archaeological skeletal collection is an appropriate and accurate proxy for the Thai population as a whole. In short, gaining a greater understanding of patterns of variation which exist within the modern Thai population, both between modern Thai males and females and among regional Thai subpopulations, is a matter of both practical and academic interest.

The Samples

In order to gain a greater understanding of patterns of craniometric variation in the modern

Thai, twenty-four standard cranial measurements (Table 4-1; Figures 4-1 to 4-3) (Martin and

Saller 1956; Howells 1973; Moore-Jansen et al. 1994) were collected on a total of 429 known individuals from the modern anatomical skeletal collections maintained by the Departments of

Anatomy at Naresuan University1 (NU), Chiang Mai University (CMU) and Khon Kaen

University (KKU), Thailand (Table 4-2). The source collections are composed of individuals of known age and sex who have donated their bodies to the respective university‘s medical schools for gross anatomy dissection; the majority of individuals have dates-of-death between 1992 and

2006, with a few individuals having dates of death as early as 1979. Likewise, dates of birth range between 1899 and 1983, with the majority of individuals (70%) having been born between

1926 and 1956. These individuals represent local subpopulations in and around three of

Thailand‘s largest cities (Bangkok, Chiang Mai and Khon Kaen, respectively) and also broadly reflect the populations of three of Thailand‘s vernacular sociopolitical regions: the Central,

North and Northeast, respectively (Figure 4-4) (Bunge 1981; Higham and Thosarat 1998; Baker

1 The skeletal collection at Naresuan University consists of primarily of skeletons loaned to the University from Siriraj Hospital, Mahidol University, Bangkok, along with a small number of individuals (n=8) on loan from the collections at Khon Kaen University. These latter have been added to the sample of individuals measured from the KKU collection; the others are designated as the Naresuan University sample but represent, inasmuch as is possible, the population of Bangkok.

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and Phongpaichit 2005). As such, these craniometric data are representative of a substantial portion of the total range of morphological variation within the modern Thai population, and therefore provide some insight into the degree of morphological variation among several regional subpopulations within Thailand. All craniometric data were collected by this author between

June 2006 and November 2009, using GPM analog sliding and spreading calipers, with measurements recorded to the nearest millimeter. Measurements were taken following the landmark and measurement definitions provided by Moore-Jansen et al. (1994); these are reproduced in Appendix A for reference.

In the total sample, males outnumber females by a ratio of 2.13 to 1; this approximate ratio holds for each of the regional subsamples, with the exception of Khon Kaen, in which specimens were measured in alternating male-female order due to time constraints. In males, age-at-death ranged from 22 to 95 years, with a mean age-at-death of 65.58 years (standard deviation [s.d.] =

13.46 years); in females, age-at-death ranged between 27 and 94 years, with a mean age-at-death of 64.35 years (s.d. = 14.08 years). Mean ages-at-death are not significantly different between males and females (assessed using an independent samples t-test for the equality of group means; p = .41), or among regional subsamples (assessed using a one-way analysis of variance

[ANOVA]; p = .83). The distribution of age-at-death is significantly skewed to the left, and fails the Shapiro-Wilk test for normality (p > 0.000). However, both the sex ratio and the age distribution are typical of anatomical collections (Hunt and Albanese 2004; Buikstra and Komar

2008; Komar and Grivas 2008).

Statistical Methods

The majority of statistical analyses used in this dissertation were performed in SPSS

Release Version 19.0.0 (SPSS, Inc. 2010). Some preliminary data cleaning and summary statistics were performed using Microsoft Office Excel 2007.

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Basic Assumptions

Craniometric data lend themselves to a variety of exploratory and analytical multivariate statistical methods. These various methods allow for both the description of patterns of variation within and between groups of known individuals, and the classification of unknown individuals into one of the constituent groups in an analysis. In general, these multivariate methods are underpinned by the paired assumptions that the variables in the analysis are characterized by normally distributed response values (i.e., measurement values) and homogenous variances among the different groups in the analysis (Kachigan 1991; Feldesman 1997; Field 2009).

As there is no efficient way to test for multivariate normality, it is generally accepted that if the individual variables in an analysis are each normally distributed, then the assumption of multivariate normality holds (Kachigan 1991; Feldesman 1997). Normality of the individual craniometric variables was tested using the Shapiro-Wilk test (Shapiro and Wilk 1965) at p ≤ 0.05 on separate male and female samples; the results are summarized in Table 4-3. As is clear from this table, the majority of cranial vault measurements are normally distributed (the exceptions generally being breadth measurements such as maximum cranial breadth), while the majority of facial measurements have test values indicating significant deviations from normality. These cases of non-normality are attributable to any of several potential causes. The first of these are simple limitations of the Shapiro-Wilk test, which becomes more sensitive to small deviations from normality with increasing sample sizes. The sensitivity of the Shapiro-

Wilk test is also significantly affected by large numbers of ties in the dataset (i.e., numerous individuals in the dataset with identical measurement values for a given variable). Ties are common in the present dataset due to the relatively narrow range of human cranial variation overall and the fact that all measurements were recorded to the nearest millimeter. Large sample sizes and large numbers of ties within the dataset may lead to artificial indications of non-

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normality under the Shapiro-Wilk test, when in fact the sample values do not deviate from the normal distribution to an extent sufficient to affect robust multivariate analytical methods (Field

2009).

In addition to these immediate statistical methodology considerations, there are underlying evolutionary and developmental factors that are likely to have a significant impact on the distribution of response values for craniometric variables. The functional matrix model of cranial development suggests that the growth and mature morphology of the skull is driven by the growth and development of neural tissues, oral and nasal passages, and special sense organs such as the eyes, olfactory apparatus and glossopharyngeal structures (Moss 1973, 1997a-c;

Humphrey 1998). Accordingly, the intimate association between these soft tissue structures and the basicranium and portions of the facial skeleton places these cranial regions under significant, but independent, developmental and morphological constraints. As such, it is reasonable to expect a narrowed range of morphological and metric variation in features such as the orbits and cranial base (Moss 1973, 1997a-c). Therefore, the distribution within a population of measurement values for these cranial features may be truncated at both the upper and lower extremes, resulting in a statistically significant deviation from univariate normality for these craniometric variables. It seems certain that this consideration would apply to all human populations, and as it does not seem to have posed a problem in previous craniometric studies, it is assumed it will not have a significant impact on the present study.

Both of these sets of concerns regarding the normality of the dataset can be ameliorated somewhat by examining the magnitude of skewness (asymmetry) and kurtosis (peakedness) values for individual craniometric variables, as well as by examining the quantile (Q-Q) plots and histograms of response values for individual variables for their extent of deviation from the

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normal curve. With regard to the present dataset, several observations can be made. The majority of instances of non-normality appear to be due to positive kurtosis values, indicating a higher-than-normal peak to the distribution curve. This is a further indication that the individual variables‘ distributions are substantially affected by large numbers of specimens in the sample with identical measurement values at, or very close to, the mean. Such tight grouping of response values around the mean is additionally suggestive of tight constraint on the range of variation for certain variables.

Furthermore, the fact that the male sample had twice as many variables fail the test of normality than the female sample suggests that the validity of the test is unduly affected by size of the male sample relative to the female sample. This is reinforced by the observation that, with regard to many of the variables in which the male sample deviates significantly from normal but the female sample does not, the male sample actually has lower absolute skewness and kurtosis values than the female sample. This indicates relatively smaller deviations from normality in the male sample than in the female sample, despite the determination of significant non-normality for males under the Shapiro-Wilk test. In light of these considerations, it is believed the non- normality of these variables is insufficient in magnitude to have a serious negative impact on the statistical analyses herein, which generally are robust to slight departures from normality.

Homogeneity of variances (homoscedasticity) between male and female samples and was tested using Levene‘s test (Levene 1960). This method is appropriate for use with this dataset as it is relatively insensitive both to differences in sample size and deviations from normality (Ott and Longnecker 2001). Homoscedasticity was confirmed for all variables at except cranial base length (BNL), which fails Levene‘s test at p = .001 (Table 4-4). For this variable, variance in the male sample (20.822) is nearly twice that in the female sample (11.810), indicating a narrower

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range of variation in cranial base length in females. The male sample contains several outliers with extreme values for this variable, including one individual with a cranial base length of 88 mm and three with BNL measurements of 112 or 113 mm. However, removal of these individuals from the sample did not resolve the heteroscedasticity of this variable. An alternate explanation is that a developmental factor is at work. It is broadly accepted that females generally display a narrower range of morphological variation than males (the hypothesis of female canalization; Waddington 1942; Hamilton 1982; Stinson 1985; John Byrd, pers comm.); this effect may be especially profound with regards to the cranial base, which is intimately associated with a number of critical neural and endocrine structures, such as the cranial nerves, brainstem, pituitary gland and hypothalamus (Moss 1973, 1997a-c).

Data transformations and non-parametric approaches

Clearly, failure of a given dataset to meet the basic assumptions of normally distributed variables with equal variances introduces significant doubt into the validity and reliability of the results any subsequent multivariate statistical analyses performed on that dataset. Due to the complexity of the methods involved, it can be difficult to assess to what degree the violations of these assumptions may be driving significant results in the multivariate analyses and masking or exaggerating the true patterns of variation within the dataset. To a certain degree, these doubts can be mitigated by a close understanding both of the limitations of the tests used to establish the dataset‘s conformity to these assumptions, and of the nature of the dataset itself, as reflected in the preceding discussion of the Shapiro-Wilk and Levene tests (Field 2009). However, these considerations are generally (and correctly) regarded as inadequate for fully eliminating all doubts regarding the dataset‘s suitability for multivariate statistical analyses and the veracity of the results of those analyses.

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Many methods exist for resolving these issues of non-normality and heteroscedasticity using various data transformations. These methods generally involve the global application of a mathematic operator, such as logarithms, square roots, or trigonometric functions (angular or arcsine transformations), to the dataset, which renders the variables into normal, homoscedastic distributions (Conover and Imam 1981; Sokal and Rohlf 1995). Although these methods are quite powerful from an analytical standpoint, they can have a significant drawback for practical statistical applications (Conover and Imam 1981), such as the use of discriminant functions in forensic anthropology identifications. In this applied context, if an analyst wishes to apply a discriminant function derived from transformed data to a novel unknown individual, the battery of craniometric data collected on that novel individual must also undergo the same transformation, thereby creating an extra computational step that 1) presents an additional opportunity for the introduction of error into the classification analysis; and 2) may be challenged in court as an undesirable or unwarranted manipulation of the skeletal data.

Fortunately, there exists a simpler and analytically expedient means of evaluating the effects of non-normality and heteroscedasticity on the outcomes of multivariate analyses, as well as for teasing out what violations of these assumptions are due to the limitations of the test methods themselves, using rank ordering. Rank ordering involves sorting the response values for each variable, across all groups, from smallest to largest and then assigning the smallest response value the rank of 1, the next highest the rank of two, the next highest the rank of 3, and so forth. Ties in the response values are assigned the mean values of their total ranks (i.e., four individuals with maximum cranial breadths (XCB) of 131 mm and ranked as 5 through 8 in the ordering would each be given the rank of 6.5). Rank ordering mitigates the non-normality and heteroscedasticity of the underlying data, and reduces the disproportionate effects of outliers on

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both of these characteristics, yet preserves a faithful representation of the central tendency and spread of the data (Ott and Longnecker 2001; Field 2009).

Typically, rank-ordered datasets are then analyzed using non-parametric statistical tests

(e.g., the Mann-Whitney test) that are homologous to the more familiar parametric tests (e.g., student‘s t-test), but which are freed from the constraints of assumed normality and heteroscedasticity. However, Conover and Iman (1981), have shown that standard parametric tests, such as the t-test, can be applied to rank-ordered data and are fundamentally equivalent to non-parametric tests in such applications, which they call rank transformations. This is due to the preservation in the rank ordered dataset of faithful measures the central tendency and spread of the original data—two criteria that are essential to parametric statistics. In rank transformations, if the parametric tests of the rank-ordered data return results that are essentially identical to the original parametric tests on the unranked data, then it can generally be assumed that the original parametric tests have not been unduly affected by violations of the assumptions of normality and homoscedasticity in the underlying dataset. The rank transformation technique will be applied to the present dataset as warranted within the context of each of the analyses outlined in the following chapter.

Multivariate Statistical Analyses

Despite their long history as an integral part of the practice of physical anthropology, for many years the potential of craniometric data to explore, describe and utilize patterns of intra- and interpopulation variation was severely constrained by the limitations of available computational technology (Hrdlička 1939; Martin and Saller 1956; Howells 1969; Gould 1996;

Pietrusewsky 2000, 2005). Such analyses as were possible often relied on a single measurement or on a single index, such as the cephalic index, which combined a few measurements in relatively simple fashion. Such simplistic analyses, unavoidable as they may have been,

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necessarily led to simplistic interpretations of patterns of human variation and equally simplistic typological classification rubrics (Howells 1969; Gould 1996; Pietrusewsky 2000, 2005). Not until the development of several essential multivariate statistical methods—including analysis of variance (ANOVA) (Fisher 1925), discriminant function analysis (DFA) (Fisher 1936; Rao

1948, 1952), principle components analysis (PCA) (Hotelling 1933) and Mahalanobis‘

Generalized Distance (D2) (Mahalanobis 1936)— in the 1920s to 1940s, and the subsequent advent of increasingly powerful computing systems, could craniometric analyses begin to mature into the powerful analytical tool we recognize today (Pietrusewsky 2000:377; Howells 1973;

Kachigan 1991; Feldesman 1997; Pietrusewsky 2005).

As noted previously, multivariate statistical analyses can be descriptive or predictive, depending on the aims of the analysis, and they have numerous practical and theoretical advantages for the interpretation of craniometric data, regardless of their specific approaches to data analysis (Feldesman 1997:86; Huberty 1984). First, serial pairwise statistical analyses of suites of craniometric variables in multiple populations are, by their nature, lacking in statistical power, due to the geometric increase in the rate of Type I (α) errors (i.e., falsely rejecting the null hypothesis) with each additional pairwise test (Ott and Longnecker 2001; Field 2009). For example, if one were to compare the mean values for maximum cranial length (GOL) in three populations by calculating three independent Student‘s t-tests for the equality of means, comparing populations 1 and 2, 1 and 3, and 2 and 3, with a level of significance of p = 0.05, the probability of making a Type I (α) error2 for each test is 5%, (α = 0.05) and the probability of not making a Type I error is therefore 95% (1-α = 0.95). Because the tests are independent, the

2 In this example, the null hypothesis would be that the two populations being compared do not have significantly different mean values for maximum cranial length. A Type I error, falsely rejecting the null hypothesis, would entail the conclusion that these two populations do have significantly different mean values for maximum cranial length, when in actuality they do not.

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multiplicative rule of probability applies, such that the total probability of not making a Type I error for the three tests is actually 85.7% (0.95 x 0.95 x 0.95 = .857) and the probability of making a Type I error is therefore 14.3% (α = 1 - .857 = 0.143). This is far greater than the initial condition of α = 0.05, and is well above even the most liberal accepted levels of significance at p = 0.1 (example adapted from Field 2009:348; Ott and Longnecker 2001). To counter the effects of this compounding error in serial pairwise comparisons, the initial α-values must be set to a much lower value, creating a much more stringent threshold for concluding statistical significance.

More importantly, such serial analyses have the undesired effect of decomposing an integrated whole (i.e., a skull and also a discrete individual) into a number of quasi-independent parameters, the sum of which is much, much less than the whole. As a result, it is profoundly difficult to gain a true appreciation of the morphological character of the skull of a given individual, much less that of a population of individuals (Howells 1969, 1973; Pietrusewsky

2000). As Howells (1969:312) has eloquently stated, in univariate statistical analyses,

the individual is sunk. Nor is a population vector created…This is the fundamental limitation of univariate statistics: there is no real vector, or profile, representing either individuals or populations.

But while ―univariate statistics are the statistics of measurements,‖ multivariate statistics are the statistics of individuals and populations (Pietrusewsky 2000:377; Howells 1969:312;

Howells 1973). Multivariate statistical methods achieve this integration of separate measurements into whole individuals and collected populations by permitting the analysis of many variables simultaneously, while accounting for both the nature and extent of the relationships among the variables (Howells 1969, 1973; Kachigan 1991; Pietrusewsky 2000).

This is accomplished by various means depending upon the specific structure of a particular multivariate statistical method, but in general is the result of the reduction of dimensionality in a

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dataset. To clarify what this means by way of an example: if we consider an individual cranium as a multidimensional object (which it clearly is), with each dimension defined by each one of the 24 craniometric variables in a dataset, then the given cranium can be described by its position in a 24-dimensional multivariate space. In this multivariate space, each craniometric variable corresponds to an individual coordinate axis of the space (x-axis, y-axis, z-axis and so on), and the position of a given individual is determined by its measurement values for each of the metric variables, which act as Cartesian coordinates for the dimensional axes. A population of individuals in a craniometric analysis would create a cluster or ―swarm‖ (Howells 1973:3-4) of positions within a particular region of this same 24-dimensional space, and different populations would occupy different regions of that space (Howells 1969, 1973; Zegura 1971; Corruccini

1973; Kachigan 1991; Feldesman 1997; Pietrusewsky 2000).

Obviously, 24-dimensional space is exceedingly difficult to comprehend logically, and in fact is nearly equally difficult to manage computationally. Thus, many multivariate statistical methods reduce this troublesome dimensionality by creating a smaller number of composite or transformed ―variables,‖—known variously as linear combinations, factors, or variates—based typically on patterns of covariation among the individual variables. When applied to craniometric data, these variates summarize the morphological character (i.e., the size and shape) of an individual, accentuate certain aspects of the patterns of morphological variation within and among populations, and reduce redundancy and noise in the data. This reduction in dimensionality is possible because craniometric measurements overlap somewhat in their information content, simply by virtue of the fact that they are all measuring some aspect of the same object. As Feldesman (1997:80) states, ―Each variable contributes some information about the individual; however, the information is not totally unique.‖ That is to say, many of the

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individual craniometric variables are intercorrelated. For example, there is a high degree of intercorrelation between nasal height (NLB), which is the distance from nasion to nasospinale in the sagittal plane, and upper facial height (UFHT), which is the distance from nasion to prosthion in the sagittal plane (Figure 4-1), as the latter measurement includes the same distance measured by the former. In multivariate analyses, the newly generated variates account and adjust for such intercorrelation, thereby optimizing the amount of information contained within. Thus, the craniometric dataset containing 24 measurements (and hence information in 24 dimensions) may be reduced to, say, 5 variates, all of which act as new coordinate axes that allow us to consider the individuals and populations in a 5-dimensional space. A 5-dimensional space is still rather difficult to grasp, but is certainly much more intuitive and much less unwieldy than a 24- dimensional space (Howells 1969, 1973; Zegura 1971; Kachigan 1991; Feldesman 1997;

Pietrusewsky 2000).

An essential feature of these variates is that collectively they still capture the majority of the variation within and among the population samples under analysis, despite the reduced dimensionality. Variates generated by multivariate statistical analyses are often purposely structured to be uncorrelated or orthogonal with regards to one another. Each orthogonal variate generated in an analysis ―explains‖ a certain proportion of the total variance within a dataset, with the first variate explaining the greatest proportion of the variance, and each subsequent variate explaining a slightly smaller, yet still unique, component of the total variance. Thus, intra- and interpopulational patterns of variation within the dataset are preserved, and in many analyses are emphasized.

An additional advantage of multivariate statistical methods is that they permit not only the simultaneous analysis of multiple variables, but also the simultaneous analysis and comparison

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of three or more groups or populations at a time. The results of such multigroup analyses are much more reliable and more statistically powerful than are pairwise comparisons of each population in turn. Furthermore, the economization of information contained in the orthogonal variates permits the identification of factors that are correlated with specific aspects of biological variation, such as differences in size or in specific shape parameters. All of this in turn allows the exploration and interpretation of complex patterns of morphological variation within and among populations in a manner that is morphologically holistic and therefore biologically meaningful, as well as statistically powerful and more logically intuitive (Kachigan 1991;

Feldesman 1997; Pietrusewsky 2000).

From a practical standpoint, many multivariate statistical methods are also very robust to

(i.e., unaffected by) deviations from normality and from homogeneity of variances among individual variables, to a much greater degree than univariate methods. This is especially important in physical anthropology analyses, where skeletal samples can often be quite small and statistically irregular. For the current research, the gathered craniometric data from modern and archaeological populations in Thailand will be analyzed using the following methods:

Analysis of variance

Analysis of variance (ANOVA) is the statistical solution to the problem of compounding error rates that occur when performing serial pairwise statistical tests of three or more groups. It allows the simultaneous testing of the equality of all group means while maintaining a fixed

Type I error rate, as established by the significance level for the test set by the analyst, and is therefore the foundation for nearly all other multivariate statistical methods (Kachigan 1991;

Feldesman 1997; Ott and Longnecker 2001; Field 2009). Without delving too deeply into the mathematical basis for ANOVA, suffice it to say that the approach is built on a statistical model that states that the total variance (the spread or dispersion of data) within a dataset is equal to the

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sum of the within-group variance plus the among-group variance. When used as a statistical test to compare populations, ANOVA tests the equality of group means by considering the ratio of among-group variance to within-group variance; this is known as the F-ratio. If the among- group variance is approximately equal to the within-group variance, the F-ratio will be close to 1.

This result is interpreted as indicating that the groups in the analysis share a common mean and variance, and therefore are not significantly different from one another. However, as the amount of among-group variance increases relative to the amount of within-group variance, so too does the F-ratio, indicating that the groups have significantly different means and therefore are truly distinct from one another. Like all multivariate statistical methods, ANOVA is underpinned by the assumptions of multivariate normality and homogeneity of variances (homoscedasticity), but it is also quite robust to substantial deviations from these conditions if sample sizes are sufficiently large (i.e., greater than 20) and approximately equal (Kachigan 1991; Feldesman

1997; Ott and Longnecker 2001; Field 2009).

Discriminant function analysis

Discriminant function analysis (DFA) is one of the most widely used multivariate statistical methods in physical anthropology. The broad appeal of DFA stems from its combination of descriptive and predictive capabilities in a single analysis, providing an efficient means both for expressing the patterns of variation among separate populations (or among groups within a population), and for assigning an unknown individual to one of these populations or groups (Zegura 1971; Huberty 1984; Kachigan1991; Feldesman 1997). Discriminant function analysis was first applied to forensic anthropology identifications by Giles and Elliot in 1962 for purposes of race determination, and in 1963 for purposes of sex determination. The power of discriminant function analysis for exploring population relationships and illuminating subtle patterns of human intra- and interpopulation variation was highlighted by Howells‘ landmark

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study Cranial Variation in Man (Howells 1973) and subsequent volumes (Howells 1989, 1995).

Today, DFA finds continued application in a broad variety of analytical contexts, including questions germane to functional morphology and human evolution (Feldesman 1997), population history and global and regional patterns of human morphological variation (Pietrusewsky 2000,

2005, 2006, 2008a,b, etc; Zegura 1971), and forensic identification (Jantz and Ousley 2005).

Discriminant function analysis was initially developed by Fisher (1936) and was enhanced by contributions from Hotelling (1931), Mahalanobis (1936) and Rao (1948, 1952). The central goal of DFA (also known as canonical variates analysis when applied to three or more groups, following Rao 1948, 1952) is to create orthogonal variates, or canonical axes, which maximize the amount of among-group variance and minimize the amount of within-group variance for each population or group, to ensure maximal separation and minimal overlap of all groups in an analysis. These canonical axes define a transformed multivariate space in which each dimension or axis accounts for some proportion of the total variance in the dataset. As was noted above, the first variate or canonical axis explains the greatest proportion of the total variance, with each subsequent variate/axis explaining a lesser portion of the remaining variance. Usually the number of variates or axes generated in a specific DFA is one less than the number of groups in the analysis. Mathematically, the creation of a small number of orthogonal variates or canonical axes achieves the desired reduction in dimensionality and allows the identification of the specific individual variables that are driving the separation between groups. Each canonical axis is defined by ―loadings‖ on the variables, which provide an indication of the direction and magnitude of each variable‘s influence on group separation (Zegura 1971; Howells 1973;

Kachigan 1991; Pietrusewsky et al. 1992; Feldesman 1997; Pietrusewsky 2000).

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Once the groups are separated in the multivariate space created by the canonical axes, linear or quadratic equations—the actual discriminant functions themselves—can be derived that define and maximize the geometric distance and minimize the overlap between the ―swarms‖ or clusters of individual points representing the different populations or groups. The line or curve described by the discriminant function is usually oriented obliquely to all the canonical axes, so that it captures a summary of all the variation among groups described by the canonical axes

(Zegura 1971; Howells 1973; Kachigan 1991). These linear or quadratic discriminant functions are also the means by which an unknown individual is classified as belonging to one of the groups in the analysis. This is accomplished by substituting the unknown individual‘s craniometric values into the discriminant function and determining which population swarm the unknown individual falls within or is closest to (Howells 1969, 1973; Kachigan 1991; Feldesman

1997; Pietrusewsky 2000).

Discriminant functions can be constructed in one of two ways. The first is to include all the variables in a given dataset in the discriminant function, regardless of how informative an individual variable is with regard to the separation and discrimination among populations. A more powerful approach is called stepwise DFA, in which only those variables which contribute the most to the separation among populations are included in the equation. Determining which variables make the greatest contributions can be achieved through either of two approaches: forward selection or backward elimination (Sokal and Rohlf 1995). In forward-selected stepwise

DFA, which is the approach used in this dissertation, variables are added into the equation one at a time through a sequence of iterative analyses, until the point of diminishing returns is reached where the inclusion of subsequent variables does not significantly contribute to the separation between groups or improve the discriminatory power of the equation. With the addition of each

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new variable, the contributions of all of the variables already included in the equation are re- evaluated. If the addition of a new variable significantly decreases the contribution of a variable already in the equation below the level of significance, that lesser variable is then removed from the equation. Backward elimination stepwise methods take the opposite approach: all variables are considered in the initial analysis, and each subsequent iteration removes from the equation the variable that contributes the least to the discriminatory power of the discriminant function.

Stepwise approaches thus provide further insight into which specific variables are driving the separation between populations, and which variables therefore have the greatest discriminatory power and the greatest potential for future investigation. They also help reduce the redundancy of data in the analysis by avoiding the inclusion of pairs of variables that are highly intercorrelated with one another, such that little additional information is gained by including both variables (Kachigan 1991; Pietrusewsky 1996, 2000; Field 2009). Stepwise approaches additionally reduce the risk of ―overfitting‖ the function, which occurs when too many variables are included in the discriminant function relative to the number of individuals in the sample. In instances of overfitting, the model (i.e., the discriminant function) is characterized by a greater degree of complexity than that found in the data, such that the model ends up explaining noise or error in the dataset due to the intercorrelation of the individual variables, rather than explaining only the underlying pattern of variation in the data (Feldesman

1997; Ott and Longnecker 2001).

There are many statistical means of evaluating which variables make the greatest contribution to the discrimination between populations in a forward-selected stepwise analysis.

One of the most common approaches, and the method used herein, relies on a statistic known as

Wilks‘ Lambda, which calculates the ratio of within-group variance to total variance—also

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regarded as the total ―unexplained‖ variance—for an individual variable or for the discriminant function as a whole (Field 2009). Wilks‘ Lambda is derived from the same statistical model as

ANOVA, namely that the total variance in a dataset is the sum of the within-group variance plus the among-group variance. Therefore, Wilks‘ Lambda is intimately related mathematically to the F-ratio used in ANOVA (i.e., the ratio of among-group variance to within-group variance), but in an inverse fashion. Just as a larger F-ratio indicates that there is greater distinction between groups (the among-group variance is significantly greater than the within-group variance), so too does a smaller Wilks‘ Lambda value (the within-group variance is a smaller proportion of the total variance, such that a greater proportion of the total variance is accounted for by the among-group variance). In other words, the lower the Wilks‘ Lambda value, the less unexplained variance there is, and the greater the proportion of the total variance that is explained by a specific variable or by the discriminant function as a whole. Since the general aim of discriminant function analysis is to maximize the among-group variation and minimize within-group variation to facilitate discrimination between the two groups, forward selection stepwise methods using Wilks‘ Lambda as a selection parameter will select the variable that minimizes this ratio for each iterative step in building the function.

Integral to DFA is Mahalanobis‘ Generalized Distance (D2) (Mahalanobis 1936), which is the computational means of ―determining the generalized distance between the centroids3 of two or more populations‖ in multivariate space (Feldesman 1997:86; Zegura 1971). By maximizing the ratio of between-group variance to within-group variance in pairwise comparisons of all the populations in an analysis, Mahalanobis distances provide a single, objective, quantitative

3 That is, the center point of the swarm of points representing a population in multivariate space. The centroid does not—indeed, will not—correspond to a particular individual in that population. Rather it is a multivariate indicator of central tendency, comparable (but not exactly equivalent) to the mean in a univariate analysis.

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indicator of the similarity or dissimilarity that exists between each of the different populations in an analysis (Pietrusewsky 1996:70; Howells 1973; Feldesman 1997; Pietrusewsky 1994, 2000,

2006). Hotelling‘s T2 test (1931) provides a test of significance for D2 values, facilitating the identification and interpretation of biologically meaningful distances among populations.

Similarly, when using discriminant functions to classify an unknown individual, that individual‘s similarity to the known groups is frequently calculated in terms of the unknown individual‘s

Mahalanobis distance from each group‘s centroid. The individual will then be classified by the discriminant function analysis as belonging to the population from which it has the smallest D2 distance. Measures of significance can be applied to these individual-to-population centroid generalized distances to generate probabilistic statements, in the form of posterior and typicality probabilities, regarding the likelihood that the unknown individual belongs to the population into which they have been classified (Howells 1973; Albrecht 1992; Feldesman 1997; Pietrusewsky

2000; Jantz and Owsley 2001).

Discriminant function analyses may also incorporate Bayesian statistical approaches in the form of informed prior probabilities, which allow the analyst to weight the classification function according to demographic characteristics of the dataset and/or the underlying population from which it was derived. For example, in discriminant analyses for classification by sex, prior probabilities are often set to 0.5 (equal priors) for both sexes as a default, but may be adjusted for analyses of strongly sex-biased populations to reflect the actual ratio of males to females in the population. Thus, in a population in which males outnumber females at a ratio of 3:1, the prior probabilities could be set to 0.75 for males and 0.25 for females, to accommodate for the probability that an unknown individual from this population is more likely to be a male simply by chance. Similar informed prior probabilities can be applied to discriminant function analyses

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for purposes of race assessment, to reflect the actual racial/ethnic composition of a given population.

As with all multivariate statistical methods, DFA carries with it the twin assumptions of multivariate normality and homogeneity of variances, but adds the additional assumption that all populations in the analysis have equal covariance matrices, an assumption which can be tested using Box‘s M Test for the equality of covariance matrices. Unequal covariance matrices are primarily an issue when using DFA for classification of unknown individuals, as the groups with larger covariance matrices will tend to exert a stronger ―pull‖ over unknown individuals, leading to more frequent misclassification. However, this is generally not a significant concern unless the covariance matrices are extremely different; unequal covariance matrices between groups can also be accommodated for by using quadratic, rather than linear, discriminant functions (Zegura

1971; Kachigan 1991; Feldesman 1997; Jantz and Ousley 2005; Field 2009). It is additionally fortunate for the present analysis that DFA is relatively robust to departures from normality due to kurtosis, rather than due to skew, which is the case for most of the craniometric variables under consideration here (if they are indeed significantly non-normal, as questioned above)

(Feldesman 1997:87-90).

When using discriminant functions to classify unknown individuals, it is additionally assumed that unknown individual is reasonably derived from one of the reference populations from which the discriminant function was generated. Discriminant functions will always assign an unknown individual to one of the groups in the analysis, regardless of whether the individual actually belongs to any of those groups (Zegura 1971; Giles and Elliot 1963; Jantz and Ousley

2005). This is of little concern when using DFA for sex determination, as all individuals are either male or female, but is a persistent problem when the method is used for ancestry

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determination, since many ancestry groups are underrepresented or wholly unrepresented in current databases of normative craniometric data. In this regard, it is incumbent upon the analyst to review not only the classification results, but also the posterior probability and typicality probability as a means of assessing the validity of the classification.

Final Considerations

The complexity of these multivariate methods and their flexibility for application in a variety of analytical contexts means that each statistical approach presents the researcher with an array of operational parameters that can be adjusted to fine-tune the analysis to the specific demands of the research question and/or the dataset at hand. As the present research is intended as an exploratory study of patterns of craniometric variation in the modern Thai, rather than one which is focused on addressing specific hypotheses, the specific methodological approach to the statistical analysis of this dataset evolved with each subsequent analysis, in concert with the author‘s evolving understanding of the structure of that craniometric variation. Thus, it is not possible here to have provided more than the foregoing broad summary of the salient statistical methods employed in this research. Rather, the specific statistical analytical steps followed in each individual analysis will be outlined in greater detail in the following chapter, within the framework of each specific exploratory analysis. Although this is an unconventional structure for a scientific paper, it is felt that this will lend greater lucidity to both the structure of the analyses and the interpretation of the results.

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Table 4-1. Standard cranial measurements used in this study. # Measurement namea Codeb # Measurement name Code 1 Maximum cranial length GOL 13 Nasal height NLH 2 Maximum cranial breadth XCB 14 Nasal breadth NLB 3 Bizygomatic breadth ZYB 15 Orbital breadth OBB 4 Basion-bregma height BBH 16 Orbital height OBH 5 Cranial base length BNL 17 Biorbital breadth EKB 6 Basion-prosthion length BPL 18 Interorbital breadth DKB 7 Maxillo-alveolar breadth MAB 19 Frontal chord FRC 8 Maxillo-alveolar length MAL 20 Parietal chord PAC 9 Biauricular breadth AUB 21 Occipital chord OCC 10 Upper facial height UFHT 22 Foramen magnum length FOL 11 Minimum frontal breadth WFB 23 Foramen magnum breadth FOB 12 Upper facial breadth UFBR 24 Mastoid height MDH a,b Measurement names and codes are following FORDISC 3.0 (Jantz and Ousley 2005; Moore-Jansen et al. 1994)

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Figure 4-1. Anterior views of the skull, detailing cranial measurements. Measurement numbers correspond to Table 4-1; see Appendix A for landmark and measurement definitions.

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Figure 4-2. Left lateral view of the skull, detailing cranial measurements. Measurement numbers correspond to Table 4-1; see Appendix A for landmark and measurement definitions.

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Figure 4-3. Inferior view of the skull, detailing cranial measurements. Measurement numbers correspond to Table 4-1; see Appendix A for landmark and measurement definitions.

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Table 4-2. Summary statistics of modern Thai population samples. Population Males n = Age range Females n = Age range Totals (mean) (mean) Chiang Mai 242 22 - 95 years 104 27 - 93 years 346 (65.87 years) (63.74 years) Khon Kaen 30 40 - 80 years 24 36 - 94 years 54 (61.47 years) (67.50 years) Naresuana 20 N/A 9 N/A 29 N/A N/A Totals 292 137 429 a Age data not available for the Naresuan skeletal collection.

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Figure 4-4. Vernacular sociopolitical regions of Thailand. (Redrawn from original map data obtained from the CIA World FactBook (2010) and used with permission. Regional boundaries following Bunge 1981; Higham and Thosarat 1998; Baker and Phongpaichit 2005).

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Table 4-3. Summary of Shapiro-Wilk tests of normality for individual craniometric variables. Variable Males Females Variable Males Females GOL Ya Y NLH N(.006) N(.001) XCB N(.003)b Y NLB N(.000) N(.004) ZYB Y Y OBB N(.000) N(.002) BBH Y Y OBH N(.000) N(.001) BNL Y Y EKB N(.013) Y BPL Y Y DKB N(.000) N(.003) MAB Y Y FRC Y(.041) Y MAL Y Y PAC Y Y AUB Y(.020) Y OCC N(.000) Y UFHT Y Y FOL N(.001) N(.001) WFB Y Y FOB N(.000) N(.003) UFBR Y(.021) Y MDH Y(.026) Y a Normally distributed at p ≥ 0.05, unless otherwise stated b p-values for significant deviations from normality given in parentheses.

Table 4-4. Summary of Levene‘s tests of normality for individual craniometric variables. Levene Levene Variable Statistic df1 df2 Sig. Variable Statistic df1 df2 Sig. GOL 3.817 1 424 .051 NLH 2.703 1 421 .101 XCB 1.774 1 406 .184 NLB .150 1 416 .699 ZYB .357 1 424 .550 OBB .014 1 427 .905 BBH .494 1 424 .483 OBH 3.280 1 427 .071 BNL 11.298 1 427 .001 EKB .770 1 427 .381 BPL 1.172 1 168 .281 DKB .308 1 427 .579 MAB .020 1 83 .888 FRC .004 1 425 .952 MAL 2.680 1 163 .104 PAC 1.888 1 423 .170 AUB .410 1 426 .522 OCC .195 1 423 .659 UFHT .005 1 154 .946 FOL 3.245 1 424 .072 WFB .829 1 421 .363 FOB .667 1 425 .415 UFBR 1.518 1 427 .219 MDH 3.141 1 424 .077

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CHAPTER 5 RESULTS AND DISCUSSION

The whole of science is nothing more than a refinement of everyday thinking. -Albert Einstein Physics and Reality (1936)

Numerous multivariate statistical analyses using the methods discussed in Chapter 4 were performed in order to explore patterns of morphological variation between the sexes and among regional subpopulations within the modern Thai population. These analyses were performed in an iterative fashion in order to ascertain both the analytical parameters and the craniometric variables that provide the greatest illumination of this morphological variation; each of these will be detailed in turn below. As all of the multivariate statistical methods employed in this study are, at their core, rooted in the differences in mean craniometric dimensions between the sexes and among regional subpopulations, these data are provided in Tables 5-1 and 5-2.

Discriminant Function Analysis – By Sex

Discriminant Function 1: All Variables

Patterns of sexual dimorphism in the modern Thai were first explored using a linear discriminant function analysis incorporating all 24 craniometric variables and applied to the total sample of 429 individuals (292 males/137 females). However, due to missing measurement data for several variables in a number of individuals, this initial analysis included only 38 males and

32 females who had recorded values for all 24 craniometric variables. Nevertheless, Box‘s M test for the equality of the covariance matrices for the male and female samples failed to reject the null hypothesis of equal matrices at p = 0.426, indicating that this reduced sample still meets the basic assumptions for the appropriate use of linear discriminant function analysis. This is particularly reassuring in light of the possible non-normality of many of the individual craniometric variables, as discussed in Chapter 4, and confirms the likelihood that the Shapiro-

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Wilk test is being overly sensitive to small, non-significant deviations from univariate normality due to the relatively large total sample size. Because the male and female samples included in this specific analysis are nearly equal in size, prior probabilities were left at the default value of

0.5 for each sex.

An independent samples t-test for the equality of means was calculated for each of the 24 craniometric variables prior to DFA, as a preliminary means of probing which variables differ significantly between males and females (Table 5-3). This t-test revealed significant differences at p < 0.011 between the male and female means for each craniometric variable except maxillo- alveolar length, which was significant at p < 0.05. Despite the non-homogeneity of variances between male and female samples for cranial base length (BNL) (Levene‘s test significant at p ≤ 0.001), the t-test is still significant at p < 0.001 under analytical parameters that do not assume equality of variances. Likewise, independent samples t-tests executed on the rank- ordered data for each of the variables shown in Table 4-3 to be non-normally distributed at p < 0.05 (nominally, XCB, AUB, UFBR, NLH, NLB, OBB, OBH, EKB, DKB, FRC, OCC,

FOL, FOB, MDH) also shows significant differences at p <0.01 between the male and female mean ranks for each of these variables (Table 5-4). Because the results of this rank transformed t-test mirror the results of the t-test as applied to the unranked data, it can be concluded that the non-normality and heteroscedasticity detected in the dataset are likely of an insufficient

1It may be questioned at this point whether a Bonferroni correction (or similar) is required here to adjust the pairwise error rate to maintain an experimentwise error rate of α = 0.05. However, these t-tests are not being used here for hypothesis testing—i.e., to establish whether craniometric differences exist between males and females—as this is already known a priori. Nor is it being argued here that these craniometric variables are collectively significantly different between males and females at p < 0.05. Rather, these univariate t-tests are simply being used as a preliminary tool for data exploration, to provide a general preview of which individual craniometric variables may be emphasized by the subsequent DFA to generate a classification function for sex determination. As the results of the t-tests are not being considered in a summary fashion for hypothesis testing, the compounding increase in the pairwise error rate is of little concern here, and it is felt that no Bonferroni correction is needed for this or subsequent univariate tests which are used for the same purpose of general data exploration.

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magnitude to have undesired effects on the results of the following parametric multivariate analyses. This also further supports the initial interpretation that the non-normality of so many craniometric variables, as indicated by the Shapiro-Wilk test, is due to the over-sensitivity of this test to large sample sizes and large numbers of ties in the dataset. Last, these indications of significant sexual dimorphism for all craniometric variables in turn implies that the DFA should be successful in generating an accurate and reliable function for sex determination in unknown individuals.

Similarly, as part of the DFA procedure in SPSS, Wilks‘ Lambda was calculated as an alternate test of equality of group means for each craniometric variable (Table 5-5). As Wilks‘

Lambda is rooted in the same statistical model as ANOVA (total variance = among-group variance + within-group variance), which in turn underpins DFA, the results of this test may be more directly relevant than the t-test for evaluating which variables will contribute significantly to group separation under DFA. This analysis revealed significant differences at p < 0.05 between the male and female mean values for each of craniometric variables except orbital height (OBH), interorbital breadth (DKB), occipital chord (OCC) and foramen magnum breadth

(FOB). As such, these four variables may be considered to display a low degree of sexual dimorphism and therefore are expected to contribute little to the separation between males and females.

Interestingly, three of these four variables (OBB, DKB, FOB) are intimately associated with organs of special sense (the eyes for OBB and olfactory apparatus for DKB) or significant neural structures (the proximal spinal cord and brainstem for FOB). Under the functional matrix hypothesis (Moss 1973, 1997a-c) these craniometric dimensions are arguably under significant developmental constraint, and might be less likely to display significant sexual dimorphism as a

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result. Conversely, Wilks‘ Lambda reveals that 14 craniometric variables are significantly different between males at females at p < 0.01, indicating that these variables are highly sexually dimorphic and should be significant components of the discriminant function.

Review of the discriminant function structure matrix (Table 5-6), which lists in rank order the pooled within-groups correlation between each craniometric variable and the discriminant function, provides the clearest insight into the relative importance of each variable to the separation of male and female clusters in multivariate space (Feldesman 1997:93; Field 2009;

SPSS, Inc 2010). As expected, the four variables with non-significant Wilks‘ Lambda results

(OBH, OCC, FOB, DKB) also have the lowest structure correlation coefficients, indicating that they provide little discriminatory power to the analysis. Structure coefficients for variables with significant ANOVA results are all positive and range from 0.511 to 0.193. This overall moderate range of correlation coefficients indicates that all these variables are contributing somewhat more or less equally to population separation. In other words, the separation between males and females is based on many moderately dimorphic craniometric variables, rather than only a few highly sexually dimorphic cranial dimensions.

Curiously, according to the structure matrix the variable contributing the most to the separation between males and females is nasal height (NLH), which is a relatively small, somewhat specific cranial dimension overall. Typically, discriminant functions for sex determination are found to be driven by the larger, more generalized dimensions of the cranial vault and facial skeleton that contribute significantly to the overall size differences between males and females (Giles and Elliot 1963). In the current analysis, this can be seen in the relatively high correlations (second and third highest overall) for bizygomatic breadth (ZYB) and cranial base length (BNL), as well as the presence of maximum cranial length (GOL), basion-

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bregma height (BBH) and biauricular breadth (AUB) among the ten craniometric variables with the strongest correlations. Mastoid height (MDH), which is considered a strongly dimorphic non-metric trait (Buikstra and Ubelaker 1994; Walker 2008), also has a relatively strong correlation in this analysis, indicating its significant metric discriminatory power between males and females.

Because only two groups (males and females) are under consideration, only a single discriminant function was generated in the analysis and accounts for 100% of the variation between males and females. By multiplying the cranial measurements of an unknown individual by their respective coefficients from Table 5-7 and then adding the constant term, the discriminant function score for the unknown individual is obtained. This discriminant function score is then compared to the provided sectioning point of -0.108, which is simply the midpoint between the respective mean discriminant function scores for males and females (Figure 5-1). If the unknown‘s discriminant function score is greater than the sectioning point, the individual is classified as a male; if smaller than the sectioning point, the individual is classified as female.

The classification accuracy of Discriminant Function 1 was tested using both resubstitution and leave-one-out cross-validation (Table 5-8). Under resubstitution, nearly 90% (34/38) of males and 94% (30/32) of females were classified into their correct sex group, for an overall correct classification rate of 91%. However, estimates of classification accuracy based on resubstitution are artificially high, as the same individuals which were used to create the discriminant function initially are also used to test its performance—in effect, individuals are being tested against themselves, leading to inflated accuracy rates. In contrast, under leave-one- out cross-validation, one individual is held out of the analysis and the discriminant function is generated from n-1 individuals in the sample; the held-out individual is then classified into one

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of the reference groups using the n-1 discriminant function. This process is repeated with each of the n individuals in the dataset, so that the discriminant function is iteratively tested against an independent ―unknown‖ sample, resulting in an unbiased estimate of model performance.

Classification accuracy rates are almost always lower under leave-one-out cross-validation than under resubstitution, but are a much more reliable indicator of the true predictive accuracy of the discriminant function (Feldesman 1997; Pietrusewsky 2000; Jantz and Ousley 2005). Using leave-one-out cross-validation, 74% (28/38) of males and 81% (26/32) of females were correctly classified by Discriminant Function 1, for an overall accuracy rate of 77% (54/70 correctly classified by sex). This accuracy rate is still significantly greater than chance (0.5), indicating the basic utility of this function for sex determination in modern Thai populations.

Though the inclusion of all 24 craniometric variables in this discriminant allows us the opportunity to explore the overall pattern of sexual dimorphism in the cranial dimensions of the modern Thai, this is not necessarily the most powerful analysis for sex determination in this population. Indeed, the 77.1% correct classification rate, although considerably better than chance, is nonetheless somewhat poor in comparison to the performance of other models developed on other populations. The discriminant functions for sex determination generated by

Giles and Elliot (1963:63) for use on American Whites and Blacks, for example, had classification accuracy rates of 82 - 89%, appreciably better than the performance of this Thai- specific discriminant function. Likewise, discriminant functions generated by FORDISC 3.0

(Jantz and Ousley 2005) for purposes of sex determination often have classification accuracy rates greater than 90%. This suggests that the Thai-specific sex determination function can be considerably improved upon.

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There are several potential causes for the lower classification rate in this Thai-specific function. There is, of course, always the possibility that the modern Thai population is not strongly sexually dimorphic, and is less so than other populations. If this were the case, a greater degree of craniometric and morphological overlap between males and females in the modern

Thai would translate to greater difficulties distinguishing between them using discriminant functions. Given the overall gracility of the Thai population—and, indeed, of all Southeast

Asian populations in general—this is a very real possibility which must be considered in any attempt to develop metric or morphological standards for the determination of sex in unknown skeletal remains from this population.

It is much more likely, however, that this poorer correct classification rate is simply due to the structure of this initial analysis. As was noted above, many individuals were removed from this DFA due to missing measurement values for one or more of the craniometric variables. This resulted in a reduction in sample size from 429 individuals to only 70 individuals (38 males/32 females), meaning that 84% of the total sample was excluded from the analysis. There is no doubt that the removal of these individuals also equates to the loss of a significant proportion of the total pattern of variation within the modern Thai sample. As such, it is possible that

Discriminant Function 1 would have an even lower classification accuracy over a period of long- term application to a number of unknown individuals from forensic contexts. Clearly, this is an undesirable situation. Fortunately, both of these concerns can be addressed through various adjustments of the analytical parameters for generating the discriminant function for sex determination. By increasing the number of individuals in the analysis and/or selecting only those variables with the greatest discriminatory power, it should be possible to generate other discriminant functions with greater classification accuracy rates. However, a failure of these

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adjustments to produce any significant increase in model performance for the sex determination function may be considered an indicator of a relatively lower degree of sexual dimorphism in the modern Thai population.

Discriminant Function 2: 20 variables

One way to increase the number of individuals included in the discriminant function analysis is simply to exclude those variables that are missing for large numbers of the individuals in the dataset. In the present study, the majority of the individuals are missing values for basion- prosthion length (BPL), maxillo-alveolar length (MAL), maxillo-alveolar breadth (MAB) and upper facial height (UFHT) (see Table 5-1). These measurements all involve osteometric points that are positioned on the alveolar processes of the maxilla—particularly the landmark prosthion—and are therefore strongly affected by antemortem tooth loss and subsequent alveolar resorption, as well as by dental conditions such as periapical abscesses and advancing periodontal disease. Like most anatomical collections, the Thai skeletal collections from which the current sample is drawn are composed primarily of older adults who display varying degrees of dental pathology, antemortem tooth loss and accompanying alveolar resorption, resulting in a large number of individuals with missing values for one or more of these four variables. By removing BPL, MAL, MAB and UFHT, the sample size is increased from 70 individuals to 385 individuals (262 males/123 females), representing approximately 90% of the original dataset.

Examination of the structure matrix for Discriminant Function 1 (see Table 5-6) shows that these variables contributed only moderately to the separation between males and females in the first discriminant function analysis. Therefore, the cost of removing these variables, in terms of lost morphological information, is far outweighed by the gain of information in terms of the improved representation of the overall pattern of variation within the Thai population that is provided by an enlarged sample size.

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As in the previous analysis, Box‘s M test for the homogeneity of covariance matrices was performed to ensure the data meet the underlying assumptions of DFA. This test failed to reject the null hypothesis of equal covariance matrices at p = 0.153, indicating that this sample conforms to the assumption of equal covariance matrices, in addition to the previous established assumptions of normality and homoscedasticity. Calculation of Wilks‘ Lambda for each of the craniometric variables revealed significant differences between the male and female mean values for all 20 measurements included in the analysis (Table 5-9), indicating that all variables are contributing to the separation between males and females. Of these, 19 variables were significantly different at p < 0.01; orbital height (OBH) was significant at p < 0.05. Review of the structure matrix (Table 5-10) finds that all variables have positive correlation coefficients ranging from 0.655 to 0.136. This range of correlation coefficients extends somewhat higher than the range of coefficients from the first analysis; indeed, six variables (ZYB, NLH, BNL,

MDH, AUB, BBH) have correlation coefficients that are greater than the highest correlation in the first analysis (NLH correlation coefficient: 0.511; see Table 5-6). This indicates that in this second analysis there is a stronger relationship between certain variables and the statistical separation between males and females; these variables therefore have a correspondingly greater influence in the discriminant function.

This is an important practical consideration, because the more a given discriminant function is driven by a few highly correlated variables, the more susceptible it becomes to classification errors due to inaccurate values for those variables. For example, the true measurement value of bizygomatic breadth is often affected by antemortem or perimortem cranial trauma. If an analyst is not cognizant of this distortion and enters an inaccurate value into a discriminant function which is strongly driven by bizygomatic breadth, this small measurement

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error can have a disproportionately large effect on the classification of an unknown individual, increasing the likelihood of a classification error. However, this is not a significant concern for

Discriminant Function 2 due to the overall moderate magnitude of the correlation coefficients.

In general, the suite of variables contributing the most to the separation between males and females in Discriminant Function 2 is nearly identical to that in Discriminant Function 1, with only slight differences in the rank order of the variables. In Discriminant Function 2, bizygomatic breadth (ZYB), nasal height (NLH), cranial base length (BNL), mastoid height

(MDH), biauricular breadth (AUB), basion-bregma height (BBH) and maximum cranial length

(GOL) have the highest correlation coefficients in the structure matrix. As expected,

Discriminant Function 2 is largely driven by measurements of overall cranial vault and facial size, as well as by measurements of the strongly sexually dimorphic mastoid processes. Also as in the first analysis, interorbital breadth (DKB), occipital chord (OCC) and orbital height (OBH) have the lowest correlation coefficients, reinforcing the interpretation that that these craniometric dimension are minimally sexually dimorphic and therefore contribute little to the discrimination between males and females.

The unstandardized canonical coefficients, male and female group means, and sectioning point for Discriminant Function 2 are presented in Table 5-11. Under leave-one-out cross- validation, 82.1% (215 / 262) of males and 83.7% (103 / 123) of females were classified into their correct sex group, for an overall correct classification rate of 82.6% (318 / 385 individuals correctly classified by sex) (Table 5-12; Figure 5-2). This represents a marked increase over the accuracy rate for Discriminant Function 1, indicating a substantial improvement in model performance when the discriminant function is derived from a larger sample size. The accuracy rate for Discriminant Function 2 also falls within the range of correct sex classification rates for

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discriminant functions based on American Whites and Blacks reported by Giles and Elliot

(1963:63). This generally eliminates the concern that the modern Thai population displays a lower degree of sexual dimorphism than the American White and Black populations. Further, it indicates that the lower correct classification rate seen in Discriminant Function 1 was the result of the restricted pattern of craniometric variation in the small sample from which this function was derived.

It also should be noted that these correct classification rates for Discriminant Function 2 were calculated using equal prior probabilities for males and females. However, since the male sample size is twice as large as the female sample size, classification rates were recalculated using informed prior probabilities, based on sample size, of 0.681 for males and 0.319 for females. This led to a 7.6% increase in the correct classification rates for males, to 89.7%, but also resulted in a 10.3% decrease in the correct classification rates for females, to only 73.2%.

Despite a slight increase (1.8%) in overall accuracy rate to 84.4%, the significant reduction in correct classification of females indicates that while the use of informed unequal priors may be statistically justified, in practice it is unwarranted for this equation. Equal prior probabilities of

0.5 for males and females also much more accurately reflect the percentages of males (49.2%) and females (50.8%) in the modern Thai population (based on 2010 Thailand government census data, available at http://203.113.86.149/stat/pk/pk53/ pk_53.pdf, accessed 25 June 2011), and thus are more appropriate for use when analyzing unknown skeletal remains originating from this population.

Discriminant Function 3: Forward-Selected Stepwise Analysis

An alternate and generally more powerful approach to DFA is to use a stepwise selection process to generate a discriminant function incorporating only those variables which collectively account for the majority of separation between the groups under analysis. A discriminant

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function generated through stepwise selection or elimination of variables can be loosely regarded as an optimal solution to the competing analytical demands for the highest possible classification accuracy using the fewest possible variables to account for most of the separation between groups.

Therefore, a third discriminant function for sex determination in the modern Thai population was generated via forward-selected stepwise analysis, using Wilks‘ Lambda as the selection criterion. In order to maximize the sample size at the outset of the analysis, basion- prosthion length (BPL), maxillo-alveolar length (MAL), maxillo-alveolar breadth (MAB) and upper facial height (UFHT) were again excluded from the analysis due to large numbers of missing values. Thus, the forward-selected stepwise analysis started from the same initial conditions as Discriminant Function Analysis 2.

A total of eight variables were selected by the forward stepwise analysis: bizygomatic breadth (ZYB), nasal height (NLH), mastoid height (MDH), cranial base length (BNL), biorbital breadth (EKB), biauricular breadth (AUB), frontal chord (FRC) and minimum frontal breadth

(WFB) (Table 5-13). This suite of variables is generally similar to the craniometric dimensions with the highest correlation coefficients for Discriminant Functions 1 and 2 (see Tables 5-6 and

5-10), a level of internal consistency that would seem to confirm the general validity of all the discriminant functions generated in this study. Interestingly, biorbital breadth, frontal chord and minimum frontal breadth all had relatively low correlations with Discriminant Functions 1 and 2, so their prominence in the stepwise function is intriguing. Their inclusion, however, does preserve the general balance between cranial vault and facial dimensions present in the full measurement battery, and is a clear indication that both regions of the cranium are significantly sexually dimorphic. It is additionally interesting to note that breadths of the vault and face

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appear to be more sexually dimorphic than are length measurements, as this equation includes four breadth measurements but only two lengths. This echoes the pattern seen in the postcranial skeleton, where epiphyseal breadths and other measurements in the transverse plane are more diagnostic for sex determination than measurements in the sagittal plane (Stewart 1979; France

1983; Krogman and İşcan 1986; Spradley and Jantz 2011).

The unstandardized canonical coefficients, male and female group means, and sectioning point for Discriminant Function 3 are presented in Table 5-14. Under leave-one-out cross- validation, 82.0% (228 / 278) of males and 84.7% (111 / 131) of females were classified into their correct sex group, for an overall correct classification rate of 82.9% (339 / 409 individuals2 correctly classified by sex; Table 5-15, Figure 5-3). These correct classification rates for the stepwise-selected discriminant function are approximately equal to those for Discriminant

Function 2, which was based on 20 craniometric variables. Although the stepwise-selection of craniometric variables did not lead to a significant improvement in model performance,

Discriminant Function 3 has the considerable advantage of only requiring eight measurements for a high level of accuracy in sex determination of unknown remains. As such, Discriminant

Function 3 has greater utility in the analysis of remains from forensic contexts, as it can be readily applied to crania in which trauma or postmortem damage preclude measurement of the 20 variables necessary for Discriminant Function 2, but which retain the eight stepwise-selected variables.

2 The male and female sample sizes in the cross-validation sample represent the individuals with recorded measurements for all eight stepwise-selected variables, hence the difference here from the initial analytical sample size of 385 individuals 20 of 24 cranial measurements.

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Discriminant Function Analysis – By Regional Subpopulation

Intrapopulation variation in the modern Thai was also explored using linear discriminant function analysis, in order to assess the nature and extent of any craniometric differences among the three regional subpopulations from Bangkok (Naresuan University collection), Chiang Mai

(Chiang Mai University collection) and Khon Kaen (Khon Kaen University collection). This exploration was undertaken on the premise that any significant differences that exist among these regional subpopulations may be reflective of semi-unique population histories, traceable to relative differences in the degree of genetic influence of various prehistoric and historic non-Thai populations, such as the Mon or Khmer. As with the discriminant function analyses performed for purposes of sex determination, patterns of intrapopulation variation were addressed beginning with broad approaches including the maximum possible number of variables and progressing to more focused, more powerful approaches relying on forward stepwise selection of the most diagnostic variables. Though it might be more efficient, and certainly more expedient, to proceed directly with the stepwise analysis, it is felt that there are nevertheless some important insights into patterns of morphological variation that are revealed by the non-stepwise approaches.

From the outset, analyses of intrapopulation variation in the present sample were somewhat more challenging than analyses of sexual dimorphism. Because of the obvious and demonstrated craniometric differences between males and females, discriminant function analyses of regional subpopulations were separated by sex. The sample sizes for each of the regional subpopulations are already rather unequal (see Table 4-2), with the Chiang Mai sample roughly six times larger than the Khon Kaen sample and 12 times larger than the sample from

Naresuan. However, when the data are parsed by sex the differences in sample size become even more dramatic, and some, such as the Naresuan females (n = 9), drop well below reasonable

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lower limits for sample size in DFA (Feldesman 1997). These small, markedly unequal sample sizes cause further problems for meeting the underlying assumptions of DFA, including univariate and multivariate normality of the craniometric variables and the homogeneity of variances and of covariance matrices. Failure of the data to meet these analyses causes problems throughout all phases of data analysis but is particularly problematic for formal tests of significance, which can become somewhat unpredictable and decidedly unreliable as a result.

Though data transformations and removing individuals from larger groups to make sample sizes more equal can remedy some of these issues, they also result in a certain loss of information and statistical power, and may further complicate interpretation of results (Kachigan 1991;

Feldesman 1997; Field 2009).

Despite the likelihood that these critical assumptions are untenable in this instance, it is still a worthwhile initial exercise to see how the data behave as a whole under DFA, as long as the interpretation of the results does not overreach the limits of the analysis under these non-ideal conditions. This initial exercise also forms an important baseline for evaluating how later adjustments to the dataset have affected the outcomes of the analyses. The three regional subpopulations were first assessed for univariate normality of the individual craniometric variables using the Shapiro-Wilk test (see Table 5-2). Non-normally distributed variables for the

Chiang Mai male sample includes XCB, AUB, UFBR, NLH, NLB, OBB, OBH, EKB, DKB,

OCC, FOL, FOB and MDH; non-normally distributed variables for the Chiang Mai female sample include NLH, NLB, OBB, OBH, EKB, DKB, FOL and FOB. This pattern of non- normality matches almost exactly that for the pooled male and female samples discussed in

Chapter 4 (see Table 4-3). As Chiang Mai males and females are the largest of the six population*sex samples, many of the same statistical influences on the reliability of the Shapiro-

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Wilk test discussed in Chapter 4 obtain in this analysis as well. These influences include large sample sizes and large numbers of ties within the dataset leading to oversensitivity of the

Shapiro-Wilk test to only slight deviations from univariate normality. This oversensitivity is further suggested by the fact that far fewer variables display significant deviations from normality in the much smaller Naresuan and Khon Kaen male and female samples.

Non-normality for MAB in the Khon Kaen sample is certainly the result of only five individuals (3 males, 2 females) having recorded values for this variable; this is of little concern as this variable will be excluded from later analyses due to a high number of individuals with missing measurement values for this variable overall. Other non-normalities, such as foramen magnum length (FOL) in the Khon Kaen male sample and biauricular breadth (AUB) in the

Khon Kaen female sample, are due to flat (platykurtic) distributions with a high number of ties.

The remaining deviations from normality are due primarily to skewed distributions. In general, non-normality is of greater concern when DFA is being used for classification purposes (again, a greater issue where formal tests of significance are concerned), than when it is used merely for exploratory purposes, as here (Feldesman 1997). As such, it is believed that these suspected non-normalities do not represent an insurmountable impediment to further analysis.

The regional Thai subpopulation samples were also tested for homogeneity of variances among the three samples using Levene‘s test for homogeneity of variances (Levene 1960). As was noted above, this method is appropriate for use with this dataset as it is relatively insensitive both to differences in sample size and deviations from normality (Ott and Longnecker 2001). In males, homogeneity of variances was confirmed for all variables except nasal breadth (NLB) and parietal chord (PAC), which fail Levene‘s test at p = 0.031 and p = 0.025, respectively. In

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females, homogeneity of variances among the regional subpopulations was confirmed for all variables except orbital breadth (OBB), which fails Levene‘s test at p = 0.029.

Prior to DFA, a one-way ANOVA was also calculated for each of the 24 craniometric variables, as a preliminary means of exploring which variables differ significantly among the regional Thai subpopulations (Table 5-16). As with the t-tests comparing males and females above, these individual ANOVAs are not being considered in a summary fashion for purposes of hypothesis testing, and it is felt that a Bonferroni correction for the pairwise error rates is not needed in this application. These ANOVAs revealed significant differences at p < 0.05 among group means only for cranial base length (BNL), basion-prosthion length (BPL), minimum frontal breadth (WFB) and foramen magnum breadth (FOB). With the exception of foramen magnum breadth (FOB), these variables meet all assumptions of normality and homogeneity of variance, so the corresponding significant ANOVA results are generally considered valid and reliable. However, given the disparity in sample sizes for the different populations, these significant differences may not actually be meaningful in biological terms or even within the statistical context of the DFA. In fact, the overwhelming number of non-significant ANOVA results, as well as their magnitude (with p-values as high as 0.899), suggest that there is little to no meaningful craniometric differentiation among the three regional subpopulations. This is reinforced by the ANOVA results for female groups, in which none of the craniometric variables were significantly different among the regional subpopulations (p = 0.200 to 0.955; full data not shown).

Unfortunately, ANOVA is one of the few parametric tests that cannot be readily applied to rank-ordered data, as the F-ratio at the core of ANOVA lacks robustness when calculated on data of this type (Conover and Iman 1981:128). Therefore, a rank-transformed ANOVA cannot be

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executed here to further probe the potential impact of the violations of normality and heteroscedasticity on the outcomes of the subsequent multivariate analyses, as with the rank- transformed t-tests described above. Instead, the Kruskal-Wallis test, a non-parametric procedure that is roughly homologous with, but less powerful than, ANOVA (Field 2009), was applied to the rank-ordered data for those variables shown in Table 5-2 to be significantly non- normally distributed. The results of the Kruskal-Wallis test mirror those of the ANOVA, with the test failing to reject the null hypothesis of no differences among the three Thai subpopulations, at significance values ranging from 0.1 to 0.864 for males and 0.85 to 0.904 for females. This similarity of results between the parametric and nonparametric tests suggests that the results of the ANOVA are generally valid and reliable in spite of any underlying violations of normality and/or heteroscedasticity. Furthermore, since violations of these assumptions in non- equal sample sizes typically reduce the statistical power3 of ANOVA and bias the test towards a larger Type I error rate—meaning that the critical p-value would have to be set lower than the standard p < 0.05 to maintain the stringency of the test—the profound non-significance of the majority of the ANOVA results suggests that we need exercise little concern over the possibility that the test is falsely indicating significant differences where none actually exists.

In spite of this discouraging outlook, discriminant function analyses were performed for the multiple purposes of comparison with the ANOVA results and gaining the opportunity to examine the relationships of the population clusters in multivariate space. The first DFA was conducted using 20 craniometric variables, with basion-prosthion length (BPL), maxillo-alveolar breadth (MAB), maxillo-alveolar length (MAL) and upper facial height (UFHT) excluded, as before, due to large numbers of individuals with missing values for one or more of these four

3 ―Statistical power refers to the ability of a test to find an effect that genuinely exists‖ (Field 2009:551)

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variables. In males, this led to effective sample sizes of n = 216 from Chiang Mai, n = 29 from

Khon Kaen, and n = 17 from Naresuan (total n = 262); in females, the working sample sizes were n = 93 (Chiang Mai), n = 23 (Khon Kaen), and n = 7 (Naresuan), for a total sample size of n = 123. Although the small sample sizes for Khon Kaen and especially Naresuan compared to the number of variables in the analysis means that there is a very high risk of overfitting the function in this analysis, this concern will be addressed by a subsequent forward-selected stepwise DFA.

The small sample sizes for the Naresuan samples also preclude the calculation of Box‘s M test for the homogeneity of covariance matrices among the three regional subpopulations.

However, when Box‘s M test calculated only for the Chiang Mai and Khon Kaen male samples, the test fails to reject the null hypothesis of equal matrices at p = 0.176; for the female Chiang

Mai and Khon Kaen samples, the null hypothesis is rejected at only p = 0.048, as the covariances for the Chiang Mai sample are somewhat greater than those for the Khon Kaen sample. Field

(2009:604) and Tabachnick and Fidell (2007) indicate that in analyses where the differences in sample size and covariances are concordant (i.e., larger samples have larger covariances), the probability values for multivariate tests of significance will be more conservative and therefore less likely to indicate significance when this is not the case. When examining patterns of intrapopulation variation, falsely concluding the existence of significant distinctions among subpopulations when no such differences exist would seem to be a more serious error than falsely concluding the opposite. This conservativism as a result of the unequal covariances is therefore deemed reasonable and tolerable with regards to the aims of present analysis.

In any case, the results of the initial DFA clearly and consistently point to the absence of any significant differences among the three regional Thai subpopulations. Calculation of Wilks‘

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Lambda to test the equality of group means demonstrates that the only variables to display significant differences among the three subpopulations are basion-nasion length (BNL) among males (p = 0.033), and frontal chord (FRC) among females (p = 0.038). These results largely echo those of the ANOVA, both in the overwhelming non-significance of the results as a whole, and in the magnitude of that non-significance, with p-values as high as p = 0.818 among males and p = 1.0 in females. In addition to its utility in assessing the discriminatory power of individual variables, a Wilks‘ Lambda value can also be calculated as a test of significance for the discriminatory power of the canonical variates. In this analysis, Wilks‘ Lambda is non- significant at p = .262, indicating that the two discriminant functions4 generated here do not achieve any meaningful differentiation of the three subpopulations in the analysis. For comparison, the Wilks‘ Lambda values for the discriminant functions generated above for sex determination were all significant at p < 0.001, a clear indication of the strong discriminatory power of these functions.

Further, review of the structure matrices for the two discriminant functions finds only low to moderate correlations between the individual craniometric variables and the discriminant function; these range from -0.418 to 0.435 for males and -0.383 to .0481 for females (Table 5-

17). These low correlations are an additional indication that the subpopulations are not well- differentiated by any variables on either canonical axis. This is confirmed by examination the canonical scatter plots for males and females (Figures 5-4 and 5-5), which show only a slight separation of group centroids along the canonical axes and a near-total overlap in scatter among the three subpopulations. Lastly (and thoroughly unsurprising at this point), the results of leave- one-out cross-validation show that the overall correct classification rate for these discriminant

4 As three groups are included in this analysis, two discriminant functions are needed to separate them on horizontal (Function 1) and vertical (Function 2) axes.

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functions is 45.4% for males and 46.3% for females, both of which are only slightly greater than chance (0.33) (Tables 5-18 and 5-19).

An attempt at generating a more powerful discriminant function using forward selection stepwise analysis, again employing Wilks‘ Lambda as the selection criterion, fared no better.

Although the analysis did generate functions which achieved statistically significant separation among the three regional Thai subpopulations, these functions are of little practical utility as they each only contain a single variable. For males, the stepwise analysis selected basion-nasion length (BNL) as the discriminating variable. Selection of this measurement is unsurprising, given that it was identified as differing significantly among the three subpopulations by both the initial ANOVA and the Wilks‘ Lambda test for the equality of group means. Similarly, for females, the stepwise analysis selected frontal chord (FRC) as the discriminating variable, which was also shown by calculation of Wilks‘ Lambda (but not by ANOVA) to differ significantly among the subpopulations. Both of these variables have confirmed normal distributions and homogeneity of variances among the three subpopulations, and both the male and female stepwise discriminant function analyses had non-significant results for Box‘s M test, indicating equal population covariances. Therefore, all the underlying assumptions of DFA are met in this case, and the outcomes of the tests of significance can be taken at face value, that these variables truly do differ significantly among the regional subpopulations.

However, statistical significance does not necessarily equate to biological significance. In discriminant functions for sex determination, where the statistical separation of populations by sex can be traced to real, biologically meaningful differences in absolute size and relative proportions between males and females. Likewise, in discriminant functions analyses for race determination, the separation among different ancestry groups is a function of subtle yet specific

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patterns of shape (and to a lesser degree, size) differences that persist over large numbers of subpopulations across broad geographic areas, and even across broad time intervals. These patterns were established through a variety of evolutionary mechanisms (e.g., genetic drift, founder effects, selective pressures) and can be traced back to the distinct population histories of different ancestry groups since at least the terminal Pleistocene or earliest Holocene (Howells

1973, 1989, 1995, 1997; Pietrusewsky 2000; Roseman 2004; Jantz and Ousley 2005).

In contrast to these established patterns of sexual dimorphism and racial variation, there is no readily apparent, biologically meaningful explanation as to why two measurements, cranial base length in males and frontal chord in females—and these two measurements alone—should differ significantly among regional subpopulations of the modern Thai. The population differences in these single craniometric dimensions cannot be related to general patterns of broad morphological variation among the populations of Southeast Asia, or even of Asia as a whole.

And there is the additional consideration that while the complexity of craniometric variation among modern human populations is ultimately a function of our complex evolutionary history, it is also more proximately a function of the complexity of the polygenic basis of craniometric traits in general. By the very nature of their polygenic inheritance, individual craniometric dimensions do not exist in a vacuum, and the meaningful shape and size differences between sexes and among ancestry groups are driven by coherent patterns of variation across numerous craniometric variables, believed to be underpinned by coherent (if poorly understood) patterns of variation in numerous genes (Howells 1973, 1989, 1995, 1997; Pietrusewsky 1994, 1999, 2000;

Jantz and Ousley 2005; Matsumura 2006). Therefore, if we are using patterns of craniometric variation as a surrogate for patterns of genetic variation, we should not expect meaningful differences between populations to be revealed in terms of a single craniometric variable.

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Though a single measurement can be highly informative in some circumstances (e.g., use of the humeral head diameter for purposes of sex determination), in an analysis of this scope, seeking meaningful patterns of intrapopulation variation to illuminate broader questions of population history, a single craniometric variable has so little real information content as to be practically useless, regardless of its statistical significance.5

In this case, the forward-selected stepwise discriminant functions are more informative for the variables that they do not include than they are for the single variables that they do select. As has been noted, stepwise DFA is generally considered the most powerful approach to developing functions which create meaningful separation between populations in an analysis (Kachigan

1991; Pietrusewsky 2000; Jantz and Ousley 2005). The fact that even this approach was unable to detect a suite of variables with which to differentiate, in any meaningful way, among regional subpopulations of the modern Thai speaks more clearly than any of the prior analyses to the overall similarity of these subpopulations. As if to underscore this, the leave-one-out cross- validation of the stepwise discriminant functions had total correct classification rates among the three regional subpopulations of only 25.0% for males and 24.8% for females—these rates, obviously, are substantially lower than correct classifications due simply to chance, and reiterate the absence of any meaningful separation between the three regional Thai subpopulations.

Collectively, all of these results for the various aspects of discriminant function analysis are so resoundingly unequivocal as to leave little doubt to the conclusion that there is an overall lack of differentiation among the regional Thai subpopulations—even in light of the concerns about the overall normality and homoscedasticity of the dataset and their impacts on the analysis.

However, a single analysis, no matter how convincing, generally does not a robust conclusion

5 And this, of course, is to say nothing of the scientific and methodological unsoundness of purposefully calculating a discriminant function to separate populations on the basis of a single variable.

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make. In order to address the lingering possibility that the compounded effects of the unequal sample sizes and various suspected and confirmed violations of the underlying assumptions of

DFA have rendered the above analyses totally invalid and unreliable—and in spite of the rank transformations and non-parametric tests which suggest this is not the case—a final approach to the discriminant function analysis of intrapopulation variation in the modern Thai was undertaken. Because the majority of these structural concerns stem from the extremely disproportionate size of the Chiang Mai sample relative to the Khon Kaen and Naresuan samples, the former was parsed into smaller datasets comparable in size to these latter, and the forward-selected stepwise discriminant function analysis was rerun using these smaller datasets.

Only the stepwise DFA approach was used, in order to eliminate the probability of overfitting the function to these small sample sizes. Additionally, due to the unmanageably small size of the

Naresuan female sample, this final analytical approach was applied only to the male samples.

This procedure is somewhat similar in principle, though certainly not in scale, to the much more robust bootstrap methods of resampling. In bootstrapping, statistics such as the means and standard deviations of individual variables are calculated on a large number of random subsamples drawn from within the total sample in an iterative fashion; depending on the dataset and the variables, a typical bootstrap might involve 1000 to 10,000 sampling iterations. The means and standard deviations (and their sampling distributions) as calculated over such large numbers of bootstrapped samples 1) can be used to resolve issues of unequal sample sizes and underlying non-normalities and heteroscedasticities; and 2) have been shown to be highly accurate estimates of the true population parameters of central tendency and dispersion, from which formal tests of significance can be calculated (Sokal and Rohlf 1995; Field 2009).

However, bootstrapping can be computationally and interpretively demanding, and with regards

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to the present study, it is reserved as an avenue for future expansion of the analysis of intrapopulation variation within the modern Thai.

For this final analysis, the Chiang Mai male sample was culled to create four datasets of 30 individuals each; these were designated Culled Samples (CS) 1 through 4. Individuals were sorted by accession number and those with missing measurement values were deleted from the initial dataset, leaving 216 individuals in the sample. These 216 individuals represent the same set of individuals used in all of the above analyses once the BPL, MAB, MAL and UFHT variables were excluded. From this pool, individuals were selected for inclusion in the culled samples using a random number table (provided by Ott and Longnecker 2001) to pick 120 numbers between 1 and 216. The 120 individuals in the culled samples represent 56% of the original dataset, which should reduce sampling bias concerns, and should also allow any patterns observed across the analyses of these reduced datasets to be reasonably generalized to the modern Thai dataset as a whole.

Unfortunately, the culled samples did not fully resolve the issues of non-normality and heterogeneity of variances. The non-normal distributions of maximum cranial breadth (XCB) and orbital height (OBH) previously observed in the Naresuan sample and noted above (see

Table 5-2) persist in this analysis, as this sample has not changed. Additionally, at least one and as many as four non-normally distributed variables were revealed in the culled Chiang Mai samples by the Shapiro-Wilk test (Table 5-20). These variables differed by culled dataset, and all but maximum cranial length (GOL; CS 3) and basion-bregma height (BBH; CS 4) had been previously noted for the complete Chiang Mai dataset. The novel non-normality of these two variables is likely due to sampling error introduced during the creation of the culled samples.

Likewise, heterogeneity of variance was revealed by Levene‘s test, for parietal chord (PAC),

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foramen magnum breadth (FOB), and occipital chord (OCC) in CS 2, CS 3, and CS 4 respectively. The heterogeneous variances for PAC had been previously observed in the original dataset, but the varying pattern of heteroscedasticity across the four culled samples is again likely due to sampling error.

Despite this, the collective results of the stepwise DFAs of the four culled samples are generally in accordance with the analyses of the modern Thai dataset in its entirety. As in all previous analyses, cranial base length (BNL) was found to differ significantly among the regional Thai subpopulations in each of the four culled samples, based on the calculation of

Wilks‘ Lambda as a test for the equality of group means (see Table 5-20). This test identified additional significant differences among the three subpopulations in bizygomatic breadth (ZYB) in CS 2, and minimum frontal breadth (WFB) and occipital chord (OCC) in CS 4. Of these, however, only minimum frontal breadth had been previously identified (by a one-way ANOVA applied to the entire dataset; see Table 5-16) as differing significantly among the subpopulations.

These results are echoed in the variables selected by the forward stepwise analysis for inclusion in the discriminant functions. Cranial base length (BNL) was selected in all four stepwise analyses, while the functions generated for CS 4 also included minimum frontal breadth (WFB) and occipital chord (OCC). Curiously, the stepwise analysis of CS 2 also selected interorbital breadth (DKB), rather than bizygomatic breadth (ZYB), for inclusion in the functions, even though DKB was not indicated by the Wilks‘ Lambda test to differ significantly among the populations (at p = 0.2). Review of the scatter plot on the canonical variates for the CS 2 functions (not shown) reveals that the individual data points are aligned in regular rows, suggesting there is an underlying issue with the data, such as too small a sample size.

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The discriminant functions generated for each of the culled datasets all have significant values for Wilks‘ Lambda as a test of the discriminatory power of the model, indicating that the stepwise discriminant functions do differentiate amongst the three regional subpopulations to a statistically significant degree. Raised again, however, is the concern over the distinction between statistical significance and biological meaning. As in the stepwise analysis of the entire

Thai dataset, the discriminant functions for CS 1 and CS 3 contain only a single variable, cranial base length (BNL), which is, as noted above, an inadequate basis for arguments for a meaningful pattern of morphological variation and differentiation among the regional Thai subpopulations, correlated with aspects of population history. If there were to be biologically meaningful differences among these three regional subpopulations, related to local differences in the genetic influence of various prehistoric and historic non-Thai populations, we should expect a pattern of variation across several cranial dimensions simultaneously (Howells 1973, 1989, 1995;

Pietrusewsky 2000, 2006; Matsumura 2006).

Of particular yet cautious interest, therefore, were the 3-variable discriminant functions generated from CS 4, which, with a Wilks‘ Lambda value significant at p < 0.001, appear from a purely statistical standpoint to have discriminatory power comparable to that of the sex determination functions discussed above. Additionally, this model has a 53.2% correct classification rate under cross-validation; this is somewhat greater than chance and is the highest correct classification rate seen in any of the DFAs addressing variation among the regional subpopulations. At this point, then, it would be an easy thing to get carried away by the seeming potential of this model, as it appears superficially to have several analytical strengths. Aside from being the most substantial stepwise function generated in these intrapopulation analyses, the three variables contained in this model (minimum frontal breadth, occipital chord and cranial

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base length) could be argued to approximate a reasonable minimum selection of cranial vault dimensions (i.e., breadth, height, length) with which to capture something of the essential shape and/or size differences which might exist among populations. This could in turn suggest that the population separation generated by this model may be biologically meaningful as well as statistically significant. The discriminant functions appear to achieve slight separation of Chiang

Mai and Naresuan from Khon Kaen on the first canonical axis and Chiang Mai and Khon Kaen from Naresuan on the second axis (Figure 5-6). Examination of the function coefficients indicates that higher scores on the first axis are driven by large measurements for WFB and small measurements for OCC, while higher scores on the second axis are driven by large measurements for BNL and small measurements for WFB (Table 5-21).

However, a deeper and more thorough consideration of this model‘s performance reveals that it is clearly of little real analytical value or power. First, there is marked overlap among the scatters for each of the regional subpopulations, a strong sign that this model is not as powerful in achieving separation among the regional subpopulations as its statistical significance would imply. In order to verify the actual discriminatory potential of this model, a discriminant function analysis using only minimum frontal breadth (WFB), occipital chord (OCC) and cranial base length (BNL) was executed on the entire modern Thai male sample (n = 284, after removing individuals with missing values for these three variables). The covariance matrices for these three variables are equal among the three subpopulations (Box‘s M Test: p = 0.863);

Wilks‘ Lambda indicates that the model does significantly differentiate among the three subpopulations (p = 0.022). However, leave-one-out cross-validation reveals an overall correct classification rate of only 37%—only marginally better than chance (0.33). Thus, despite the initial indications of statistical robusticity and the faintest possibility of biological validity, this

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discriminant function model does not appear to have any real utility for distinguishing among regional subpopulations when applied to the actual modern Thai sample as a whole.

Viewed collectively and at a distance, these discriminant function analyses of the regional

Thai subpopulations appear rather unpredictable and inconsistent in their results, with different suites of variables emphasized by the discriminant functions in each analysis (see Tables 5-17 and 5-20), wide fluctuations in the correct classification rates for each of the subpopulations (see

Tables 5-18 to 5-20 and 5-22), and a frustrating juxtaposition between the high statistical significance of some of the functions and their overall poor performance in classifying individuals into their correct subpopulation. These observations give the impression that, as applied to the Thai subpopulations, the discriminant function analysis itself is ―grasping at straws‖— its analytical power severely undermined by the conflict between the method‘s explicit design to mathematically identify and emphasize morphological distinctions among population samples and its application, in this instance, to a set of subpopulations where no such distinctions actually exist. If there were a true pattern of meaningful differences among these regional Thai subpopulations, we should expect it to have emerged over the course of these several analytical approaches. That it does not, that no larger pattern of population segregation can be detected across these multiple analyses—save for the repeated identification of a single variable, cranial base length, that varies significantly among the subpopulations (but is of dubious physical anthropological significance)—serves as something of a final confirmation of the craniometric homogeneity of the modern Thai population as represented in the current dataset.

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Table 5-1. Means, standard deviations, and number of missing cases, for 24 cranial measurements for the modern Thai sample, pooled by sex. Males (n = 292) Females (n = 137) Variable Mean s.d. # Missing Mean s.d. # Missing GOL 171.77 6.71 3 165.15 5.60 0 XCB 144.51 5.31 13 140.72 5.54 8 ZYB 133.56 4.94 1 126.85 4.67 2 BBH 138.26 4.85 3 133.12 5.18 0 BNL 99.64 4.56 0 94.52 3.44 0 BPL 97.87 4.70 183 94.39 5.43 76 MAB 66.29 3.25 244 63.24 3.34 100 MAL 54.10 2.94 187 52.82 3.69 77 AUB 126.56 5.00 1 121.09 4.67 0 UFHT 72.47 4.13 191 68.31 4.05 82 WFB 93.12 4.67 4 90.73 4.35 2 UFBR 105.36 4.05 0 101.91 4.33 0 NLH 53.02 3.07 5 49.20 2.71 1 NLB 27.19 1.92 9 26.27 1.81 2 OBB 40.19 1.79 0 38.80 1.81 0 OBH 34.53 2.00 0 33.98 1.73 0 EKB 98.57 3.80 0 95.64 3.96 0 DKB 21.28 2.24 0 20.57 2.29 0 FRC 112.59 4.42 2 108.33 4.26 0 PAC 108.35 5.85 4 106.18 6.33 0 OCC 97.61 5.96 4 95.55 5.96 0 FOL 34.89 2.30 1 33.41 1.99 2 FOB 29.89 2.05 0 28.73 1.93 2 MDH 28.41 3.24 2 24.84 2.84 1

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Table 5-2. Means and standard deviations for 24 cranial measurements for the modern Thai sample, by sex and population. Chiang Mai (n = 242 M / 104 F) Khon Kaen (n = 30 M / 24 F) Naresuan (n = 20 M / 9 F) Variable Males s.d. Females s.d. Males s.d. Females s.d. Males s.d. Females s.d. GOL 171.75 6.91 164.99 5.82 172.57 4.98 165.54 5.01 170.72 6.74 165.89 4.76 XCB 144.32a 5.47 140.52 5.84 145.73 3.93 140.92 4.49 144.88 5.15 142.63 4.84 ZYB 133.40 4.87 126.95 4.47 135.43 5.06 126.54 4.96 132.58 5.23 126.43 7.02 BBH 138.36 4.84 133.11 5.28 138.83 4.04 132.50 4.43 136.06 5.78 134.89 5.99 BNL 99.52 4.47 94.43 3.49 101.50 4.41 95.21 3.01 98.25 5.29 93.67 3.94 BPL 98.22 4.62 94.48 5.47 98.90 4.68 94.00 6.60 93.70 3.59 93.75 4.79 MAB 66.22 3.37 63.41 3.35 67.33 1.16 64.50 2.12 66.25 3.40 60.67 3.51 MAL 54.28 2.86 52.89 3.82 54.71 3.35 53.50 2.65 52.27 2.97 50.67 1.53 AUB 126.51 5.05 121.33 4.77 127.67 4.47 120.17 3.88 125.37 5.04 120.78 5.61 UFHT 72.79 4.21 68.54 4.01 71.33 2.73 68.33 2.08 70.64 3.83 65.50 5.45 WFB 92.84 4.63 90.74 4.10 95.07 4.55 91.08 5.36 93.55 4.82 89.78 4.49 UFBR 105.19 3.97 101.88 4.16 106.73 4.01 101.88 4.86 105.35 4.87 102.44 5.34 NLH 53.00 3.05 49.09 2.68 53.27 3.01 49.33 2.55 52.95 3.53 50.11 3.55 NLB 27.14 1.82 26.25 1.79 27.55 2.15 26.29 1.63 27.35 2.64 26.56 2.60 OBB 40.15 1.80 38.91 1.68 40.67 1.61 38.21 1.53 39.95 1.88 39.11 3.41 OBH 34.55 1.98 33.98 1.77 34.37 2.03 34.04 1.78 34.45 2.31 33.78 1.20 EKB 98.40 3.68 95.77 3.87 99.83 3.72 95.00 3.62 98.60 5.00 95.78 5.89 DKB 21.22 2.21 20.56 2.31 21.40 2.31 20.96 2.27 21.85 2.52 19.67 2.00 FRC 112.43 4.52 108.33 4.18 113.73 3.49 107.38 4.28 112.79 4.38 110.89 4.60 PAC 108.34 6.01 105.86 6.59 109.69 3.85 107.12 5.34 107.32 5.05 107.33 5.81 OCC 97.76 5.90 95.69 6.04 95.97 5.48 94.62 6.45 98.32 7.16 96.44 3.36 FOL 34.79 2.28 33.30 1.97 35.50 2.15 33.75 1.89 35.25 2.77 33.78 2.54 FOB 29.76 1.98 28.72 2.02 30.70 2.34 28.92 1.56 30.25 2.20 28.44 1.81 MDH 28.48 3.22 24.92 2.94 28.53 3.59 24.61 2.41 27.40 2.93 24.44 2.88 a Red values indicate the variable fails the Shapiro-Wilk test for normality at p < 0.05.

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Table 5-3. Independent samples t-tests for equality of group means, by sex, for 24 craniometric variables. Levene's Test t-test for Equality of Means 95% Confidence Degrees of Interval (C.I.) of the Freedom Sig. Mean Std. Error Difference F Sig. t (df) (2-tailed) Difference Difference Lower Upper GOL Equal variances 3.817 .051 10.011 424 .000 6.622 .662 5.322 7.922 assumed XCB Equal variances 1.774 .184 6.601 406 .000 3.784 .573 2.657 4.911 assumed ZYB Equal variances .357 .550 13.258 424 .000 6.708 .506 5.714 7.703 assumed BBH Equal variances .494 .483 10.012 424 .000 5.146 .514 4.136 6.157 assumed BNL Equal variances 11.298 .001 11.674 427 .000 5.122 .439 4.260 5.985 assumed Equal variances 12.906 344.034 .000 5.122 .397 4.342 5.903 not assumed BPL Equal variances 1.172 .281 4.375 168 .000 3.478 .795 1.909 5.048 assumed MAB Equal variances .020 .888 4.239 83 .000 3.048 .719 1.618 4.479 assumed MAL Equal variances 2.680 .104 2.446 163 .015 1.279 .523 .247 2.311 assumed AUB Equal variances .410 .522 10.777 426 .000 5.469 .507 4.472 6.467 assumed UFHT Equal variances .005 .946 6.042 154 .000 4.156 .688 2.797 5.515 assumed WFB Equal variances .829 .363 5.005 421 .000 2.385 .476 1.448 3.321 assumed UFBR Equal variances 1.518 .219 8.026 427 .000 3.444 .429 2.600 4.287 assumed NLH Equal variances 2.703 .101 12.423 421 .000 3.826 .308 3.221 4.431 assumed NLB Equal variances .150 .699 4.663 416 .000 .920 .197 .532 1.308 assumed OBB Equal variances .014 .905 7.436 427 .000 1.385 .186 1.019 1.752 assumed OBH Equal variances 3.280 .071 2.759 427 .006 .549 .199 .158 .941 assumed EKB Equal variances .770 .381 7.348 427 .000 2.930 .399 2.146 3.714 assumed DKB Equal variances .308 .579 3.063 427 .002 .715 .233 .256 1.174 assumed FRC Equal variances .004 .952 9.391 425 .000 4.258 .453 3.367 5.149 assumed PAC Equal variances 2.225 .137 3.608 422 .000 2.232 .619 1.016 3.449 assumed OCC Equal variances .195 .659 3.325 423 .001 2.056 .618 .841 3.272 assumed

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Table 5-3. Continued. Levene's Test t-test for Equality of Means 95% C.I. of the Sig. Mean Std. Error Difference F Sig. t df (2-tailed) Difference Difference Lower Upper FOL Equal variances 3.245 .072 6.423 424 .000 1.479 .230 1.026 1.931 assumed FOB Equal variances .667 .415 5.507 425 .000 1.154 .209 .742 1.565 assumed MDH Equal variances 3.141 .077 11.028 424 .000 3.572 .324 2.935 4.209 assumed

Table 5-4. Rank transformed independent samples t-tests for equality of group means, by sex, for 14 craniometric variables. Levene's Test t-test for Equality of Means 95% C.I. of the

Sig. Mean Std. Error Difference F Sig. t df (2-tailed) Difference Difference Lower Upper XCB .628 .428 6.477 406 .000 77.392 11.948 53.904 100.880 AUB 2.368 .125 10.760 426 .000 122.242 11.361 99.911 144.573 UFBR .607 .436 7.906 427 .000 94.682 11.976 71.142 118.222 NLH 9.628 .002 12.798 421 .000 137.839 10.770 116.669 159.010 NLB .085 .771 4.625 416 .000 56.308 12.176 32.374 80.242 OBB .354 .552 7.380 427 .000 88.076 11.934 64.618 111.533 OBH 4.026 .045 3.132 427 .002 39.266 12.538 14.623 63.909 EKB .110 .741 7.115 427 .000 86.204 12.115 62.391 110.017 DKB 1.445 .230 3.172 427 .002 39.909 12.582 15.179 64.639 FRC .917 .339 9.098 425 .000 106.385 11.693 83.402 129.368 OCC .138 .710 2.870 423 .004 36.219 12.621 11.411 61.028 FOL 3.126 .078 6.726 424 .000 81.350 12.095 57.578 105.123 FOB 1.573 .210 5.472 425 .000 67.164 12.274 43.039 91.290 MDH 7.987 .005 11.372 424 .000 127.017 11.169 105.063 148.971

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Table 5-5. Wilks‘ Lambda test for equality of group means between males and females in Discriminant Function Analysis (DFA) 1. Wilks'

Lambda F-value df1 df2 p = GOL .780 19.201 1 68 .000 XCB .915 6.290 1 68 .015 ZYB .732 24.842 1 68 .000 BBH .805 16.481 1 68 .000 BNL .757 21.865 1 68 .000 BPL .831 13.870 1 68 .000 MAB .813 15.663 1 68 .000 MAL .876 9.627 1 68 .003 AUB .815 15.392 1 68 .000 UFHT .776 19.679 1 68 .000 WFB .928 5.265 1 68 .025 UFBR .872 10.018 1 68 .002 NLH .703 28.760 1 68 .000 NLB .937 4.596 1 68 .036 OBB .857 11.361 1 68 .001 OBH .964 2.575 1 68 .113 EKB .935 4.747 1 68 .033 DKB 1.000 .016 1 68 .901 FRC .897 7.802 1 68 .007 PAC .943 4.079 1 68 .047 OCC .964 2.546 1 68 .115 FOL .931 5.006 1 68 .029 FOB .974 1.791 1 68 .185 MDH .768 20.575 1 68 .000

Table 5-6. Structure matrix for Discriminant Function 1. Correlation Correlation Variable coefficient Variable coefficient NLH .511 MAL .296 ZYB .475 FRC .266 BNL .446 XCB .239 MDH .433 WFB .219 UFHT .423 FOL .213 GOL .418 EKB .208 BBH .387 NLB .204 MAB .377 PAC .193 AUB .374 OBH .153 BPL .355 OCC .152 OBB .321 FOB .128 UFBR .302 DKB .012

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Table 5-7. Unstandardized canonical coefficients for Discriminant Function 1. Variable Coefficient Variable Coefficient GOL .060 NLH .165 XCB -.036 NLB .224 Male group mean: -1.150 ZYB .143 OBB -.109 Female group mean: -1.366 BBH .099 OBH .117 BNL .122 EKB -.278 Sectioning point: BPL -.099 DKB -.175 Females < -0.108 < Males MAB -.044 FRC -.009 MAL .264 PAC -.047 AUB .023 OCC -.089 UFHT -.047 FOL .034 WFB -.055 FOB -.094 UFBR .151 MDH .127 (Constant) -31.270

Figure 5-1. Frequency distribution of male and female discriminant function scores for Discriminant Function 1.

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Table 5-8. Classification results for Discriminant Function 1. Predicted Group Membership Total Sex Males Females Resubstitution Count M 34 4 38 F 2 30 32 % M 89.5 10.5 100.0 F 6.2 93.8 100.0 Cross-validated Count M 28 10 38 F 6 26 32 % M 73.7 26.3 100.0 F 18.7 81.3 100.0 91.4% of resubstituted grouped cases correctly classified. 77.1% of cross-validated grouped cases correctly classified.

Table 5-9. Wilks‘ Lambda test for equality of group means between males and females in Discriminant Function Analysis 2. Wilks'

Lambda F-value df1 df2 p = GOL .815 86.719 1 383 .000 XCB .907 39.199 1 383 .000 ZYB .722 147.723 1 383 .000 BBH .808 90.922 1 383 .000 BNL .761 120.148 1 383 .000 AUB .807 91.483 1 383 .000 WFB .950 20.322 1 383 .000 UFBR .873 55.492 1 383 .000 NLH .735 138.023 1 383 .000 NLB .949 20.419 1 383 .000 OBB .891 47.092 1 383 .000 OBH .984 6.370 1 383 .012 EKB .892 46.483 1 383 .000 DKB .976 9.373 1 383 .002 FRC .831 78.057 1 383 .000 PAC .973 10.580 1 383 .001 OCC .979 8.357 1 383 .004 FOL .906 39.862 1 383 .000 FOB .935 26.477 1 383 .000 MDH .776 110.644 1 383 .000

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Table 5-10. Structure matrix for Discriminant Function 2. Correlation Correlation Variable coefficient Variable coefficient ZYB .655 EKB .367 NLH .633 FOL .340 BNL .591 XCB .337 MDH .567 FOB .277 AUB .515 NLB .243 BBH .514 WFB .243 GOL .502 PAC .175 FRC .476 DKB .165 UFBR .401 OCC .156 OBB .370 OBH .136

Table 5-11. Unstandardized canonical coefficients for Discriminant Function 2. Variable Coefficient Variable Coefficient GOL .023 OBB .225 XCB -.002 OBH -.052 Male group mean: -0.648 ZYB .183 EKB -.208 Female group mean: -1.380 BBH .030 DKB .139 BNL .064 FRC .022 Sectioning point: AUB -.070 PAC -.004 Females < -0.366 < Males WFB -.063 OCC -.029 UFBR .008 FOL .016 NLH .119 FOB -.005 NLB -.028 MDH .126 GOL .023 (Constant) -22.677

Table 5-12. Classification results for Discriminant Function 2. Predicted Group Membership Total Sex Males Females Resubstitution Count M 220 42 262 F 17 106 123 % M 84.0 16.0 100.0 F 13.8 86.2 100.0 Cross-validated Count M 215 47 262 F 20 103 123 % M 82.1 17.9 100.0 F 16.3 83.7 100.0 84.7% of original grouped cases correctly classified. 82.6% of cross-validated grouped cases correctly classified.

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Figure 5-2. Frequency distribution of male and female discriminant function scores for Discriminant Function 2.

Table 5-13. Stepwise selection matrix for variables included in Discriminant Function 3. Wilks‘ Step Entered Lambdaa df1 df2 df3 F-ratiob df1 df2 Sig. 1 ZYB .722 1 1 383.000 147.723 1 383.000 .000 2 NLH .651 2 1 383.000 102.507 2 382.000 .000 3 MDH .609 3 1 383.000 81.556 3 381.000 .000 4 BNL .586 4 1 383.000 67.053 4 380.000 .000 5 EKB .567 5 1 383.000 57.863 5 379.000 .000 6 AUB .559 6 1 383.000 49.795 6 378.000 .000 7 FRC .551 7 1 383.000 43.911 7 377.000 .000 8 WFB .544 8 1 383.000 39.387 8 376.000 .000 a At each step, the variable that minimizes the overall Wilks' Lambda is entered. b Pairwise group comparisons

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Table 5-14. Unstandardized canonical coefficients for Discriminant Function 3. Variable Coefficient ZYB .183 BNL .101 Male group mean: -0.626 AUB -.075 Female group mean: -1.333 WFB -.050 NLH .100 Sectioning point: EKB -.091 Females < -0.354 < Males FRC .048 MDH .126 (Constant) -24.996

Table 5-15. Classification results for Discriminant Function 3. Predicted Group Membership Total Sex Males Females Resubstitution Count M 230 48 278 F 17 114 131 % M 82.7 17.3 100.0 F 13.0 87.0 100.0 Cross-validated Count M 228 50 278 F 20 111 131 % M 82.0 18.0 100.0 F 15.3 84.7 100.0 84.1% of original grouped cases correctly classified. 82.9% of cross-validated grouped cases correctly classified.

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Figure 5-3. Frequency distribution of male and female discriminant function scores for Discriminant Function 3.

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Table 5-16. One-way analysis of variance (ANOVA) results comparing from three regional Thai subpopulations, males only. Sum of Variable Squares df Mean square F Sig. GOL Between Groups 38.929 2 19.465 .430 .651 Within Groups 12944.538 286 45.261 Total 12983.467 288 XCB Between Groups 55.714 2 27.857 .987 .374 Within Groups 7786.028 276 28.210 Total 7841.742 278 ZYB Between Groups 129.385 2 64.693 2.678 .070 Within Groups 6958.312 288 24.161 Total 7087.698 290 BBH Between Groups 99.592 2 49.796 2.137 .120 Within Groups 6664.422 286 23.302 Total 6764.014 288 BNL Between Groups 145.642 2 72.821 3.559 .030 Within Groups 5913.601 289 20.462 Total 6059.243 291 BPL Between Groups 195.696 2 97.848 4.739 .011 Within Groups 2188.506 106 20.646 Total 2384.202 108 MAB Between Groups 3.476 2 1.738 .159 .854 Within Groups 492.441 45 10.943 Total 495.917 47 MAL Between Groups 42.058 2 21.029 2.503 .087 Within Groups 856.990 102 8.402 Total 899.048 104 AUB Between Groups 64.264 2 32.132 1.288 .277 Within Groups 7183.551 288 24.943 Total 7247.814 290 UFHT Between Groups 53.107 2 26.554 1.571 .213 Within Groups 1656.022 98 16.898 Total 1709.129 100 WFB Between Groups 136.560 2 68.280 3.183 .043 Within Groups 6113.426 285 21.451 Total 6249.986 287 UFBR Between Groups 63.910 2 31.955 1.960 .143 Within Groups 4711.049 289 16.301 Total 4774.959 291

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Table 5-16. Continued. Sum of Variable Squares df Mean square F Sig. NLH Between Groups 2.013 2 1.006 .106 .899 Within Groups 2692.817 284 9.482 Total 2694.829 286 NLB Between Groups 4.965 2 2.482 .671 .512 Within Groups 1035.346 280 3.698 Total 1040.311 282 OBB Between Groups 8.379 2 4.190 1.307 .272 Within Groups 926.261 289 3.205 Total 934.640 291 OBH Between Groups 1.063 2 .531 .131 .877 Within Groups 1167.718 289 4.041 Total 1168.781 291 EKB Between Groups 54.483 2 27.241 1.899 .152 Within Groups 4145.281 289 14.344 Total 4199.764 291 DKB Between Groups 7.707 2 3.854 .768 .465 Within Groups 1449.700 289 5.016 Total 1457.408 291 FRC Between Groups 46.341 2 23.171 1.185 .307 Within Groups 5610.004 287 19.547 Total 5656.345 289 PAC Between Groups 71.443 2 35.721 1.072 .344 Within Groups 9463.860 284 33.323 Total 9535.303 286 OCC Between Groups 95.967 2 47.983 1.356 .259 Within Groups 10088.478 285 35.398 Total 10184.444 287 FOL Between Groups 16.240 2 8.120 1.535 .217 Within Groups 1523.457 288 5.290 Total 1539.698 290 FOB Between Groups 26.605 2 13.302 3.207 .042 Within Groups 1198.666 289 4.148 Total 1225.271 291 MDH Between Groups 22.006 2 11.003 1.050 .351 Within Groups 3008.163 287 10.481 Total 3030.169 289

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Table 5-17. Structure matrices for discriminant function analysis of regional subpopulations, by sex, based on 20 craniometric variables. Function Function Males 1 2 Females 1 2 FOB .435* -.016 OBB -.383* -.085 WFB .395* .026 AUB -.212* -.131 ZYB .345* -.262 EKB -.211* -.126 FOL .316* .104 BBH -.159* .053 UFBR .305* -.031 OCC -.122* .046 FRC .303* .014 FOL .121* .028 EKB .297* .008 ZYB -.084* -.081 OCC -.272* .171 UFBR -.040* -.037 OBB .239* -.118 OBH .030* -.010 XCB .226* .042 FRC -.291 .481* NLB .198* .110 DKB .116 -.252* GOL .114* -.109 BNL .185 -.244* OBH -.112* .045 GOL .057 .230* BBH .049 -.418* XCB .043 .213* BNL .368 -.384* FOB .065 -.188* MDH -.018 -.348* PAC .130 .155* AUB .194 -.281* NLH .034 .108* DKB .040 .276* MDH -.087 -.101* NLH .112 -.142* WFB .022 -.058* PAC .067 -.122* NLB -.004 .006* * largest absolute correlation between each variable and any discriminant function.

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Figure 5-4. Scatter plot of discriminant function scores for males, by regional subpopulation, for discriminant function analysis using 20 craniometric variables.

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Figure 5-5. Scatter plot of discriminant function scores for females, by regional subpopulation, for discriminant function analysis using 20 craniometric variables.

Table 5-18. Classification results for males, by regional subpopulation, from discriminant function analysis using 20 variables. Predicted Group Membership POP CM KK NU Total Resubstitution Count CM 118 58 40 216 KK 5 18 6 29 NU 5 2 10 17 % CM 54.6 26.9 18.5 100.0 KK 17.2 62.1 20.7 100.0 NU 29.4 11.8 58.8 100.0 Cross-validateda Count CM 102 66 48 216 KK 10 13 6 29 NU 8 5 4 17 % CM 47.2 30.6 22.2 100.0 KK 34.5 44.8 20.7 100.0 NU 47.1 29.4 23.5 100.0 55.7% of original grouped cases correctly classified. 45.4% of cross-validated grouped cases correctly classified.

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Table 5-19. Classification results for females, by regional subpopulation, from discriminant function analysis using 20 variables. Predicted Group Membership POP CM KK NU Total Original Count CM 54 20 19 93 KK 7 15 1 23 NU 1 1 5 7 % CM 58.1 21.5 20.4 100.0 KK 30.4 65.2 4.3 100.0 NU 14.3 14.3 71.4 100.0 Cross-validateda Count CM 44 25 24 93 KK 11 11 1 23 NU 4 1 2 7 % CM 47.3 26.9 25.8 100.0 KK 47.8 47.8 4.3 100.0 NU 57.1 14.3 28.6 100.0 60.2% of original grouped cases correctly classified. 46.3% of cross-validated grouped cases correctly classified.

Table 5-20. Summary of stepwise discriminant function analyses of Culled Samples (CS) 1 through 4 (males only). CS 1 CS 2 CS 3 CS 4 Non-normal variables: Chiang Mai FOL NLB GOL BBH EKB XCB OBH MDH OBB DKB OCC Khon Kaen XCB XCB XCB XCB OBH OBH OBH OBH Naresuan ------Levene‘s test p ≤ 0.05 -- PAC FOB OCC (≠ variance) Wilks‘ Lambda p ≤ 0.05 BNL BNL BNL BNL (≠ means) ZYB WFB OCC Variable selected: BNL BNL BNL WFB (in sequence) DKB OCC BNL Wilks‘ Lambda p = 0.036 p = 0.008 p = 0.008 p = 0.001 (test of model) Cross-Validation 42.5 52.5 42.5 53.2 (% correct)

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Figure 5-6. Scatter plot of discriminant function scores for males in Culled Sample 4, by regional subpopulation, for stepwise discriminant function analysis selecting 3 variables.

Table 5-21. Unstandardized canonical coefficients for stepwise discriminant function analysis of Culled Sample 4. Function 1 Function 2 Variable Coefficient Coefficient Function 1 Function 2 BNL .012 .241 WFB .177 -.170 CM group centroid: -0.501 0.226 OCC -.128 -.039 KK group centroid: 0.605 0.132 (Constant) -5.222 -4.543 NU group centroid: -0.111 -0.617

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Table 5-22. Cross-validated classification results for stepwise discriminant function analyses of Culled Samples 1 through 4. Predicted Group Membership POP CM KK NU Total Culled Sample 1 Count CM 4 12 14 30 KK 3 18 9 30 NU 2 6 12 20 % CM 13.3 40.0 46.7 100.0 KK 10.0 60.0 30.0 100.0 NU 10.0 30.0 60.0 100.0 Culled Sample 2 Count CM 13 9 8 30 KK 6 17 7 30 NU 4 4 12 20 % CM 43.3 30.0 26.7 100.0 KK 20.0 56.7 23.3 100.0 NU 20.0 20.0 60.0 100.0 Culled Sample 3 Count CM 3 14 13 30 KK 2 21 7 30 NU 2 8 10 20 % CM 10.0 46.7 43.3 100.0 KK 6.7 70.0 23.3 100.0 NU 10.0 40.0 50.0 100.0 Culled Sample 4 Count CM 15 7 8 30 KK 4 19 7 30 NU 4 7 8 19 % CM 50.0 23.3 26.7 100.0 KK 13.3 63.3 23.3 100.0 NU 21.1 36.8 42.1 100.0

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CHAPTER 6 SUMMARY AND CONCLUSIONS

Know then thyself, presume not God to scan; The proper study of Mankind is Man. Plac’d on this isthmus of a middle state, A being darkly wise, and rudely great: With too much knowledge for the Sceptic side, With too much weakness for the Stoic’s pride… In doubt his Mind or Body to prefer, Born but to die, and reas’ning but to err. -Alexander Pope An Essay on Man (1733-34)

In physical anthropology, the comparison of proximate and ultimate levels of analysis is a familiar construct that pervades nearly all scientific enquiry within the field, and though the analogy is imperfect, this dissertation is no exception. The foregoing research was initially motivated by forensic anthropology‘s proximate need for a representative craniometric dataset for the modern Thai population, from which to generate population specific standards for determination of the biological profile in unknown skeletal remains from this population. This objective has, in considerable measure, been accomplished. A dataset of over 400 individuals has been compiled, and from this, discriminant functions have been generated that can be used immediately by forensic anthropologists in Thailand for the determination of sex in unknown remains within their country. The eventual addition of these data to the FORDISC forensic database will introduce the first Southeast Asian female sample (and only the second sample of

Asian females overall) to the database, and will permit all forensic anthropologists to apply this dataset—and its representation of a small, yet important portion of global human craniometric diversity—to their own casework, improving our ability to correctly identify individuals who fall outside the typical forensic anthropologist‘s scope of experience. Intended future research will investigate whether these functions can also be validly generalized and applied to other populations Southeast Asian populations as well. Given the Thais‘ unique and complex

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population history, they may be suitably representative of a much greater degree of the total span of human craniometric variation in the collective Southeast Asian populations than is currently appreciated.

Additional impetus for this research was derived from the broader questions regarding the ultimate origins of the Thai and the other modern populations of Southeast Asia. For here, in

Southeast Asia, we find a compelling distillation of one of the essential debates in all of anthropology: what roles, and of what magnitude, have the in situ evolution of endogenous populations and the migratory influences of exogenous populations played in the development and structure of the populations of a region as we know them today. To have any hope of advancing towards a resolution of this debate, we must delve deeper into the available skeletal material, and consider the salient populations in greater detail than we have previously, to include considerations of patterns of regional and temporal variation possibly extant within them.

In this regard, this dissertation has also achieved a measure of success, albeit of somewhat lesser direct impact than the forensic applications noted above, but hopefully still of some significance in its implications for our understanding of Southeast Asian population history.

As has been noted in previous chapters, the hypothesis that significant morphological differences may exist among regional subpopulations within the modern Thai population emerges proximately from first-hand observations of the phenotypic diversity extant within the living population of Thailand, and is reinforced by the perceptions of this diversity expressed by the living Thais themselves. An understanding of the archaeology and linguistics of Southeast

Asia leads almost ineluctably to the hypothetical corollary that this perceived diversity, if it is in fact real, should be deeply rooted in the intricacies of Southeast Asian history and prehistory, in the procession of human civilizations and the peregrinations of human populations across the

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landscape. The plausibility of this corollary is in turn reinforced by the varied cultural identities which exist within the modern Thai population, and by the differences in dialect and custom which characterize the populations of each of Thailand‘s internal, informal sociopolitical regions—all of which appear, at least superficially, to be correlated with aspects of Thailand‘s geography and its modern and ancient history (Kaplan 1981; Rinehart 1981; Higham and

Thosarat 1998; Baker and Phongpaichit 2005).

Thus, the populations in the North and Northeast of Thailand are reported to look more

―classically‖ Asian, with lighter skin, flatter and broader faces, prominent cheekbones, low nasal bridges and noticeable epicanthic folds, echoing the proximity of these regions to, and the accordingly greater influence of, the peoples of East Asia. Chiang Mai and the surrounding regions of Northern Thailand look to the ancient kingdom of Lanna, which itself had its capital at

Chiang Mai, as their legendary antecessor. The pride of this place, first of the Tai kingdoms in the ur-Thailand, always beyond the reach of great Angkor, is evoked by the prominent display throughout the region of the unique Lanna script and architectural style (Figures 6-1 and 6-2)

(Kaplan 1981; Rinehart 1981; Higham and Thosarat 1998; Baker and Phongpaichit 2005). Khon

Kaen is located on the Khorat Plateau in Northeastern Thailand, in the heart of what is perhaps the most culturally distinctive region of the country, Isan. Here, unique styles of food, dress, music and custom combine with a singular, predominantly Lao-influenced dialect (also known as

Isan) to create a regional culture which is quite distinct from—and quite proud of its distinction from—mainstream Thai culture. But the Khorat Plateau—ever the thoroughfare for the movements of peoples and the expansion of empires—is home not just to the Isan culture (which shares ancient, common Daic roots with the northern Tai), but also to peoples who trace the paths of their descent to the ancient Austroasiatic Khmer of Angkor. Central Thailand,

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birthplace of the modern kingdom in all its incarnations—beginning with Sukhothai and ending with Bangkok, reborn from the ashes of Ayutthaya—and for so long the point of entry for so much of the external influences from the great civilizations of China and India, is home to a cosmopolitan population of intermediate phenotype, neither wholly Asian nor wholly Indic in character, with perceived darker skin, stronger noses and sharper features than their northern counterparts. The populations of Southern Thailand, not addressed in the present study due to the social and political upheaval that precluded travel to the region, are known to be heavily influenced, culturally and biologically, by their Muslim Malay neighbors and the other

Austronesian peoples of adjacent regions of island Southeast Asia (Kaplan 1981; Rinehart 1981;

Higham and Thosarat 1998; Baker and Phongpaichit 2005).

With such seemingly direct correspondences between the known ebbs and tides of peoples, cultures and languages, into and across Thailand over the course of millennia, and the human biogeography of modern Thailand‘s variegated regional subpopulations, the notion that the Thai perceive meaningful phenotypic distinctions within their own population becomes almost intuitively obvious. From there, the plausibility of real, discernable skeletal morphological differences among the subpopulations seems equally intuitive. And yet, as the results of the foregoing analyses have made abundantly clear, no such morphological distinctions can be detected. Given the analytical power of discriminant function analyses for teasing apart subtle patterns of human morphological variation (Howells 1973), and the breadth of the skeletal sample under analysis here, these findings of the absence of any clear patterns of differentiation among the regional subpopulations of the modern Thai cannot go unexplained.

There is, of course, always the possibility that such patterns of differentiation never existed to begin with, and that the population of Thailand has always been relatively homogenous. This

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is, of course, a thoroughly unsatisfactory answer. To suggest that a region characterized by such complexity and reticulation in its population history has been craniometrically homogenous throughout that entire history is to disregard all that we understand about the patterning of human craniometric diversity, along with all that we know about the prehistoric and historic development of the populations of Southeast Asia (Kenyhurcz et al. 2010). It is simply untenable to argue that, all else being equal, the myriad peoples—the Mon, Khmer, Cham, Tai,

Lao, Vietnamese, Chinese, Indian, Malay, the unnamed prehistoric populations of Austronesians and Austroasiatics, and the Australo-Melanesian hunter-gatherers before them—who have contributed, in greater or lesser degrees, to the pool of modern Thai genetic, linguistic and cultural diversity, have left no corresponding pattern of discernable craniometric diversity within this population. Something is clearly missing.

As was stated at the outset of this dissertation, to understand the biological history of the

Thai population it is essential that we first understand the cultural history of the people. For herein lies the most plausible explanation for the pattern (or perhaps lack of patterning) of craniometric variation observed in the modern Thai. According to historical accounts, the battles for political and cultural supremacy that roiled among the early Tai kingdoms of Lanna,

Sukhothai and Ayutthaya, and between Ayutthaya and Angkor in the fourteenth and fifteenth centuries, were generally accompanied by the displacement of large segments the defeated kingdom‘s population, as the people fled the violence or were carried off as prisoners and slaves of the conquering armies. This is particularly true of the sacking of Ayutthaya by the Burmese in

AD 1559 and again in AD 1767, the latter of which reduced the entire city to rubble and left it completely abandoned. A succession of similar attacks on the great Lanna capital of Chiang Mai likewise left that city destroyed and deserted, ―reduced to a village, and…not resettled fully until

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the 1870s‖ (Baker and Phongpaichit 2005:23). By the time the Burmese were finally expelled from Siam in the early 1800s, a similar fate had befallen most of the towns and cities once under

Ayutthaya‘s sway (Rinehart 1981; Baker and Phongpaichit 2005).

Over the last quarter of the eighteenth century and the first of the nineteenth century, as the

Thai fought to contain the Burmese and reclaim their lands, areas that had formerly been only loosely tributary to Ayutthaya—such as Lanna and the Khorat frontier—were brought under the firm control of the new Siamese capital at Bangkok. But reclaiming their devastated cities was only the first step in rebuilding what would in short order become the modern nation of Thailand.

Siam also needed to reclaim its people, driven off and carried away during the sieges. To do this, the Siamese armies pushed beyond the boundaries of Ayutthaya‘s sphere of influence to the north and east and engaged in their own wars of aggression against the Lao and Khmer, conquering the former in 1828 and the latter in 1833. After each conquest, thousands of people—Thai and non-Thai, commoners and elites alike—were again forcibly displaced from the annexed territories and resettled throughout Siam, to rebuild its cities and replant its fields

(Baker and Phongpaichit 2005:26-29). As Baker and Phongpaichit (2005:29) summarize,

In the 1770s and 1780s, Taksin‘s1 armies captured many thousands of Lanna Yuan, Lao Wiang, Lao Phuan, Black Tai, and Khmer. The southern expeditions brought back several thousand Malays. Possibly 30,000-40,000 Mons voluntarily migrated to Siam. In the early 1800s, the Bangkok and Lanna troops went further north to seize Khoen, Lu, and Shan. After the 1827 war against Vientiane [the Lao capital], over 150,000 were captured and some 50,000 marched down to the Chao Phraya basin. In the 1830s, the Bangkok armies made six expeditions into the Lao regions, depopulating the left bank of the Mekong, and bringing back Lao Phuan from the Plain of Jars, Tai Dam from Sipsongchutai, Khmer, and Vietnamese.

1 Taksin was the first King of Siam in the post-Ayutthaya era, who established an interim capital at Thonburi, on the west bank of the Chao Phraya river. He was deposed in a coup in 1782, whereupon the capital was shifted east across the river to Bangkok, and the Chakri Dynasty—whence all subsequent kings, up to an including the present King of Thailand, Bhumipol Adulyadej (Rama IX)—was established.

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In this tumult of war and displacement, any remaining threads tying the local subpopulations of Siam (and adjacent regions of Southeast Asia) to their immediate prehistoric and historic antecessors would almost certainly have been broken. While many displaced Thais surely returned to rebuild and resettle the cities of their ancestry, thus reestablishing some semblance of regional ethnic and biological continuity with previous eras, the population of Siam was, in the main, remade anew as the ―forced movements of people transformed the ethnic mix in the Chao Phraya plain‖ (Baker and Phongpaichit 2005:26). The severing of local and regional ties and the great shuffle of displaced and replaced populations across the Chao Phraya basin and

Khorat Plateau therefore must also have dramatically altered the structure of human biological variation within Thailand. The post-reconstruction population of Siam likely contained a greater range of individual morphological diversity, due to the forced influx of thousands of non-Thai, but this diversity must have been less patterned along regional and historic population lines, therefore making the modern population more homogenous overall.

Throughout the nineteenth and twentieth centuries, this physical homogeneity would have been sustained and reinforced—and in all likelihood, deepened—by further population movements, first out of the urban areas and forested lands and into the rural provinces along the rapidly advancing modern agricultural frontier, and then later, back to the cities during the recent era of industrialization and urbanization (Baker and Phongpaichit 2005). Yet throughout nearly all of this change in the structure of Thailand‘s population, continuing right up to the present day, areas such as Isan and Lanna have still cleaved tightly to their unique regional ethnic and linguistic identities. It is hard to say to what extent these regional cultural complexes were disrupted by the earlier population upheavals, but like the population of Thailand itself, these cultural identities must to some extent have been reclaimed, reconstructed, and reimagined by

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the newly resettled populations following the ouster of the Burmese. In becoming the modern nation of Thailand, these regional cultural identities would be alternately celebrated as a measure of the cosmopolitan breadth of Siam or subdued in order to present to the world a singular Thai nation unified in language, custom and heritage (Baker and Phongpaichit 2005).

Today, Thailand has achieved a delicate balance between both aspects of its character, acknowledging the richness of the manifold cultural influences and identities that shape Thai society as a whole, all the while asserting the overarching unity of a single Thai nation. And both aspects are of great import to the collective self-image of the modern Thai. On one hand, internal unity has always been the means by which the Thai have resisted external pressure, whether from conquest, colonialism or globalization. Not surprisingly, it is this same desire for unity within the modern Thai society that also motivates their desire for a direct connection to, through continuous descent from, the ancient populations of Thailand. On the other hand, the diversity of the Thais‘ cultural, linguistic, spiritual and biological heritages are the crucial ties that bind them to the other great cultures of southern and eastern Asia, and root them in their unique place among the modern nations of Southeast Asia. In turn, the Thais‘ appreciation of and esteem for their diverse heritage is what drives their recognition of patterns of internal cultural and physical variation structured by internal regional boundaries. This perception of suites of regionally distinctive characteristics within the modern Thai population acts to reify the people‘s sense of deep-time connections to the myriad historic and prehistoric cultures of mainland Southeast Asia.

Given a greater understanding of these historical and cultural considerations in the population history of the modern Thai, we can at last return to the pursuit of a corresponding understanding of the biological aspects of that population history. The critical finding we seek to

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understand is that despite the colloquial assertions to the contrary, there appears to be no pattern of phenotypic distinctions within the modern population of Thailand—or at least, none that we can describe craniometrically—which can be correlated with the historic ethnic and linguistic distinctions among the internal subregions of Thailand. By now, the reasons for this should be clear. The homogenizing effects of the deliberate upheaval and forced migrations of tens of thousands of dispossessed people, inflicted over three quarters of a century of intermittent warfare across the whole of Southeast Asia, represents such a fundamental and profound disruption in the historic trajectories of peoples and cultures, and of the historic connections between peoples and places, that any correlation that might once have existed between them has long since been erased. Regardless of the cultural perceptions which exist among the Thai today, the inescapable biological truth is that there simply hasn‘t been enough time for a new pattern of regional craniometric differentiation to have evolved within the Thai population in the post-

Ayutthayan era. The modern Thais‘ perception of regional patterns of variation within their population, though attributed largely to differences in phenotype, is in truth likely driven much more by the observation of cultural and ethnic factors, such as differences in dress, accent, dialect and manner, which were more easily preserved and which have more readily persisted in spite of the population upheaval of the eighteenth and nineteenth centuries.

The new, yet still incomplete, understanding of the patterns of morphological variation within the modern Thai population that has been developed within this dissertation leads to several other important considerations. First and foremost, the skeletal populations from Chiang

Mai, Khon Kaen and Bangkok, in addition to representing the populations of those cities and surrounding areas, can now be more comfortably accepted as representative of the modern Thai

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population in general.2 The genuine and justifiable concern that the labyrinthine subtleties of

Southeast Asian population history have created craniometrically distinct subpopulations within the modern Thai is somewhat ameliorated, as is the concern over the impact of such subpopulations on our ability to ultimately resolve the central question of essential continuity versus discontinuity between the pre- and post-Neolithic populations of Southeast Asia. Had the regional subpopulations examined in this study been found to differ significantly in terms of cranial morphology, this finding would compel us to consider them as quasi-independent populations in our broader studies of biological distance among the varied populations of Asia and the Pacific. But this is not so, and while the small sample sizes (ca. N ≤ 50) common to many broad-scale craniometric population studies still must necessarily fall far short of capturing a true picture of the full spectrum of craniometric variation within the Thai population, we are reassured that such samples can nevertheless reasonably stand as a measure of the Thai population as a whole, and not just as the sum of one of its parts.

Yet as reassuring as this should be, this finding also highlights the critical importance of approaching craniometric population studies with a thorough understanding of the subtleties of the individual histories of each population under consideration. Recognizing that certain populations speak related languages, or that they share ethnic, cultural or genetic characteristics inherited from a common ancestral civilization, are only the starting points for disentangling population relationships in a region so humanly complex as Southeast Asia. Historic events such as the Burmese conquest and the Siamese wars of reclamation add yet another layer of complexity to the picture of population history in Thailand, and we underestimate the ripple effects of that history on the peoples outside of Thailand‘s borders at our analytical peril. We

2 Ethnic minorities such as the Hill Tribes still notwithstanding, of course.

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need to delve deeper into our own anthropological knowledge of the forces which shape populations, to look not just at the ebb and tide of ancient civilizations, but also into the much more recent diasporas wrought by warfare, changing environmental conditions, political unrest.

In the case of Thailand, an understanding of historical events in Siam in the eighteenth and nineteenth centuries leads us to the dawning comprehension that we can‘t consider the regional populations of the modern nation as long-term residents in their present locations. Yet not a single one of the craniometric studies cited in Chapter 3 even mentions these events, much less gives any consideration to their potential impact on our understanding of population history in

Southeast Asia (though such events, and their impact on Southeast Asian population prehistory are broadly alluded to by Kenyhercz et al. 2010).

It was noted previously that Matsumura (2006) has argued for a more temporally robust and geographically focused approach to craniometric data sampling as the way forward in resolving the central question of endogenous versus exogenous origins for the modern populations of Southeast Asia. This argument is supported, indirectly, by the results of this dissertation. Analysis of a tightly concentrated geographic and ethnic population, both as a whole and broken down into regional subpopulations, has found a pattern of morphological variation which runs counter to that which might be expected based on extrapolation from the prehistoric and early historic archaeological and linguistic data, but which is in broad accord with expectations produced by an understanding of the previously unacknowledged recent historical events shaping that population. These results have assuaged some of the analytical concerns regarding the validity of using a small pool of craniometric data derived from a single anatomical collection as representative of the entire population as a whole, but it has also raised other concerns about the thoroughness of our understanding of the history of the populations

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under examination. So to Matsumura‘s advice, I would add the further suggestion of giving greater attention to the recent historic sociopolitical factors relevant to the development of the specific geographic and ethnic populations we are analyzing. We are already quite adept at this when it comes to our own population—for example, applying our knowledge of eighteenth to twentieth century American history to understand the composition of the Terry or Hamann-Todd collections. But it becomes especially important when dealing with non-Western populations, whose recent historic trajectories may not be so readily apparent to researchers venturing into new territories, or seeking new insight into familiar ones. My suspicion is that many of the nagging questions and incongruences which cause so much distraction and misdirection in so many craniometric population studies will resolve themselves if we only understood a little more about the populations involved.

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Figure 6-1. Sign in front of the International Center Hostel at Chiang Mai University, with text in the Thai (top), Lanna (middle), and Roman scripts. Photograph by the author.

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Figure 6-2. Great Stupa of Wat Suandok, Chiang Mai; an example of Lanna-style religious architecture. Photograph by the author.

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APPENDIX A DEFINITIONS OF CRANIOMETRIC LANDMARKS AND MEASURMENTS

For reference, the craniometric landmark and measurement definitions followed during data collection for this dissertation are reproduced here, with minor edits, as provided by Moore-

Jansen et al. (1994). Citations provided within each definition identify the original source of the landmark or measurement, as cited by Moore-Jansen et al. (1994).

Definitions of Cranial Landmarks

Alare (al) The most laterally positioned point on the anterior margin of the nasal aperture. This point should be marked on both the right and left sides of the nasal aperture (after Bass 1971:60; Howells 1973:176).

Alveolon (alv) The point where the midline of the palate is intersected by a straight tangent connecting the posterior borders of the alveolar crests. This point is used in the measurement of maxilla alveolar length and determined in practice, as the point where the mid-sagittal plane intersects a wire needle placed against the posterior margins of the alveolar processes of the maxilla (Martin and Saller 1956:451).

Auriculare (au) A point on the lateral aspect of the root of the zygomatic process at the deepest incurvature, wherever it may be.

Basion (ba) The point where the anterior margin of the foramen magnum is intersected by the mid-sagittal plane. The point is located on the inner border of the anterior margin of the foramen magnum direction opposite of opisthion (o). In height measurements of the braincase, basion is positioned somewhat farther onto the underside of the margin of the foramen magnum (hypobasion), so that the observer may distinguish between an inferior and a posterior basion for reasons of convenience and technical demands (Martin and Saller 1956:446).

Bregma (b) The point where the sagittal and coronal sutures meet. In those cases where the most anterior segment of the sagittal suture deflects to one side, the point of the junction of the two sutures must be projected. Bregma is impossible to determine exactly on children‘s crania with open fontanelles, skulls with ―fontanelle‖ bones, and in skulls with total obliteration of the sutures. In the latter case it may be possible to see existing traces of the sutures by slightly moistening the area (Martin and Saller 1956:444)

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Dacryon (d) The point on the medial border of the orbit at which the frontal, lacrimal, and maxilla intersect. In other words, dacryon lies at the intersection of the lacrimomaxillary suture and the frontal bone. There is often a small foramen at this point (Martin and Saller 1956:450).

Ectoconchion (ec) The intersection of the most anterior surface of the lateral border of the orbit and a line bisecting the orbit along its long axis. To mark ectoconchion, move a toothpick or other thin, straight instrument up and down, keeping it parallel to the superior orbital border, until you divide the eye orbit into two equal halves. Mark the point on the anterior orbital margin with a pencil (Howells 1973:168)

Ectomolare (ecm) The most lateral point on the lateral surface of the alveolar crest. This point is generally positioned on the alveolar margin of the section maxillary molar.

Euryon (eu) The most laterally positioned point on the side of the braincase. Euryon always falls on either the parietal bone or on the upper portion of the temporal bone and may be determined only by measuring maximum cranial breadth. The area of the root of the zygomatic arch, the supramastoid crest, and the entire adjacent region above the external auditory meatus, which sometimes exhibit excessive symmetrical lateral expansion, should be avoided when determining the position of euryon (Martin and Saller 1956:447).

Frontomalare The most laterally positioned point on the frontomalar suture temporale (fmt) (Martin and Saller 1956:451).

Frontotemporale (ft) A point located generally forward and inward on the superior temporal line directly above the zygomatic process of the frontal bone (Martin and Saller 1956:451).

Glabella (g) The most frontwardly projecting point in the mid-sagittal plane at the lower margin of the frontal bone, which lies above the nasal root and between the superciliary arches. The point of glabella is depressed between the confining bony ridges, and is often delineated superiorly by a shallow gutter or a transversely running indentation on the surface of the frontal bone (Martin and Saller 1956:442-443).

Lambda (l) The point where the two branches of the lambdoidal suture meet with the sagittal suture. The determination of this point is uncertain in cases with strongly serrated sutures, as well as cases where sutures are totally obliterated. Locating lambda may be further complicated in crania with wormian or sutural bones at the apex of the occipital squama. In such cases the general direction of the two branches of the lambdoidal sutures is determined and two straight lines are projected along the branches of the

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suture placing lambda at the point where these lines meet with one another and with the sagittal suture (Martin and Saller 1956:444)

Nasion (n) The point of intersection of the nasofrontal suture and the mid-sagittal plane. Nasion corresponds to the nasal root (Martin and Saller 1956:448)

Nasospinale (ns) The lowest point on the inferior margin of the nasal aperture as projected in the mid-sagittal plane. In crania with slight to moderate development of the anterior nasal spine, this point is easily determined by connecting the lowest point on the inferior margin of the nasal aperture right and left of the nasal spine. Nasospinale is located wherever this line is intersected by the mid-sagittal plane at the base of the nasal spine. If the nasal spine is well developed, mark the point of nasospinale on the lateral wall of the projecting nasal spine. However, if the nasal spine is at or below the line connecting the lowest point on the inferior margins of the aperture, nasospinale is found on the upper margin of the nasal spine (Martin and Saller 1956:448)

Opisthocranion (op) The most posteriorly protruding point on the back of the braincase, located in the mid-sagittal plane. Opisthocranion almost always falls on the superior squama of the occipital bone, and only occasionally on the external occipital protuberance. Opisthocranion can generally be established while obtaining the measurement of maximum cranial length. However, in some cases where the superior squama forms a partial sphere with glabella as its midpoint, opisthocranion cannot be determined in this manner. In such cases, each point on the partial sphere represents the maximum distance and opisthocranion is located arbitrarily approximately at a point in the middle of the spheric segment (Martin and Saller 1956:445).

Opisthion (o) The point at which the mid-sagittal plane intersects the posterior margin of the foramen magnum. Opisthion is located on the inner border of the posterior margin of the foramen magnum facing basion.

Prosthion (pr) The most anterior point on the alveolar border of the maxilla between the central incisors in the mid-sagittal plane. Note that in measuring basion- prosthion length and palate length, prosthion is not positioned on the inferior margin of the advanced bony parts between the incisors, but is more anteriorly located on the anterior surface of the alveolar process. In measuring upper facial height, however, prosthion is located on the inferior tip of the alveolar process. In cases of a defective or resorbed alveolar process, determination of prosthion becomes uncertain or impossible, and upper facial height cannot be measured (Martin and Saller 1956:449).

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Zygion (zy) The most laterally positioned point on the zygomatic arches. The position of zygion is defined from the measurement of bizygomatic breadth (Martin and Saller 1956:450)

Definitions of Cranial Measurements

Maximum cranial length The distance of glabella from opisthocranion in the midsagittal (GOL; g-op) plane measured in a straight line.

Maximum cranial The maximum width of the skull perpendicular to the midsagittal breadth plane wherever it is located, with the exception of the inferior (XCB; eu-eu) temporal line and the immediate area surrounding the latter.

Bizygomatic breadth The direct distance between both zygia located at the most lateral (ZYB; zy-zy) points of the zygomatic arches.

Basion-bregma height The direct distance from the lowest point on the anterior margin of (BBH; ba-b) the foramen magnum, basion, to bregma.

Cranial base length The direct distance from basion to nasion. (BNL; ba-n)

Basion-prosthion length The direct distance from basion to prosthion. (BPL; ba-pr)

Maxillo-aveolar breadth The maximum breadth across the alveolar borders of the maxilla, (MAB; ecm-ecm) measured on the lateral surfaces at the location of the second maxillary molars.

Maxillo-aveolar length The direct distance from prosthion to alveolon. (MAL; pr-alv)

Biauricular breadth The least exterior breadth across the roots of the zygomatic (AUB; au-au) processes, wherever found.

Upper facial height The direct distance from nasion to prosthion. (UFHT; n-pr)

Minimum frontal breadth The direct distance between the two frontotemporale. (WFB; ft-ft)

Upper facial breadth The direct distance between the two frontomalare. (UFBR; fmt-fmt)

Nasal height The direct distance from nasion to nasospinale. (NLH; n-ns)

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Nasal breadth The maximum breadth of the nasal aperture. (NLB; al-al)

Orbital breadth The laterally sloping distance from dacryon to ectoconchion. (OBB; d-ec)

Orbital height The direct distance between the superior and inferior orbital (OBH) margins, perpendicular to orbital breadth.

Biorbital breadth The direct distance between left and right ectoconchion. (EKB; ec-ec)

Interorbital breadth The direct distance between left and right dacryon. (DKB; d-d)

Frontal chord The direct distance from nasion to bregma, taken in the midsagittal (FRC; n-b) plane.

Parietal chord The direct distance from bregma to lambda, taken in the (PAC; b-l) midsagittal plane.

Occipital chord The direct distance from lambda to opisthion, taken in the (OCC; l-o) midsagittal plane.

Foramen magnum length The direct distance of basion from opisthion. (FOL; ba-o)

Foramen magnum The distance between the lateral margins of the foramen magnum breadth (FOB) at the point of greatest lateral curvature.

Mastoid length The projection of the mastoid process below and perpendicular to (MDH) the Frankfurt Horizontal in the vertical plane.

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BIOGRAPHICAL SKETCH

Laurel Elizabeth Freas graduated summa cum laude with a Bachelor of Arts degree in anthropology and archaeology from Cornell University in 2001. After working as a park ranger for the National Park Service at Walnut Canyon National Monument near Flagstaff, Arizona,

Ms. Freas enrolled in the graduate program in the Department of Anthropology at the University of Florida. There, she served as a graduate analyst at the C. A. Pound Human Identification

Laboratory (CAPHIL). Upon earning her Master of Arts degree in anthropology in 2005, Ms.

Freas was certified into the Department of Anthropology‘s doctoral program and was appointed

Laboratory Manager of CAPHIL. While at the University of Florida, Ms. Freas also taught courses in human osteology, forensic anthropology and human anatomy.

In 2009, Ms. Freas joined the scientific staff of the Joint Prisoner of War/Missing In

Action (POW/MIA) Accounting Command‘s Central Identification Laboratory (JPAC-CIL), in

Honolulu, Hawaii. At the JPAC-CIL, Ms. Freas serves as a deploying forensic anthropologist and has led recovery missions to Laos and Cambodia in search of the remains of Americans missing and unaccounted-for as a result of the nation‘s past conflicts.

Ms. Freas is the recipient of a National Science Foundation Graduate Research Fellowship

(2002-2005), the J. Lawrence Angel Student Paper Award (2004), the Forensic Science

Foundation‘s Emerging Forensic Scientist Award (2006-2007), the Ellis R. Kerley Scholarship

(2007), a University of Florida Graduate Student Teaching Award (2007-2008) and the William

R. Maples Scholarship (2009).

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