Genomic and Climatic Effects on Human Crania from South America: a Comparative

Home , Aguazuque, Amantani, Cockburn Channel, Karitiana, Korubo, Macushi

Genomic and Climatic Effects on Human Crania from South America: A Comparative

Microevolutionary Approach

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

Brianne C. Herrera, M.A.

Graduate Program in Anthropology

The Ohio State University

2019

Dissertation Committee

Mark Hubbe, Advisor

Clark Spencer Larsen

Debbie Guatelli-Steinberg

Jeffrey McKee

Copyrighted by

Brianne C. Herrera

2019

Abstract

Cranial morphology has been widely used to estimate phylogenetic relationships among and between populations. When compared against genetic data, however, discrepancies arise in terms of population affinity and effects of microevolutionary processes. These discrepancies are particularly apparent in studies of the human dispersion to the New World. Despite the apparent discrepancies, research has thus far been limited in scope when analyzing the relationship between the cranial morphology and genetic markers. This dissertation aimed to fill this void in research by providing a necessary broad comparative approach, incorporating 3D morphological and climate data, mtDNA, and Y-chromosome DNA from South America. The combination of these data types allows for a more complete comparative analysis of microevolutionary processes.

Correlations between these different data types allow for the assessment their relatedness, while quantitatively testing microevolutionary models permit determining the congruence of these different data types.

I asked the following research questions: 1) how consistent are the patterns of population affinity when comparing different regions of the crania to each type of DNA for populations in

South America? 2) If they are not consistent, why not? How are different evolutionary forces affecting the affinities between them? Collectively, both the cranial and genetic data demonstrated patterns of isolation-by-distance when viewed from a continent-wide scale.

However, once the continent was broken into different eco-geographic regions, differing patterns emerged. The patterns seen at this regional scale were not the same between the cranial and genetic data, just as their correlations to different climate variables were different. These results demonstrate that cranial morphology, mtDNA, and Y-chromosome DNA are affected by climate and geography to different extents.

Dedication

To my parents, who always encouraged me to follow my own path in life. Thank you for all of the love, support, encouragement, and dedication.

To my husband, who has been there for me throughout this whole journey, across continents, and all with love and patience.

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Acknowledgements

I like to express my deep appreciation and gratitude to my dissertation advisor, Dr. Mark

Hubbe, for the patience, guidance, and mentorship he has shown me from the time of first applying to the PhD program in the Department of Anthropology through the completion of this dissertation. Dr. Hubbe’s skill in research and data analysis is matched only by his humor and positive nature, and I truly appreciate having the opportunity to work with him.

I would also like to thank my committee members, Dr. Clark Larsen, Dr. Debbie

Guatelli-Steinberg, and Dr. Jeff McKee, for their provoking feedback, support, and guidance that was offered to me over the years. Similarly, I would like to recognize Dr. Julie Field, who truly made me think-outside-the-box in both her classes and my candidacy exam. These professors collectively made me a better, more knowledgeable student.

My sincere thanks also goes to several people for providing me with access to numerous museum collections. Without their help attaining these data, this dissertation would not be possible. Specifically, I would like to thank Christopher Philipp and Dr. Ryan Williams at The

Field Museum in Chicago; Vivien Standen at the Museo Arqueológico San Miguel de Azapa,

Chile; Drs. Verónica Silva and Francisco Garrido at the Museo Nacional de Historia Natural in

Santiago, Chile; Susan Kuzminsky at the R.P. Gustavo Le Paige Museum in San Pedro de

Atacama, Chile; and Dr. German Alberto Peña Leon at the Museo de Historia Natural in Bogotá,

Colombia. An additional thanks to Drs. Christina Torres-Rouff, Will Pestle, and Mark Hubbe, whose grant funded my research trip to the R.P. Gustavo Le Paige Museum in San Pedro de

Atacama, Chile.

I gratefully acknowledge the funding I received toward my PhD and travel associated with my research: the Graduate Enrichment Fellowship, the Global Gateway Grant, and the iii

Alumni Grants for Graduate Research and Scholarship from The Ohio State University; the

Elizabeth A. Salt Anthropology Travel Award from the Department of Anthropology; and the

Science Visiting Scholarship from the Field Museum in Chicago.

I would also like to thank my friends, both at The Ohio State University and elsewhere, for always supporting me. I value the moments I’ve shared with my friends in the department, even if was just to simply grab lunch or commiserate about grad school. More specifically, I would like to thank Daniel Peart and Nicole Hernandez for always being there to share a meal, take my mind off the stress of academia, and support me through tougher times. They were also gracious enough to take care of various errands for my husband and I while we were out of the country, which took an immense weight off our shoulders. Of course, I would like to thank my husband, Mark Anthony, for encouraging and talking me through problems when I needed it most. I would also like to thank Alex Frye for always lifting my spirits and having unwavering faith in my abilities. I deeply value our friendships.

Lastly, I’m deeply appreciative of my family and husband for their support through this roller-coaster of a journey and who believed in me even when I didn’t believe in myself.

Vita

May 2010 ...... B.Sc. Astronomy, University of Texas at Austin

May 2013 ...... M.A. Anthropology, Texas State ...... University – San Marcos

August 2013 ...... Graduate Enrichment Fellowship, ...... The Ohio State University

August 2014-May 2019 ...... Graduate Teaching Associate, ...... Department of Anthropology, The ...... Ohio State University

Publications

Herrera B, Peart D, Hernandez N, Spradley K, Hubbe M. 2016. Morphological variation among Late Holocene Mexicans: implications for the discussion about the human occupation of the Americas. Am J Phys Anthropol 163: 75-84.

Herrera B, Hanihara T, Godde K. 2014. Comparability of multiple data types from the Bering Strait region: cranial and dental metrics and nonmetrics, mtDNA, and Y-chromosome DNA. Am J Phys Anthropol 154:334-348.

Herrera B. 2013. Genetic and craniometric comparative analysis of three Mexican populations. Master’s thesis, Texas State University-San Marcos.

Field of Study

Major Field: Anthropology

Table of Contents

Abstract ...... ii Dedication ...... iii Acknowledgements ...... iii Vita...... v List of Tables ...... xiii List of Figures ...... xv Chapter 1 : Introduction ...... 1 1.1 Microevolutionary History in South America ...... 1 1.2 Summary of Dissertation ...... 7 Chapter 2 : Background on the Settlement of the Americas ...... 12 2.1 Osteological Perspective on the Settlement of the Americas ...... 12 2.1.1 Biological Diversity ...... 13 2.1.2 Geographic Origin ...... 14 2.1.3 Timing of Dispersal into the Continent ...... 15 2.1.4 Number of Founding Populations ...... 17 2.2 Molecular Anthropology Perspective on the Settlement of the Americas ...... 19 2.2.1 Genetic Diversity ...... 20 2.2.2 Geographic Origin ...... 23 2.2.3 Timing of Dispersal into the Continent ...... 24 2.2.4 Number of Founding Populations ...... 25 2.3 Conclusion ...... 26 Chapter 3 : Population Backgrounds ...... 30 3.1 Populations Used for MtDNA Analyses ...... 30 3.1.1 Argentina: Choroti ...... 30 3.1.2 Argentina: Fuegian ...... 31 3.1.3 Argentina: Mapuche ...... 32 3.1.4 Argentina: Mataco (Chaco, Formosa) ...... 33 3.1.5 Argentina: Pilaga (Formosa) ...... 34 3.1.6 Argentina: Quebrada de Humahuaca ...... 34 3.1.7 Argentina: San Salvadore de Jujuy ...... 35

3.1.8 Argentina: Tehuelche ...... 35 3.1.9 Argentina: Toba (Chaco, Formosa) ...... 36 3.1.10 Bolivia: Aymara ...... 37 3.1.11 Bolivia: Chimane ...... 37 3.1.12 Bolivia: Ignaciano ...... 38 3.1.13 Bolivia: Moseten ...... 38 3.1.14 Bolivia: Movima ...... 39 3.1.15 Bolivia: Trinitario ...... 39 3.1.16 Bolivia: Quechua ...... 40 3.1.17 Bolivia: Yuracare ...... 41 3.1.18 Brazil: Guarani-Tupian ...... 42 3.1.19 Brazil: Jean ...... 43 3.1.20 Brazil: Kaingang ...... 43 3.1.21 Brazil: Krahô ...... 44 3.1.22 Brazil: Macushi ...... 44 3.1.23 Brazil: Marubo ...... 45 3.1.24 Brazil: Ticuna ...... 45 3.1.25 Brazil: Tupi ...... 46 3.1.26 Brazil: Wapishana ...... 47 3.1.27 Brazil: Yanomama ...... 47 3.1.28 Chile: Atacameños ...... 48 3.1.29 Chile: Aymara ...... 48 3.1.30 Chile: Huilliches ...... 49 3.1.31 Chile: Mapuche ...... 50 3.1.32 Chile: Pehuenche ...... 50 3.1.33 Chile: Yaghan ...... 51 3.1.34 Colombia: Antioquia ...... 51 3.1.35 Colombia: Embera ...... 52 3.1.36 Colombia: Ingano ...... 52 3.1.37 Colombia: Piaroa ...... 53 3.1.38 Colombia: Ticuna ...... 53 3.1.39 Colombia: Wayuu ...... 54 3.1.40 Colombia: Zenu ...... 55

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3.1.41 Ecuador: Cayapa ...... 55 3.1.42 Peru: Ancash ...... 56 3.1.43 Peru: Arequipa ...... 56 3.1.44 Peru: Tayacaja ...... 56 3.1.45 Peru: Tupe ...... 57 3.1.46 Peru: Quechua ...... 57 3.1.47 Venezuela: Makiritare ...... 58 3.2 Populations Used for Y-Chromosome DNA Analyses ...... 58 3.2.1 Argentina: Choroti ...... 58 3.2.2 Argentina: Colla ...... 59 3.2.3 Argentina: Fueguian ...... 59 3.2.4 Argentina: Mapuche ...... 59 3.2.5 Argentina: Mataco (Chaco, Formosa) ...... 59 3.2.6 Argentina: Pilaga (Formosa) ...... 59 3.2.7 Argentina: Quebrada de Humahuaca ...... 60 3.2.8 Argentina: San Salvadore de Jujuy ...... 60 3.2.9 Argentina: Tehuelche ...... 60 3.2.10 Argentina: Toba (Chaco, Formosa) ...... 60 3.2.11 Bolivia: Chimane ...... 60 3.2.12 Bolivia: Mojeño ...... 60 3.2.13 Bolivia: Trinitario ...... 61 3.2.14 Brazil: Arara ...... 61 3.2.15 Brazil: Asurini ...... 61 3.2.16 Brazil: Awa-Guajá ...... 62 3.2.17 Brazil: Gavião ...... 62 3.2.18 Brazil: Ipixuna ...... 63 3.2.19 Brazil: Karitiana ...... 63 3.2.20 Brazil: Kayapó-Xikrin...... 63 3.2.21 Brazil: Parakanã ...... 64 3.2.22 Brazil: Terena ...... 64 3.2.23 Brazil: Tiriyo ...... 65 3.2.24 Brazil: Urubu-Kaapor ...... 65 3.2.25 Brazil: Waiãpi ...... 66

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3.2.26 Brazil: Yanomama ...... 66 3.2.27 Brazil: Zoé ...... 66 3.2.28 Colombia: Embera-Chami ...... 67 3.2.29 Colombia: Guambiano ...... 67 3.2.30 Ecuador: Kichwa ...... 67 3.2.31 Ecuador: Waorani ...... 68 3.2.32 Peru: Chumbivilca ...... 68 3.2.33 Peru: Chuquibamba ...... 68 3.2.34 Peru: Huanca ...... 69 3.2.35 Peru: Huancavelica ...... 69 3.2.36 Peru: Santiago de Chuco ...... 69 3.2.37 Peru: Shipibo-Conibo ...... 69 3.2.38 Venezuela: Bari Boxi ...... 70 3.2.39 Venezuela: Wayuu ...... 70 3.3 Populations Used for Cranial Analyses ...... 71 3.3.1 Chile: Araucania (Mid Chile) ...... 71 3.3.2 Chile: Azapa (Arica) ...... 72 3.3.3 Chile: Camarones (Arica) ...... 73 3.3.4 Chile: Costa Sur (Mid Chile) ...... 73 3.3.5 Chile: Coyo Oriente ...... 73 3.3.6 Chile: Estancia los Vicunas (Patagonia) ...... 74 3.3.7 Chile: Halakwulup (Patagonia) ...... 74 3.3.8 Chile: Fueginos (Patagonia) ...... 75 3.3.9 Chile: Isla Dawson (Patagonia) ...... 76 3.3.10 Chile: Isla Grande Bahia (Patagonia) ...... 76 3.3.11 Chile: Magallanes (Patagonia) ...... 78 3.3.12 Chile: Morro (Arica) ...... 78 3.3.13 Chile: Playa Miller (Arica) ...... 79 3.3.14 Chile: Puerto de Hambre (Patagonia) ...... 80 3.3.15 Chile: Selk’nam (Patagonia) ...... 80 3.3.16 Chile: Yamana (Patagonia) ...... 81 3.3.17 Colombia: Aguazuque ...... 81 3.3.18 Peru: Ancón ...... 82

3.3.19 Peru: Aramburu ...... 82 3.3.20 Peru: Cerro del Oro ...... 83 Chapter 4 : DNA: Sub-Regional Population Structure within South America ...... 85 4.1 Introduction ...... 85 4.1.1 Natural Selection and Genomics ...... 86 4.1.2 Regional Genetic Diversity ...... 90 4.2 Materials and Methods ...... 91 4.2.1 DNA Data ...... 91 4.2.2 Climate Data ...... 95 4.2.3 Comparing DNA to Climate ...... 99 4.3 Results ...... 99 4.3.1 MtDNA Results ...... 99 4.3.2 Y-Chromosome Results ...... 109 4.4 Discussion ...... 112 4.4.1 MtDNA ...... 112 4.4.2 Y-Chromosome ...... 115 4.4.3 Implications to the Hypotheses ...... 116 4.5 Conclusion ...... 118 Chapter 5 : Cranial Morphometrics ...... 120 5.1 Introduction ...... 120 5.1.1 Modularity and Integration ...... 120 5.2 Materials and Methods ...... 125 5.2.1 Crania: Samples ...... 125 5.2.2 Analytical Procedures ...... 139 5.2.3 Comparisons with Geographic and Climate Data ...... 144 5.3 Results ...... 146 5.3.1 PCA: Pooled Samples ...... 146 5.3.2 PCA: Male Samples ...... 166 5.3.3 PCA: Female Samples ...... 187 5.3.4 Fst Analysis: Pooled Samples ...... 207 5.3.5 Fst Analysis: Male and Female Samples ...... 209 5.3.6 MDS: Pooled Samples ...... 211 5.3.7 MDS: Male Samples ...... 224

5.3.8 MDS: Female Samples ...... 236 5.3.9 RB Plots: Pooled Samples ...... 248 5.3.10 RB Plots: Male Samples ...... 260 5.3.11 RB Plots: Female Samples ...... 270 5.3.12 Mantel Tests ...... 282 5.4 Discussion ...... 293 5.4.1 Fst: Pooled Cranial Series ...... 293 5.4.2 Fst: Male Cranial Series ...... 295 5.4.3 Fst: Female Cranial Series ...... 295 5.4.4 Comparisons: Pooled Cranial Series with Climate Variables ...... 296 5.4.5 Comparisons: Male Cranial Series with Climate Variables ...... 297 5.4.6 Comparisons: Female Cranial Series with Climate Variables ...... 297 5.4.7 MDS Analysis: Pooled Cranial Series ...... 298 5.4.8 MDS Analysis: Male and Female Cranial Series ...... 299 5.4.9 Contributions to the Hypotheses ...... 300 5.5 Conclusion ...... 302 Chapter 6 : Comparing Cranial Morphology to DNA ...... 304 6.1 Introduction ...... 304 6.1.1 Concordance and Discrepancies Between DNA and Cranial Morphology ...... 304 6.1.2 Chapter Goals ...... 309 6.2 Materials and Methods ...... 310 6.2.1 Crania ...... 310 6.2.2 DNA ...... 312 6.2.3 Climate ...... 315 6.2.4 Comparisons Between Data Types ...... 316 6.3 Results ...... 317 6.3.1 Comparisons: Mantel Tests of Pooled Cranial Series with DNA ...... 317 6.3.2 Comparisons: Mantel Tests of Male Cranial Series with DNA ...... 318 6.3.3 Comparisons: Mantel Tests of Female Cranial Series with DNA ...... 320 6.3.4 Comparisons: DNA with Climate ...... 321 6.4 Discussion ...... 323 6.4.1 Comparisons: Pooled Cranial Series with DNA ...... 323 6.4.2 Comparisons: Male Cranial Series with DNA ...... 323

6.4.3 Comparisons: Female Cranial Series with DNA ...... 324 6.4.4 Comparisons: DNA with Climate ...... 324 6.4.5 Implications to the Hypotheses ...... 326 6.5 Conclusion ...... 328 Chapter 7 : Conclusion...... 332 7.1 Overall Conclusions ...... 332 References ...... 338

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List of Tables

Table 4.1: MtDNA Populations and Source Papers...... 93 Table 4.2: Y-Chromosome Populations and Source Papers ...... 94 Table 4.3: Mantel and Partial Mantel test results for the Northern Andes...... 103 Table 4.4: Mantel and Partial Mantel test results for the Southern Andes...... 104 Table 4.5: Mantel and Partial Mantel test results for the Andes...... 105 Table 4.6: Mantel and Partial Mantel test results for the Amazon...... 106 Table 4.7: Mantel and Partial Mantel test results for Central South America...... 107 Table 4.8: Mantel and Partial Mantel test results for Southern South America...... 108 Table 5.1: Cranial Series Information ...... 127 Table 5.2: Landmarks used in Cranial Analyses ...... 136

Table 5.3: FST Results for both male and female cranial samples. The table is ordered from highest to lowest FST...... 209

Table 5.4: FST Results for the male cranial samples. The table is ordered from highest to lowest FST...... 210

Table 5.5: FST Results for the female cranial samples. The table is ordered from highest to lowest FST...... 211 Table 5.6: Mantel and Partial Mantel test results for the Whole Cranium, sorted by pooled male and female samples, male samples, and female samples...... 283 Table 5.7: Mantel and Partial Mantel test results for the Basicranium, sorted by pooled male and female samples, male samples, and female samples...... 284 Table 5.8: Mantel and Partial Mantel test results for the Neurocranium, sorted by pooled male and female samples, male samples, and female samples...... 285 Table 5.9: Mantel and Partial Mantel test results for the Anterior Neurocranium, sorted by pooled male and female samples, male samples, and female samples...... 286 Table 5.10: Mantel and Partial Mantel test results for the Medial Neurocranium, sorted by pooled male and female samples, male samples, and female samples...... 287 Table 5.11: Mantel and Partial Mantel test results for the Posterior Neurocranium, sorted by pooled male and female samples, male samples, and female samples...... 288 Table 5.12: Mantel and Partial Mantel test results for the Face, sorted by pooled male and female samples, male samples, and female samples...... 289 Table 5.13: Mantel and Partial Mantel test results for the Right or Left Masticatory Complex, sorted by pooled male and female samples, male samples, and female samples...... 290 Table 5.14: Mantel and Partial Mantel test results for the Nasal, sorted by pooled male and female samples, male samples, and female samples...... 291

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Table 5.15: Mantel and Partial Mantel test results for the Right or Left Orbit, sorted by pooled male and female samples, male samples, and female samples...... 292 Table 6.1: Table showing cranial populations and the analogous mtDNA populations...... 313 Table 6.2: Table showing cranial populations and the analogous Y-Chromosome populations. 314 Table 6.3: Mantel test results between the crania, mtDNA, and Y-Chromosome DNA for both male and female samples...... 318 Table 6.4: Mantel test results between the crania, mtDNA, and Y-Chromosome DNA for male samples...... 320 Table 6.5: Mantel test results between the crania and mtDNA for female samples ...... 321 Table 6.6: Mantel and Partial Mantel test results between climate, mtDNA, and Y- Chromosome DNA...... 322 Table 7.1: Table summarizing the hypotheses, with the results from Chapters 4, 5, and 6...... 333

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List of Figures

Figure 4.1: Map of South America showing mtDNA and Y-chromosome populations. These populations are organized by region. Gold dots represent mtDNA populations; Red dots represent Y-chromosome populations...... 98 Figure 4.2: Nonmetric MDS for mtDNA ...... 102 Figure 4.3: Nonmetric MDS for the Y-Chromosome...... 111 Figure 5.1: Image of landmarks on skull ...... 135 Figure 5.2: PCA for the Whole Cranium using both male and female samples...... 147 Figure 5.3: PCA for the Basicranium using both male and female samples...... 148 Figure 5.4: PCA for the Neurocranium using both male and female samples...... 149 Figure 5.5: PCA for the Anterior Neurocranium using both male and female samples...... 150 Figure 5.6: PCA for the Medial Neurocranium using both male and female samples...... 151 Figure 5.7: PCA for the Posterior Neurocranium using both male and female samples...... 152 Figure 5.8: PCA for the Face using both male and female samples...... 153 Figure 5.9: PCA for the Right Masticatory Complex using both male and female samples...... 154 Figure 5.10: PCA for the Nasal using both male and female samples...... 155 Figure 5.11: PCA for the Left Orbit using both male and female samples...... 156 Figure 5.12: PCA Polygons for the Whole Cranium using both male and female samples...... 157 Figure 5.13: PCA Polygons for the Basicranium using both male and female samples...... 158 Figure 5.14: PCA Polygons for the Neurocranium using both male and female samples...... 159 Figure 5.15: PCA Polygons for the Anterior Neurocranium using both male and female samples...... 160 Figure 5.16: PCA Polygons for the Medial Neurocranium using both male and female samples...... 161 Figure 5.17: PCA Polygons for the Posterior Neurocranium using both male and female samples...... 162 Figure 5.18: PCA Polygons for the Face using both male and female samples...... 163 Figure 5.19: PCA Polygons for the Right Masticatory Complex using both male and female samples...... 164 Figure 5.20: PCA Polygons for the Nasal using both male and female samples...... 165 Figure 5.21: PCA Polygons for the Left Orbit using both male and female samples...... 166 Figure 5.22: PCA of the Whole Cranium using male samples...... 167 Figure 5.23: PCA of the Basicranium using male samples...... 168 Figure 5.24: PCA for the Neurocranium using male samples...... 169

Figure 5.25: PCA for the Anterior Neurocranium using male samples...... 170 Figure 5.26: PCA for the Medial Neurocranium using male samples...... 171 Figure 5.27: PCA for the Posterior Neurocranium using male samples...... 172 Figure 5.28: PCA for the Face using male samples...... 173 Figure 5.29: PCA for the Right Masticatory Complex using male samples...... 174 Figure 5.30: PCA for the Nasal using male samples...... 175 Figure 5.31: PCA for the Right Orbit using male samples...... 176 Figure 5.32: PCA Polygons for the Whole Cranium using the male samples...... 177 Figure 5.33: PCA Polygons for the Basicranium using the male samples...... 178 Figure 5.34: PCA Polygons for the Neurocranium using the male samples...... 179 Figure 5.35: PCA Polygons for the Anterior Neurocranium using the male samples...... 180 Figure 5.36: PCA Polygons for the Medial Neurocranium using the male samples...... 181 Figure 5.37: PCA Polygons for the Posterior Neurocranium using the male samples...... 182 Figure 5.38: PCA Polygons for the Face using the male samples...... 183 Figure 5.39: PCA Polygons for the Right Masticatory Complex using the male samples...... 184 Figure 5.40: PCA Polygons for the Nasal using the male samples...... 185 Figure 5.41: PCA Polygons for the Right Orbit using the male samples...... 186 Figure 5.42: PCA for the Whole Cranium using female samples...... 188 Figure 5.43: PCA for the Basicranium using female samples...... 189 Figure 5.44: PCA for the Neurocranium using female samples...... 190 Figure 5.45: PCA for the Anterior Neurocranium using female samples...... 191 Figure 5.46: PCA for the Medial Neurocranium using female samples...... 192 Figure 5.47: PCA for the Posterior Neurocranium using female samples...... 193 Figure 5.48: PCA for the Face using female samples...... 194 Figure 5.49: PCA for the Left Masticatory Complex using female samples...... 195 Figure 5.50: PCA for the Nasal using female samples...... 196 Figure 5.51: PCA for the Left Orbit using female samples...... 197 Figure 5.52: PCA Polygons for the Whole Cranium using the female samples...... 198 Figure 5.53: PCA Polygons for the Basicranium using the female samples...... 199 Figure 5.54: PCA Polygons for the Neurocranium using the female samples...... 200 Figure 5.55: PCA Polygons for the Anterior Neurocranium using the female samples...... 201 Figure 5.56: PCA Polygons for the Medial Neurocranium using the female samples...... 202 Figure 5.57: PCA Polygons for the Posterior Neurocranium using the female samples...... 203

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Figure 5.58: PCA Polygons for the Face using the female samples...... 204 Figure 5.59: PCA Polygons for the Left Masticatory Complex using the female samples...... 205 Figure 5.60: PCA Polygons for the Nasal using the female samples...... 206 Figure 5.61: PCA Polygons for the Left Orbit using the female samples...... 207 Figure 5.62: MDS for the Whole Cranium using both male and female samples...... 213 Figure 5.63: MDS for the Basicranium using both male and female samples...... 214 Figure 5.64: MDS for the Neurocranium using both male and female samples...... 215 Figure 5.65: MDS for the Anterior Neurocranium using both male and female samples...... 217 Figure 5.66: MDS for the Medial Neurocranium using both male and female samples...... 218 Figure 5.67: MDS for the Posterior Neurocranium using both male and female samples...... 219 Figure 5.68: MDS for the Face using both male and female samples...... 220 Figure 5.69: MDS for the Left Orbit using both male and female samples...... 221 Figure 5.70: MDS for the Nasal using both male and female samples...... 222 Figure 5.71: MDS for the Right Masticatory Complex using both male and female samples. ... 223 Figure 5.72: MDS for the Whole Cranium using male samples ...... 225 Figure 5.73: MDS for the Basicranium using male samples...... 226 Figure 5.74: MDS for the Neurocranium using male samples...... 227 Figure 5.75: MDS for the Anterior Neurocranium using male samples...... 229 Figure 5.76: MDS for the Medial Neurocranium using male samples...... 230 Figure 5.77: MDS for the Posterior Neurocranium using male samples...... 231 Figure 5.78: MDS for the Face using male samples...... 232 Figure 5.79: MDS for the Right Orbit using male samples...... 233 Figure 5.80: MDS for the Nasal using male samples...... 234 Figure 5.81: MDS for the Right Masticatory Complex using male samples...... 235 Figure 5.82: MDS for the Whole Cranium using female samples...... 237 Figure 5.83: MDS for the Basicranium using female samples...... 238 Figure 5.84: MDS for the Neurocranium using female samples...... 239 Figure 5.85: MDS for the Nasal using female samples...... 240 Figure 5.86: MDS for the Left Orbit using female samples...... 241 Figure 5.87: MDS for the Left Masticatory Complex using female samples...... 242 Figure 5.88: MDS of the Neurocranium using female samples...... 243 Figure 5.89: MDS of the Anterior Neurocranium using female samples...... 245 Figure 5.90: MDS using Medial Neurocranium using female samples...... 246

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Figure 5.91: MDS of the Posterior Neurocranium using female samples...... 247 Figure 5.92: RB Plot of the Whole Cranium using both male and female samples...... 249 Figure 5.93: RB Plot of the Basicranium using both male and female samples...... 250 Figure 5.94: RB Plot of the Neurocranium using both male and female samples...... 251 Figure 5.95: RB Plot of the Anterior Neurcranium using both male and female samples...... 253 Figure 5.96: RB Plot of the Medial Neurocranium using both male and female samples...... 254 Figure 5.97: RB Plot of the Posterior Neurocranium using both male and female samples...... 255 Figure 5.98: RB Plot of the Face using both male and female samples...... 256 Figure 5.99: RB Plot of the Right Masticatory Complex using both male and female samples. . 257 Figure 5.100: RB Plot of the Nasal using both male and female samples...... 258 Figure 5.101: RB Plot of the Left Orbit using both male and female samples...... 259 Figure 5.102: RB Plot of the Whole Cranium using male samples...... 261 Figure 5.103: RB Plot of the Basicranium using male samples...... 262 Figure 5.104: RB Plot of the Neurocranium using male samples...... 263 Figure 5.105: RB Plot of the Face using male samples...... 264 Figure 5.106: RB Plot of the Anterior Neurocranium using male samples...... 265 Figure 5.107: RB Plot of the Medial Neurocranium using male samples...... 266 Figure 5.108: RB Plot of the Posterior Neurocranium using male samples...... 267 Figure 5.109: RB Plot of the Right Masticatory Complex using male samples...... 268 Figure 5.110: RB Plot of the Nasal using male samples...... 269 Figure 5.111: RB Plot of the Right Orbit using male samples...... 270 Figure 5.112: RB Plot of the Whole Cranium using female samples...... 272 Figure 5.113: RB Plot of the Basicranium using female samples...... 273 Figure 5.114: RB Plot of the Neurocranium using female samples...... 274 Figure 5.115: RB Plot of the Face using female samples...... 275 Figure 5.116: RB Plot of the Anterior Neurocranium using female samples...... 276 Figure 5.117: RB Plot of the Medial Neurocranium using female samples...... 277 Figure 5.118: RB Plot of the Posterior Neurocranium using female samples...... 278 Figure 5.119: RB Plot of the Left Masticatory Complex using female samples...... 279 Figure 5.120: RB Plot of the Nasal using female samples...... 280 Figure 5.121: RB Plot of the Left Orbit using female samples...... 281

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CHAPTER 1 : INTRODUCTION

1.1 MICROEVOLUTIONARY HISTORY IN SOUTH AMERICA

The context for this chapter of the dissertation relies, in part, on the broader study of the microevolutionary history of modern humans who spread around the globe during the late Pleistocene. Worldwide cranial variation has been shaped during the short time modern humans expanded out of Africa into Asia (70-55ka), Europe (65-40ka), Australia

(~50ka), the Americas (15-13ka), and Oceania (4.5-0.5ka) (Armitage et al., 2011;

Dillehay, 2009; Duggan & Stoneking, 2014; Mellars, Gori, Carr, Soares, & Richards,

2013). This global expansion of modern humans mostly replaced previously existing archaic humans and hominins, supporting that all Late Pleistocene/Early Holocene groups, and most of our phenotypic diversity, derive from this expansion (Hubbe,

Harvati, & Neves, 2011). Nonetheless, during this expansion period, humans interbred with Neandertals, Denisovans, and possibly H. erectus present in Asia (Green et al.,

2010; Reich et al., 2010; Sankararaman et al., 2014), further affecting human variation on a worldwide scale. However, current evidence suggests that most cranial morphological diversity observed in current human populations developed during the last 10kyr

(Harvati, Hubbe, Bernardo, & Hanihara, 2009; Hubbe, Harvati, et al., 2011; Reyes-

Centeno et al., 2014).

Many studies have assessed whether modern human cranial diversity patterns fit a selectively neutral (evolutionary changes that are not caused by natural selection), or more simply, neutral evolutionary model when compared to neutral genetic loci (Harvati

& Weaver, 2006a, 2006b; Roseman, 2004; Smith, 2011; Smith, Terhune, & Lockwood,

2007; Weaver, Roseman, & Stringer, 2007; Weaver, Roseman, & Stringer, 2008). In general, the majority of cranial measurements reflect patterns of differentiation coherent with neutral evolutionary process (e.g., Harvati & Weaver, 2006a; Harvati & Weaver,

2006b; Roseman & Weaver, 2004) , with a few regions showing significant effects from the climate or diet (e.g., Hubbe, Neves, Bernardo, & Harvati, 2011; Perez, Lema, Diniz-

Filho, et al., 2011; von Cramon-Taubadel, 2014). For example, Roseman (2004) and

Harvati and Weaver (2006a, 2006b) found the facial region and cranial vault to be highly influenced by cold climate. Colder climates likely affect cranial shape through Bergman and Allen’s rules (Hubbe, Hanihara, & Harvati, 2009), with skull shape becoming more globular in extremely cold climates. Furthermore, in terms of the best specific traits for demographic use, Betti, Balloux, Amos, Hanihara, and Manica (2009) was in accordance with Harvati and Weaver (2006a) in finding the face to be most affected by climate, and therefore morphological studies concerned with reconstructing phylogenetic relationships should not consider this region of the skull. In contrast to the face, many studies have found the basicranium to be the most neutrally evolving and least influenced by the environment and epigenetic effects (e.g., Jacobson, 1993; Lieberman, Pearson, &

Mowbray, 2000; Strait, 1998; von Cramon-Taubadel, 2009).

In terms of the neurocranium, Hubbe, Hanihara, et al. (2009) found statistically significant correlations between measurements from the neurocranium and geographic/genetic distance (not climate), supporting Harvati and Weaver (2006a).

However, the neurocranium results from Hubbe, Hanihara, et al. (2009) and Harvati and

Weaver (2006a, 2006b) are contrary to Smith (2009)’s, who found that the neurocranium reflects climate to a greater degree than population history, emphasizing the importance of future studies examining relationships between different regions of the skull to both selective forces and genetic background. Selective pressures to the crania are not limited to climate though, and diet has been suggested as is another important contributor

(González-José, Ramírez-Rozzi, et al., 2005; Menéndez, Bernal, Novellino, & Perez,

2014; Perez, Lema, Diniz-Filho, et al., 2011; Perez & Monteiro, 2009), with some suggesting a potentially rapid (within a lifetime) response rate in morphological diversification following diet change (Lieberman, Krovitz, Yates, Devlin, & Claire, 2004;

Perez, Lema, Diniz-Filho, et al., 2011). However, this cranial shape response is mostly developmental plasticity rather than selective pressure. Because developmental plasticity can also affect morphological changes (in addition to evolutionary forces), it can be a strong confounding factor in biological affinity studies.

In spite of these known environmental effects, natural selection has not been observed to have a dominant effect on structuring variance between modern human populations (e.g., Betti et al., 2009; Manica, Amos, Balloux, & Hanihara, 2007;

Relethford, 1994, 2001), justifying the use of crania for reconstructing past population affinities. In general, the majority of cranial measurements reflect patterns of

3 differentiation coherent with neutral evolutionary process (Harvati & Weaver, 2006a,

2006b; Hubbe, Hanihara, et al., 2009; Roseman, 2004, among others), leading to the consensus that global patterns of cranial variation are primarily shaped by neutral evolutionary forces (von Cramon-Taubadel, 2014). This consensus is determined through four avenues:

1) Similar apportionment of cranial morphological data has been seen when

compared with neutral genetic DNA (e.g., Hubbe, Hanihara, et al., 2009;

Relethford, 2002, 2004b; Roseman & Weaver, 2004).

2) When comparing craniometric distance matrices to neutral genetic distance

matrices, congruence can be found (e.g., Herrera, Hanihara, & Godde, 2014;

Roseman, 2004; Smith, 2009; von Cramon-Taubadel, 2009, 2011a).

3) Craniometrics show a strong fit to theoretical neutrality (classic examples

include Lande, 1976; Lande, 1977, 1979; Lynch, 1989). More recent articles

continue to support this, as demonstrated by Roseman (2004), Harvati and

Weaver (2006a), Weaver et al. (2007), Weaver et al. (2008), Weaver (2014), and

Smith (2011).

4) Manica et al. (2007) demonstrated that modern human cranial within-

population diversity fits a model of iterative founder effects, which are repeated

bottlenecking effects, with an origin in Africa. This result is supported by Betti et

al. (2009) and von Cramon-Taubadel and Lycett (2008).

Nevertheless, using cranial morphology to investigate the human dispersal into the

Americas has led to disparate results, especially when comparing morphological data against molecular data.

The Americas were the last continents to be occupied by humans, with the human dispersal into the continents occurring approximately 14,000 years ago (Dillehay, 2009).

In spite of this, the Americas have high amounts of cranial variation when compared to the rest of world across time (Relethford, 2002; Ross, Ubelaker, & Falsetti, 2002; Sardi,

Rozzi, González-José, & Pucciarelli, 2005), especially within South America (González-

José, Bortolini, Santos, & Bonatto, 2008; Hubbe, Okumura, Bernardo, & Neves, 2014a;

Hubbe, Strauss, Hubbe, & Neves, 2015; Sardi et al., 2005; A. Strauss, Hubbe, Neves,

Bernardo, & Atuí, 2015). Some genetic evidence from the Americas support the same high levels of between-group variation (Cavalli-Sforza, Menozzi, & Piazza, 1994; Wang et al., 2007), but most demonstrate low levels of between-group variation (e.g., O'Rourke

& Raff, 2010). This is not to be confused with the average within-group variation, which seem to be smaller in the Americas than in the rest of the planet, when most molecular markers are considered (Betti et al., 2009).

Explanations for this high level of cranial variation are wide-ranging. While some authors attribute the high variation to one cause, such as diet (Sardi, Novellino, &

Pucciarelli, 2006), most authors assume a multi-cause scenario. For instance, González-

José et al. (2008) and Pucciarelli et al. (2006) argue that the observed morphological variation is a combined result of different microevolutionary processes, including genetic drift, gene flow, and directional selection. Others add the possibility of multiple ancestral

5 populations migrating to the Americas (e.g., Hubbe, Neves, & Harvati, 2010; Sardi et al.,

2005; von Cramon-Taubadel, Strauss, & Hubbe, 2017), persistent contact with Asian populations (e.g., de Azevedo et al., 2011; González-José et al., 2008), an earlier dispersion event than typically accepted (e.g., Sardi et al., 2005), or climate and diet differences (e.g., Perez & Monteiro, 2009). Morphological variation in the Americas is further complicated when considering the effects of genetic drift, which appears to have had a larger impact on cranial variation in the Americas compared to other continents

(Sardi et al., 2005).

On the other hand, genetic variation in the Americas tends to show the opposite trend: molecular variation in the Americas is lower than global estimates due to a bottleneck that occurred during the expansion from Asia (González-José & Bortolini,

2011). This pattern is evident in mtDNA (Fagundes, Kanitz, Eckert, et al., 2008), Y- chromosome DNA (Karafet, Zegura, & Hammer, 2008), and autosomal DNA (Conrad et al., 2006; Li et al., 2008a). While these results run counter to many cranial morphological studies (e.g., Ross et al., 2002; Sardi et al., 2005), genetic evidence is usually not considered with regard to temporality as in cranial morphological studies. Logically, directly comparing results of two different data types, one with a temporal depth and one without, would lead to inconsistencies. Indeed, recent DNA studies that included early

American skeletons found evidence of high biological diversity in the continent (Posth et al., 2018). As such, it is not surprising that genomic data show lower variation than global estimates, and cranial morphological data shows higher variation than global estimates. Additionally, these results, while generally interpreted to be incongruent, may

6 not be contradictory. Cranial traits are likely determined from polygenic effects of several autosomal genes, and thus, have greater effective population sizes (González-José et al.,

2008). This would result in the variation from phenotypic traits appearing to be higher when compared to genetic traits.

While the American populations are known to have a wide range of cranial variation compared to the rest of the world (Relethford, 2002; Ross et al., 2002; Sardi et al., 2005), many authors reduce this range of variation down to two distinctive morphologies present in the Americas – one labeled Paleoamerican and characterized by a long, narrow cranial vault and face, and the other labeled Amerindian and having a short, wide cranial vault and face (e.g., González-José et al., 2003; Hubbe, Harvati, et al.,

2011; Perez, Bernal, Gonzalez, Sardi, & Politis, 2009). Restricting the variation to only two morphologies is clearly an underrepresentation of the total variation present, as is easily demonstrated in South America. For instance, Pucciarelli et al. (2006) and Ross,

Ubelaker, and Guillen (2008) found distinct morphologies present on each side of the

Andes, which did not match the two morphologies generally discussed.

1.2 SUMMARY OF DISSERTATION

The Americas therefore provide a unique situation from which to study cranial variation, due to the unexpected higher levels of morphological variation present, especially when compared to the levels that are typically reported in molecular studies.

This dissertation aims to fill the void in knowledge regarding the origin of cranial shape variation in South America by employing a multidisciplinary comparative approach, 7 incorporating 3D morphological data collected in this project, with geographic distance and climatic variables (all coming from public databases) from different macro regions.

South America was chosen as the focus of this project because it has large enough skeletal collections, as well as a range of diverse environments. The research questions of interest are: (1) How consistent are the patterns of population affinity when comparing different regions of the crania to geographic distances for populations in South America?

(2) If they are not consistent, what are the possible causes of the observed differences? In order to test this, comparisons will be made between the different regions of the cranium and geographic distance for each population studied.

This dissertation will test the following hypotheses, by contrasting different cranial anatomical regions with the neutral molecular data available for the geographic regions studied. In other words, all hypotheses test how neutral the different cranial regions are (e.g., Francalacci et al., 2013; Hubbe, Hanihara, et al., 2009; Kuruppumullage

Don, Ananda, Chiaromonte, & Makova, 2013; Smith, 2009; von Cramon-Taubadel,

2009). This project is not interested in looking for particular regions of the DNA associated with cranial development, but rather uses the genetic data as a proxy for a neutral model of evolution. For this end, this project only considers DNA regions assumed to have differentiated via stochastic evolutionary processes (e.g., Kim &

Stephan, 2002; Scally & Durbin, 2012). Therefore, the strength of the correlation between the various genetic data and the cranial data across the continent should indicate how closely cranial differentiation across the continent is following neutral, non-selective evolution.

Uniparental data, such as mtDNA and Y-Chromosome DNA, is ideal for this dissertation because while admixture from Europeans cannot be parsed out using only crania, admixture can be determined using DNA sequences and haplogroups. A caveat to the uniparental inheritance with mtDNA is that a recent study found biparental inheritance of mtDNA in three families (Luo et al., 2018). The biparental inheritance appears to be very rare and left no detectable mark on the evolutionary genomic record

(Luo et al., 2018: 13043) and therefore is not expected to have affected any results in this dissertation. To remedy the admixture problem with crania, only crania dated to before

European contact were analyzed.

The null hypotheses tested are as follows:

1. Regional patterns of population structure will match global patterns. For both cranial morphology and DNA studies, the global pattern of population structure follows an isolation by distance model (e.g., Betti et al., 2009; Li et al., 2008b; Prugnolle, Manica, & Balloux, 2005; Ramachandran et al., 2005; von Cramon-Taubadel & Lycett, 2008). Generally, even studies relating to the peopling of the New World show these patterns (e.g., Wang et al., 2007).

2. Basicranium variation is mostly the product of neutral evolutionary processes. The basicranium interacts the least with the environment, partially due to it being the first part of the cranium to form in utero (Lieberman, Pearson, et al., 2000; McCarthy, 2001; Strait, 2001). Due to this lack of interaction, I do not expect correlations between the basicranium and any of the climate variables.

3. The neurocranium is the product of neutral evolutionary processes with some influence of diversifying selection. The neurocranium has been shown to co-vary with some environmental aspects, particularly temperature (Betti, Balloux, Hanihara, & Manica, 2010; Hubbe, Hanihara, et al., 2009; Roseman, 2004; Smith, 2009), but is also restricted by the shape of the basicranium. Taking this into account, neurocranium differentiation is expected to show strong correlation with neutral molecular data and moderate correlation with climate data as a response to natural selection. The climate variables I expect a correlation with are temperature, temperature range, and

isothermality. Precipitation and altitude are used as a proxy for differing ecological zones, and I would not expect strong correlations to these variables.

4. The facial region will show stronger evidence of diversifying selection in response to adaptation to climate and diet variation. The face interacts with environmental factors, such as diet and temperature more than other regions of the cranium (González-José, Ramírez-Rozzi, et al., 2005; Hubbe, Hanihara, et al., 2009). Given this interaction, I expect to find correlations with temperature, temperature range, and isothermality. Precipitation and altitude are used as a proxy for differing ecological zones, and I would expect correlations to these variables as well.

A strong assumption of this study, that complement the hypotheses being tested is that all DNA data selected for this study follow neutral evolutionary processes. This includes mtDNA (e.g., Papadopoulou et al., 2011; Pearson, 2013; Weaver, 2014) and Y-chromosome DNA (e.g., Francalacci et al., 2013; Jobling & Tyler-Smith, 2003; Wilder, Kingan, Mobasher, Pilkington, & Hammer, 2004).

While this dissertation is not directly testing hypotheses related to the first human dispersion to the Americas, there are implications from this work. In that vein, it is important to provide an overview of the current state of studies about the human dispersion to the Americas (Chapter 2). A discussion of the population histories for each population is also necessary to properly contextualize this dissertation, and is presented in

Chapter 3. Chapter 3 is divided into three sections in order to discuss the populations used for the mtDNA, Y-chromosome DNA, and the cranial morphological analysis. All the populations used for the DNA analyses were chosen from previously published data.

Additionally, they were chosen to represent a wide range of environments present in

South America. The populations for the crania were chosen from locations present all along the Andes mountain range. To properly do a comparative analysis between all data types, analogous populations had to be chosen since it was not possible to match all populations. The justification and explanation for which populations were chosen to be 10 compared, as well as any known relationship between the populations, are provided in

Chapter 5.

To understand the relationships between genetics and the environment, comparisons were performed between mtDNA, Y-chromosome DNA, and various environmental variables (Chapter 4). These correlation analyses involved comparing mtDNA haplogroup and Y-chromosome STR frequencies from a number of South

American populations from five macro-regions, linear geographic distances, and five climatic variables (mean annual temperature, annual temperature range, annual precipitation, isothermality, and altitude).

Chapter 5 covers the cranial morphological analyses. Seventy-eight 3D landmarks were measured on samples from three countries (nine sites) and divided into ten datasets

(face, neurocranium, basicranium, whole cranium, nasal, orbit, masticatory complex, anterior neurocranium, medial neurocranium, and posterior neurocranium). Each region of the cranium was then compared to the climate variables. In Chapter 6, I test the association of different anatomical regions of the cranium to mtDNA haplogroup frequencies and Y-chromosome STR frequencies using samples from western South

America. In the final chapter, Chapter 7, I conclude by bringing all these elements into a broader context and provide the next steps that can be taken moving forward in this research.

CHAPTER 2 : BACKGROUND ON THE SETTLEMENT OF THE AMERICAS

While this dissertation is not directly testing or analyzing patterns to provide information relating the settlement of the Americas, there are implications for this, given that all samples used are from South America. Further, a goal of the dissertation is to elucidate the relationship that exists between genomic and phenomic data, which is a problem plaguing studies on the settlement of the Americas. Thus, to fully place this dissertation in a broader context of meaning, a brief background on the settlement of the

Americas is provided in this chapter.

2.1 OSTEOLOGICAL PERSPECTIVE ON THE SETTLEMENT OF THE AMERICAS

The osteological record in the Americas ranges in date from the Late Pleistocene through the Holocene. Interestingly, the oldest remains have been found in South

America (Neves & Hubbe, 2005; Sardi et al., 2005), and not North America (Jantz &

Owsley, 2001). Although, more recently, older skeletons from North America have been presented, pushing the date of the oldest skeletal remains in North America. One example of this is from Chatters et al. (2014) where a skeleton dated to approximately 12-13 kya from Mexico’s eastern Yucatan Peninsula. South America also has more human skeletal remains found than North America (Neves, Hubbe, & Pilo, 2007), with unusually high between-group variation being found in their cranial samples when compared to other 12 continents (e.g., Hubbe et al., 2014a; Perez et al., 2009; Pucciarelli, González-José,

Neves, Sardi, & Rozzi, 2008; Sardi et al., 2005).

Many studies concerning the human dispersal to the New World discuss two distinctive morphologies that are present in the Americas – one labeled Paleoamericans and being characterized by a long, narrow cranial vault and narrow face, and the other being Amerindians and having a short, wide cranial vault with wide faces (e.g.,

González-José et al., 2003; Hubbe, Harvati, et al., 2011; Perez & Monteiro, 2009). These two morphologies have caused a long-standing debate concerning the origins of biological variation in the continents (e.g., de Azevedo et al., 2011; González-José et al.,

2008; Hubbe et al., 2010), but are likely an oversimplification of the variation that is and was present in the continents (Herrera, Peart, Hernandez, Spradley, & Hubbe, 2017;

Pucciarelli et al., 2006; Ross et al., 2008; Varela & Cocilovo, 2002).

2.1.1 Biological Diversity

Even though the Americas was one of the last continents occupied by humans, it shows high amounts of cranial variation across time when compared to the rest of world

(Jantz & Owsley, 2001; Relethford, 2002; Ross et al., 2002; Sardi et al., 2005), especially when South America is considered (González-José et al., 2008; González-José, Dahinten,

Luis, Hernández, & Pucciarelli, 2001; González-José et al., 2003; Sardi et al., 2005).

South America, and to some extent North America, has a high degree of diachronic diversity, with early populations differing significantly from the late and/or modern counterparts (Hubbe et al., 2010; Hubbe et al., 2015; Neves, Hubbe, & Correal, 2007; 13

Neves, Hubbe, & Pilo, 2007). Even when only late period populations are considered, the high level of variation is observed (Hubbe et al., 2014a; Perez & Monteiro, 2009;

Pucciarelli et al., 2008; Ross et al., 2002; Ross et al., 2008; Sardi et al., 2005).

There is no consensus on the reasons behind this high level of cranial variation. Some authors attribute the variation to a single cause, such as diet (Larsen, 2015; Sardi et al.,

2006), but most cite multiple causes. As an example, González-José et al. (2008) and

Pucciarelli et al. (2006) discuss morphological variation as occurring from microevolutionary events, including genetic drift, gene flow, and directional selection.

These microevolutionary events can complicate the picture because drift is thought to have had a stronger effect in the Americas than in other continents (e.g., compared to the dispersal to Sundaland; Sardi et al., 2005). Others add the possibility of multiple founding populations (e.g., Hubbe et al., 2010; Sardi et al., 2005), contact between Asian and

Beringian populations (de Azevedo et al., 2011), an earlier dispersion (Sardi et al., 2005), diet (e.g., Perez et al., 2009; Perez & Monteiro, 2009), or climate (Perez, Bernal, &

Gonzalez, 2007; Perez et al., 2009; Perez & Monteiro, 2009).

2.1.2 Geographic Origin

Morphological studies have not been able to provide estimates of the geographic origin of the first peoples to the Americas on the same scale as molecular data, which indicate very specific areas of the Siberian region as the origin point for the dispersal into the Americas. Researchers have, however, traced a general area of the world where the first Americans likely arose. Through studying the early Paleoamericans, researchers 14 found that they are more morphologically similar to modern Australians than to northeast

Asians (e.g., Neves et al., 2004; Neves & Hubbe, 2005; Neves, Hubbe, & Correal, 2007).

Moreover, Amerindians showed stronger affinities to east and north Asians (e.g., Jantz &

Owsley, 2001; Steele & Powell, 2002) and are considered to have a more derived morphology (e.g., Hubbe, Harvati, et al., 2011; Neves, Hubbe, & Correal, 2007). These results were sometimes interpreted as counter to many of the genetic studies which demonstrated a clear originating region from Siberia (e.g., Santos et al., 1999;

Starikovskaya et al., 2005). However, the morphology of the Paleoamericans is not unique to the Americas, as it corresponds to cranial morphologies present in early China

(Hubbe, Harvati, et al., 2011; Neves et al., 2005; Neves & Pucciarelli, 1998) and Japan

(Seguchi, McKeown, Schmidt, Umeda, & Brace, 2011). These results do not indicate contradictions to molecular evidence when, taken as a whole, most morphological evidence supports the early Paleoamericans being most related to ancient northeast

Asians (Seguchi et al., 2011; von Cramon-Taubadel et al., 2017).

2.1.3 Timing of Dispersal into the Continent

Researchers have also used the dichotomy of morphologies within the Americas to estimate the timing of dispersal. A popular interpretation is that the early Paleoamericans arrived in the Americas before the more derived morphology, which characterizes northeast Asia, arose (Dillehay, 2009; Hubbe, Harvati, et al., 2011; Hubbe et al., 2010;

Neves & Hubbe, 2005; Neves, Hubbe, & Correal, 2007). It is thought that the derived

15 morphology arose around the Pleistocene/Holocene transition (Neves et al., 2004; Neves,

Hubbe, & Correal, 2007).

The dates for early archaeological sites also provide timing estimates. First, it has been established that humans occupied the entirety of Siberia by around 32 ka (e.g.,

Goebel, Waters, & O'Rourke, 2008; Kuzmin & Keates, 2005). The most well-known site related to the possible origins of the dispersal to the Americas is the Yana Rhino Horn site, primarily because the people from this site produced Clovis-like foreshafts made from rhino horn (Goebel et al., 2008; Pitblado, 2011).

The earliest undisputed Beringian site is located in eastern Beringia, central Alaska.

The Swan Point site is approximately 14 ka (Speakman, Holmes, & Glascock, 2007), with many artifacts sharing traits with Upper Paleolithic sites in Siberia (Goebel et al.,

2008). There are few North American sites situated either geographically or temporally between the Yana Rhino Horn site and Swan Point, which has left researchers debating why this is the case. Some authors cite rapid depopulation in Siberia as the cause (e.g.,

Goebel, 2002; Hoffecker, 2005; Mandryk, Josenhans, Fedje, & Mathewes, 2001), although it is likely that many sites on and around the Beringian land bridge are currently underwater (e.g., Dixon & Monteleone, 2014). In any case, these two sites provide a window of time for the movement of people across Beringia (>14 ka). South America, however, contains several sites that are dated to between 11-15 ka, such as Las Vegas in

Ecuador (Stothert, 1985), Quebradas Tacahuay (Keefer et al., 1998) and Jaguay

(Sandweiss et al., 1998) in southern Peru, Huaca Prieta (Dillehay et al., 2012; Grobman et al., 2012) in northern Peru, Quebrada de Las Conchas (Llagostera, 1992; Llagostera et

16 al., 1998) and Huentelauquén (Llagostera, Weisner, G., Cervellino, & Costa J, 2000) in northern Chile, and Monte Verde (Dillehay, 1984; Dillehay & Collins, 1988; Meltzer,

1997) in Southern Chile. The timings for these sites have forced researchers to view the timing for first entry into the Americas as occurring prior to 15 ka.

2.1.4 Number of Founding Populations

The high level of cranial variation has also been used as evidence for multiple founding populations (e.g., Hubbe et al., 2010; Jantz & Owsley, 2001; Sardi et al., 2005).

Generally, morphologists have supported two waves or founding populations – one for each morphology present in the Americas. Morphometric studies have seen morphologies that are not consistent with the traditional two morphologies (Pucciarelli et al., 2006;

Ross et al., 2008), raising question of a potential third wave into the continents. However, this could also indicate a single founding population with a diverse set of morphologies present rather than two waves, each with a specialized morphology.

A potential source of this discrepancy is that many of the traditional morphometric papers did not consider microevolutionary forces to the degree that was needed. More recently, discussion of the dichotomous morphologies has turned to whether the

Amerindian morphology arose in the Americas, as a response to human occupation of distinct environments within the continents, gene flow occurring between Asia and

Beringia, or a general loss of diversity from the founding population (again, referencing the debate about whether one dispersal event occurred, or two or more; González-José et al., 2008; Powell, 2005). 17

Neves and Blum (2000) offer an interesting view on the number of founding populations. They too support two waves, but that are closely spaced. This allows for two waves from the same source population, but with one being temporally older than the other. The older wave would bring the Paleoamerican morphology, and the newer wave would bring the more specialized Amerindian morphology. Sardi et al. (2005), González-

José et al. (2008), de Azevedo et al. (2011), and von Cramon-Taubadel et al. (2017) support a similar model, but extend the possibility to a source population giving rise to a higher number of morphologies. They argue these morphologies could have risen in situ and maintained through recurrent gene-flow with Asia. This could potentially solve the discrepancy that is seen with the genomic data, as multiple waves from the same source population would genetically appear the same.

In 2010, Hubbe and colleagues tested a single wave versus multiple wave dispersal scenarios. They found that a model where the Paleoamerican and Amerindian morphologies share a last common ancestor outside of the Americas to be the most likely in term of explaining the present morphological variation present within the Americas.

Results supporting a multiple wave scenario have been corroborated in subsequent studies as well (e.g., Hubbe, Neves, et al., 2011; Hubbe, Okumura, Bernardo, & Neves,

2014b; Hubbe et al., 2015; von Cramon-Taubadel & Schroeder, 2016; Von Cramon-

Taubadel, Strauss, & Hubbe, 2015), but criticized by other studies (de Azevedo et al.,

2011; González-José et al., 2008).

2.2 MOLECULAR ANTHROPOLOGY PERSPECTIVE ON THE SETTLEMENT OF THE

AMERICAS

Molecular anthropologists use a wide range of DNA data to investigate past human history, including the dispersal to the Americas. Putting each piece of genetic information in context creates a robust analysis covering many aspects of inheritance. Mitochondrial

DNA (mtDNA) is the most commonly studied given that it decays at half the rate of nuclear DNA (Allentoft et al., 2012) and has hundreds of thousands of copies per cell

(Reynier et al., 2001). This allows for better preservation, allowing more opportunities to obtain sequence information from ancient mtDNA. Different sections of the mtDNA have been analyzed, such as the D-loop region (Derenko et al., 2001; Starikovskaya et al.,

2005) or haplogroup or hypervariable sequences (Fagundes, Kanitz, & Bonatto, 2008;

Fagundes, Kanitz, Eckert, et al., 2008; Reidla et al., 2003; Starikovskaya et al., 2005).

One restriction with using mtDNA is that it is only inherited from the maternal side, which inherently creates a bias in this data type, but this aspect also makes it easier to control for admixture.

Y-chromosome DNA is analogous in some ways to mtDNA. The Y-chromosome is only present in males, and is passed down only on the paternal side. The uniparental inheritance could again creates a bias in this data type. To best study phylogenetic or population relationships, the nonrecombining portion of the Y-chromosome is used

(Bortolini et al., 2003; Lell et al., 1997; Lell et al., 2002). Y-chromosome DNA is also easier to obtain from ancient specimens than autosomal DNA, but more recently, studies using autosomal DNA have become more common as methods for collection have

19 improved (e.g., Bolnick et al., 2012; Reich et al., 2012; N. M. Silva, Rio, Kreutzer,

Papageorgopoulou, & Currat, 2018; Wang et al., 2007).

Genetic studies of Native Americans are also complicated because there was a demographic collapse following European colonization, which started toward the end of the 15th century (Llamas et al., 2016). These early Europeans mostly arrived from the

Iberian Peninsula. By the 16th century, the European conquest had reached the Andean region and created a population bottleneck among the Native Americans (Cade, 1992).

This led to a loss in genetic diversity (Livi-Bacci, 2006) as well as admixture with a variety of European populations and West Africans brought to the Americas via the slave trade (Homburger et al., 2015). Prior to this, Inca state practices allowed the Inca Empire to routinely move males, females, and whole families throughout their different territories. This included moving people into and out of both highland and coastal communities (Cabana et al., 2014). These different demographic events could obscure some of the patterns that were present among earlier populations.

2.2.1 Genetic Diversity

Generally, most DNA studies agree that genetic diversity within the Americas is among the lowest in the world (e.g., Ramachandran et al., 2005; Wang et al., 2007), standing in contrast with the cranial morphological studies discussed above. However, there is still discordance with the research concerning diversity estimates, as different results and different interpretations of the results are common (e.g., Fuselli et al., 2003;

Lewis & Long, 2008; Mesa et al., 2000). 20

There are five primary mtDNA haplogroups present in modern Native Americans from both North and South America: A, B, C, D, and X (Bailliet, Rothhammer, Carnese,

Bravi, & Bianchi, 1994; Fagundes, Kanitz, & Bonatto, 2008; Perego et al., 2009; Perego et al., 2010; Schurr & Sherry, 2004; Starikovskaya et al., 2005). Similarly, there are two primary nonrecombining Y-chromosome haplogroups present in Native Americans: P and Q (e.g., Battaglia et al., 2013; Lell et al., 2002; Schurr & Sherry, 2004; Zegura,

Karafet, Zhivotovsky, & Hammer, 2004). In the Y-chromosome, in general, there is low diversity in the Americas with only two primary haplogroups present (Cabana et al.,

2014; Francisco M. Salzano, 2002; Underhill et al., 2001). Few autosomal DNA studies have been done on either Native North or South Americans. However, the results of these studies support those of the Y-chromosome studies previously discussed in that Native

Americans are less diverse than other populations in the world, with a higher heterozygosity in North America compared to South America (Reich et al., 2012).

When it comes to the New World, mtDNA results have led to particularly interesting patterns in population history. In general, mtDNA shows an isolation-by- distance trend (e.g., Malhi et al., 2002), but this view is complicated due to the fact that several mutations arose in situ within the New World and were then reshaped by genetic drift (de Saint Pierre et al., 2012), and possibly gene flow with Asia (Ray et al., 2010;

Tamm et al., 2007). Additionally, there is a range of variation estimates reported from mtDNA studies (e.g., Fuselli et al., 2003 and Tarazona-Santos et al., 2001 versus

Demarchi & Ministro, 2008; Hunley et al., 2008; Lewis & Long, 2008), showing an inconsistency in the mtDNA data and which makes it difficult to compile population

21 histories. The range of reported variation also highlights inconsistencies in how information pertaining to the settlement of the Americas is interpreted. Part of this range of estimates is likely due to the populations utilized in the studies. Although there was a general loss of diversity after the arrival of Europeans due to the massive decrease in native population size, populations today have variable amounts of admixture present.

While these results tend to run counter to the majority of cranial morphological studies (e.g., Ross et al., 2002; Sardi et al., 2005), temporality is not always as strong of a consideration in genetic studies as it is for morphological ones, leading to a potential disagreement about the interpretation of data. Furthermore, there is a lack of aDNA studies (although they are becoming more prevalent) to use when trying to directly compare results from morphological and genomic studies. Logically, directly comparing results of two different data types, one with a temporal depth and one without, would lead to inconsistencies. So, it is not surprising that genomic data show lower variation than global estimates, and cranial morphological data show higher variation than global estimates. Additionally, these results, while generally interpreted to be incongruent, may not be contradictory. Cranial traits are likely determined from polygenic effects of several autosomal genes, and thus, have greater effective population sizes (González-José et al.,

2008). This would result in the variation from phenotypic traits appearing to be higher when compared to genetic traits.

2.2.2 Geographic Origin

Researchers have tried to trace the geographic origin of the first Americans through mtDNA, Y-chromosome DNA, and nuclear DNA. There is no consensus in the literature on the exact origin point. In general, though, there is agreement that the origin point is in southcentral or southeastern Siberia, with no study suggesting an origin outside of northeast Asia. However, the degree of specificity to which researchers are willing to suggest an origin point varies. For example, researchers have suggested as possible sources: northern Siberia (Willis, Bennett, Walker, & Forster, 2004), central Siberia (Lell et al., 2002), southcentral Siberia (Bortolini et al., 2003; Schurr, 2004), eastern Siberia

(Wang et al., 2007), Asia (O'Rourke, 2009), around Lake Baikal (Derenko et al., 2001), the Altai Mountains (Dulik et al., 2012; Starikovskaya et al., 2005; Zegura et al., 2004), or western Beringia (Schroeder et al., 2009; Tamm et al., 2007).

The conclusion that Siberia is the likely origin of the first humans to the Americas is primarily based on haplotype analysis. The haplogroup frequencies recorded in modern

Native Americans only matches frequencies from people in the Altai or Lake Baikal region, leading researchers to conclude Siberia as the origin location (Merriwether, 2006;

Zegura et al., 2004).

Genetic evidence also points to a population bottleneck occurring during the dispersal period. Some have referred to this period as the ‘Beringian pause’ or ‘Beringian standstill’ (e.g., Mulligan, Kitchen, & Miyamoto, 2008; Tamm et al., 2007). It is thought that this ‘pause’ lasted long enough for population differentiation to take place (Mulligan et al., 2008). The ‘pause’ is also a possible explanation (González-José & Bortolini,

2011; Pitblado, 2011) for the lower levels of diversity seen within the Americas (Wang et al., 2007). Evidence for an event like the ‘pause’ can be seen in mtDNA (Fagundes,

Kanitz, & Bonatto, 2008), Y-chromosome DNA (Karafet, Zegura, & Hammer, 2009), and autosomal DNA (Conrad et al., 2006; Li et al., 2008a).

2.2.3 Timing of Dispersal into the Continent

Calculating the actual timing of the dispersal to the New World has been difficult, with different rates reported by different researchers. To calculate these estimates based on molecular data, a few assumptions must be made. The primary assumption is the mutation rate of DNA, which is used to calculate how long it has been since different populations diverged. Unfortunately, there is no consensus on what the mutation rate is for different types of DNA data (Achilli et al., 2008; Ho & Larson, 2006; Kemp et al.,

2007), which leads to disparate results.

Earlier works estimating an entry time for the colonization of the Americas tended to report early dates, ranging from 20 – 30 ka (Eshleman, Malhi, & Smith, 2003). More recently, the accepted date range is not only smaller, 18 – 15 ka, but more consistent across studies (e.g., Fagundes, Kanitz, Eckert, et al., 2008; O'Rourke, 2009; Perego et al.,

2009; Perego et al., 2010). The inconsistency again lies in the specificity to which researchers are willing to claim a time period. Some researchers offer a wider range of dates, such as 28.7 – 8.1 ka by Kemp et al. (2007) or 20 – 15 ka by Schurr (2004). These wider ranges tend to encompass the more frequently cited date of 18 – 15 ka.

2.2.4 Number of Founding Populations

There are two primary sides that molecular research falls into when estimating the number of founding populations moving into the Americas. The majority of studies support a single population moving into the Americas, and subsequently giving rise to all

Pleistocene humans and Native Americans living in North and South America through divergence and gene flow (e.g., Fagundes, Kanitz, Eckert, et al., 2008; Rasmussen et al.,

2014; Wang et al., 2007). The other supports two or more populations or movements of people, who all contributed to the biological variation in the Americas (e.g., Achilli et al.,

2013; Raghavan et al., 2015; Reich et al., 2012). Despite this disagreement, it is usually accepted that one migration wave could explain the variation seen in the Arctic populations (Raghavan et al., 2015; Rasmussen et al., 2014; Reich et al., 2012).

Several papers using mtDNA data support a single population founder for the

Americas (e.g., Fagundes, Kanitz, Eckert, et al., 2008; Kitchen, Miyamoto, & Mulligan,

2008; Lewis et al., 2007). Some Y-chromosome DNA studies also support a single founder (e.g., Bortolini et al., 2003; Schroeder et al., 2009; Schroeder et al., 2007; Zegura et al., 2004). Autosomal DNA studies concerning the initial human dispersion to the New

World are rare, but three have shown results consistent with a single founder wave

(Schroeder et al., 2009; Schroeder et al., 2007; Wang et al., 2007). It is important to note that multiple dispersal pulses originating from the same ancestral population would leave the same genetic signatures as a single founding population (Goebel et al., 2008).

A multiple founding population hypothesis has many supporters as well, ranging from mtDNA analyses (e.g., Derenko et al., 2007; O'Rourke, 2009; Perego et al., 2009;

Schurr, 2004; Tamm et al., 2007), Y-chromosome DNA analysis (Lell et al., 2002;

Schurr, 2004), and nuclear DNA (Ray et al., 2010). Two of these papers, Schurr (2004) and Perego et al. (2009), each support a model where two movements of people took place. One movement was along the coast from Asia and then into the Americas, and the other is through the ice-free corridor.

Each side of this debate does not necessarily have different data or results. There is a tendency for researchers to interpret the information differently though. For example, there are a few rare haplogroups present in the Americas, all of which can be traced back to the Siberian region (Pitblado, 2011). This has been interpreted as indicating multiple founding populations (Perego et al., 2009; Perego et al., 2010). Others have seen the same pattern but interpret it as a single founding population (Malhi et al., 2010), arguing for small population sizes and drift to keep those rare types as relatively high levels in the

Americas.

2.3 CONCLUSION

Discrepancies are found not only within and between the different DNA types, but also within both molecular and morphological studies, demonstrating that the problem does not lie with a single data type. There are incongruent estimates of genetic variation within the Americas, particularly within the Y-chromosomal studies (e.g.,

Cabana et al., 2014; Tarazona-Santos et al., 2001) and mtDNA studies (e.g., Fuselli et al.,

2003 and Tarazona-Santos et al., 2001 versus Lewis & Long, 2008), but also between each DNA type. Furthermore, cranial variation in the Americas has been used to argue for one, two, or more dispersal events (e.g., González-José et al., 2008; Hubbe et al.,

2010; Pucciarelli, Sardi, Jimenez López, & Sanchez, 2003). With few studies addressing these apparent discrepancies, a void in our knowledge of the relationship between the environment, morphology, and genetics needs to be filled, as it is impossible to truly address the discrepancies without this knowledge.

After reviewing the literature, it is clear that even within a data type, researchers approach how to analyze and interpret it differently. Comparing all results from mtDNA analyses, for instance, may not be appropriate. Research from mtDNA shows D-loop sequences, non D-loop sequences, whole sequencies, or haplogroup/haplotypes frequencies (e.g., Derenko et al., 2001; Fagundes, Kanitz, & Bonatto, 2008; Reidla et al.,

2003; Starikovskaya et al., 2005). In an ideal situation, all of these different parts of the mtDNA would lead to the same result. However, this is not the case, because there are different influences and biases. Again, we do not understand exactly what each type of information best conveys in terms of population histories or affinities, or their relationship to other data types.

There is currently no unifying model for human dispersal to the Americas, as exemplified by the discrepancies present, and described above, between morphological and molecular data. These discrepancies are not necessarily expected according to evolutionary theory, since cranial morphology has been shown to vary mostly according to the expectations of neutral evolutionary processes (e.g., Harvati & Weaver, 2006a;

Harvati & Weaver, 2006b; Hubbe, Hanihara, et al., 2009; Roseman, 2004) and population geneticists primarily use neutral molecular data (e.g., Francalacci et al., 2013;

Kuruppumullage Don et al., 2013; Lohmueller et al., 2011; Pearson, 2013; Weaver, 2014;

Wilder et al., 2004). One possibility for why these discrepancies are occurring is because the crania could show environmental effects or adaptations, which could overwhelm the genetically neutral signal that is expected (Roseman, 2016). The cranium could also not carry enough evolutionary information for these types of studies, as most cranial traits have only been shown to have a heritability of 20-60% (Roseman, 2016).

To circumvent the lack of biological remains, particularly in North America, a primary avenue of research has been DNA analysis, such as mtDNA, Y-chromosome

DNA, and autosomal DNA analyses (Battaglia et al., 2013; de Saint Pierre et al., 2012;

Dulik et al., 2012; Wang et al., 2007), with differing interpretations of the results. Most of these studies only use DNA from modern people, leaving some amount of speculation up to the researcher and less accurate results than if aDNA were being used instead. The same situation holds true for skeletal remains. There are few remains dating to the

Pleistocene-Holocene transition, with many more dating to later periods (Jantz &

Owsley, 2001; Pucciarelli et al., 2006). The lack of remains means that for both genomic and skeletal data, reconstructions of biological diversity often rely on material dating to much later than the known entry.

With the lack of both skeletons and DNA dating to the late Pleistocene and early

Holocene in the Americas, it is important to use an interdisciplinary approach, analyzing a combination of different data types. An interdisciplinary approach allows for a more

28 complete and comprehensive analysis to be performed. Consideration of the context from archaeological sites is important too, as this adds much needed context about the populations. In terms of the biological interdisciplinary approach, there is some previous work analyzing both DNA and skeletal morphology, but as reviewed above these studies come with disparate results depending on the statistical analyses performed (e.g., Herrera et al., 2014; Perez et al., 2009; Ricaut et al., 2010; Roseman & Weaver, 2007).

Furthermore, experts in the field need to agree upon the best information to use or incorporate into their analyses. If we are talking about mtDNA, researchers should decide which type of information (sequences, haplogroup frequencies, etc.) is the most valuable.

If not the most valuable, then which type is most affected by selection, which type is least affected by selection, etc. The discovery of this information could only be accomplished by doing extensive studies comparing the effectiveness of each data type when testing core assumptions in population genetics, such as assumptions of biological inheritance in each type of data. The same can be said about cranial morphological studies. This will allow for a better comparability between data types, and within each data type. My dissertation is a first step toward resolving these issues.

CHAPTER 3 : POPULATION BACKGROUNDS

The purpose of this chapter is to provide a brief background on all populations that are used in the analyses in Chapters 4, 5, and 6. The populations are discussed alphabetically first by country, then by population name. All names used for populations in mtDNA and Y-chromosome analyses are names used in the original source material.

All names used for the populations in the cranial analyses were the names used by the museum that houses the collection.

3.1 POPULATIONS USED FOR MTDNA ANALYSES

3.1.1 Argentina: Choroti

The Choroti, or Chorote, currently number about 2800 people. The Chorote language belongs to the Mataca-Macá family, and there are currently two dialects present depending on if the population is in Argentina or Paraguay (Siffredi, 2017).

The aboriginal Choroti lived in the semi-arid southern Chaco region of Argentina off the bank of the Río Bermejo until the second half of the seventeenth century.

Traditionally, the Choroti lived in semisedentary villages during the rainy season and temporary camps of fragmented family groups during the dry season (Siffredi, 2017).

Following colonial expeditions carried out by the Spanish, the Choroti were displaced to 30 the bank of the Río Pilcomayo, on the border of Argentina and Paraguay (Siffredi, 2017).

The climate there is very tropical and characterized by seasonal rain.

During the early twentieth century, several ethnic groups had territories bordering the Choroti: the Toba, Chiriguano, Tapieté, Ayoreo, Nivaclé, and Mataco-Guisnay

(Siffredi, 2017). The Choroti maintained peaceful relations with the Nivaclé and Tapieté through frequent intermarriage and military alliances, but the relationship with the other surrounding groups was hostile (Siffredi, 2017). The Chaco War (1932-1935) eventually forced the Choroti to move into evangelical, Mennonite, or Catholic missions in

Argentina and Paraguay (Siffredi, 2017).

3.1.2 Argentina: Fuegian

Fuegian populations refer to the peoples living in the Tierra del Fuego-Patagonia area, extending from the grasslands of central Argentina down to Patagonia and Isla

Grande de Tierra del Fuego. The groups consisted of the Pampas or Puelches, which lived in the area north of the Colorado River in central Argentina, the Tehuelches whose territory extended from the Colorado River to the Straits of Magellan, the Selk’nam who lived in the steppe regions of Isla Grande Tierra del Fuego, the Alakaluf who primarily lived on the islands and channels on the Chilean Pacific coast, and the Yahgan who inhabited coastal areas near the Beagle channel (González-José, García-Moro, Dahinten,

& Hernández, 2002). These groups did not develop agriculture or pastoralism, with many being hunter-gatherer or marine hunter-gatherer (González-José et al., 2002).

3.1.3 Argentina: Mapuche

Sometimes called Araucanian, the Mapuche currently have 113, 680 members living in Argentina (Nakashima Degarrod, 2009). The primary language of the Mapuche is Mapudungun, which belongs to the Mapuche Stock. There are several dialects of this language used by the Picunche, Pehuenche, Huilliche, and Chilote (Nakashima Degarrod,

2009).

Prior to European contact, the Mapuche occupied a large area of south-central

Chile, and therefore had different terms to distinguish members living in the different areas. Huilliche were ‘people of the south,’ Pehuenche were ‘piñon-eating people in the mountains,’ Lafquenche were ‘people of the coast,’ and Picunche were ‘people of the north’ (Nakashima Degarrod, 2009). It is debated if these different groups were originally part of the Mapuche, if the Spanish called all of these potentially separate groups

Mapuche, or if these were separate groups that the Mapuche conquered.

Aboriginal Mapuche were hunter-gatherers, but also practiced horticulture and agriculture. During the sixteenth century, the Mapuche were divided into three ethnic groups: the Picunche, the Mapuche, and the Huilliche (Nakashima Degarrod, 2009).

Shortly after, the Spanish arrived to the area and conquered the Picunche. They were eventually fully incorporated into the Spanish settlement, and the Picunche disappeared as an ethnic group (Nakashima Degarrod, 2009). The other two Mapuche groups, the

Mapuche and Huilliche, kept their independence from the Spanish for another four centuries (Nakashima Degarrod, 2009).

The four centuries of fighting the Spanish led to the Mapuche and Huilliche to migrate into Argentina. By the end of the eighteenth century, the Pehuenche, Puelche, and Pampa all spoke Mapudungun (Nakashima Degarrod, 2009). Eventually, a treaty was signed between the Spanish and the Mapuche, but in 1818 Chile won its independence from Spain. Chile took a large portion of the Huilliche and Mapuche land and allowed

German settlers to live there (Nakashima Degarrod, 2009). The Mapuche eventually lost their autonomy and military power to the Chileans. Chile then created a reservation system and moved the Mapuche there. In Argentina, the Mapuche were arrested and confined to the more remote regions (Nakashima Degarrod, 2009). At this point, the

Argentinean and Chilean Mapuche are relatively differentiated.

3.1.4 Argentina: Mataco (Chaco, Formosa)

There are currently about 12,000 Mataco living in Argentina, although that number is considered to be too low. The Mataco language belongs to the Mataco-Mak’a branch of the Macro-Guaicuruan family (Alvarsson & Beierle, 1997). There are currently three dialects present.

The Mataco traditionally lived in the Gran Chaco region, specifically in an area spanning from the foothills of the Andes in Bolivia to a town called Las Lomitas in

Argentina. This area is the hottest region of South America, with little precipitation and only a few colder days a year. Officially, this area is considered to be a semidesert climate.

While the Spanish made official contact with the Mataco in 1628, the area was not considered to be fully colonized until after the Chaco War (1932-1935) (Alvarsson & 33

Beierle, 1997). Pre-contact archaeological and ethnographic evidence indicates that the

Mataco had contacts with several of the Andean peoples, but mainly the Quechua.

Throughout history, it is known that they traded goods with and worked for the

Chiriguano (Alvarsson & Beierle, 1997). Despite this, it seems they have exchanged very little of their language and culture with other ethnic groups and resisted integration with the colonialists (Alvarsson & Beierle, 1997).

3.1.5 Argentina: Pilaga (Formosa)

The Pilaga are a group of people who live in the central part of Formosa, which is a province in Northern Argentina (Filipov, 1994). This region is characterized by a xerophytic deciduous forest, with a drought period for eight months out of the year

(Filipov, 1994). The Pilaga language belongs to the Guaycurú language family, along with the Toba, Mocoví, and the Mbayá.

Traditionally, the Pilaga were semi-nomadic but occasionally practiced horticulture. During the present century, Spanish South Americans began to settle on their land (Filipov, 1994). This forced the Pilaga to transition to a sedentary way of life, where they are dependent on urban centers to meet their needs (Filipov, 1994).

3.1.6 Argentina: Quebrada de Humahuaca

The Quebrada de Humahuaca is a narrow valley in the Andes, situated in the province of Jujuy and connecting the eastern plains, the Puna, and the southern hills

(Mendisco et al., 2014). The population of Quebrada de Humahuaca lived at altitudes around 2500-2500 m above sea level (Dipierri et al., 1998). The Quebrada de Humahuaca valley was a very strategic position, and it became a crossroads for the exchange of 34 cultural, economic, and social interactions (Cocilovo, Varela, & Valdano, 2001).

Between 1000-1450 CE, enough social changes had occurred that there were several local cultures (Mendisco et al., 2014). During the thirteenth and fourteenth centuries, many of the groups in this area were in constant war with each other. This is thought to have occurred due to severe droughts that created conflict over control for water sources

(A. E. Nielsen, 2001).

The Incas eventually controlled the Quebrada area, followed by the European settlers, which again shifted the demographics through war and relocation of indigenous groups.

3.1.7 Argentina: San Salvadore de Jujuy

The San Salvadore de Jujuy population has the same general background as the

Quebrada de Humahuaca (above) but are located at a different altitude level than the

Quebrada de Humahuaca (Dipierri et al., 1998). The Quebrada extend across altitudes around 2500-3500 m above sea level, whereas San Salvadore de Jujuy is in a lower altitude, around 1248 m above sea level (Dipierri et al., 1998).

3.1.8 Argentina: Tehuelche

The Tehuelche today are almost extinct, with less than 200 people currently alive

(Adem, 2009), largely due to conflict with colonialists and disease. The Tehuelche are one of three populations, with the others being the Poya and Ona, which comprise the

Chonan linguistic group (Gordon Jr, 2005). In the 1800s, it was reported that there were two Tehuelche groups, the Northern and Southern Tehuelche, with slightly different dialects (Adem, 2009). 35

The Tehuelche lived in an area spanning from north of the Chubut River down to the Strait of Magellan (Adem, 2009). Traditionally, the Tehuelche were hunter-gatherers and did not practice agriculture. After contact with Magellan and the Spaniards, the

Tehuelche adopted use of the horse for hunting and transportation (Adem, 2009).

3.1.9 Argentina: Toba (Chaco, Formosa)

The Toba lived in the central and southeastern Gran Chaco region of Argentina.

This area is made up of grasslands, semi-arid forests, and forests along riverbanks, with marked seasonal changes in temperature. Currently, the Toba currently live primarily in the Chaco and Formosa provinces (Valeggia & Tola, 2004). Toba languages are categorized in the Guaycurú language family, which also includes the Pilaga, the Mocoví, and the Mbayá (Mason, 1963). There are numerous languages and dialects spoken by the

Toba, with at least four languages mutually unintelligable between groups (Mendoza,

2002).

The Toba traditionally were organized into bands, with leadership extended to familial heads. They were nomadic or semi-nomadic hunter-gatherers, although they sometimes practiced horticulture (Valeggia & Tola, 2004). The Toba were able to resist

Spanish colonialization until the late 1800s and maintained a hunting-gathering lifestyle

(Valeggia & Tola, 2004). More recently, their traditional lifestyle and ecological habitat were disrupted, forcing communities to become more sedentary (Valeggia & Tola, 2004).

3.1.10 Bolivia: Aymara

The Aymara are located on the Bolivian, Chilean, and Peruvian Altiplano, primarily centered around Lake Titicaca. As with most other indigenous South American populations, the Spanish colonizers had lasting effects on the demographics of this population through epidemics, warfare, and exploitation (Johnsson, 1995). The current demographic state is difficult to assess, with the last accurate count of their population (in

Bolivia) being 1,785,000 and taking place in 1987 (Johnsson, 1995). Their language is grouped in the Andean-Equatorial language family and has a number of local dialects

(Johnsson, 1995).

The Aymara were organized into a series of independent states, likely each with different dialects prior to the Incan invasion (Johnsson, 1995). The conquest by the Inca led to a significant degree of acculturation until the Spanish arrived, which further affected the Aymaran culture. From 1820 until now, the Aymara have lived under the

Peruvian and Bolivian Republics (Johnsson, 1995). These governments have both started programs to develop rural areas and incorporate the indigenous populations into the mainstream population (Johnsson, 1995).

3.1.11 Bolivia: Chimane

There are four distinct ethnic populations living in the Piedmont region of

Bolivia: the Chimane, Mosten, Aymara, and Quechua (Corella, Bert, Pérez-Pérez, Gené,

& Turbón, 2007). The Piedmont area, which is situated between the tropical forests and the Andes, is known to have had populations living here since pre-ceramic periods

(Corella et al., 2007). The Chimane hunt and fish, but also practice agriculture. Currently,

37 there are around 4000 Chimane primarily located along several upstream rivers in the

Beni Department.

3.1.12 Bolivia: Ignaciano

The Ignaciano are a population living in the Beni Department of Bolivia. The

Ignaciano, along with the Trinitario and Movima, live in a seasonally overflowing savanna, while the Chimane, Moseten, and Yuracare live in the foothills of the Andes

(Bert et al., 2001). Early attempts to missionize the Ignaciano and Trinitario were unsuccessful until 1686 when upwards of 20 missions were built in the area over time to convert these two groups (Bert, Corella, Gené, Pérez-Pérez, & Turbón, 2004). These two groups were combined in the missions around the villages of San Ignacio and Trinidad

(hence the names Ignaciano and Trinitario).

Ignaciano and Trinitario were both labeled as ‘Moxo’ by the Jesuit missionaries, who they categorized as groups living in the forests and foothills of the upper Mamoré river. They both speak languages belonging to the Arawak language group, though each group has still maintained different, but very similar, languages (Bert et al., 2004). They are led by a chief with a division of labor, and have built complex structures, artificial hills, and elevated walkways (Bert et al., 2004).

3.1.13 Bolivia: Moseten

There are four distinct ethnic populations living in the Piedmont region of

Bolivia: the Chimane, Mosten, Aymara, and Quechua (Corella et al., 2007). The

Piedmont area, which is situated between the tropical forests and the Andes, is known to have had populations living here since pre-ceramic periods (Corella et al., 2007). The 38

Moseten are estimated to currently have a population of 1300 people. They live in Alto

Beni, which is on the Andean hills. Moseten are hunter-gatherers who also practice agriculture. They move frequently from place to place due to soil productivity for their agricultural plants (Corella et al., 2007). There are few people today who still speak

Moseten, which belong to the Macro-Panoan language family (Corella et al., 2007).

3.1.14 Bolivia: Movima

The history of this group is poorly understood. There are Spanish documents that briefly mention this group, but provide very little information (Bert et al., 2004). Toward the end of the 17th century and beginning of the 18th century, Jesuit missionaries

‘reduced’ or combined the Movima with other Moxo groups (likely with the Ignaciano and Trinitario). However, the language used by the Movima limited interaction with these groups and they remained relatively unmixed (Bert et al., 2004). Today, there is likely no more than 1000 people who still speak the Movima language, and it remains unclassified but belonging to the Equatorial-Tucanoan subgroup (Bert et al., 2004).

3.1.15 Bolivia: Trinitario

The Trinitario are a population living in the Beni Department of Bolivia. The

Ignaciano, along with the Trinitario and Movima, live in a seasonally overflowing savanna, while the Chimane, Moseten, and Yuracare live in the foothills of the Andes

(Bert et al., 2001). Early attempts to missionize the Ignaciano and Trinitario were unsuccessful until 1686 when upwards of 20 missions were built in the area over time to convert these two groups (Bert et al., 2004). These two groups were combined in the

39 missions around the villages of San Ignacio and Trinidad (hence the names Ignaciano and

Trinitario).

Semo, & Noza, 2009). Today, they still perform agriculture, but it is supplemented with fishing and hunting (Thomas et al., 2009). Many Trinitario can be found in the Territorio

Indígena Parque Nacional Isiboro-Securé. They share this land with the Yuracare,

Tsimané, Quechua, and Aymara (Thomas et al., 2009).

3.1.16 Bolivia: Quechua

There are four distinct ethnic populations living in the Piedmont region of

Bolivia: the Chimane, Mosten, Aymara, and Quechua (Corella et al., 2007). The

Piedmont area, which is situated between the tropical forests and the Andes, is known to have had populations living here since pre-ceramic periods (Corella et al., 2007).

Quechua-speaking peoples are the primary Amerindian population in the Andes, and

Quechua has become one of the most spoken native languages in the Americas (Corella et al., 2007). The Quechua language is also known as Inca language because it was the

40 language used by the Inca, and subsequently used by the different populations that the

Inca had influence over.

3.1.17 Bolivia: Yuracare

While writing about the Moxos (see Ignaciano or Trinitario), the Jesuits also mentioned a group called the Yuracare. It is thought that the Jesuits were not particularly interested in bringing the Yuracare into the ‘reduction’ system with the Moxos (Bert et al., 2004). In other words, they were not interested in combining all of these populations together, partially because they were a small population, but they also had a different culture than the Ignaciano or Trinitario (Bert et al., 2004). Missions were eventually set up with the Yuracare in mind, but they were unsuccessful in being missionized in large part because the missionary system was stationary (Bert et al., 2004).

It is unknown how widespread the Yuracare were prior to the arrival of the

Spanish. Today, the Yuracare live between the Secure and Ichilo rivers, near the foothills of the Andes (Bert et al., 2004). They are still nomadic, and in general have been more adaptive to forest living than either the Moxo or Movima. Although, some claim that calling them nomadic is an exaggeration and that they are territorially flexible (DOBES).

Their language is unclassified, but it belongs to the Ecuatorial-Tucanoan language stock

(Bert et al., 2004; Ruhlen, 1991). Their current population size is around 3000 people. In general, ethnographers and archaeologists have found that the Yuracare are more similar to populations found in the Chaco region than to their neighbors such as the Chimane,

Moseten, and Tacana.

3.1.18 Brazil: Guarani-Tupian

Early in Guarani history, the Guarani migrated out of the Amazon Basin to the

Paraná plateau. The Tupí-Guarani groups then moved east to the Atlantic (Reed &

Beierle, 1998). Due to conflicts with the Chaco, the Guarani did not move further west than the Paraguay river, although they did still participate in trade with the Inca and had extensive trade routes with other indigenous groups (Reed & Beierle, 1998). By the time the Spanish arrived, there were over a million Guarani present in southern South

America. While the first contact with the Europeans was peaceful, the Spanish quickly tried to use the Guaraní as forced labor, ending their peaceful contact (Reed & Beierle,

1998).

Disease from the Europeans killed some Guarani, while others were assimilated into the colonial system (Reed & Beierle, 1998). Still others were able to escape the colonialists by living in the forests and the Paraná plateau. These communities have maintained independence throughout this time, even with more recent attempts to exploit their living areas for timber, yerba mate, and essential oils (Reed & Beierle, 1998).

In total, there are currently more than 50,000 Guarani. All Guarani share a common language heritage, but there are many different dialects present (Reed & Beierle,

1998). The Guarani are proficient in gardening a variety of crops, and supplement their diet with hunting, gathering, and fishing (Reed & Beierle, 1998).

The Tupi-Guarani is technically defined as a cultural tradition based on specific pottery, but has been used interchangeably with both Tupi and Guarani, and sometimes

42 described as a specific linguistic stock (Noelli, 1998). See Brazil: Tupi below for a description of this culture.

3.1.19 Brazil: Jean

The Jean live in southern Brazil and belong to the Jean language family. Little is known about this population.

3.1.20 Brazil: Kaingang

The term Kaingang did not exist until 1882 and was used as a way to help distinguish all non-Guarani indigenous groups in southern Brazil. During contact period, this group was known as Guaianás or Coroados (Marrero et al., 2007). The Kaingang belong to the Jean language family, which is estimated to have originated around 3000 ybp (Marrero et al., 2007).

The Kaingang are recognized as descendants of native peoples in the Brazilian

Central-South plateau who lived in subterraneous houses (Schmitz and Becker, 1997) and were called Guayaná (Steward, 1963, p. 450). The Kaingang and their ancestors were primarily sedentary agriculturalists, although one ancestral group called the Botocudo relied solely on hunting (Steward, 1963, p. 450). As is typical of Native American populations, the Kaingang population was severely affected by European contact. Those who survived were moved to reservations in the states of Rio Grande do Sul, Santa

Catarina, Paraná, and São Paulo (Marrero et al., 2007). The Kaingang have been neighbors with the Guarani since the 17th century, but have remained both culturally and genetically distinct (Marrero et al., 2007).

3.1.21 Brazil: Krahô

The Krahô belong to a subgroup of the Timbira language family (Mistry et al.,

2005). Despite the Europeans arriving to the area in the 1500s, the Krahô did not have contact with Europeans until the beginning of the 18th century (Melatti, 1972; Mistry et al., 2005). They were originally seminomadic, but were involved with violent crashes with Europeans until the land they live today was given to them in 1944 (Mistry et al.,

2005).

The Krahô are hunters and gatherers, as well as shifting cultivators, who live in a savanna-forest environment in the state of Tocantíns. They are currently divided into two groups: the Wakmejê, which is the dry season or eastern group, and the Katamjê, which are the rainy season or western group (Mistry et al., 2005). Marriage is supposed to be between members of the opposite group, and decision-making is cycled through the males of the two groups depending on the season (Mistry et al., 2005).

3.1.22 Brazil: Macushi

The earliest confirmed reports about the Macushi are dated to 200 years ago, and place the Macushi on the Upper Essequibo River of Guyana. They were since displaced by the Wapishana and moved northwest. Some speculate that the Macushi were actually described by Raleigh in 1595 while he was in the lower Orinoco (Brett, 1868; Neel et al.,

1977). If this is true, then they moved much further south sometime around 300 years ago.

The Macushi currently live in the Roraima territory of northern Brazil/southern

Guyana. This is a savanna area, and is drained by the Rio Branco (Neel et al., 1977). 44

Currently, the territory of the Macushi and Wapishana are greatly overlapping. There is also admixture present with the Taurepan and Ingariko. The Macushi belong to a geographical grouping known as the Central Caribs and are a Carib speaking ethnic group (Neel et al., 1977). Two dialects are present, with one occurring in the Maú River area and Guyana, and the other occurring westward (Neel et al., 1977). It is estimated that there are around 3500 Macushi living in Brazil and scattered in approximately 68 villages

(Neel et al., 1977).

3.1.23 Brazil: Marubo

The Marubo remained relatively isolated until around the 1950s (Spielman et al.,

1982). In 1960, the New Tribes Mission made permanent contact with this group

(Spielman et al., 1982). Currently, this group is located near the Paraguaçu River. The

Marubo language is part of the Pano family, which also includes the languages of

Korubo, Mayoruna, and Shipibo. Currently, the men speak some Portuguese and have contact with the outside society, whereas women have little to no contact with anyone outside of the group. There is little known about their prehistory, but it is thought that they dispersed and regrouped periodically.

3.1.24 Brazil: Ticuna

The Ticuna originally lived in small tributaries on the left side of the part of the

Arawakan language branch, but it is now believed that the Ticuna just had a history of

45 interacting with the Arawakan tribes but with a separate language (Luedtke, 1990) . It is also possible that their language is related to the now extinct Yuri language (Kaufman,

1990).

The Ticuna are horticulturalists and fisher hunter-gatherers (Fajardo Reyes,

2010). Traditionally, the left bank of the Amazon was occupied by the Omagua, which was an enemy of the Ticuna (Fajardo Reyes, 2010). Other neighbors included the

Arawak, Mariaté, Yumana, Pasé, Peba, and Yagua. The Omagua, and several other neighbors have since either gone extinct or been assimilated into the wider Brazilian population (Fajardo Reyes, 2010). The Yagua, though, remain neighbors. Because the

Ticuna live relatively inland within the Amazon, they were less affected by the European colonists than other Amazonian tribes. However, the end of the 19th century saw a rise in rubber cultivation, through which many Ticuna were used as slave labor (Luedtke, 1990).

3.1.25 Brazil: Tupi

The Tupi refer to the broad group of peoples who belong to the Tupi language stock, which encompasses 41 languages spread throughout Brazil, Peru, Bolivia,

Paraguay, Uruguay, and Argentina (Noelli, 1998). This is a result of the large amount of territory the Tupi had in prehistory. Today, the Tupi are largely confined to various reservations.

During prehistory, the Tupi were thought to have originated in the Amazon rainforest and gradually spread southward and to the coast of Brazil, although the mechanism for this is poorly understood (Noelli, 1998). When the Europeans arrived, it was thought that their population was approximately 1 million individuals, and the first 46 contact was actually with the Tupi people. Starting from the 16th century, the Tupi were severely affected by European contact, largely due to disease, enslavement, or assimilation to European culture. For instance, there were few Portuguese women who came to the Americas with the Portuguese men, so the men commonly used the

Amerindian women as wives.

3.1.26 Brazil: Wapishana

European contact with the Wapishana is thought to have occurred around 200 years ago. As mentioned above in the description of the Macushi, the Wapishana displaced them from their land on the Upper Essequibo River of Guyana. While this was occurring, the Wapishana were also in the process of absorbing another group called the

Atorai (Neel et al., 1977). It has also been described that they incorporated the

Paraviyana (Neel et al., 1977; Rivet, 1924). Because of this, they are a very admixed group. During the first contact period, the Macushi and Wapishana were quite hostile with each other, but marriage between the two tribes were on the rise (Neel et al., 1977).

The Wapishana were one of four Arawak tribes present in this region (Wapishana,

Atorai, Amariba, Maopityan) (Neel et al., 1977). Each of tribes are Carib speaking and can understand each other’s dialects. It is estimated that there are around 1200 Wapishana in Brazil.

3.1.27 Brazil: Yanomama

The Yanomami have been particularly well studied by anthropologists, starting with Alexander von Humboldt in 1800, with the height of interest occurring in the 1970s and 80s (Sponsel, 1998). Most of these studies were on violence, as the Yanomami are 47 considered to be a highy violent society, although the degree of violence is often debated

(Chagnon, 1968; Ferguson & Farragher, 1988; Monaghan, 1994; Sponsel, 1998). The

Yanomami currently live along the border of Brazil and Venezuela in the Amazonian rainforest (Crews et al., 1993). Their territory is between the mountain regions of the

Orinoco and Amazon River Basins. There are around 20,000 people living in about 363 villages in this region, with only around 8500 in Brazil (Sponsel, 1998). The most isolated members live in the Surucucu plateau region in northwest Brazil (Crews et al.,

1993). There are four linguistic groups that the Yanomami belong to: Yanomamo,

Yanomam, Yanam, and Sanuma (Crews et al., 1993). The Yanomami are seminomadic and practice slash and burn agriculture.

3.1.28 Chile: Atacameños

The Atacameños people live in the Atacama Desert and associated altiplano region in northern Chile, Argentina, and southern Bolivia. Today there are currently around 21,000 Atacameño people living in Chile. While the Atacameno culture dates back to at least 500 AD, they were conquered and somewhat assimilated into the Inca culture at the beginning of the 15th century. During this time, the Inca introduced the

Atacameno to coca leaves and built roads leading from the Salar de Atacama to what is now eastern Argentina.

3.1.29 Chile: Aymara

The Aymara are located on the Bolivian, Chilean, and Peruvian altiplano, primarily centered around Lake Titicaca. The Aymara were organized into a series of independent states, likely each with different dialects prior to the Incan invasion

(Johnsson, 1995). The conquest by the Inca led to a significant degree of acculturation until the Spanish arrived, which further affected the Aymaran culture. However, they were able to maintain some degree of autonomy under the Inca. From 1820 until now, the

Aymara have lived primarily under the Peruvian and Bolivian Republics (Johnsson,

1995). After the War of the Pacific (1879-1883), Chile acquired some territory occupied by the Aymara. This is a smaller region with less people than either Bolivia or Peru. The

Aymara language is grouped in the Andean-Equitorial language family and has a number of local dialects (Johnsson, 1995). There is currently only one related language left, which is in the village of Tupe in the mountains around Lima, Peru.

3.1.30 Chile: Huilliches

The Mapuche occupied a large area of south-central Chile, and therefore had different terms to distinguish members living in the different areas. Huilliche was ‘people of the south,’ Pehuenche were ‘piñon-eating people in the mountains,’ Lafquenche were

‘people of the coast,’ and Picunche were ‘people of the north’ (Nakashima Degarrod,

2009). It is debated if these different groups were originally part of the Mapuche, if the

Spanish called all of these potentially separate groups Mapuche, or if these were separate groups that the Mapuche conquered. At one point, the Huilliche and Mapuche spoke the same language, but now Huilliche is a separate branch of the Araucanian language family.

A full description of this population is under 3.1.3 Argentina: Mapuche.

3.1.31 Chile: Mapuche

Sometimes called Araucanian, the Mapuche currently have 604,349 members living in Chile (Nakashima Degarrod, 2009). During the sixteenth century, the Mapuche were divided into three ethnic groups: the Picunche, the Mapuche, and the Huilliche

(Nakashima Degarrod, 2009). However, it is debated if these different groups were originally part of the Mapuche, if the Spanish called all of these potentially separate groups Mapuche, or if these were separate groups that the Mapuche conquered. Shortly after, the Spanish arrived to the area and conquered the Picunche. The other two

Mapuche groups, the Mapuche and Huilliche, kept their independence from the Spanish for another four centuries (Nakashima Degarrod, 2009).

A full description of this population is under 3.1.3 Argentina: Mapuche.

3.1.32 Chile: Pehuenche

‘people of the coast,’ and Picunche were ‘people of the north’ (Nakashima Degarrod,

2009). It is debated if these different groups were originally part of the Mapuche, if the

Spanish called all of these potentially separate groups Mapuche, or if these were separate groups that the Mapuche conquered.

A full description of this population is under 3.1.3 Argentina: Mapuche.

3.1.33 Chile: Yaghan

The Yahgan (sometimes spelled Yaghan) were a group of people who occupied the southern part of the island of Tierra del Fuego (Beierle, 2003). The Yahgan refer to themselves as Yámana, with term Yahgan being coined by a missionary named Thomas

Bridges and coming from the word “Yahga” which is a term to describe the Murray

Narrows region they often occupy (Beierle, 2003). After European contact, many individuals succumbed to disease brought over by the Europeans. It is currently thought that the Yahgan people have been completely intermixed with other ethnic populations

(Beierle, 2003), but there are disputed reports if this is the case. The Yahgan had five subdivisions within the tribe, and even though each had a different dialect they were mutually understood by each group (Beierle, 2003). Their language belongs to an isolated language group with no relationship to any other one (Beierle, 2003).

The Yahgan were fisher hunter-gatherers with no domesticated plants or animals except for the dog (Beierle, 2003). They lived in nomadic small groups with nonpermanent living structures. The participated in trade both within and outside of the tribe. The Alacaluf and Ona were other ethnic groups known to participate in trade with the Yahgan (Beierle, 2003).

3.1.34 Colombia: Antioquia

Antioquia is a province in northwestern Colombia. The most prominent settlements in Antioquia were established in the mid-17th century (Alvarez, 1996) and were located in two different valleys. From there, the populations expanded and followed the central cordillera (Christie, 1978).

Data from blood groups shows that the background for the Antioquia population consists of approximately 70% white, 15% Amerindian, and 15% African (Sandoval, De la Hoz, & Yunis, 1993). Additional research has shown differences in the origin of male and female founders to the Antioquia area (Carvajal-Carmona et al., 2000). The vast majority of the female founders were Amerindian (>90%) whereas male founders were primarily European (Carvajal-Carmona et al., 2000). Carvajal-Carmona et al. (2000) also demonstrated that the genetic distance between this Antioquia sample and a neighboring

Embera sample were not statistically significant (Carvajal-Carmona et al., 2000).

3.1.35 Colombia: Embera

The Embera belong to the linguistic family of Chocó (Frausin, Trujillo, Correa, &

Gonzalez, 2008). They live in tropical lowland forests near the Pacific coast in northwest

Colombia (Frausin et al., 2008). Traditionally, the Embera are hunter-gatherers that also practice horticulture and fishing (Herlihy, 1995).

3.1.36 Colombia: Ingano

The Ingano speak a Quechua language and reportedly migrated to Colombia from

Peru more than 100 years ago (Yunis, Yunis, & Yunis, 2001). Most currently reside in the Sibundoy valley and near the Putumayo River in the southern part of Colombia

(Keyeux, Rodas, Gelvez, & Carter, 2002). The Ingano currently live near a group called the Siona, who also live along the Putumayo River. The close proximity of the groups has meant there is a high degree of admixture (Keyeux et al., 2002).

3.1.37 Colombia: Piaroa

The Piaroa live near the Orinoco River Basin in Colombia and Venezuela, and specifically live near the Parguaza, Cuao, Sipapo, Autana, Cataniapo, and Carinagua rivers (Rodd & Sumabila, 2011). Their territory covers tropical highland areas, as well as lowland rainforests and savannahs. Traditionally, the Piaroa lived primarily in the highlands, but they moved out of these more isolated zones in 1960s and 80s (Rodd &

Sumabila, 2011).

Most of the subsistence activities revolve around cassava, which is a focus of women in the group (Rodd & Sumabila, 2011). Women also gather fruits, while men hunt and fish (Kaplan, 1975). The Piaroa language is one of only a few surviving Saliva languages, but many contemporary Piaroa also speak Spanish and the languages of neighboring groups, such as Guahibo, Curripaco, and Mapoyo (Rodd & Sumabila, 2011).

3.1.38 Colombia: Ticuna

The Ticuna originally lived in small tributaries on the left side of the part of the

Amazon River that flows into the Putumayo (Fajardo Reyes, 2010). Today, the Ticuna can be found in Brazil, Colombia, and Peru, with the majority residing in Brazil (~36,000 people). Currently, there are around 8,000 Ticuna in Colombia. The Ticuna’s language is a language isolate. It was once thought to be part of the Arawakan language branch, but it is now believed that the Ticuna just had a history of interacting with the Arawakan tribes but with a separate language (Luedtke, 1990). It is also possible that their language is related to the now extinct Yuri language (Kaufman, 1990).

The Ticuna are horticulturalists and fisher hunter-gatherers (Fajardo Reyes,

2010). Traditionally, the left bank of the Amazon was occupied by the Omagua, which was an enemy of the Ticuna (Fajardo Reyes, 2010). Other neighbors included the

3.1.39 Colombia: Wayuu

The Wayuu live in northern Colombia and Venezuela on the Guajira Peninsula.

Their territory follows a typical equatorial weather pattern of a rainy and dry season. The

Wayuu are organized in matrilineal families and still maintain many aspects of a tribal system (decentralized political system, clan affiliation, a recognition of different levels of wealth or prestige acquired through both pastoralism and social ties) (Bates, 2004). The

Wayuu language is called wauyynaiki and is part of the Arawak language family.

The Wayuu have historically resisted European domination (Harker & Curvelo,

1998) even though they were one of the first groups to have contact with the Europeans in South America. This, however, does not mean that appropriation of European elements have not occurred. For example, the Wayuu were horticulturalists and hunter-gatherers, including coastal fishing (Perrin, 1980). Approximately 50 years after European contact, the Wayuu began incorporating livestock into their economic system (Barrera, 2000).

3.1.40 Colombia: Zenu

The current population of the Zenu is approximately 53,000 people (Keyeux et al., 2002). The Zenu live close to Venezuela in the Córdoba and Sucre Departments in the

Santander Department (Keyeux et al., 2002). The Zenu have been shown to not be significantly differentiated from the Embera, Warao, or Yukpa when using Y- chromosome DNA (Ascunce, Gonz, xc, Lez-Oliver, & Mulligan, 2008).

In prehistory, the Zenu produced a lot of gold artifacts, which lured the Spanish to their settlements. The Zenu mostly died out after this contact in the 16th century. While the Spanish were there, they wrote some about the Zenu they came in contact with but did not record their history.

3.1.41 Ecuador: Cayapa

The Cayapa, also called Chachi, have a somewhat vague history (Skoggard,

2012). According to oral tradition, the Cayapa originated in the Ibarra region. When the

Spanish invaded, they fled from the Ibarra to their current home along rivers or tributaries of the Cayapa basin (Skoggard, 2012). This movement of the Cayapa caused the Indios

Bravos to be displaced. However, some ethnohistorians argue that the Cayapa were living along the coastal areas and were pushed upriver by a population of marooned slaves in the late 18th century (Skoggard, 2012). Archaeological evidence supports this more recent arrival to the Cayapa area (Skoggard, 2012).

Currently, the Cayapa live along the Rio Cayapas in the northwestern Esmeraldas

Province in Ecuador. This is a tropical rainforest area with daily rain. They use the language Cha’palaachi, which is in the Barbacoan linguistic stock of the Paezan 55 subfamily and the Macro-Chibchan family (Skoggard, 2012). Although, some linguists find similarity between their language and Mayan language (Skoggard, 2012). The

Cayapa utilize fishing, hunting, and farming for subsistence, and grow a wide variety of crops including plantain, maize, papaya, and cherimoya.

3.1.42 Peru: Ancash

Ancash is a region in northern Peru that is both coastal and mountainous. The

Quechua (although a broad group encompassing numerous cultural populations; see Peru:

Quechua below for more information) are generally ascribed to living in this area, although groups belonging to the Yunca linguistic family may have lived here as well

(Steward, 1950, p. 194). One of the most prominent groups known to inhabit the Ancash region are the Chavín (named after Chavín de Huántar) (Steward, 1949, p. 417). The most habitable areas in this region are mountain piedmont valleys (Steward, 1950, p.

328).

3.1.43 Peru: Arequipa

The Chuquibamba culture was a pre-Colombian culture in what is now the

Arequipa area. The population the DNA was extracted from in this area is unknown and may not have been related to the Chuquibamba.

3.1.44 Peru: Tayacaja

The Tayacaja province in the Peruvian Andes lies approximately 3,800 meters above sea level. Individuals from this area are Quechua speakers and largely belong to farming communities that were in place prior to the arrival of the Spanish (Luiselli,

Simoni, Tarazona-Santos, Pastor, & Pettener, 2000). Biodemographic studies on this 56 area, such as Pettener, Pastor, and Tarazona-Santos (1998), show a low level of interethnic marriage occurring in the area during the last few centuries (Luiselli et al.,

2000).

3.1.45 Peru: Tupe

The District of Tupe is a highland plaza located approximately 3,000 m above sea level. The current population size in this district is quite low due to an increase in violence and people moving to larger cities (Hardman, McCoy, Beck, & Legg, 2007).

This district contains three populations: Tupe, Ayza, and Colca (Hardman et al., 2007).

The Tupe speak the language of Jaqaru, which belongs to the language family Jaqi.

Jaqaru is currently an endangered language (Hardman et al., 2007).

3.1.46 Peru: Quechua

Quechua refers to anyone who speaks the Quechua language. The Quechua language is also known as Inca language because it was the language used by the Inca, and subsequently used by the different populations that the Inca had influence over. Most

Quechua people live in the Andean highlands from Peru, Ecuador, and Bolivia. However, there are also Quechua in Chile, Argentina, and Colombia. When the Spaniards first arrived and began their conquest, Quechua was used as a lingua franca between them and the Indigenous people (Hornberger & King, 2001). As the Spanish conquered more and more people, the Spanish language began to spread and the Quechua language began to be ignored and/or oppressed (Hornberger & King, 2001). Even today, when there are at least eight million Quechua speakers, Quechua is associated with rural, poor people

(Hornberger & King, 2001).

The highlands of Peru have several ethnic groups that are considered Quechua, including: Anqaras, Amantani, Q’ero, Huanca, Chanka, and Taquile. See Peru: Huanca in

“Populations Used for Y-Chromosome DNA Analysis” for more information on the

Huanca.

3.1.47 Venezuela: Makiritare

The Makiritare, also called the Yekuana, first had contact with European explorers in the mid 1700s. At the time, the Makiritare lived along different tributaries in the tropical rainforests of the Upper Orinoco, which is an area that covers present day

Venezuela, Colombia, and Brazil (Guss, 1982). They are both a hunter-gatherer and a horticultural society. The Makiritare are one of many groups who speak a language in the

Cariban linguistic family (Guss, 1982).

Today, there are approximately 6,250 Makiritare living in Venezuela. Most live on the Alto Orinoco-Casiquiare Biosphere Reserve, which was designed with the goal of preserving the traditional territory and lifestyle of both the Yanomami and the Makiritare.

This means that today, they live as neighbors to the Sanumá (a subgroup of the

Yanomami), who were their former enemies.

3.2 POPULATIONS USED FOR Y-CHROMOSOME DNA ANALYSES

3.2.1 Argentina: Choroti

See 3.1.1 Argentina: Choroti in the above section for complete population description.

3.2.2 Argentina: Colla

The Colla currently live in the mountainous region of the Tucemán district of

Argentina (Toscanini et al., 2008). They are descended from the ‘Calchaquí’ people, who are one of the original groups of the Diaguita culture in the Calchaqui valleys (Toscanini et al., 2008). Although they specifically came in contact with the Spanish in 1540, they were able to resist Spanish forces for over 100 years. The Colla, specifically, are the descendants of the first cultural and/or genetic exchange between the the Calchaquí and the Spanish, as the original culture is now extinct in its non admixted form (Toscanini et al., 2008).

3.2.3 Argentina: Fueguian

See 3.1.2 Argentina: Fuegian in the above section for complete population description.

3.2.4 Argentina: Mapuche

See 3.1.3 Argentina: Mapuche in the above section for complete population description.

3.2.5 Argentina: Mataco (Chaco, Formosa)

See 3.1.4 Argentina: Mataco (Chaco, Formosa) in the above section for complete population description.

3.2.6 Argentina: Pilaga (Formosa)

See 3.1.5 Argentina: Pilaga (Formosa) in in the above section for complete population description.

3.2.7 Argentina: Quebrada de Humahuaca

See 3.1.6 Argentina: Quebrada de Humahuaca in the above section for complete population description.

3.2.8 Argentina: San Salvadore de Jujuy

See 3.1.7 Argentina: San Salvadore de Jujuy in the above section for complete population description.

3.2.9 Argentina: Tehuelche

See 3.1.8 Argentina: Tehuelche in the above section for complete population description.

3.2.10 Argentina: Toba (Chaco, Formosa)

See 3.1.9 Argentina: Toba (Chaco, Formosa) in the above section for complete population description.

3.2.11 Bolivia: Chimane

See 3.1.11 Bolivia: Chimane in the above section for complete population description.

3.2.12 Bolivia: Mojeño

The Mojeño have been in contact with Catholic missions and missionaries, as well as cattle ranchers for several centuries (Jones, 1991). Because of this contact, the

Mojeño currently have a denser population than other indigenous populations in the area, and their communities feature more out-migrants (Godoy, Franks, & Claudio, 1996). The

Mojeño practice swidden farming along river banks, but also incorporate foraging

(Alvarado, 1996). The Mojeño can be split into subgroups, such as Ignaciano or

Trinitario, with the primary difference between these populations being language.

3.2.13 Bolivia: Trinitario

See 3.1.15 Bolivia: Trinitario in the above section for complete population description.

3.2.14 Brazil: Arara

The Arara are a Karib-speaking group living in the Amazon. Historically, the

Arara have been involved in several conflicts with non-Indigenous people who invaded their territory (Ribeiro-dos-Santos, Guerreiro, Santos, & Zago, 2001). This is particularly true during the construction of the Transamazonica highway (Ribeiro-dos-Santos et al.,

2001). Largely due the construction of this highway, the Arara territory and population size was greatly reduced, which was followed by fission and fusion events (Ribeiro-dos-

Santos et al., 2001).

3.2.15 Brazil: Asurini

The Asurini are located in the Pará district of Brazil, primarily between the Xingu and Bacajá Rivers. The Asurini language is Asurini do Xingu, which belongs to the Tupi-

Guarani language family (F. A. Silva, 2008). Their primary subsistence strategy is agriculture, with the addition of hunting, fishing, and gathering (F. A. Silva, 2008).

The Asurini have a history of conflict and periods of depopulation (F. A. Silva,

2008). They were officially first contacted by non-indigenous groups in the 1970s, which caused a further decline in their population due to flu, malaria, and tuberculosis (B. G.

Ribeiro, 1982). By the 1980s, their population had been reduced to 52 individuals

(Müller, 1990). Since this time, partially due to the increase of medical assistance and cultural changes, the population has increased to approximately 126 by 2006.

3.2.16 Brazil: Awa-Guajá

The Awa-Guajá are a group of hunter-gatherers that are transitioning to agriculture (as of 2011) (Hernando, Politis, Ruibal, & Coelho, 2011). While they traditionally hunt, gather, and fish, other food sources have been produced for them by the FUNAI agency. They are currently cultivating some of these foods themselves. It has been suggested that the Awa-Guajá were once horticulturalists, but had to resort to hunting and gathering to avoid colonization (Balée, 1994). The Awa-Guajá reside in the state of Maranhão, on the eastern side of the Amazon in land that was set aside as a reservation (Hernando et al., 2011).

3.2.17 Brazil: Gavião

The Gavião are a population in northern Brazil. Gavião was attributed to some of the Timbira groups, and they are part of the Gê peoples. Currently, they are divided into two different groups based on the Brazilian state they are residing: the Parakáteye who live in the state of Pará and the Pukobyé who live in the state of Maranhão. Historically, the Gavião were divided into three groups due to warfare amongst themselves, but all three groups are currently reunited. Their population brought to near extinction in the

1970s due to clashes with extractivists claiming resources on their land (Ferraz, 1998), but currently the Gavião have a population of approximately 340.

3.2.18 Brazil: Ipixuna

The Ipixuna is a river that flows into the Xinghu River in northern Brazil. One of the primary populations living along the Ipixuna River is the Araweté (Balee, 2013). The

Araweté are a Tupi-Guarani people and have lived in the general area for several centuries. Between 1976 and 1977, a third of the Araweté population was lost to disease from contact with non-Indigenous populations and attacks from the Parakanã (de Castro,

1992). More currently, their population size is reportedly around 276. Their history in general involves successive conflict with neighboring groups, typically requiring them to relocate to another nearby area. By the mid-1970s, the area of land they were living on was not conducive to growing Maize, which is why they initially started contact with non-Indigenous people (de Castro, 1992).

The Araweté live close to the Asurini. These populations share similar, but distinct, language and cultural practices (Balee, 2013; de Castro, 1992).

3.2.19 Brazil: Karitiana

The Karitiana currently reside in the state of Rondônia in the southwestern

Amazon on a reservation covered in rain forest (Ferrari, Ferreira, Tanaka, & Mizokami,

1999). Their language belongs to the Tupi-Arikén language family.

3.2.20 Brazil: Kayapó-Xikrin

The Kayapó-Xikrin are a subgroup of the Kayapó and speak a language of the Jê family (Vallinoto et al., 1999). They live in the State of Pará on a tributary along the

Bacajá and Xingu River (Vallinoto et al., 1999). This area of land is a reservation created

63 just for this group. They live in two villages: the Bacajá and Cateté (D. M. Ribeiro,

Figueiredo, Costa, & Sonati, 2003).

The Kayapó were previously split into two main groups: the northern Kayapó and southern Kayapó (Steward, 1963, p. 477). The southern Kayapó are now extinct

(Steward, 1963, p. 478). Currently, it is recognized that there are four subgroups of

Kayapó: Xikrin, Gorotire, Mekranoti, and Metyktire.

3.2.21 Brazil: Parakanã

The Parakanã belong to the Tupi language family. While it is sometimes assumed that there is admixture with Europeans, due to their light colored skin, there is no evidence of genetic influence from Europeans and their first known contact with non- indigenous groups was in 1971 (D. M. Ribeiro et al., 2003). The Parakanã live in the southern portion of the State of Pará in northern Brazil. They are located in three villages

(Paranati, Marudjewara, and Bom Jardim) (D. M. Ribeiro et al., 2003). Their nearest neighbors are the Kayapo.

3.2.22 Brazil: Terena

In pre-Colombian times, the Terena were located in the northern portion of the

Chaco area (Francisco M. Salzano & de Oliveira, 1970). During the late 17th century, the

Terena crossed the Paraguay River into the State of Mato Grosso near the Miranda River

(Francisco M. Salzano & de Oliveira, 1970). This is the same general area where they are living today (Francisco M. Salzano & de Oliveira, 1970).

The Terena’s language belongs to the Arawak language family. In general, the

Terena are agriculturalists, but some communities supplement this lifestyle with hunting and gathering.

3.2.23 Brazil: Tiriyo

The Tiriyo speak a language belonging to the Carib language family. During the

1970s, there were approximately 800 individuals living in their communities (F. M.

Salzano et al., 1974). They live in savannahs near the Brazil-Surinam border, north of the

Amazon River (Black, Woodall, Evans, Liebhaber, & Henle, 1970). It is understood that there was a lot of violence between the Tiriyo and neighboring indigenous communities, sometimes resulting in the Tiriyo taking captives (mostly women) and incorporating them into their own community (Black et al., 1970).

3.2.24 Brazil: Urubu-Kaapor

The Urubu-Kaapor live in the northwest portion of the State of Maranhão. This area can be characterized as an interfluvial rain forest, bordered by the Gurupi and

Turiaçu rivers (Aguiar & Neves, 1991). They speak an Urubu language belonging to the

Tupi-Guarani language family (Aguiar & Neves, 1991). The Urubu-Kaapor live in the northern part of a reservation that is also shared with the Tembé and Guajá (Aguiar &

Neves, 1991). The reservation area, however, was not their original location. During the

1870s, the Urubu-Kaapor had to move due to colonists invading their territory (Aguiar &

Neves, 1991). In spite of this, the Urubu-Kaapor have been able to maintain many aspects of their cultural history, including their social organization and ritual ceremonies (Aguiar

& Neves, 1991). The Urubu-Kaapor’s marriage strategy is intratribal endogamy, although some tribes are known to have a high male migration rate (Aguiar & Neves, 1991).

3.2.25 Brazil: Waiãpi

The Waiãpi live in an area between the States of Pará and Amapá, north of the mouth of the Amazon. Some members of this population currently live in Guyana as well. They belong to the Tupian linguistic family and were originally living south of the

Amazon but fled north due to fear of the Portuguese settlers (Steward, 1948, pp. 814-

815). They later began working with the Portuguese, providing them with slaves from neighboring Carib tribes (Steward, 1948, p. 815). They now live as a single nation with the Emerillon (Steward, 1948, p. 815).

3.2.26 Brazil: Yanomama

The Yanomama live in a tropical forest area of Brazil, near the border of

Venezuela. Unlike many other Amazon groups, the Yanomama do not have a watercraft tradition and instead mostly travel by foot (Chagnon, Neel, Weitkamp, Gershowitz, &

Ayres, 1970). The Yanomama live in villages and an exact population count has been difficult to estimate due to the lack of contact in some locations (Chagnon et al., 1970).

Their territory intersects that of the Makiritare of Venezuela. While relations have been tense between these two groups in the past, more recently the two are amicable and some villages are now a mixed group of Yanomama and Makiritare (Chagnon et al., 1970).

3.2.27 Brazil: Zoé

The Zoé live in the northwest portion of the State of Pará bordered by the

Cuminapanema and Erepecuru rivers. Their language is in the language family Tupí 66

(Vallinoto et al., 1999). The Zoé were not in contact with any non-indigenous group until

1982. Currently, their population is around 150 individuals (Vallinoto et al., 1999).

3.2.28 Colombia: Embera-Chami

The Embera-Chami are a subgroup of the Embera (described briefly in section

3.1.35 Colombia: Embera). The Embera are divided into three different subgroups, with each one denoting a different lifestyle: Embera Dóbida, meaning “riverside’s inhabitant”;

Embera Chami, meaning “forest’s inhabitant”; and Embera Eyábida, meaning

“mountain’s inhabitant (Hernández Sarmiento et al., 2013).

3.2.29 Colombia: Guambiano

The Guambiano primarily live in the Cauca Department of Colombia. Currently, population estimates for the Guambiano range from 10,000-18,000 individuals (Yunis et al., 2001). Traditionally, the Guambiano language was classified as part of the Chibcha language family, but some studies suggest that there is no classification for their language as of yet (Yunis et al., 2001).

3.2.30 Ecuador: Kichwa

The Kichwa are a large group of people, with a population estimate reaching around three million people (Gonzalez-Andrade, Sanchez, Gonzalez-Solorzano, Gascon,

& Martinez-Jarreta, 2007). Their language is a mix of languages used by local populations and the language of the Inca empire (Gonzalez-Andrade et al., 2007). In Peru and Bolivia this is called Quechua, and while these are related languages, the Kichwa’s is distinct (Gonzalez-Andrade et al., 2007).

The Kichwa currently live in both the Andean highlands and the Amazonian region (Gonzalez-Andrade et al., 2007). There are distinct cultural and linguistic patterns between the highland and Amazonian populations.

3.2.31 Ecuador: Waorani

The Waorani are a group of people who live in the Amazonian region, south of the Rio Napo, in Ecuador (Davis & Yost, 1983). The Waorani remained relatively isolated from both indigenous and non-indigenous peoples until the 1950s and had a reputation for being violent toward outsiders (Davis & Yost, 1983).

Waorani are hunter-gatherer horticulturalists and participate in fishing as well.

Fishing seemed to be a smaller portion of their activities though because they had taboos toward the majority of fish and aquatic animal species (Davis & Yost, 1983). The

Waorani consider themselves to be people of the forest and many subsistence and cultural practices revolve around the forest (Davis & Yost, 1983).

3.2.32 Peru: Chumbivilca

Chumbivilcas is a province in the Andes in southern Peru. The population DNA was extracted from in this area is unknown.

3.2.33 Peru: Chuquibamba

The Chuquibamba culture was a pre-Colombian culture in what is now the

Arequipa area. The population DNA was extracted from in this area is unknown.

3.2.34 Peru: Huanca

The Huancas are also called Wancas or Wankas. Prior to the Inca conquest, the

Huanca population were a series of decentralized groups with competing polities

(D'Altroy & Hastorf, 1984). In total, there were about 135,000 people comprising the population. They were living in the Xuaxa region of Peru, which was a region that was logistically important for the Inca to control (D'Altroy & Hastorf, 1984). In addition, the topography of this region allowed for several different products to be grown, such as quinoa, maize, and camelids (D'Altroy & Hastorf, 1984). Later, the Huanca aided the

Spaniards during the conquest of Peru.

The Huanca today speak their own dialect of Quechua (Knapp, 1987).

3.2.35 Peru: Huancavelica

Huancavelica is a region and city near the coast in southern-central Peru. The population DNA was extracted from in this area is unknown.

3.2.36 Peru: Santiago de Chuco

Santiago de Chuco is a region and city near the coast in northern Peru. The population DNA was extracted from in this area is unknown.

3.2.37 Peru: Shipibo-Conibo

Originally, the Shipibo and Conibo were two separate populations. The Shipibo originally lived on the upper Aguaytia River, but were driven from the area by the

Cashibo (Steward, 1948, p. 561). The Shipibo then drove the Conibo from the mouth of the Aguaytia River, who then moved to the Ucayali River (Steward, 1948, p. 561). The

Shipibo were highly resistant to the missionaries when they arrived in the mid-1600s, and even joined other groups in attacking them (Steward, 1948, p. 561). However, they eventually joined a mission but later killed the missionary. Other missionaries arrived in the late 1700s, and many Shipibo eventually joined various missions (Steward, 1948, p.

562).

The Conibo had a slightly different history, with stories describing that they were descended from the Inca (Steward, 1948, p. 563). However, the Conibo, and several neighboring populations, joined the Shipibo on many of the revolts against the missionaries (Steward, 1948, p. 563). These two separate groups eventually formed one larger group via intermarriage, and are currently known as the Shipibo-Conibo.

3.2.38 Venezuela: Bari Boxi

The Barí are a group of people living in northwastern Venezuela, as well as northeastern Colombia. They are agriculturalists, hunters, and fishers who engage in cash cropping and cattle raising so as to not lose their territory (Lizarralde, 1992). Cash cropping and cattle raising are considered “improvements to the land” by the Venezuelan government, through which people are then allowed to claim ownership over (Lizarralde,

1992).

3.2.39 Venezuela: Wayuu

The Wayuu, also known as Goajiro, live along the border of Colombia and

Venezuela. Their language belongs to the Arawak family of languages. However, members in the northern and southern areas of their territory speak distinct, but mutually intelligible, languages (Perrin, Abate, Beierle, & Doyon, 2012). The Wayuu’s early 70 history is relatively unknown, but based on linguistics alone, it is likely they originated in the Amazonian region (Perrin et al., 2012).

Wayuu society was previously based on horticulture, gathering, hunting, or fishing. More currently, the Wayuu have a strict hierarchical pastoral system. This shift was a gradual change brought on initially by raids and theft of livestock on European settlers by the Wayuu (Perrin et al., 2012). After this, pastoralism became more popular through their contact by Dutch, French, and English pirates who were all hostile with the

Spanish, as well as African slaves who had come to live with the Wayuu (Perrin et al.,

2012).

3.3 POPULATIONS USED FOR CRANIAL ANALYSES

3.3.1 Chile: Araucania (Mid Chile)

At the turn of the nineteenth century, Britain found themselves in a position where they had great influence and knowledge of many places around the world, but knew little of South America. Their knowledge of the continent was relayed by historians and mostly revolved around the colonization by Spain and Portugal a few hundred years prior

(Fulford, 2008). Looking to advance their influence, Britain found Chile particularly enticing, as it was believed to be the most vulnerable (Fulford, 2008). Viewing themselves as more mild-mannered than Spain and/or Portugal, Britain believed that the

Native Americans of South America would be more welcoming toward them (Fulford,

2008). This view was completely focused on the Native American group that the

Europeans called Araucanians. However, in reality, the Araucanians are actually divided 71 into three different groups called the Mapuche, Huilliche, and Picunche. As stated above, the Mapuche were able to resist European invaders for a number of years (Nakashima

Degarrod, 2009). The British viewed this group as the opportunity for influence, which was only intensified by the popularity of an epic poem written by one of the conquistadors called La Araucana (1589) (Fulford, 2008). However, Britain was soon faced with invasion by the French and Spanish, and so their focus shifted away from

South America (Fulford, 2008).

Most information that is known about these groups are from various European conquerors or explorers to the region. See 3.1.3 Argentina: Mapuche in above for complete population description.

3.3.2 Chile: Azapa (Arica)

The Azapa Valley contains a series of burial sites. The inhabitants of the Azapa

Valley practiced a mix of agropastoral and maritime subsistence practices (Sutter &

Mertz, 2004). These sites are dated either to the Formative Period, Middle Horizon, or the

Late Intermediate Period. The Formative Period began with the arrival of ceramics to the region, and many of these ceramics have been found in a burial context (Sutter and

Mertz, 2004). The Middle Horizon within the south-central Andes is defined as the presence of Tiwanaku V ceramics, although many grave sites have Cabuza and Maitas-

Chiribaya artifacts (Sutter & Mertz, 2004). The Late Intermediate Period has been defined, within the context of the Azapa Valley, as the emergence of the San Miguel,

Pocoma, and Gentilar ceramics (Sutter & Mertz, 2004). These ceramic traditions are restricted to the coastal valley of Chile and southern Peru and did not reach the altiplano.

3.3.3 Chile: Camarones (Arica)

The Camarones sites are all located in the Camarones River Valley, about 100 km south of the city of Arica (near to where the collections are held in Azapa) (Byrne et al.,

2010). The Camarones valley was the primary location for the Chinchorro culture. The

Chinchorros were a fishing-hunting-gathering coastal group that lived along the coast of southern Peru and northern Chile (Arriaza et al., 2010). Around 7500 years B.C., the

Chinchorro developed a system of mummification (Arriaza et al., 2010).

3.3.4 Chile: Costa Sur (Mid Chile)

Costa Sur was historically populated by the Promaucaes, which was a Mapuche group. They traditionally lived south of the Maipo River valley. This area may have also had Chango and Huilliches populations living in the area.

See 3.1.3 Argentina: Mapuche above for complete population description.

See 3.1.30 Chile: Huilliches above for complete population description of the

Huilliches people.

3.3.5 Chile: Coyo Oriente

The site of Coyo Oriente is located near the oases around San Pedro de Atacama,

Chile. These oases are located between the San Pedro river (to the west) and the Vilama river/Andean Cordillera (to the east) (Rodman, 1992) and at the northern tip of the Salar de Atacama (Torres-Rouff & Hubbe, 2013). There are several other oases and oasis communities that were located in the general area, including Solcor, Quitor, and Tulor

(Rodman, 1992). These oases were occupied for about 2,500 years (Torres-Rouff &

Hubbe, 2013).

The Coyo Oriente cemetery was in use from A.D. 500 to A.D. 900 (Rodman,

1992). This time period is also when the Tiwanaku had the most influence, and it has been well established that the tombs held large quantities of Tiwanaku influenced items

(Rodman, 1992; Torres-Rouff, 2002). While the extent of this influence is still unknown, the Atacameños did appear to maintain a separate cultural identity from the Tiwanaku

(Torres-Rouff, 2002).

3.3.6 Chile: Estancia los Vicunas (Patagonia)

Estancia los Vicunas is an area, and part of the Karukinka Natural Park on Isla

Grande in Tierra del Fuego. Two groups lived on Isla Grande: the Selk’nam and the

Haush. Estancia los Vicunas is located in what was traditionally the Selk’nam territory.

For more information on Isla Grande, see 3.3.10 Chile: Isla Grande Bahia

(Patagonia) below.

For more information on the Selk’nam, see 3.3.15 Chile: Selk’nam (Patagonia) below.

3.3.7 Chile: Halakwulup (Patagonia)

The Halakwulup (also known as Alacalufe) refer to themselves as Kawéssqar.

They first encountered Europeans when an explorer named Jofré de Loaysa reached their territory in 1526. At that time, the Halakwulup numbered around 4,000 and remained that way until the end of the 18th century (Precolombino, n.d.). After that time, colonists were

74 always in the area, and the Halakwulup numbers began decreasing due to conflict with the colonists and disease (Precolombino, n.d.). By 1925, there were only 150 Halakwulup alive. The Chilean government later moved all of the Halakwulup to Puerto Edén on

Wellington Island (Precolombino, n.d.).

Historically, the Halakwulup territory extended from the Gulf of Penas to the

Cockburn Channel (Precolombino, n.d.). There are very few beaches and dense forests cover most of the islands. Additionally, the climate is very cold, with yearly temperatures ranging from 23 to 50˚F.

The Halakwulup were nomadic sea-faring people who hunt, gather, and fish

(Kelleher, 2017). Families of around 10 people typically traveled from place to place together (Precolombino, n.d.). They speak a language called Kawéssqar, but was not mutually inteligible with Yaghan or Selk’nam languages (Precolombino, n.d.). There are currently only 7 people who can speak this language.

3.3.8 Chile: Fueginos (Patagonia)

The Fueginos is a general name for the peoples living in Tierra del Fuego.

According to historical and ethnographic records, there were four main ethnic groups: the

Selk’nam, Yaghan, Alakalufe, and Haush.

The Alakalufe (or Halakwulup) refer to themselves as Kawéssqar. For more information, see 3.3.7 Chile: Halakwulup above.

For more information on Feuginos, see 3.1.2 Argentina: Fuegian in the above section.

For information on the Haush or Selk’nam, see 3.3.10 Chile: Isla Grande Bahia

(Patagonia) or 3.3.15 Chile: Selk’nam below.

For information on the Yaghan, see 3.1.33 Chile: Yaghan in the above section.

3.3.9 Chile: Isla Dawson (Patagonia)

Isla Dawson is characterized by plains, with mountainous areas located to the south of the island. Isla Dawson is located on border of the territories of the Alakalufe (or

Kawésqar), Yamana, and Selk’nam. It has been proposed that the inhabitants of this island are called Selkkar, which is a combination of Selk’nam and Kawésqar, but it’s been subsequently argued that this designation does not reflect reality (Legoupil,

Christensen, & Morello, 2011). It is actually not clear how to distinguish between some ethnic groups in Patagonia and Tierra del Fuego due to how little we know about their mobility, cultures, or land/sea territory (Legoupil et al., 2011).

There is archaeological evidence on Isla Dawson that people were hunting large birds, such as albatrosses, and fishing for deep-sea fishes (Legoupil et al., 2011). It is thought that to perform deep-sea fishing, fish hooks are required, but there is not physical evidence for this yet. After the colonization period, there are links between the culture on

Isla Dawson to some of the land hunters of Tierra del Fuego (Legoupil et al., 2011).

3.3.10 Chile: Isla Grande Bahia (Patagonia)

Isla Grande was inhabited by both the Selk’nam and the Haush. The Haush inhabit the southeastern tip of the island, with the Selk’nam inhabiting the rest of the island. As with the entirety of Patagonia, population sizes were relatively small. It is

76 thought that the Haush never numbered more than about 300 (Macnaughtan, 2014). The

Huash moved from mainland Patagonia and arrived to Isla Grande first, followed by the

Selk’nam, but the exact timing of these movements is unknown (Macnaughtan, 2014).

The languages of both the Selk’nam and Haush belong to the Chonan language family, but were not mutually intelligible (Macnaughtan, 2014).

Both the Selk’nam and Haush hunted marine mammals and guanaco

(Macnaughtan, 2014). They were also both targeted by extermination campaigns in the late 19th and early 20th centuries where sheep farmers were paid a sterling per ear ("The

Qawasqar Indians of Tierra del Fuego," 1987). The Selk’nam and Haush survivors of the genocide then formed one group (Macnaughtan, 2014). This group then had a measles outbreak, with reduced their numbers even more. The Haush are now completely gone, and only approximately 35 Selk’nam survive and live on Isla Grande, but form no identifiable community ("The Qawasqar Indians of Tierra del Fuego," 1987).

Since the Haush are now gone, most of what we currently know about them is derived through the Selk’nam. While there are cultural similarities between the two groups, they were distinct cultures. The Haush lived in tribes that were collectively made up of 11 lineages (Macnaughtan, 2014).

For more information on the Selk’nam, see 3.3.15 Chile: Selk’nam (Patagonia) below.

3.3.11 Chile: Magallanes (Patagonia)

Originally called Punta Arenas, then renamed Magallanes, and now named Punta

Arenas again, the southern corner of Chile was inhabited by four different groups of people. These include the Tehuelche, Selk’nam, Yamanas, and Qawasqar or Alakalufes.

For more information on the Tehuelche, see 3.1.8 Argentina: Tehuelche in the above section.

For more information on the Selk’nam and Alakalufes, see 3.3.15 Chile: Selknam

(Patagonia) below, 3.3.10 Chile: Isla Grande Bahia (Patagonia), and 3.3.9 Chile (Isla

Dawson (Patagonia) above.

For more information on the Yamanas, see 3.1.33 Chile: Yaghan in the above section.

3.3.12 Chile: Morro (Arica)

The Morro sites are along the coast on the extreme northern part of Chile and are associated with the Chinchorro culture. Many ancient towns in the area chose a particular rocky outcrop, called Morro de Arica, as the place for their dead to rest (Standen, 2003).

This particular site has views of both the sea and the mouth of the Azapa and Lluta valleys. Uhle excavated more than one hundred individuals from this area (Uhle, 1919), with later people performing additional excavations (Allison et al., 1984; Focacci, 1974;

Guillen, 1992; Munizaga & Martinez, 1961; Standen, 1991).

Based on the grave goods, the people at the Morro sites primarily relied on marine resources. They also exploited aquatic plants, from the sea and swampy areas in the

78 nearby valleys (Standen, 2003). More women seem to be buried with fishing equipment, while the men had more marine hunting devices (harpoons, spears, etc) (Standen, 2003).

There is also evidence that there were exchange relationships with some Andean hunters

(Standen, 2003).

Rivera (1975) and Rivera and Rothhammer (1986) have suggested that the

Chinchorros are originally derived from populations in the Amazon. As an alternative, others have suggested that the Chinchorros were present along the Chilean/Peruvian coasts for around 9,000 years, but that their ancestors were from a tropical forest area

(Arriaza, 1995; Guillen, 1992; Núñez, 1999; Standen, 1991). More recently, though, ancient DNA analysis shows strong similarities between prehistoric populations of northern Chile to populations in the Amazonian basin (Moraga et al., 2001).

3.3.13 Chile: Playa Miller (Arica)

The Playa Miller sites are cemetery sites, with Playa Miller 7 being a Formative period site, and Playa Miller 2, 4, 6, and 9 being Late Intermediate period sites (Focacci,

1974; Focacci & Cordova, 1982). These sites are coastal sites in Northern Chile, near the border of Chile and Peru, and it is known that people in this area participated in maritime economic activities supplemented with terrestrial plants and animals (Varela, Cocilovo,

Santoro, & Rothhammer, 2006). The graves for some of the sites, such as Playa Miller 4, were lined with rock slabs. Each grave had a single, flexed burial and the skeletons were in a seated position. Ceramics from at least some of these sites are associated with the

San Miguel ceramic tradition.

3.3.14 Chile: Puerto de Hambre (Patagonia)

Puerto de Hambre was named due to European settlers landing in this area and subsequently starving and freezing to death due to the harsh terrain. It is located halfway between the South Pole and the northern border of Chile. While little can be found describing any indigenous cultures living in this area, it is located in what is traditionally

Yaghan territory.

See 3.1.33 Chile: Yaghan in the above section for complete population description.

3.3.15 Chile: Selk’nam (Patagonia)

The Selk’nam have lived in Tierra del Fuego, Chile, for approximately 11,000 years (Garcés Feliu, 2012). Tierra del Fuego is an archipelago off of the southernmost area of Chile, and is divided between Chile and Argentina. This series of islands includes a large, main island called Isla Grande de Tierra del Fuego. It is this island that the

Selknam seemed to primarily use, although there is linguistic evidence of them utilizing other islands (Barceló, del Castillo, Mameli, Moreno, & Videla, 2009). They also had symbolic explanations for their separation from populations on the mainland (Barceló et al., 2009).

Little is known about this population due to their extinction after European contact, with much of the knowledge coming from historical records (García-Moro,

Hernández, & Lalueza, 1997). According to missionary accounts of Tierra del Fuego, most Native Americans in the area succumbed to infectious disease (García-Moro et al.,

1997). Other accounts refer to the Selknam as ending due to invasion and fights for territory by settlers (Garcés Feliu, 2012).

It is known that the Selk’nam were nomadic, and were adapted to survive in an extreme-cold environment. They were skilled in crafting a variety of weapons, with a particular focus on bows and arrows (Garcés Feliu, 2012). While many aspects of the

Selk’nam’s life appear to be unknown, one ritual that was recorded is a rite of passage for young people to enter adulthood, where they secluded and tested on various skills such as courage, endurance, and handling bows and arrows (Garcés Feliu, 2012).

3.3.16 Chile: Yamana (Patagonia)

See 3.1.33 Chile: Yaghan in the above section for complete population description.

3.3.17 Colombia: Aguazuque

The site of Aguazuque in Colombia was excavated by Gonzalo Correal in 1985 and is described in Correal (1990b). Dated to between 7500 and 3000 BP, it is located in the Soacha municipality and is southwest of the famous Sabana de Bogotá site (Correal,

1990b). The site itself has evidence of five successive occupations, with evidence of both hunters and gatherers and agriculturalists (Correal, 1990b). Aguazuque is considered to be the site with the most elaborate bone industry during the preceramic period of Sabana de Bogotá (Neves, Hubbe, & Correal, 2007).

Fifty-nine sets of human remains were excavated from the Aguazuque site

(Neves, Hubbe, & Correal, 2007). Twenty-three of these individuals were deposited in a

81 circular pattern in one large pit (Neves, Hubbe, & Correal, 2007). The reason for this is unknown, but was theorized to be due to ritual (Correal, 1990a).

3.3.18 Peru: Ancón

Ancón is an extensive archaeological site on the central coast of Peru. From 1800-

300BC, Ancón was a fishing village and a cemetery (Druc, Burger, Zamojska, & Magny,

2001). The particular collection used for this analysis is held at the Field Museum of

Chicago and was excavated by Kroeber. In total, approximately 3,000 individuals have been excavated from this site. Many of the associated materials excavated from the site include fish hooks and nets, leading researchers to believe that the people of Ancón primarily relied upon maritime resources (Slovak & Paytan, 2011). In addition to this, the

Ancóneros participated in agriculture of lúcuma, quinoa, maní, avocado, and beans

(Slovak & Paytan, 2011).

There is evidence of cultural changes occurring at the site between the Early

Horizon and Middle Horizon period, such as changes in burial practices (moving from simple to more complex with burial goods and displays of wealth), the settlement moved from the hillside to the flat plains, and they became less dependent on shellfish (Slovak &

Paytan, 2011). These changes were likely due to increased trade between the Ancón and

Wari polity (Slovak & Paytan, 2011).

3.3.19 Peru: Aramburu

The particular collection used for this analysis is held at the Field Museum of

Chicago and was excavated by Kroeber. The Aramburu site has also been called Huatica,

Huadca, and Maranga (Kroeber & Wallace, 1954). This is considered to be a proto-Lima 82 site and there are several (three main) pyramids at the site as well (Kroeber & Wallace,

1954).

3.3.20 Peru: Cerro del Oro

On the southern coast of Peru lies the Cañete River Valley. This area can be divided into three zones: the high and middle mountain zone, and the alluvial plain

(Hundman, 2016). The Cañete River Valley area also receives little rain throughout the year, but the nearby Cañete river acts as a year-round water source. The Cañete river is approximately 220 km in length, running from Ticllacicha to the Pacific Ocean

(Hundman, 2016).

The site of Cerro del Oro is located in the Quilmaná Rocky Spurs and lies approximately 13 km from the Cañete River in the alluvial plain (Hundman, 2016). It is one of at least 53 archaeological sites in the lower Cañete valley spanning from the

Formative (1800-200 BC) to Inca occupation (AD 1500-1532) (Fernandini, 2015). Cerro del Oro occupation itself is dated to AD 55-850 with a later occupation by the Wari, and is a natural rocky formation that was used for settlement (Fernandini, 2015). Access to water was limited, given the height of the hill and distance to the Cañete River, but the first residents at Cerro del Oro built a complex system of water canals surrounding the site (Fernandini, 2015).

One interesting aspect to this site, and the Cañete River Valley as a whole, is that it fell within two spheres of influence: the Nasca and the Wari. The Nasca were a confederation of valleys that shared a religious ideology (Vaughn, 2006). It was comprised of self-sufficient villages with Cahuachi being the socio-religious center, 83 rather than the typical forms of administrative control being imposed (Vaughn, 2006).

Temporally, the Nasca held influence across the southern coast of Peru from around AD

1-700, although their reach also made it to the higher altitudes (Vaughn, 2006). The Wari, on the other hand, was an imperial state located in the Andean highlands. Around AD

600, the urban center was considered to be Huari (or Wari), which held a complex and hierarchical social system (Covey et al., 2013). The Wari took control over the production and distribution of elite materials, which gave them authority in those regions.

To maintain control specifically over Nasca territory, the Wari chose to incorporate some of the Nasca ideologies to help legitimize their occupation in local villages and valleys

(Hundman, 2016; Vaughn, 2004).

Initial excavations at the Cerro del Oro site were done by Alfred Kroeber in 1925 while working for the Field Museum in Chicago, Illinois. His focus, while excavating at

Cerro del Oro, was on the material culture in tombs and human cranial and mummified remains. He largely felt that the site of Cerro del Oro was a cemetery, as he found no residences during his excavations. Since its arrival at the Field Museum, the collection has been relatively untouched, with more recent studies done by Fernandini (2015) and

Hundman (2016).

CHAPTER 4 : DNA: SUB-REGIONAL POPULATION STRUCTURE WITHIN SOUTH AMERICA

4.1 INTRODUCTION

Genetic structure within South America have not only been affected by gene flow due to the influx of European and African peoples in post-Columbian South America, but also by the entry of peoples into the Americas and their subsequent internal movements.

These movements are more complicated than the typically discussed single migration along the Pacific coast (Bodner et al., 2012; Erlandson & Braje, 2011; Hubbe et al., 2010;

See Chapter 2). One possibility is multiple migration events, which could explain some of the distinct genetic patterns (Perego et al., 2009; Perego et al., 2010; Ray et al., 2010;

Schurr, 2004). Additionally, multiple lines of data have corroborated an idea of a later genetic exchange occurring separately from the initial influx (linguistic: Fortescue, 1998; morphometric: González-José et al., 2008; autosomal: Rasmussen et al., 2010).

For both North and South America, an eastward expansion is thought to have occurred to explain the spread of people throughout the interior of each continent (e.g.,

Bodner et al., 2012), although this may be too simplistic as well (Bodner et al., 2012;

Gruhn, 1994). Multiple population replacements, extinctions, and expansions have shaped and reshaped genetic variance in these different population groups (e.g., Schurr &

Sherry, 2004; Volodko et al., 2008), leaving no general consensus on a peopling model

85 from either mtDNA or Y-chromosome DNA (Lewis, 2010). Further complicating the models is the lack of information on the extent to which natural selection has played a role in structuring the genetic variance we see.

Therefore, in this chapter I will be testing if population affinity patterns and structure for mtDNA haplogroup and Y-chromosome STR frequencies are different on a regional scale versus a continental one. I will further test if population structure is defined by anything other than geographic distance.

4.1.1 Natural Selection and Genomics

Natural selection has been known to affect both phenotypes and genomes, with the two typically forming a feedback loop of the phenotype interacting with the environment, selection occurring on the phenotype, and subsequently affecting the genotype. One of the typical examples of selection occurring is through skin pigmentation. Skin pigmentation seems to have been driven by a balancing act of protection from ultraviolet radiation while still enabling the manufacture of enough vitamin D. The equator receives the most ultraviolet radiation, and populations living near the equator receive a fitness advantage to having darker skin to protect against skin damage (Jablonski & Chaplin, 2010). However, ultraviolet radiation also plays an important role in the production of vitamin D, which is necessary for both bone and overall health (Holick et al., 2011). This role for vitamin D production may be the potential driver for higher latitude populations having lighter skin pigmentation, since higher latitudes receive less ultraviolet radiation and lighter skin would allow for more 86 efficient vitamin D production. Selection has not only been occurring on the phenotype, in this instance, but several genes (e.g., MC1R, SLC24A5, MATP) have been linked to skin pigmentation as well (Graf, Voisey, Hughes, & van Daal, 2007; Lamason et al.,

2005; Rees, 2003).

While genomic studies in the past used to report that selection tends to act immediately on any new mutation, more recent studies have shown that selection acts on variation that was present for some time prior to being favored (Hamblin, Thompson, &

Di Rienzo, 2002; R. Nielsen, 2005; R. Nielsen et al., 2017; Prezeworski, Coop, & Wall,

2005). This pattern has become increasingly apparent in studies involving interbreeding with other hominins, biology, immunology, among others. For instance, during the past

20,000 years, a species of stickleback fish moved from marine environments to freshwater environments. This movement created isolated freshwater populations, but all isolated populations experienced a parallel reduction in body armor plates (Colosimo et al., 2005). When marine sticklebacks were studied, the allele representing the adaptive phenotype for freshwater sticklebacks were found in frequencies ranging from 0.2-3.8%.

Therefore, directional selection likely took place on variation already present in the ancestral population (Colosimo et al., 2005). This process likely occurred numerous times throughout human history, with some examples including alleles necessary for lactose tolerance (e.g., Bersaglieri et al., 2004; Itan, Powell, Beaumont, Burger, & Thomas,

2009; Tishkoff et al., 2006), the Duffy blood group locus (Hamblin et al., 2002), genes associated with skin pigmentation and hair morphology (e.g., Beleza et al., 2013; Norton et al., 2016), and genes related to infectious disease resistance and immune response

87 variation (e.g., Fumagalli & Sironi, 2014; Karlsson, Kwiatkowski, & Sabeti, 2014;

Quintana-Murci & Clark, 2013).

Many of the genetic changes due to selection seemed to have occurred by changes in the environment produced by humans. For instance, cultural innovation that led to changes in diet, such as agriculture or new hunting technologies, have affected the genome (R. Nielsen et al., 2017). A direct example of this is selection for lactase persistence, which affected dairy farming populations in Europe and Africa (R. Nielsen et al., 2017; Ranciaro et al., 2014; Tishkoff et al., 2006). Cultural transitions toward or away from vegetarian diets have also led to selection of genes controlling the synthesis of polyunsaturated fats (FADS) (Ameur et al., 2012; Fumagalli et al., 2015; Kothapalli et al., 2016; Mathias et al., 2012). These genes, having been found in Greenlandic Inuit populations, were first thought of as a genetic and physiological adaptation to a cold environment, where their diets were rich in protein and fatty acids (Fumagalli et al.,

2015). A later study (Amorim et al., 2017) found FADS genes in populations throughout the Americas, suggesting that they were instead present in the ancestral population prior to arrival in the New World and was subsequently spread throughout the diverse set of environments in the New World. Dietary changes are not the only human induced environmental change, as the emergence of civilizations and cities increased the spread of disease, thus changing genes involved in immunity and pathogenic responses.

It is also possible that natural selection has affected both mtDNA and Y- chromosome DNA. For instance, the genes in mtDNA are linked to energy production, both in terms of generating heat to maintain body temperature, but also to generate ATP

88 from dietary calories in order to perform work (Mishmar et al., 2003). Thus, it is possible that regional patterns to mtDNA haplogroups have not only been shaped by neutral forces, but natural selection as well. A few studies have even demonstrated a deviation from neutrality (e.g., Mishmar et al., 2003; Pakendorf & Stoneking, 2005), which only further complicates the use of mtDNA for reconstructing population phylogenies.

In terms of the Y-chromosome, the extent to which natural selection may have affected different alleles has been questioned numerous times (Charlesworth &

Charlesworth, 2000; Jobling & Tyler-Smith, 2000; Poznik et al., 2016; Repping et al.,

2006; Wilson Sayres, Lohmueller, & Nielsen, 2014). Evolutionary biologists in particular have been writing about the extent to which selection could have acted on the Y- chromosome because the Y-chromosome lost all of its active genes that were present in its ancestors. A distinct possibility of this degeneration is natural selection, and several theories have been suggested describing different ways and degrees to which natural selection could have acting (e.g., Bachtrog, 2013; Hughes & Rozen, 2012; Hughes et al.,

2012; Hughes et al., 2010). Some of these theories include the Hill-Robertson effect with weak selection, the hitchhiking of deleterious alleles by more favorable mutations, background selection, and Muller’s ratchet (Charlesworth & Charlesworth, 2000). While most studies tend to support the idea that selection has acted to some extent on the Y- chromosome, few have shown any direct evidence for selection acting or been able to quantify the extent to which selection could be happening (Poznik et al., 2016).

For Y-chromosome DNA, there is a high mutation rate of short tandem repeats

(STRs) and many of the ancient single-nucleotide polymorphism (SNP) defined lineages

89 show phylogeographic relationships that are very homoplastic. In the Americas, these patterns have resulted in SNP-based phylogenies representing little genealogical information, such as with the Q-M3 lineage (Jota et al., 2016). These complications make the phylogenies of ancient peoples unclear (Jota et al., 2016).

4.1.2 Regional Genetic Diversity

World-wide genetic studies have shown a general trend of isolation by distance after the out-of-Africa movement (e.g., Li et al., 2008a; Prugnolle et al., 2005;

Ramachandran et al., 2005). This process greatly affected the general genetic patterns of neutral genetic diversity in humans, causing a general decay in biological diversity with increasing distance from Africa (DeGiorgio, Degnan, & Rosenberg, 2011; DeGiorgio,

Jakobsson, & Rosenberg, 2009; Hunley, Healy, & Long, 2009; Ramachandran et al.,

2005). However, the admixture that has taken place throughout the long-range movements of the last several thousand years have affected patterns of regional genetic diversity. This is particularly true in the Americas (e.g., Hunley & Cabana, 2016; Pickrell

& Pritchard, 2012; Wang et al., 2007).

In addition to more recent admixture, long-distance dispersals, contractions and expansions of populations associated with the last glacial maximum, and range expansions occurring due to the rise of agriculture could have also affected regional genetic diversity (Alves et al., 2016; Hunley & Cabana, 2016). One last key factor affecting regional genetic diversity is localized gene flow. Not only can gene flow eliminate traces of isolation by distance patterns, but it can be easily confused with 90 geographic patterns, such as isolation by distance, since both gene flow and isolation by distance affect the relationship between geographic distance and local divergence between populations (Hunley & Cabana, 2016; Meirmans, 2012; Ramachandran et al.,

2005).

Given this overall trend of isolation by distance, the genetic structure of South

America has been primarily explored in the context of human dispersion patterns within the Americas, with the assumption that the local environment does not significantly affect genetic structure. In other words, the impact of the partitioning of variance and population structure according to eco-geographical factors has not been examined extensively in the continent. In this chapter, I explore potential climatic and geographic factors that are correlated with mtDNA haplogroup frequencies and Y-chromosome STR frequencies from a large number of series representing different regions in the continent.

4.2 MATERIALS AND METHODS

4.2.1 DNA Data

Mitochondrial haplogroup frequencies from 51 populations were collected from previously published literature (Table 4.1). The haplogroups included were A, B, C, and

D. The series were grouped into six regions that represent different environments on the continent (Northern Andes, Southern Andes, Andes, Amazon, Central South America,

Southern South America), with the Andes being composed of both the North and South

Andes populations. Y-chromosome STR frequencies from 35 populations were also

91 collected from previously published literature (Table 4.2) and grouped into the same regions listed above (also shown in Figure 4.1). The genetic distances for both mtDNA and Y-chromosome DNA were calculated in R, based on the haplogroup and STR frequencies.

Table 4.1: MtDNA Populations and Source Papers.

Country MtDNA Populations (N) MtDNA Sources Embera (22), Ingano (27), Lewis et al., 2004; Mesa et al., Piaroa (10), Ticuna (54), 2000; Torroni et al., 1993; Colombia Wayuu (40), Zenu (37), Carvajal-Carmona et al., 2000, Antioquia (113) 2003 Lewis et al., 2004; Fuselli et Ecuador Cayapa (30) al., 2003 Lewis et al., 2004; Torroni et Venezuela Makiritare (10) al., 1993 Kraho (14), Macushi (10), Marubo (10), Ticuna (28), Wapishana (12), Yanomama Lewis et al., 2004; Torroni et Brazil (24), Jean (85), Tupi (339), al., 1993; Marrero et al., 2007 Kaingang (78), Guarani-Tupian (200) Ancash (33), Arequipa (22), Lewis et al., 2004; Torroni et Peru Tayacaja (61), Quechua (19) al., 1993 Aymara (33), Quechua (32), Chimane (41), Moseten (20), Bolivia Lewis et al., 2004 Ignaciano (22), Trinitario (35), Movima (22), Yuacare (28) Atacamenos (113), Aymara (172), Huilliches (38, 80), Lewis et al., 2004; Fuselli et Chile Mapuche (111), Peheunche al., 2003 (205), Yaghan (21) Choroti (20), Fueguian (45), Mapuche (155), Mataco Chaco (28), Mataco (72), Mataco Formosa (44), Pilaga Formosa Argentina (41), Quebrada de Humahuaca Lewis et al., 2004 (46), San Salvadore de Jujuy (19), Tehuelche (29), Toba (8), Toba Chaco (30), Toba Formosa (26)

Table 4.2: Y-Chromosome Populations and Source Papers

Country Y-Chromosome Populations (N) Y-Chromosome Sources Embera-Chami (24), Guambiano Colombia Roewer et al. (2013) (16) Ecuador Kichwa (42), Waorani (40) Roewer et al. (2013) Venezuela Wayuu (19), Bari Boxi (16) Roewer et al. (2013) Kayapó-Xikrin (13), Arara (20), Zoé (25), Waiãpi (13), Urubu- Kaapor (27), Awa-Guajá (46), Brazil Asurini (15), Gaviao (18), Terena Roewer et al. (2013) (32), Yanomami (10), Tiriyo (35), Parakana (38), Karitiana (17), Ipixuna (21) Shipibo-Conibo (21), Chumbivilca Peru (10), Chuquibamba (16), Huanca Roewer et al. (2013) (13) Bolivia Chimane, Trinitario, Mojeño Roewer et al. (2013) Chile N/A N/A Mapuche (49), Guarani (84), Toba (90), Pilaga (53), Pilaga-La bomba Argentina Roewer et al. (2013) (12), Wichi (45), Tehuelche (10), Colla (14)

As with the climatic data (section 4.2.2), the distance matrices for both Y- chromosome STR frequencies and mtDNA haplogroup frequencies were based on

Euclidean distances. Geometrically, Euclidean distances are the shortest distances between any two points. In addition to this, one property of Euclidean distances is that rotating the points does not change the distances between points, which is why principal component scores of Euclidean distances are the same as the original data. The most negative aspect of using Euclidean distances is that there is no consideration of how correlated the variables are. All variables carry the same weight, so any variables that are

94 highly correlated are likely biasing the analysis through essentially overweighting correlated variables (Joliffe & Morgan, 1992). To represent the distance matrix and explore the affinities among the series, a Kruskal’s bi-dimensional Non-Parametric bootstrap Multidimensional Scaling (MDS) was also performed and graphed in a scatterplot (Cox & Cox, 2000; Hubbe et al., 2015) (Figures 4.2 and 4.3). These MDS scatterplots represents the pairwise Fst matrix between all series, which allows us to see how the variance apportionment is distributed among the series. To get an expected distribution of distances, 100 distance matrices were calculated from bootstrapping the original data (Hubbe et al., 2015). Doing this creates distance matrices that take into account the expected variation in the distance between each population due to population estimation errors associated with sample size. Each of these 100 bootstrapped distance matrices also had MDS coordinates calculated, which were then superimposed over the original MDS using a Procrustes analysis. This minimizes their distances through rotation, scaling, transformation, and reflection (Mitteroecker & Gunz, 2009). To calculate the goodness-of-fit, stress was calculated for each MDS analysis.

4.2.2 Climate Data

There is a wide range of environments throughout the continent of South

America. Even if just the Andes Mountains range is considered, it demonstrates a wide range of climate variability. Rain is commonplace in Colombia, even along the Andes, whereas its neighboring country, Ecuador, is typically arid. Only a few miles away from the highest mountain peak in Ecuador is a tropical rainforest. Amongst the Andes in

Chile is the Atacama Desert, which is the world’s driest desert. In general, South

America is also home to one of the longest river in the world, the Amazon River, which has a drainage basin that covers approximately 40% of South America. This area is just part of the ecosystem making up a tropical rainforest. Further to the south are grasslands, which lead into low-elevation plateaus and glaciers in the Patagonian region. These distinctive features largely helped to define the environmental regions used in this chapter. In total, there are six regions defined: Northern Andes, Southern Andes, Andes

(comprised of both Northern and Southern Andes), Amazon, Central South America, and

Southern South America.

For each population, geographic coordinates were established to the closest reference point or was based on previously published data about the population. The initial difference matrices for each climate variable (average annual temperature, temperature range, altitude, rainfall, and isothermality) were accessed through ArcMap

10.4 (ESRI, 2018) and subsequently exported into R. This includes a matrix of geographic distances between each population, where geographic distance is the linear geographic distance in kilometers. Definitions for each of the climatic variables are available below:

Altitude: height of the landscape relative to sea level.

Isothermality: quantifies how much the day-night temperatures oscillate relative

to the summer-winter oscillations. An isothermal value of 100 means that the

diurnal temperature range is equal to the annual temperature range, whereas

anything less than 100 means there is a smaller level of temperature variability 96

within a month compared to the year. Isothermality can be helpful for determining

how influential temperature fluctuations are for a population/species. (O’Donnell

& Ignizio, 2012)

Precipitation: Sum of the total precipitation for the year. Precipitation can be

important for understanding the influence of water availability for a

population/species. (O’Donnell & Ignizio, 2012)

Temperature: the mean annual temperature. First, the average temperature for

each month is calculated. These values are then averaged over the twelve months

to get the yearly average temperature. The mean annual temperature approximates

the total energy input for an ecosystem. (O’Donnell & Ignizio, 2012)

Temperature Range: the average of the monthly temperature maxima minus the

monthly temperature minima. Temperature range can be useful for understanding

the relevance of temperature fluctuations for specific populations/species.

(O’Donnell & Ignizio, 2012)

Then, climatic distance matrices were built based on the absolute differences between coordinates for each of the climatic variables (Harvati & Weaver, 2006a) based on present and past data from BIOCLIM (Hijmans, Cameron, Parra, Jones, & Jarvis,

2005). As with the genetic data, each of the climatic distance matrices are based on

Euclidean distances, which is the commonly used distance type for climatic data

(Mimmack, Mason, & Galpin, 2001).

Northern Andes

Bari Boxi Amazon EmberaWayuu-Chami

Guambiano Tiriyo Kichwa Yanomami Zoé Waiãpi Asurini Waorani Urubu-Kaapor Awa-Guajá Ipixuna Arara Parakana Shipibo-Conibo Kayapó-Xikrin Gaviao

Chumbivilca Karitiana Huanca Trinitario Mojeño Chuquibamba Chimane

Terena Central TobaWichi Guarani South America Southern Andes Guarani Colla Pilaga Pilaga-La bomba

Mapuche Southern South Tehuelche America

Figure 4.1: Map of South America showing mtDNA and Y-chromosome populations. These populations are .organized by region. Gold dots represent mtDNA populations; Red dots represent Y-chromosome populations. 98

4.2.3 Comparing DNA to Climate

All distance matrices were compared using a two-way Mantel and Partial Mantel test (Mantel, 1967). The Mantel test provides an estimation for the correlation between two matrices (r-value), while also providing a p-value to represent the significance of that correlation. In order to properly study biological affinity, any confounding effects from geographic distance needed to be controlled for, which was accomplished with the Partial

Mantel test. The Mantel and Partial Mantel tests were performed in R using the vegan package (Oksanen et al., 2018).

4.3 RESULTS

4.3.1 MtDNA Results

When all mtDNA series are analyzed together, there is significant albeit weak geographic signal (r = 0.17, p = 0.004), supporting previous genetic studies that suggest isolation by distance fits well with the partitioning of variance on a continental level (e.g.,

Jakobsson et al., 2008; Li et al., 2008a; Ramachandran et al., 2005; Relethford, 2004b;

Wang et al., 2007). Note, however, that while these data supports the general trend of decreasing variation with increasing distance from Africa, the amount of variance explained by the correlation is small (R2 = 0.029).

Once each region is analyzed individually, the patterns of correlations change, and in most of the regions, geographic distance is no longer a good fit for the differences observed (see Tables 4.3 - 4.8). The Central South America series and the Andes as a

99 whole are the only regions that maintains a significant geographic signal (r = 0.43, p =

0.005; r = 0.45, p < 0.001, respectively), with the Andes having a slightly stronger correlation. However, the Northern Andes and Southern Andes analyzed separately do not show a significant correlation to geographic distance. The Northern Andes (Table

4.3) was significantly correlated with altitude, temperature, and temperature range, with each of these relationships increasing in significance after the Partial Mantel. The

Southern Andes series (Table 4.4) was not correlated with any climate variables, and after the Partial Mantel held only a weak correlation with temperature. In terms of climate, the entire Andes series (Table 4.5) was correlated with all variables until geographic distance was controlled for. This increased all correlation coefficients to a non-significant level.

The Southern South America series (Table 4.8) was correlated with both temperature and temperature range, but the Partial Mantel increased the correlation coefficient for temperature range to a non-significant level. However, temperature was highly correlated (r = 0.62, p < 0.001). Central South America (Table 4.7) showed significance to both geographic distance and temperature range. After the Partial Mantel, temperature range still maintained a strong correlation (r = 0.55, p = 0.007).

All other regions, with the exception of the Amazon (Table 4.6), correlate with at least one climatic variables (only weakly in the case of the Southern Andes).

Interestingly, the Amazon region shows no correlation with any of the variables tested here, suggesting that the local partitioning of variance between groups is not following linear eco-geographic factors.

100

The non-metric MDS for mtDNA shows that each series is generally clustering together. The widest range of variation seems to be coming from Central South America, where the points are spread from the bottom to the top of the graph following Coordinate

2, and with two populations being placed within the Andes clusters (Figure 4.2). There is a general pattern of the Andes clustering to the right of the graph, Central South America in the middle, and the Amazon and Southern South America toward the left. In this case, there is a clear delineation of the eastern and western populations.

101

)

MtDNA

Nonmetric ( MDS Nonmetric

Figure 4.2: NonmetricMDS for mtDNA

Figure 4.2: Nonmetric MDS for mtDNA

102

Table 4.3: Mantel and Partial Mantel test results for the Northern Andes.

Mantel Test: Northern Andes1

MtDNA Y-chromosome DNA Mantel r p r p Altitude 0.771 0.0107 -0.2610 0.5850

Geodistance 0.3985 0.1022 0.1450 0.2780

Isothermal -0.1184 0.6585 -0.3420 0.9360

Precipitation 0.1348 0.2234 -0.4970 0.9780

Temperature 0.7736 0.0031 -0.2670 0.6270

Temperature 0.6916 0.0248 0.1040 0.3710 Range

Partial r p r p Mantel Altitude 0.7273 0.0145 -0.2330 0.6020

Isothermal -0.2237 0.8787 -0.4200 0.9840

Precipitation 0.1027 0.2700 -0.5140 0.9780

Temperature 0.7852 0.0015 -0.2470 0.6520

Temperature 0.6274 0.0238 0.0700 0.3950 Range

1 Yellow shading represents statistically significant with p<0.05.

103

Table 4.4: Mantel and Partial Mantel test results for the Southern Andes.

Mantel Test: Southern Andes1

MtDNA Y-chromosome DNA Mantel r p r p Altitude 0.1167 0.1682 0.2750 0.0280 Geodistance -0.0034 0.4612 0.2370 0.2160 Isothermal 0.08925 0.3090 0.1280 0.3310 Precipitation -0.1786 0.8138 0.4020 0.0060

Temperature 0.2410 0.0698 0.3130 0.0400

Temperature -0.1627 0.8075 0.3360 0.1520 Range

Partial r p r p Mantel Altitude 0.1288 0.1652 0.2000 0.0960

Isothermal 0.1359 0.2394 0.0210 0.4570

Precipitation -0.1802 0.8250 0.3500 0.0280

Temperature 0.2819 0.0527 0.2280 0.0840

Temperature -0.1633 0.8069 0.2540 0.1700 Range

1 Yellow shading represents statistically significant with p<0.05.

104

Table 4.5: Mantel and Partial Mantel test results for the Andes.

Mantel Test: Andes1

Y-chromosome MtDNA DNA Mantel r p r p Altitude 0.1816 0.0278 0.0070 0.4470

Geodistance 0.4463 3.00E-04 0.0160 0.4290

Isothermal 0.2799 0.0106 0.1240 0.2290 Precipitation 0.3676 0.0183 -0.0260 0.5560

Temperature 0.3161 0.0040 0.0120 0.4740

Temperature 0.3121 0.0043 0.0470 0.3930 Range

Partial r p r p Mantel Altitude 0.0331 0.3368 0.0050 0.4740

Isothermal -0.0955 0.7552 0.1300 0.2210

Precipitation 0.2124 0.0978 -0.0290 0.5780

Temperature 0.0694 0.2671 0.0090 0.4480

Temperature -0.0556 0.6305 0.0490 0.3760 Range

1 Yellow shading represents statistically significant with p<0.05.

105

Table 4.6: Mantel and Partial Mantel test results for the Amazon.

Mantel Test: Amazon

MtDNA Y-chromosome DNA Mantel r p r p Altitude -0.1186 0.4388 -0.2000 0.8180

Geodistance 0.1639 0.1736 -0.0240 0.5150

Isothermal -0.0386 0.5448 -0.0410 0.5860

Precipitation 0.3588 0.0925 0.2340 0.1420

Temperature -0.0859 0.4272 -0.2510 0.9210

Temperature 0.1685 0.1863 -0.200 0.7680 Range

Partial r p r p Mantel Altitude -0.0803 0.4275 -0.2000 0.8080

Isothermal -0.0040 0.4991 -0.0350 0.5700

Precipitation 0.3261 0.1196 0.2330 0.1580

Temperature -0.0556 0.4130 -0.2510 0.9310

Temperature 0.1744 0.1805 -0.2000 0.7770 Range

106

Table 4.7: Mantel and Partial Mantel test results for Central South America.

Mantel Test: Central South America1

MtDNA Y-chromosome DNA Mantel r p r p Altitude 0.2022 0.1699 0.5850 0.1650

Geodistance 0.4296 0.0052 0.8590 0.0070

Isothermal 0.3078 0.1716 0.7240 0.0250

Precipitation 0.0161 0.4193 0.6620 0.0330

Temperature 0.1549 0.1327 0.6070 0.0630

Temperature 0.6475 0.0019 0.6600 0.0420 Range

Partial r p r P Mantel Altitude 0.1006 0.2830 0.2240 0.2940

Isothermal 0.2376 0.2442 0.3580 0.2690

Precipitation -0.4715 0.9810 -0.2960 0.7290

Temperature -0.4352 0.9767 0.3570 0.3040

Temperature 0.5547 0.0076 0.3430 0.2830 Range

1 Yellow shading represents statistically significant with p<0.05.

107

Table 4.8: Mantel and Partial Mantel test results for Southern South America.

Mantel Test: Southern South America1

MtDNA Y-chromosome DNA2 Mantel r p r p Altitude 0.1441 0.1886 N/A N/A

Geodistance 0.1937 0.1110 N/A N/A

Isothermal 0.1364 0.1941 N/A N/A

Precipitation 0.0345 0.3564 N/A N/A

Temperature 0.4472 0.0102 N/A N/A

Temperature 0.3009 0.0450 N/A N/A Range

Partial r p r p Mantel Altitude 0.1562 0.1834 N/A N/A

Isothermal 0.0897 0.2815 N/A N/A

Precipitation 0.0083 0.4244 N/A N/A

Temperature 0.6223 3.00E-04 N/A N/A

Temperature 0.2411 0.0753 N/A N/A Range

1 Yellow shading represents statistically significant with p<0.05. 2 There was too little data to run a Y-Chromosome analysis for Southern South America.

108

4.3.2 Y-Chromosome Results

When all Y-chromosome series are analyzed together, there is no geographic signal (r = 0.007, p = 0.425). World-wide patterns for the Y-chromosome are more nuanced than that of mtDNA or autosomal DNA. While studies still show a general trend of an African origin for modern human populations, the Y-chromosome does not show a strong large-scale geographic pattern (Jorde et al., 2000; Kayser et al., 1997). These results seem consistent with this patterning.

Once each region is analyzed individually, there is still very little significant correlations. When the entire Andes series is analyzed (Table 4.5), there is no correlation to any climatic variables before or after the Partial Mantel. Once the Andes are broken down into the Northern and Southern series, the Northern Andes holds the same pattern of no correlation (Table 4.3). The Southern Andes, on the other hand, is correlated to altitude, precipitation, and temperature (Table 4.5). After the Partial Mantel, the significance of the correlations for altitude and temperature increases to non-significant levels. Precipitation remains significantly correlated however (r = 0.35, p = 0.028).

There were not enough populations in Southern South America to run Mantel tests, so there are no results for this region. Central South America held significant correlations to isothermality, precipitation, and temperature range (Table 4.7). However, after the Partial Mantel tests, there were no climatic variable holding a significant correlation. Lastly, the Amazon series showed no correlation both before and after the

Partial Mantel test (Table 4.6). These results are similar to the results seen from mtDNA.

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Comparing the non-metric MDS from the Y-chromosome (Figure 4.3) to that from mtDNA (Figure 4.2) demonstrates some stark differences. For the mtDNA, Central

South America shows the widest range of variation. The Y-chromosome MDS shows the same results for the Amazon series. In fact, the Amazon series encompasses the range of variation shown for all other series. In addition, the range of variation for all series except for the Amazon is relatively small. There is also no general grouping or division among the different series and the Andes do not separate from the other populations.

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Chromosome)

Chromosome

Nonmetric MDS Nonmetric

- Figure 4.3: NonmetricMDS for the Y

Figure 4.3: Nonmetric MDS for the Y-Chromosome.

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4.4 DISCUSSION

4.4.1 MtDNA

The nonmetric MDS analysis of the mtDNA (Figure 4.2) shows strong segregation of the series according to their regions, with a significant division of series east and west from the Andes in the first dimension. This pattern is consistent with previous finds (Bodner et al., 2012; Fuselli et al., 2003; Pucciarelli et al., 2006;

Rothhammer & Dillehay, 2009; Yang et al., 2010). One explanation for this pattern is that the development of the first complex societies in South America began in the Andean region more than 4,000 years ago (Stanish, 2001). Under the Inca Empire, the Andean region achieved greater population density and socioeconomic development than the rest of the continent (Fuselli et al., 2003). The Andes also had higher cultural and linguistic homogeneity (Fuselli et al., 2003), with that homogeneity being attributed to the long history of complex societies present in the region. Others have suggested that the east- west pattern is more directly influenced by the possibility of either two distinct colonizing events or an early entry where the initial population subsequently split with some moving east and the rest moving south along the Andes (Luiselli et al., 2000;

Rodriguez-Delfin, Rubin-de-Celis, & Zago, 2001).

In terms of local environment, the Andes is much more homogeneous than the rest of the continent. The Andes can be divided into three distinct sections based on climate. The most northern end extending down into Peru is a more tropical region, with a wide range of habitats and resources. Comprising the rest of the Andes in Peru and

112 extending down into Chile and Argentina is a very dry, arid section. The most southern end is considered wet due the higher levels of humidity and precipitation present. The rest of the continent is more highly varied, which can create the east-west divisions we see if the genetic variables are strongly influenced by adaptation and/or selection based on these differing environments.

When analyzing the mtDNA series, the only climatic correlations seen are when the Andes series are broken up into a Northern and Southern region (which represent the tropical and dry regions of the Andes, respectively). Even within these two regions, the

Northern Andes was the only region holding significant correlation after the Partial

Mantel tests, which means that there is geographic structure present in the Northern

Andes but not Southern Andes. The primary reason we would see this type of pattern is that there was an outside force disrupting the geographic structure in the Southern Andes region. A typical outside force would be the introduction of a new population to the region, or a genetic restructuring of the populations already present. In the Northern

Andes, the Inca maintained control over many populations, and expanded their territory over the years. The Mapuche in the Southern Andes also maintained control of several populations, albeit in a smaller geographic area than the Inca. The Inca spread to the border of the Mapuche territory, but were unsuccessful in conquering them. However, the

Mapuche were eventually conquered by the Spanish. If the Mapuche were originally from a different area and had moved into Southern Chile sometime during prehistory, the pattern of geographic structure in the North (due to the Inca invasion) would be present, but not in the south where the Mapuche were.

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The Southern South America series was correlated with temperature and Central

South America was correlated with temperature range. These results means that all regions of South America are correlated with at least one climatic variable except the

Amazon which suggests that the local partitioning of variance between groups is not following linear eco-geographic factors. In other words, the rules defining genetic structure in South America are not strictly due to geographic distance.

In terms of the east-west partitioning, Cabana et al. (2014) suggested that the characterization of Andean diversity is incomplete. Their reasoning for this is that many studies occurring after the 1990s were based on a small number of populations representing a large area, and each population was only made up of a small number of individuals. Most of these studies were also based on either mtDNA or Y-chromosome

DNA, which only provides a limited amount of information about patterns of diversity.

Lastly, while there is plenty of archeological evidence to attest to stark differences in population structure between eastern and western South America, there is also evidence supporting the idea of the Andes being comprised of ever-changing populations with varying degrees of interaction to both each other and the environment.

While I do not disagree with these arguments, I would like to add that, based on the results presented in this chapter, we cannot assume that local environmental conditions did not play a role in the differentiation of population structure based on mtDNA. It is important to point out that the climate variables used in this chapter are not intended to test if these populations are affected by natural selection (as in Chapters 5 and

6). The climate variables are used here to test if the isolation present between groups is

114 structured by something other than geographic distance. With most populations showing correlations to various climatic variables, the eco-geographical structure that is present among the populations is not only due to geographic distance. Barriers to gene flow in

South America may be defined by ecological barriers rather than strictly geographic ones, with the exception of the Amazon. The Amazon shows no geographic or ecological structure, meaning that the structure could be defined based on sociocultural practices. It has been shown that some populations in the Amazon only marry people from specific groups, in spite of the distance between them (Arias, Barbieri, Barreto, Stoneking, &

Pakendorf, 2018). Travel via boats is also common place in the Amazon, meaning populations could have been interacting with others not based on geographic distance, but distance via boat travel (Arias et al., 2018; Schillinger & Lycett, 2018).

4.4.2 Y-Chromosome

The nonmetric MDS analysis of the Y-chromosome (Figure 4.3) does not show segregation of the series or a division of the series east and west of the Andes.

Furthermore, there is a general lack of population structure present based on the MDS, given that the variance for one population fully encapsulates all other populations and there does not appear to be any clustering occurring based on series. This pattern is somewhat consistent with Cabana et al. (2014) who also found little to no significantly different pattern between eastern and western Andes populations. Their PCO plot also visually demonstrated a lack of population structure present for their Y-chromosome

DNA. In general, it does not seem like the larger pattern of isolation by distance or serial

115 founder effects from Africa are structuring the patterns of Y-chromosome DNA on a regional scale. This has previously been alluded to in some studies (e.g., Jorde et al.,

2000) and is likely due to the high mutation rate of the Y-chromosome and effects from genetic drift.

When analyzing the Y-chromosome DNA series, there is no geographic signal present at either the continental or regional scale. This could indicate that there are possible effects of genetic drift with convergence, which would cause a loss of geographic structure. Once each region is analyzed individually, there are very few significant correlations with the climate variables. After the Partial Mantel test, only

Central South America shows a significant correlation with precipitation. Population structure from the Y-chromosome does not appear to be strongly influenced by local environmental conditions. Given the lack of evidence for either a geographic or population structure being strongly represented, using Y-chromosome DNA as the sole descriptor for past population events seems unreliable.

4.4.3 Implications to the Hypotheses

This dissertation is ultimately testing three hypotheses (full descriptions can be found in section 1.2 Summary of Dissertation):

1. Regional patterns of population structure will match global patterns.

2. Basicranium variation is mostly the product of neutral evolutionary processes.

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3. The neurocranium is the product of neutral evolutionary processes with some

influence of diversifying selection.

4. The facial region will show stronger evidence of diversifying selection in

response to adaption to climate and diet variation.

The first hypothesis can be rejected based on these results. As described in

Chapter 1, worldwide studies of DNA patterns show an isolation by distance model extending out of Africa (e.g., Li et al., 2008a; Ramachandran et al., 2005). This chapter demonstrates a population structure that is different depending on the regionality of the samples used. For instance, there are differences in the ecogeographic structure for both the Northern Andes and Southern Andes using mtDNA, with the Northern Andes showing correspondence with altitude, temperature, and temperature range and the

Southern Andes showing no correspondence to any climate variable. Although these results are more sharply defined for mtDNA, there are still some regional differences present for Y-chromosome DNA.

While this chapter is not directly addressing hypotheses 2-4, it does have implications for a core assumption of the dissertation, which is that all DNA selected for the dissertation follows neutral evolutionary processes. Prior to the analyses in this chapter, this assumption is valid and supported by numerous papers for both mtDNA

(e.g., Papadopoulou et al., 2011; Weaver, 2014) and Y-chromosome DNA (e.g.,

Francalacci et al., 2013; Jobling & Tyler-Smith, 2003; Wilder et al., 2004). The results described in this chapter do not completely nullify this assumption, but do indicate that for mtDNA some caution may be warranted since the mtDNA was regionally correlated

117 with various environmental variables. Furthermore, during the last millennia, events such as colonization by the Europeans or the conquering of populations by the Inca, have influenced haplogroup frequencies (Raff, Bolnick, Tackney, & O'Rourke, 2011). In spite of this, many studies have shown a strong geographic structure present in DNA (e.g.,

Raff et al., 2011; Ruiz-Linares et al., 2014). More studies will need to take place to determine the extent to which climate, and potentially natural selection based on local environments, affects frequencies of mtDNA haplogroups and alleles.

4.5 CONCLUSION

When analyzing mtDNA from the whole of South America, there is a significant geographic signal. However, when analyzing different regions of South America, each region displays differences in their correlations to the climatic variables. Based on these results, we can assume that local environmental conditions may have played a differential role in population structure using mtDNA.

When analyzing Y-chromosome DNA from South America, there is no geographic signal. This pattern could indicate possible effects of genetic drift with convergence, causing a loss of geographic structure. While the Y-chromosome did not show any influence from environmental conditions on population structure, using Y- chromosome DNA as the sole means for understanding population or migration events seems less reliable than either mtDNA or autosomal DNA.

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In general, population genetic structure in South America does not seem to be strictly dictated by larger-scale patterns but rather are influenced by finer-scale patterns taking place locally. These results are supported by the vast amount of archaeological, linguistic, and ethnohistorical studies that have taken place in South America. Local patterns of population differentiation in South America could be driven by ecological barriers to gene flow, with the exception of the Amazon, which is likely structured via sociocultural rules and travel via boat. Future studies should not only aim for denser coverage of the specific geographic region in question but also for more coverage temporally to better access local histories and its effect on population structure.

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CHAPTER 5 : CRANIAL MORPHOMETRICS

5.1 INTRODUCTION

This chapter addresses the relationship between cranial morphology and climate by testing population affinity patterns using 3D cranial morphology and various climate variables. The climate variables, in this case, are used to test how different ecological zones could be shaping gene flow, and therefore affecting population affinities, as well as the extent to which natural selection could be acting on parts of the cranium, or modules.

5.1.1 Modularity and Integration

While evolution is known to act on the whole phenotype, it is still important to understand evolutionary effects on each region of the cranium. ‘Evolution’ as a term encompasses effects from all evolutionary forces: mutation, natural selection, gene flow, and genetic drift. Each of these forces affect the different regions of the cranium with distinct magnitudes (von Cramon-Taubadel, 2014). Additionally, the phenotype varies in accordance to both the genotype and the local environment. While many phenotypic traits are polygenic (e.g. skin color, hair color, cranial shape), some are simpler than others.

Skin color is primarily controlled by amounts of melanin present, making it more straight-forward to study on a worldwide scale. Cranial shape, on the other hand, is controlled on multiple levels, ranging from single genes to whole integrated units 120

(Lieberman, Ross, & Ravosa, 2000; von Cramon-Taubadel, 2014). Therefore, until we better understand the evolutionary history and the sources of variance in each anatomical regions of the skull, it will be difficult to interpret results from studies utilizing the entire cranial shape.

Evolutionary effects on the cranium are, in many ways, tied to its development.

The development of the basicranium is different than the development of the neurocranium and facial region (Lieberman, Pearson, et al., 2000). The neurocranium and face develops intramembranously, with no cartilage present. The basicranium, on the other hand, develops mostly from endochondral ossification, where cartilage precursors are present in utero and then gradually replaced with bone after birth (Sperber, 1989).

The basicranium is also the first region to reach complete adult size (Moore & Lavelle,

1974), which is important when considering the integration between the different bones of the cranium.

The basicranium grows first, and, in a sense, forms a platform on which the rest of the cranium grows and attaches to (Lieberman, Ross, et al., 2000). Additionally, the basicranium contains many foramina critical to the functioning of the brain and face. The neurocranium specifically expands during growth toward the basicranium, and the two actually grow in tandem (Lieberman, Ross, et al., 2000). This makes the two regions very integrated, with two main factors influencing them: shape of the brain and the timeline of basicranium growth (Lieberman, Ross, et al., 2000). These factors may explain why research has shown the basicranium to be a better indicator of phylogenetic relationships than the other regions (e.g., Strait, 1998; von Cramon-Taubadel, 2009).

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Given that the neurocranium and basicranium are so integrated, they can have a particularly strong influence over each other early in development. The basicranium has more restriction on development than the neurocranium, partially because endochondral bones are less influenced by epigenetic effects (Thorogood, 1993). Much of the evidence for the integration of these two regions is seen through secondary sources of evidence.

For instance, neurocranial growth can be seen to affect basicranium development in studies of head-binding, where circumferential head-binding can elongate the foramen magnum (Anton, 1989; Lieberman, Pearson, et al., 2000).

The variation present in phenotypes is dependent on the genotype and the environment in which the organism develops (Perez, Lema, Diniz‐Filho, et al., 2011).

Environmental variables, such as temperature, population density, elevation, and nutrients (Nijhout, 2003) can vary over time and change the phenotypes present in an area. These environmental changes can have particularly strong effects during an organism’s development (Bogin & Rios, 2003; Carroll, Hendry, Reznick, & Fox, 2007).

Climate is not the only selective force to affect the cranium, as diet is another important contributor (González-José, Ramírez-Rozzi, et al., 2005; Menéndez et al.,

2014; Noback & Harvati, 2015; Perez, Lema, Diniz‐Filho, et al., 2011; Perez &

Monteiro, 2009). Figuring out the independent contribution from both climate and diet can be difficult, particularly for parts of the world where temperature differences are associated with diet, such as the tropical or subtropical regions (Perez, Lema, Diniz‐

Filho, et al., 2011; Price, 2009). While some have suggested that a very rapid morphological response can occur (Lieberman et al., 2004; Perez, Lema, Diniz‐Filho, et

122 al., 2011), this cranial response is likely due to developmental plasticity. Since developmental plasticity can affect morphological change, in addition to evolutionary forces, and it is difficult to distinguish each of the effects, developmental plasticity can be a strong confounding factor when using crania to assess population affinities. Other papers, however, show that the cranium can be used to reconstruct phylogenies, so on a global level, developmental plasticity likely does not have a strong confounding effect

(e.g., Collard & Wood, 2007; von Cramon-Taubadel, 2011b).

Given this evidence, there are somewhat contradictory studies revolving around the extent to which natural selection and climate has affected the cranium. While some researchers have reported effects on cranial morphology associated with climate (e.g.,

Hubbe, Hanihara, et al., 2009; Roseman, 2004), others have found no correlation between cranial morphology and climate (e.g., Betti et al., 2009; Harvati & Weaver, 2006a;

Manica et al., 2007) or that the neurocranium reflects climate to a greater degree than population history (Smith, 2009; which is counter to Hubbe et al., 2009). The case for climatic adaptation on the facial region is better supported by independent lines of evidence, where researchers have studied the relationship between facial/nasal region against temperature and humidity (Carey & Steegmann, 1981; Noback, Harvati, & Spoor,

2011; Weiner, 1954; Wolpoff, 1968).

Integration of the different regions of the cranium can also be influenced by functional strains. Because each bone/cranial region interacts directly with the neighboring bones or regions (Enlow, 1990), strains affecting one bone can directly impact and change nearby bones or regions. For example, a reduction in masticatory

123 muscle use due to decreased mechanical loading (of which one cause is a dietary shift toward softer foods) leads to decreased muscle size (González-José, Ramírez-Rozzi, et al., 2005). The decreased muscle size in turn alters the craniofacial morphology and mandible (González-José, Ramírez-Rozzi, et al., 2005), with a smaller muscle size meaning that less force is placed on the surrounding bones, allowing them to be smaller but still accomplish the same task. Experimental studies have confirmed these claims as well, since the cranium has exhibited a plastic response to different levels of mechanical stress (Ciochon, Nisbett, & Corruccini, 1997; Larsen, 2015). This has enabled researchers to use cranial morphological changes to study shifts from hunter-gatherer lifestyles to agriculture (Larsen, 2015).

The integration of various modules of the cranium can affect estimates of between group variance, particularly if that module or modules are affected by mechanical or other environmental forces. Thus, if a series of crania demonstrate a plastic response, or any response to an environmental factor, González-José, Ramírez-Rozzi, et al. (2005) suggests estimating the between group variance for both the whole cranium and various regions. These estimations are important since the cranium is integrated over many levels, where bones can both be an individual component and belong to a component

(Pucciarelli, Dressino, & Niveiro, 1990). In addition to eliminating this potential bias, dividing the cranial regions by functional components, or functional modules, has also been shown to be useful for taxonomic (Pucciarelli et al., 1990) or phylogenetic classifications (e.g., González-José et al., 2008; von Cramon-Taubadel, 2011b).

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While the classic Howells or Hanihara linear measurements have been used for a to address several questions, such as general assessments of association between populations, testing migration models, or assessing worldwide cranial patterns, these measurements are not informative for assessing the biology of variation. Linear measurements are not as informative because many of the measurements cross between multiple functional modules. For instance, Howells’ measurements span large regions of the cranium over bones and tissue with different composition, growth patterns, and functions (González-José, Ramírez-Rozzi, et al., 2005). Even though it has been shown that dividing cranial regions by development may not be useful (von Cramon-Taubadel,

2011b), dividing cranial regions by function may be quite informative because it takes into account the modularity and integration present in the skull. Obscuring this data by using the classic measurements versus landmarks may change the perspective of the analysis. To that end, the goal of this chapter is to compare functional modules of the cranium to climatic variables in order to better understand the extent to which cranial morphology reflects neutrality or natural selection.

5.2 MATERIALS AND METHODS

5.2.1 Crania: Samples

The 204 human crania (122 males, 82 females) used in this dissertation come from prehistoric populations from different parts of the Andes located in Colombia, Peru, and Chile (Table 5.1). The span of the Andes provides a more restricted sample of crania from a variety of environments. Having samples from this whole region ensured some

125 amount of variability when it came to their climate (although, most were from arid climates, this range represented tropical humid, semi-arid, and cold humid as well). Their diets were quite similar, so diet is not assumed to be strongly influencing the results with these particular populations and it was not controlled for. Most of the populations were horticulturalists, but the Patagonian populations were fisher-hunter gatherers (Zurro,

Madella, Briz, & Vila, 2009). While most of the sites are dated relatively close to contact period (Table 5.1), the Aguazuque site from Colombia is substantially older, being dated to approximately 5000 ybp (Neves, Hubbe, & Correal, 2007). For a complete background and context for these sites, see Chapter 3, section 3.3.

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Table 5.1: Cranial Series Information

Series Sites Sample Chronology Life Style Altitude Climate Institution Included Size (~kyr BP) Museo de Transitional Tropical Historia Aguazuque Aguazuque 17 5.3-3.1 400-1500 Horticulturalist Humid Natural - Bogotá Field Fisher Arid Ancon Ancon 47 3.0-0.5 0-500 Museum, Horticulturalist Coastal Chicago Field Aramburu Aramburu 34 3.0-0.5 Horticulturalist 2500-4000 Arid Museum, Chicago Azapa 8, 70, 71, 75, 115, Fisher- Museo 140 Transitional Arqueológico Arid Arica Morro 1 29 5.0 - 0.5 Horticulturalist 0-500 San Miguel Coastal Camarones 9 & Fisher de Azapa, Playa Miller Horticulturalist Azapa 3, 4, 7 Field Cerro del Cerro del 7 3.0-0.5 Horticulturalist 2500-4000 Arid Museum, Oro Oro Chicago Coyo Coyo Oriente San Pedro de 39 3.0-0.5 Horticulturalist 2500-3000 Arid Oriente Solcor Plaza Atacama Museo de Araucania Transitional Tempera Historia Mid Chile 9 3.0-0.5 0-500 Costa Sur Horticulturalist te Natural, Santiago Isla Grande Bahia Isla Dawson Puerto Museo de Patagonia Hambre Fisher-Hunter- Cold Historia and Tierra 22 1.5-0.5 0-400 Selk’nam Gatherers Humid Natural, del Fuego Fuegino Santiago Magallanas Yamana Yaghan

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Most studies on cranial variation use 2D measurements (typically based on

Howells measurements). Many of these measurements use points that either start or end on the face, meaning these measurements may be skewed due to environmental factors

(Betti et al., 2009; Harvati & Weaver, 2006a, 2006b). This dissertation used 3D scans to better assess how environmental factors affect different regions of the cranium, which enable analyses on individual bones or modules, eliminating a bias that affected previous research. Morphometric data from the skull were collected from adult crania with no evidence of cranial modification, through full surface scans with a Next Engine Surface

Scanner and the computer program ScanStudio. The complete scan of each skull in high definition (17,000 points/inch2) takes approximately 50-60 minutes and requires two scans with 8 divisions each (resting on its occipital, with the face up; and resting on its parietal, with the palate at approximately 30 degrees from the horizontal plane), to get accurate measurements for the cranial base’s complex topography. For very large crania

(e.g., Patagonia), some specimens required a third scan to get a complete image. For these, the third scan was taken with the skull resting on the opposite parietal, with the palate at approximately 30 degrees from the horizontal plane. To stabilize the skull in these positions, the skull was either placed on modeling clay or a Styrofoam ring mold.

This protocol was developed by Mark Hubbe using the teaching collection at OSU during

Autumn 2012.

The total amount of time for a complete scan of the skull (~1 hr) is taken into account when determining the length of time needed with each skeletal collection. In spite of the extended period of time it takes to complete a scan, this method is ideal

128 because the researchers obtain a virtual copy of the skull to take back with them. This ensures no further trip to these collections for most researchers since the scans will be placed in a public database at the conclusion of the dissertation. Another consideration was the size of the files, since the initial file for each skull created at this stage is between

350 and 500 MB. It is possible to decrease the file size for each skull, but the time it takes to complete this process is not always feasible in the field. Therefore, a computer and external hard drives with large storage capacity (~250GB for the total collection) were needed in the field.

Once a scan was completed, the scans had to be cleaned. Still using ScanStudio, the first step is to trim the images. There is a Trim tool that will allow the user to draw polygons around areas of the crania to remove debris and unwanted portions of the scanned specimen (e.g., the clay holding it). Care was taken to not remove too much of the actual skull during this process because this could lead to a hole in the skull once all images are combined. Once the scans were trimmed they must be aligned using the Align tool. Using this will pull up the two scans in a split screen, upon which you place a marker on each scan in the same location on the skull. A minimum of three markers must be placed. The alignment will also work best if the three markers are placed as far apart from each other as possible, as this will increase the accuracy of the alignment. All alignments for this dissertation were performed until the accuracy of the overlapped skulls was equal to or less than 0.004 mm.

Once the scans are aligned, they must be fused together using the Fuse tool in

ScanStudio. The program will then automatically fuse the scans and create a new “scan”,

129 which is the final fused cranium. To further reduce the file size, the Polish tool can be used to simplify the mesh of the final fused cranium. Doing this will reduce the final simplified file size to between 20 and 40 MB. This is an important step since the program used for landmarking the crania will not open the files if they are too large. The final step is saving the files as both a .SCN and a .PLY type.

Using the .PLY scans, type I landmarks1 were collected in the program Landmark and used in the comparative cranial analyses discussed in Chapters 5 and 6. In total, 78 different landmarks were collected and are listed in Table 5.2. The landmarks selected cover the entire skull and allow for the breakdown of the skull into distinct anatomical regions for testing the hypotheses and were based on a combination of previous work

(Harvati & Weaver, 2006a, 2006b; Hubbe, Hanihara, et al., 2009; Smith, 2009; von

Cramon-Taubadel, 2009). In Landmark, each of the 78 points must be placed on the appropriate location on the skull. If one of the points was missing, either due to a portion of the skull being broken or the markers to identify the landmark were obscured due to scanning artifacts, the point was placed nearby and marked as missing within the program. This ensures that all points remain in the same order for each skull. After each landmark on a skull was marked, the points were then exported as a .PTS file. These files were then opened in Microsoft Excel. The file opens with each X, Y, and Z component of each point in a new row. In order to run the analyses in R (R Core Team, 2018), the points must be in a single row rather than in columns. To do this, I used a macro written

1 Type I landmarks: landmarks whose homology is supported by the strongest (local) evidence. For example, the meeting of two structures. (Hoppa & Fitzgerald, 1999) Type II landmarks: landmarks whose homology is supported by geometric and not histological evidence. For example, the tip of a tooth. (Hoppa & Fitzgerald, 1999) 130 by Mark Hubbe. I then made a different Excel sheet for each population, with each point from an individual in the population listed in its own row, as well as one Excel sheet that contained all populations.

After the scans of the crania are taken, and those files have been cleaned where coordinates could be extracted, all coordinates need to be reduced, meaning any landmarks not associated with that particular module must be eliminated. To first reduce the coordinates, different Excel files are needed for each division, or module, of the cranium. For this dissertation, the first module is the entire cranium. Then the entire cranium is broken down into three major modules: 1) face, 2) neurocranium, and 3) basicranium. These were again broken down into six minor modules: 1) orbits, 2) nasal,

3) masticatory complex, 4) anterior neurocranium, 5) medial neurocranium, and 6) posterior neurocranium. Once the landmarks representing each of these modules are recorded, a file of all landmarks can be reduced down to represent only those for a particular division. Table 5.2 and Figure 5.1 shows the landmarks for each module.

Each table then needs to be optimized for analysis by ensuring that there are no outliers or missing values. I started with the file of all landmarks first in order to check for outliers. A Procrustes analysis cannot be run with missing values, so all missing values from each table were eliminated. To do this, the missing values for each column

(landmark) and each row (individual) were counted. Any column or row with more than

50% missing values were eliminated first. Then columns with more than 40% missing values were eliminated, followed by rows with more than 40% missing. This process continued in this pattern dropping by 10% missing each time. The systematic elimination

131 process allows us to maintain the maximum number of both rows and columns in the data, but slightly favors keeping higher numbers of individuals since columns were always eliminated first. Given that some of the populations used (Ex: Mid-Chile, Cerro del Oro) have smaller overall population sizes to begin with, it was important to maintain as many of these individuals as possible.

For this project, I decided not to estimate the missing values. While numerous papers have discussed estimating missing values (e.g., Clavel, Merceron, & Escarguel,

2014; Couette & White, 2010; Hubbe et al., 2015; Neeser, Ackermann, & Gain, 2009; R.

E. Strauss, 2010), there is still debate as to the accuracy and practicality of estimating values. Estimating missing data could introduce bias into the estimation of variance and covariance matrices of the data or other statistical analyses, depending on the method used. Using a mean substitution method, which typically uses a Thin Plate Spline and multivariate regression techniques, Neeser et al. (2009) found differences in the accuracy compared to a complete data set. However, researchers have found that using either

Multiple Regression or Expectation Maximization (an iterative method that substitutes means variable by variable, computing a set of parameters, and then re-estimating the missing values until the parameters converge on a value) produces results similar to that of a complete data set (Couette & White, 2010; Neeser et al., 2009). Caution is given to smaller reference samples however, as the accuracy increases with larger reference sample size. Many of the sample sizes of each of the populations used in this dissertation are small (under 100) and therefore may not be good candidates for these methods. One last method involves using a mirror reflection for points present on one side of the skull,

132 but missing on the other side of the skull. This method is relatively accurate, but it reduces the asymmetric variation between the left and right sides of an individual. While this dissertation is not directly studying fluctuating asymmetry, this type of variation is important when considering the influence of environmental adaptation, as well as the factors that lead to covariation of traits in relation to phenotypic integration and modularity (e.g., Klingenberg, 2005; Klingenberg, 2009, 2013; Klingenberg, Mebus, &

Auffray, 2003; Martínez-Abadías et al., 2012; Porto, de Oliveira, Shirai, De Conto, &

Marroig, 2009; Willmore, Buikstra, Cheverud, & Richtsmeier, 2012). Given that this dissertation is indirectly studying these factors, no estimation method was chosen.

A General Procrustes Analysis (GPA) was performed on the file with all landmarks (this file actually just contains most of the landmarks after the reduction process) with the results graphed in a 3D array. A GPA performs a rotation, scaling, transformation, and reflection of the points to put all landmarks from all individuals into the same coordinate system (Gunz, Mitteroecker, & Bookstein, 2005). When the points are first exported from Landmark, each individual has a different coordinate system so this step in necessary to put all specimens in the same coordinates system. The GPA was run in R (Team, 2014) using a script written by Mark Hubbe, which calls upon the paleomorph package (Lucas & Goswami, 2017). The GPA coordinates were plotted in a

3D scatterplot to show individual’s landmarks in this new coordinate system, with any point(s) being “out of place” representing outliers. Once any outlier was found, I went back into Landmark to double-check the placement of the landmarks in questions for the specific individuals. Sometimes a landmark was numbered in the wrong order, or placed

133 slightly off of the intended point. These were then corrected, the new landmarks exported, reduced again, and the GPA performed again. This process continued until there were no outliers. Occasionally, the placement of the landmark appeared to be correct, as well as the order of the landmarks on that individual. In these situations, where the landmark cannot be ruled out easily as a measurement mistake, the individual themselves were completely removed from the analysis. Only two of all individuals required this action. Once the file with all landmarks was free of outliers, the divisions for the different anatomical regions were made. All divisions and files were checked for outliers, and once all files were corrected, the statistical analysis could be performed.

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Figure 5.1: Image of landmarks on skull

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Table 5.2: Landmarks used in Cranial Analyses

Landmark # Landmark Anatomical Regions Neurocranium 1 Inion Posterior Neurocranium Neurocranium 2 Asterion R Posterior Neurocranium Neurocranium 3 Asterion L Posterior Neurocranium Neurocranium 4 Lambda Posterior Neurocranium 5 Opisthion Basicranium 6 Basion Basicranium 7 Occipital Condyle Posterior R Basicranium 8 Occipital Condyle Posterior L Basicranium 9 Hormion Basicranium 10 Staphylion Face 11 Stylomastoid Foramen R Basicranium 12 Jugular Foramen Anterior R Basicranium 13 Jugular Foramen Posterior R Basicranium 14 Vaginal Plate Lateral R Basicranium 15 Vaginal Plate Medial R Basicranium Neurocranium 16 Porion R Medial Neurocranium Neurocranium 17 Auriculare R Medial Neurocranium Neurocranium 18 Parietal Notch R Medial Neurocranium 19 Lateral Glenoid R Masticatory Complex Zygomatic-Temporal Suture Face 20 Inferior R Masticatory Complex Zygomatic-Temporal Suture Face 21 Superior R Masticatory Complex Face 22 Frontomalare Posterior R Masticatory Complex 23 Stylomastoid Foramen L Basicranium 24 Jugular Foramen Anterior L Basicranium 25 Jugular Foramen Posterior L Basicranium 26 Vaginal Plate Lateral L Basicranium 27 Vaginal Plate Medial L Basicranium Neurocranium 28 Porion L Medial Neurocranium Neurocranium 29 Auriculare L Medial Neurocranium Neurocranium Parietal Notch L 30 Medial Neurocranium (continued)

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Table 5.2, continued 31 Lateral Glenoid L Masticatory Complex Zygomatic-Temporal Suture Face 32 Inferior L Masticatory Complex Zygomatic-Temporal Suture Face 33 Superior L Masticatory Complex Face Frontomalare Posterior L 34 Masticatory Complex Basicranium Entoglenoid Pyramid R 35 Masticatory Complex 36 Pterygoid Canal R Basicranium Basicranium Entoglenoid Pyramid L 37 Masticatory Complex 38 Pterygoid Canal L Basicranium Face Palatine-Sphenoid R 39 Masticatory Complex Face Palatine-Sphenoid L 40 Masticatory Complex Face I2-I1 contact R 41 Masticatory Complex Face I2-I1 contact L 42 Masticatory Complex Face Canine-Premolar contact R 43 Masticatory Complex Face Canine-Premolar contact L 44 Masticatory Complex Face Premolar-Molar contact R 45 Masticatory Complex Face Premolar-Molar contact L 46 Masticatory Complex Face Distal M3 R 47 Masticatory Complex Face Distal M3 L 48 Masticatory Complex Face Midline Anterior Palatine 49 Masticatory Complex Neurocranium Bregma 50 Anterior Neurocranium Neurocranium Glabella 51 Anterior Neurocranium Face Nasion 52 Nasal Face Rhinion 53 Nasal Face Nasospinale 54 Nasal (continued)

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Table 5.2, continued Face Prosthion 55 Masticatory Complex Face Alveolare 56 Masticatory Complex Face Mid-Orbit Torus Inferior R 57 Orbit Face Dacryon R 58 Orbit Face Orbitale R 59 Orbit Face Zygoorbitale R 60 Orbit Face Frontomalare Orbitale R 61 Orbit 62 Infraorbital Foramen R Face Face Zygomaxillare R 63 Masticatory Complex 64 Alare R Face Face Jugale R 65 Masticatory Complex Neurocranium Stephanion R Anterior Neurocranium 66 Masticatory Complex Neurocranium Anterior Pterion R 67 Anterior Neurocranium Face Mid-Orbit Torus Inferior L 68 Orbit Face Dacryon L 69 Orbit Face Orbitale L 70 Orbit Face Zygoorbitale L 71 Orbit Face Frontomalare Orbitale L 72 Orbit 73 Infraorbital Foramen L Face Face Zygomaxillare L 74 Masticatory Complex 75 Alare L Face Face Jugale L 76 Masticatory Complex Neurocranium Stephanion L Anterior Neurocranium 77 Masticatory Complex Neurocranium Anterior Pterion L 78 Anterior Neurocranium

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5.2.2 Analytical Procedures

For this chapter, analyses were performed in three ways: one where males and females of all populations were pooled in order to increase sample size and another two with males and females run separately. Studies have demonstrated that the primary difference between males and females belonging to the same population is size, with no

(or very little) shape differences being due to sex differences (ex: Kimmerle, Ross, &

Slice, 2008; von Cramon-Taubadel & Smith, 2012). For cranial metric measurements, size can be corrected for by using either geometric means or regression analysis (F.

Bookstein, 1991; Smouse, Long, & Sokal, 1986), but these methods are best employed on cranial measurements rather than 3D landmarks. While there are many methods for moving 3D landmarks on individuals to a common reference frame such that the effects of variation between individuals due to scaling, translation, rotation, etc. is removed (e.g.,

GPA, Finite-Element Scaling Analysis (FESA), Thin-plate Splines (TPS)) (von Cramon-

Taubadel, Frazier, & Lahr, 2007), GPA was deemed the most appropriate for this analysis. FESA and TPS expresses shape differences in terms of deformations instead of landmark placement (F. L. Bookstein, 1989; Cheverud, Lewis, Bachrach, & Lew, 1983) whereas GPA minimizes the sum of squared distances between landmarks, which still allows these converted landmarks to be manipulated with multivariate statistics that do not necessarily operate in Euclidean space (Principal Component Analysis). Additionally, because GPA eliminates the effects of size, both males and females in a population can be analyzed together and presumably not affect the end result of the analysis.

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The first step of the analysis was to take the residuals from the GPA, which were projected into a linear space tangent to the curved shape space, and subject them to a

Principal Component Analysis (PCA). Performing this step allows us to explore the variance present in the samples, as well as act as an additional check for outliers. The principal components are also used to derive the distance matrix (see below). There are four main goals of a PCA: 1) separate the most important information in a data set; 2) compress the size of the data set by only keeping the most important information; 3) simplify the data set; and 4) analyze the structure of the observation/variables (Abdi &

Williams, 2010). To achieve these goals, a PCA uses an orthonormal transformation to convert a set of possibly correlated variables into a set of linearly uncorrelated variables, or principal components. This can show the pattern of variation of the entire sample, with the shape variation visualized along the PCA axes. Additionally, the first principal component always contains the majority of the variation, and each subsequent principal component contains less than the previous one. The PCA was run in R (Team, 2014) with a script written by Mark Hubbe.

To explore morphological affinities among the groups, Mahalanobis Squared

Distances (D2) were calculated between all pairs of series (Mahalanobis, 1936) using R

(Team, 2014). D2 matrices provide a measure of dissimilarity that considers differences observed between groups’ centroids, but also corrects the contribution of each variable to the final distance by their covariance so that the distance is not inflated by the correlation between them (Hubbe et al., 2014a). This makes the Mahalanobis distance essentially a multidimensional version of measuring the number of standard deviations a point is from

140 the mean. The morphological distances D2 were calculated using principal components representing 90-95% of the total variance (Harvati & Weaver, 2006b) so that the majority of the variance is fully represented by the distance matrix.

To represent the D2 matrix and explore the morphological affinities among the series, a Kruskal’s bi-dimensional Non-Parametric bootstrap Multidimensional Scaling

(MDS) was also performed and graphed in a scatterplot (Cox & Cox, 2000; Hubbe et al.,

2015). This MDS scatterplot represents the pairwise Mahalanobis distance matrix between all series. To get an expected distribution of the observed distances, 100 new distance matrices were calculated from bootstrapping (resampling with substitution) the original data (Hubbe et al., 2015). The bootstrapping process creates new distance matrices that take into consideration the variation expected in the distance between each population due to population estimation errors associated with sample size. Each of the

100 bootstrapped D2 matrices had MDS coordinates calculated and superimposed over the original MDS using a Procrustes analysis, which minimizes their distance (Hair,

Black, Babin, & Anderson, 2014). Unlike a tree-based structure analysis (such as a cluster analysis), an MDS analysis does not assume that the patterns represented by distances between populations must be a bifurcating branch, and therefore does not force a strong topology (Harvati & Weaver, 2006b). This makes the MDS a good complement to a Ward’s cluster analysis since it can act as a check against the groups produced by the

Ward’s analysis (Hubbe et al., 2014a). To calculate the goodness-of-fit, stress was calculated for each MDS analysis.

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Relationship matrices, or R-matrices, represent the estimate of genetic kinship that corresponds to the pattern of affinity among groups, and is calculated from the scaled variances and covariances between variables (Harpending & Jenkins, 1973). It describes the relationship between two populations relative to the average distance between individual populations (Herrera et al., 2014; von Cramon-Taubadel, 2009). Originally used to estimate genetic relationships, Relethford and Blangero (1990) and Relethford,

Crawford, and Blangero (1997) extended its use for quantitative traits derived from phenotypic data. The elements of this matrix, for populations i and j, are represented by the following equations:

(푝푖−푝̅)(푝푗−푝̅) 푟 = (1) 푖푗 푝̅(1−푝̅)

where pi is the allele frequency for population i, pj is the allele frequency for population j, and 푝̅ is the mean allele frequency averaged over all populations and described by equation (2):

푝̅ = ∑ 푤푖 푝푖푘 (2)

with wi representing the ratio of census size of population i to the total census size of all groups, pik is the frequency of one allele at locus k, and the summation is over all groups.

By averaging the principal diagonal of the R-matrix (rii), minimum Fst estimates can be calculated, which represent a measure of the amount of between-group variance in the data (Relethford, 1994; A. Strauss et al., 2015). Calculating the minimum Fst estimate using cranial metric data strongly depends on the heritability values of the measurements

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(Herrera et al., 2014; A. Strauss et al., 2015). Cranial metric data is typically estimated to be approximately 0.55 or higher (Devor, 1987), although recent research has shown distinct heritability values depending on the portion of the cranium being examined

(Carson, 2006; Martínez‐Abadías et al., 2009). The variability in heritability estimates is problematic because calculating Fst estimates based on Relethford & Blangero (1990) can only use an average heritability and cannot accommodate differential heritabilities

(A. Strauss et al., 2015). Because of this, I chose to use a narrow heritability of 1 (h2 = 1), which assumes that all observed phenotypic variation is due to genetic variation with no effect from environmental or other variables. The narrow heritability would represent the minimum possible and most conservative estimate of Fst value. It can also be argued based on Carson (2006) and Martínez‐Abadías et al. (2009), who published heritability estimates for several cranial variables, that the standard h2 = 0.55 can be seen as conservative because these studies found most h2 values to be less than 0.55 (Hubbe,

Hanihara, et al., 2009). Ultimately, this will not significantly affect the results provided there are no direct comparisons with other papers who use different heritability values.

Generating summary statistics for genetics or craniometrics are typically estimated from a small number of people relative to the entire population, or samples from a small number of populations relative to a larger metapopulation (Meirmans &

Hedrick, 2011). In the case of anthropology of past populations, the population size is likely unknown. For each of these situations, the diagonal elements of the R matrix, and by extension the Fst and square distances, have a sample bias (Varela & Cocilovo, 2002).

This can be corrected by subtracting 1/2ni from the diagonal elements, where ni is the size

143 of the ith group (Varela & Cocilovo, 2002). I have provided both the biased and unbiased

Fst values for the populations in this chapter.

In order to visualize the observed versus expected variation for each population, I utilized Bokeh plots in R (RB plots). Bokeh is a visualization library that provides the framework for creating plots. Although these are typically utilized in interfaces such as

Python, Scalia, or HTML, an R interface was recently created in the rbokeh package

(Hefen et al., 2016). Within R, the plots are constructed by first creating a figure, and then subsequently adding layers available in Bokeh on top of the figure.

With this analysis being performed on a pooled sample of males and females, there is no way to look at differences in population relatedness that could be explained by sex differences (e.g., sex-based differential migration or demographic behaviors, such as matrilocality or patrilocality). Therefore, the above methods were repeated to run a separate analysis on males and females.

5.2.3 Comparisons with Geographic and Climate Data

For each population, geographic coordinates were established to the closest reference point or was based on previously published data about the population. Climate data (average annual temperature, temperature range, altitude, rainfall, and isothermality) was extracted present and past data from the BIOCLIM database (Hijmans et al., 2005).

Geographic distance were also extracted based on the distances between the geographic coordinates. Then, climatic distance matrices were built based on the absolute differences

144 in each of the climatic variables for all possible comparisons (Harvati & Weaver, 2006a).

The initial matrices for each variable were accessed through ArcMap 10.4 and subsequently exported into R. Cranial anatomical regions were correlated with these climatic variables to test if between-group differences in these regions can be explained by any environmental parameters considered in this project.

The morphological distance matrices were correlated with climate distances using

Mantel and Partial Mantel tests (Mantel, 1967). The Mantel tests allow for an estimation of the correlation between two matrices in the form of an r-value, while also providing a p-value representing the significance of the correlation. The r-value can be positive or negative, representing a positive or negative correlation, and vary between -1 and +1. The closer the value is to either -1 or +1 the stronger the relationship is, but a value close to 0 indicates lack of correlation. The Partial Mantel test works the same way, but controls for the impact of a third variable (covariate) on the two variables being tested. In this case, geographic distances were controlled for because the strength of some relationships may have been influenced by an underlying correlation with geographic distance. These tests allow for the testing of the hypotheses (section 1.2) because comparing cranial morphology to climate variables is testing the influence from diversifying selection

(Roseman, 2004). The Mantel and Partial Mantel tests were performed in R using the vegan package (Oksanen et al., 2018).

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5.3 RESULTS

5.3.1 PCA: Pooled Samples

Figures 5.2 – 5.11 show the PCA of each combination of cranial data for the pooled samples, with the gray lines showing correlations with the original variables.

Figures 5.12 – 5.21 show the first two PCs graphed with polygons showing the limits of variation for each population. The PC scores will then act as the shape variables for assembling affinity matrices (Roseman & Weaver, 2004). Visually, the primary purpose of these particular PCAs are to ensure that there are no outliers within the populations for a given cranial module. If an individual demonstrates that they are an outlier, their landmarks were checked to ensure proper placement. Overall, there were very few outliers. Larger deviations were checked and corrected. The individuals still showing some deviation in the graphs below (basicranium: individual 101; neurocranium: individual 90; anterior neurocranium: individual 179; medial neurocranium: individual

27; left orbit: individual 101) were all checked and their landmarks were all placed correctly. These deviations are not expected to be large enough to strongly influence the analyses, which is supported by the PCA polygon graphs not demonstrating any outliers.

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Figure 5.2: PCA for the Whole Cranium using both male and female samples.

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Figure 5.3: PCA for the Basicranium using both male and female samples.

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Figure 5.4: PCA for the Neurocranium using both male and female samples.

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Figure 5.5: PCA for the Anterior Neurocranium using both male and female samples.

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Figure 5.6: PCA for the Medial Neurocranium using both male and female samples.

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Figure 5.7: PCA for the Posterior Neurocranium using both male and female samples.

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Figure 5.8: PCA for the Face using both male and female samples.

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Figure 5.9: PCA for the Right Masticatory Complex using both male and female samples.

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Figure 5.10: PCA for the Nasal using both male and female samples.

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Figure 5.11: PCA for the Left Orbit using both male and female samples.

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Figure 5.12: PCA Polygons for the Whole Cranium using both male and female samples.

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Figure 5.13: PCA Polygons for the Basicranium using both male and female samples.

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Figure 5.14: PCA Polygons for the Neurocranium using both male and female samples.

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Figure 5.15: PCA Polygons for the Anterior Neurocranium using both male and female samples.

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Figure 5.16: PCA Polygons for the Medial Neurocranium using both male and female samples.

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Figure 5.17: PCA Polygons for the Posterior Neurocranium using both male and female samples.

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Figure 5.18: PCA Polygons for the Face using both male and female samples.

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Figure 5.19: PCA Polygons for the Right Masticatory Complex using both male and female samples.

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Figure 5.20: PCA Polygons for the Nasal using both male and female samples.

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Figure 5.21: PCA Polygons for the Left Orbit using both male and female samples.

5.3.2 PCA: Male Samples

Figures 5.22 – 5.31 show the PCA of each combination of cranial data for the male samples, with the gray lines showing correlations with the original variables. Then,

Figures 5.32 – 5.41 show the first two PC scores graphed with polygons to help define

166 the limits of variation for each series. Visually, the primary purpose of these particular

PCAs are to ensure that there are no outliers within the populations for a given cranial module.

Figure 5.22: PCA of the Whole Cranium using male samples.

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Figure 5.23: PCA of the Basicranium using male samples.

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Figure 5.24: PCA for the Neurocranium using male samples.

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Figure 5.25: PCA for the Anterior Neurocranium using male samples.

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Figure 5.26: PCA for the Medial Neurocranium using male samples.

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Figure 5.27: PCA for the Posterior Neurocranium using male samples.

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Figure 5.28: PCA for the Face using male samples.

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Figure 5.29: PCA for the Right Masticatory Complex using male samples.

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Figure 5.30: PCA for the Nasal using male samples.

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Figure 5.31: PCA for the Right Orbit using male samples.

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Figure 5.32: PCA Polygons for the Whole Cranium using the male samples.

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Figure 5.33: PCA Polygons for the Basicranium using the male samples.

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Figure 5.34: PCA Polygons for the Neurocranium using the male samples.

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Figure 5.35: PCA Polygons for the Anterior Neurocranium using the male samples.

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Figure 5.36: PCA Polygons for the Medial Neurocranium using the male samples.

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Figure 5.37: PCA Polygons for the Posterior Neurocranium using the male samples.

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Figure 5.38: PCA Polygons for the Face using the male samples.

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Figure 5.39: PCA Polygons for the Right Masticatory Complex using the male samples.

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Figure 5.40: PCA Polygons for the Nasal using the male samples.

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Figure 5.41: PCA Polygons for the Right Orbit using the male samples.

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5.3.3 PCA: Female Samples

Figures 5.42 – 5.51 show the PCA of each combination of cranial data for the female samples, with the gray lines showing correlations with the original variables.

Then, Figures 5.52 – 5.61 show the first two PC scores graphed with polygons to help define the limits of variation for each series. Visually, the primary purpose of these particular PCAs are to ensure that there are no outliers within the populations for a given cranial module.

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Figure 5.42: PCA for the Whole Cranium using female samples.

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Figure 5.43: PCA for the Basicranium using female samples.

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Figure 5.44: PCA for the Neurocranium using female samples.

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Figure 5.45: PCA for the Anterior Neurocranium using female samples.

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Figure 5.46: PCA for the Medial Neurocranium using female samples.

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Figure 5.47: PCA for the Posterior Neurocranium using female samples.

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Figure 5.48: PCA for the Face using female samples.

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Figure 5.49: PCA for the Left Masticatory Complex using female samples.

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Figure 5.50: PCA for the Nasal using female samples.

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Figure 5.51: PCA for the Left Orbit using female samples.

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Figure 5.52: PCA Polygons for the Whole Cranium using the female samples.

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Figure 5.53: PCA Polygons for the Basicranium using the female samples.

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Figure 5.54: PCA Polygons for the Neurocranium using the female samples.

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Figure 5.55: PCA Polygons for the Anterior Neurocranium using the female samples.

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Figure 5.56: PCA Polygons for the Medial Neurocranium using the female samples.

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Figure 5.57: PCA Polygons for the Posterior Neurocranium using the female samples.

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Figure 5.58: PCA Polygons for the Face using the female samples.

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Figure 5.59: PCA Polygons for the Left Masticatory Complex using the female samples.

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Figure 5.60: PCA Polygons for the Nasal using the female samples.

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Figure 5.61: PCA Polygons for the Left Orbit using the female samples.

5.3.4 Fst Analysis: Pooled Samples

The results for the Fst analyses are shown in Tables 5.3, 5.4, and 5.5. The variance for the whole cranium (Table 5.3) is larger than would typically be expected, with a value of 0.3634 ± 0.0037 (a typical Fst estimate for worldwide populations is

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0.276 (Hubbe et al., 2015) or 0.1287 ((Hubbe, Hanihara, et al., 2009), although it should be noted that Hubbe et al. (2015) use a heritability of 0.55). Of the main cranial modules

(the basicranium, neurocranium, face), the neurocranium had the highest amount of variation (0.1245 ± 0.0059), followed by the face (0.1025 ± 0.0046) and the basicranium

(0.0727 ± 0.0058). Unusually, the nasal and orbit had the lowest amount of variation when looking at all modules (0.0406 ± 0.0076 and 0.0218 ± 0.0047, respectively).

Previous values, such as in Hubbe, Hanihara, et al. (2009), demonstrate Fst values that are higher than when the whole cranium is considered (orbit = 0.168; nasal = 0.2338).

Given that the nasal region is known to be influenced by temperature and humidity (von

Cramon-Taubadel, 2014), it was expected that there would be much higher amounts of variation shown.

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Table 5.3: FST Results for both male and female cranial samples. The table is ordered from highest to lowest FST.

FST Results for Crania Biased Unbiased SE Whole Cranium 0.3634 0.3265 0.0037 Neurocranium 0.1245 0.0917 0.0059 Face 0.1025 0.0673 0.0046

Anterior 0.0940 0.0630 0.0078 Neurocranium Right Masticatory 0.0836 0.0506 0.0077 Complex Posterior 0.0733 0.0427 0.0087 Neurocranium Basicranium 0.0727 0.0407 0.0058

Medial 0.0592 0.0267 0.0070 Neurocranium Nasal 0.0406 -0.0050* 0.0076 Left Orbit 0.0218 -0.0091* 0.0047 *Negative Fst values are assumed to be 0.000.

5.3.5 Fst Analysis: Male and Female Samples

Fst values for males and females showed a few key differences (see Table 5.4).

The patterns seen in males more closely resembles the overall pattern for the pooled samples than the female samples do (Table 5.5). This is likely due to the higher sample size present for males versus females. Not only is the overall pattern different between the sexes, but the actual values are starkly different. For instance, the Fst for the face on females (Biased: 0.8423; Unbiased: 0.7550) is substantially higher than for males

(Biased: 0.2739; Unbiased: 0.2066). These high Fst numbers for females are likely due to 209 outliers in the analyzed dataset that have yet to be identified. However, it appears that females demonstrate a much greater range of variation than males, which is supported by the MDS graphs as well.

Table 5.4: FST Results for the male cranial samples. The table is ordered from highest to lowest FST.

FST Results for Male Crania Biased Unbiased SE Whole Cranium 0.3608 0.2977 0.0054 Face 0.2739 0.2066 0.0075 Neurocranium 0.1507 0.0902 0.0097 Basicranium 0.1468 0.0873 0.0094

Anterior 0.1207 0.0510 0.0133 Neurocranium Right Masticatory 0.1049 0.0388 0.0107 Complex Medial 0.0882 0.0267 0.0115 Neurocranium Posterior 0.0866 0.0263 0.0139 Neurocranium Right Orbit 0.0732 -0.0068* 0.0133 Nasal 0.0477 -0.0348* 0.0121 *Negative Fst values are assumed to be 0.000.

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Table 5.5: FST Results for the female cranial samples. The table is ordered from highest to lowest FST.

FST Results for Female Crania Biased Unbiased SE Face 0.8423 0.7549 0.0016 Neurocranium 0.3225 0.2422 0.0105 Basiscranium 0.2884 0.2001 0.0108

Anterior 0.1380 0.0611 0.0134 Neurocranium Left Masticatory 0.1192 0.0347 0.0159 Complex Whole Cranium 0.1097 0.0051 0.0057 Nasal 0.0839 0.0059 0.0155

Posterior 0.0808 0.0079 0.0145 Neurocranium Medial 0.0727 -0.0019* 0.0121 Neurocranium Left Orbit 0.0488 -0.0238* 0.0121 *Negative Fst values are assumed to be 0.000.

5.3.6 MDS: Pooled Samples

When looking at the MDS analyses (Figures 5.62 – 5.71), the overarching pattern that emerges is that Aguazuque (sometimes along with Patagonia) and Cerro del Oro are typically separated from the rest of the group and on opposite sides of the graph, which means they are the most different from each other. The only exception to this is seen in the basicranium (Figure 5.63) where Arica and Aguazuque are both about the same distance away from Cerro del Oro. The rest of the populations are typically grouped between Aguazuque/Patagonia and Cerro del Oro. In some instances, these populations

211 maintain some separation from each other, such as in the masticatory complex (Figure

5.71). However, in most cases there is some amount of overlap (as seen in the anterior neurocranium, Figure 5.65). There is an additional pattern where Coyo Oriente and

Ancon typically cluster closest to each other than any other populations.

For the whole cranium (Figure 5.62), there are four primary groups within the data. Aguazuque and Cerro del Oro are the farthest away from each other. Arica,

Aramburu, Ancon, Mid Chile, and Coyo Oriente are grouped together closest to Cerro del

Oro. Lastly, Patagonia falls between them and Aguazuque.

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Figure 5.62: MDS for the Whole Cranium using both male and female samples.

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For the basicranium (Figure 5.63), Aguazuque and Mid Chile appear to be the furthest apart. Most of the other populations fall between these two, except Cerro del Oro which separates from the others but maintains a closer distance to Mid Chile.

Figure 5.63: MDS for the Basicranium using both male and female samples.

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The MDS for the neurocranium (Figure 5.64) shows the same pattern as for the whole cranium: Aguazuque and Cerro del Oro are the farthest away from each other.

Arica, Aramburu, Ancon, Mid Chile, and Coyo Oriente are grouped together closest to

Cerro del Oro. Lastly, Patagonia falls between them and Aguazuque.

Figure 5.64: MDS for the Neurocranium using both male and female samples.

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Breaking the neurocranium down into submodules shows distinct patterns. The anterior neurocranium (Figure 5.65) essentially show a triangulation with Cerro del Oro,

Aguazuque, and Patagonia all being near equivalent vertices. The rest of the population blend together, but with some separation between Arica and Aramburu with the others.

The medial neurocranium (Figure 5.66) show Coyo Oriente and Aguazuque being the furthest apart. Patagonia is sitting near Aguazuque, while Ancon is closest to Coyo

Oriente. Mid Chile and Cerro del Oro are the separated the furthest on the vertical axis.

For the posterior neurocranium (Figure 5.67), Aguazuque and Ancon are the most separated. However, there are three primary groups formed. One is Patagonia, one is

Aguazuque, and one is all the other populations.

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Figure 5.65: MDS for the Anterior Neurocranium using both male and female samples.

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Figure 5.66: MDS for the Medial Neurocranium using both male and female samples.

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Figure 5.67: MDS for the Posterior Neurocranium using both male and female samples.

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When looking at the MDS for the face (Figure 5.68), the populations blend together more than most of the other modules. That being said, Cerro del Oro does group separately, and Patagonia and Aguazuque almost separate from the rest of the group.

Figure 5.68: MDS for the Face using both male and female samples.

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When the face is broken into smaller modules, a similar occurrence happens. With the left orbit (Figure 5.69) and nasal (Figure 5.70), the populations are so well blended that they essentially show as one large population. For the orbit, it seems that Coyo

Oriente is most different from Arica and Aguazuque, whereas the nasal shows Cerro del

Oro and Aguazuque being the most different.

Figure 5.69: MDS for the Left Orbit using both male and female samples.

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Figure 5.70: MDS for the Nasal using both male and female samples.

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The right masticatory complex (Figure 5.71) also shows a triangulation between

Cerro del Oro, Aguazuque, and Patagonia. Mid Chile falls very close to Patagonia, while

Arica is between Patagonia and Aguazuque. The other populations fall between Cerro del

Oro and Aguazuque.

Figure 5.71: MDS for the Right Masticatory Complex using both male and female samples.

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When comparing the MDS analyses to the Fst results, there is some level of consistency. The two anatomical modules that demonstrated the lowest amount of variation with the Fst (nasal and orbit) show less division between populations on the

MDS. Both the nasal and orbit MDS results show basically a large cloud of points

(Figures 5.70 and 5.69) when compared to the whole cranium, which shows clear division with Aguazuque, Patagonia, and Cerro del Oro (Figure 5.62). The face and neurocranium MDS results also show a similar separation with some populations, which is also consistent with the Fst results being higher.

5.3.7 MDS: Male Samples

When looking at the MDS plots for males and females (Figures 5.72 – 5.91), some similarities exist with the pooled samples. For instance, there is still an overarching pattern that Aguazuque and Cerro del Oro are more separated from the other populations.

However, the pooled samples typically saw both Aguazuque and Cerro del Oro separated whereas the males and females tend to show one or the other being further removed.

There is simply more division between the populations, and this is consistent throughout the male samples. The MDS for females is also more varied, but in general there is less distinction between populations. This is likely due to smaller sample sizes when compared to the pooled samples.

The whole cranium (Figure 5.72) for the male samples show Aguazuque separating from the rest of the populations, although it is furthest away from Mid Chile.

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Arica and Cerro del Oro are relatively close, as is Ancon and Aramburu. Coyo Oriente is closest with Mid Chile.

Figure 5.72: MDS for the Whole Cranium using male samples

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The graph for the basicranium (Figure 5.73) essentially makes a butterfly shape, with Aguazuque being the upper right wing, Arica being the lower right wing, and

Patagonia falling between them. Cerro del Oro would be the upper left wing, Mid Chile would be the lower left wing, with Ancon and Coyo Oriente falling between them.

Aramburu is between each half, in line with where the body would be.

Figure 5.73: MDS for the Basicranium using male samples.

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The male neurocranium (Figure 5.74) shows a strong separation of Aguazuque from the rest of the populations, with a similar separation being shown by Patagonia as well. Aguazuque is approximately equally as far from Cerro del Oro as it is from Mid

Chile.

Figure 5.74: MDS for the Neurocranium using male samples.

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Each submodule of the neurocranium shows a distinct pattern. The anterior neurocranium (Figure 5.75) shows Cerro del Oro, Aguazuque, and Patagonia being separate from the rest of the groups, with Aguazuque being the most separated. All other populations are clustered and form one large, overlapping group. The medial neurocranium (Figure 5.76) shows Cerro del Oro separating from the rest of the population. While most of these blend together, there seems to be three other groups. One is Mid Chile, Ancon, and Coyo Oriente. Another is Aramburu and Arica. The last is

Aguazuque and Patagonia. The posterior neurocranium (Figure 5.77) shows the largest distance between Patagonia and Coyo Oriente. Otherwise, Patagonia and Aguazuque seem to be each separated from the rest of the populations. Mid Chile is somewhat removed, but Ancon, Arica, Aramburu, Cerro del Oro, and Coyo Oriente mostly form one large group.

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Figure 5.75: MDS for the Anterior Neurocranium using male samples.

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Figure 5.76: MDS for the Medial Neurocranium using male samples.

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Figure 5.77: MDS for the Posterior Neurocranium using male samples.

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The face (Figure 5.78) shows a triangulation between Cerro del Oro, Mid Chile, and Aguazuque. The other populations fall between these, with Coyo Oriente and Arica remaining somewhat close, as well as Ancon and Aramburu.

Figure 5.78: MDS for the Face using male samples.

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For the orbit (Figure 5.79), there seems to be three main groups. One is Mid Chile and Patagonia. Another is Cerro del Oro and Coyo Oriente. These two groups appear to be the most separated. The last group is Aguazuque, Arica, Ancon, and Aramburu.

Figure 5.79: MDS for the Right Orbit using male samples.

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For the nasal (Figure 5.80), no populations seem to be separated and the variation present in each population is overlapping.

Figure 5.80: MDS for the Nasal using male samples.

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The masticatory complex (Figure 5.81) there is little distinction between the populations. Cerro del Oro is separated from the groups, but all other populations are clustered together.

Figure 5.81: MDS for the Right Masticatory Complex using male samples.

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5.3.8 MDS: Female Samples

The whole cranium for the female samples (Figure 5.82) shows several distinct groupings: Mid Chile and Patagonia, Coyo Oriente and Ancon, Aramburu and Arica,

Aguazuque, and Cerro del Oro. The most separated populations follow the same trend as several other plots, with Cerro del Oro and Aguazuque being the most distant populations.

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Figure 5.82: MDS for the Whole Cranium using female samples.

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For the basicranium (Figure 5.83), the populations seem divided into two sections with one being Aguazuque, Aramburu, and Arica, and the other being Patagonia, Mid

Chile, Coyo Oriente, Ancon, and Cerro del Oro. Cerro del Oro is the furthest removed of all the populations.

Figure 5.83: MDS for the Basicranium using female samples.

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For the face (Figure 5.84), Cerro del Oro is most separated from all the groups.

Mid Chile, Coyo Oriente, Ancon, Aramburu, and Arica all cluster into one large group.

Patagonia and Aguazuque appear to have a wide range of variation present which overlaps each other.

Figure 5.84: MDS for the Neurocranium using female samples.

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When the face is broken down into the nasal and orbit, there is less distinction between the populations. The nasal (Figure 5.85) shows Cerro del Oro being separated with little overlap with the other populations. All other populations display somewhat overlapping ranges of variation which overlap each other. The left orbit (Figure 5.86) is similar, where all populations display overlapping ranges of variation.

Figure 5.85: MDS for the Nasal using female samples.

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Figure 5.86: MDS for the Left Orbit using female samples.

241

The left masticatory complex for females (Figure 5.87) shows the largest separation between Cerro del Oro and Patagonia. Cerro del Oro and Mid Chile are both separated, with all other populations overlapping each other.

Figure 5.87: MDS for the Left Masticatory Complex using female samples.

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The neurocranium (Figure 5.88) shows a strong separation between the groups.

Specifically, Cerro del Oro, Aguazuque, Patagonia, Mid Chile, and Arica are separated from each other. Ancon, Aramburu, and Coyo Oriente are clustered together, and are situated between all the other populations.

Figure 5.88: MDS of the Neurocranium using female samples.

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Breaking the neurocranium down into submodules shows that each have a distinct pattern, much that the pooled and male samples. The anterior neurocranium (Figure 5.89) shows Cerro del Oro and Aguazuque each maintaining distinct clusters on opposite ends of the graph. The other populations overlap with each other, although Aramburu is only partially overlapping Coyo Oriente. The medial neurocranium (Figure 5.90) has Arica,

Coyo Oriente, and Patagonia being far enough removed to have little overlap with other populations. All other populations maintain little distinction between them. The posterior neurocranium (Figure 5.91) shows Cerro del Oro and Patagonia being the most separated from each other, as well as being separated from all other populations. Arica and

Aguazuque are removed from the other populations but remain close to each other, while all other populations are overlapping each other to the point that they are one large group.

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Figure 5.89: MDS of the Anterior Neurocranium using female samples.

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Figure 5.90: MDS using Medial Neurocranium using female samples.

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Figure 5.91: MDS of the Posterior Neurocranium using female samples.

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5.3.9 RB Plots: Pooled Samples

The patterns shown by the RB plots are somewhat variable (Figures 5.92 – 5.101).

The whole cranium (Figure 5.92) shows Arica and Aguazuque having higher than expected variation and Ancon and Patagonia having the lowest, with Mid Chile falling close to the expected range. The face showed similar results (Figure 5.98) with Arica having higher than expected variation, Patagonia having the lowest, and Mid Chile,

Aramburu, and Coyo Oriente showing the closest values to expectation. The basicranium

(Figure 5.93) shows that Arica has higher than expected variation, but Cerro del Oro,

Coyo Oriente, and Patagonia are grouped with having lower than expected variation, with

Cerro del Oro being the lowest. Aguazuque almost falls right on the line of expected variation. For the neurocranium (Figure 5.94), both Arica and Cerro del Oro have higher than expected variation. Aramburu and Ancon are essentially tied for having the most amount of variance below expectation, with Mid Chile falling very close to the expected.

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Figure 5.92: RB Plot of the Whole Cranium using both male and female samples.

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Figure 5.93: RB Plot of the Basicranium using both male and female samples.

250

Figure 5.94: RB Plot of the Neurocranium using both male and female samples.

When you break the neurocranium down into smaller modules, each module shows a different pattern. The anterior neurocranium (Figure 5.95) shows Patagonia having the highest amount of variation above expectation, with Mid Chile having the lowest. No population falls near expectation. The medial neurocranium (Figure 5.96) 251 shows Cerro del Oro with the highest range of variation above expectation, Mid Chile with the lowest, and Aguazuque falling directly on the line of expectation. The posterior neurocranium (Figure 5.97) shows a stark divide in populations, with them either falling high above or below expectation. Mid Chile, Arica, and Coyo Oriente all have higher than expected values while Cerro del Oro seems to have the lowest. No populations fall near the line of expectation.

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Figure 5.95: RB Plot of the Anterior Neurcranium using both male and female samples.

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Figure 5.96: RB Plot of the Medial Neurocranium using both male and female samples.

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Figure 5.97: RB Plot of the Posterior Neurocranium using both male and female samples.

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Figure 5.98: RB Plot of the Face using both male and female samples.

The masticatory complex (Figure 5.99) demonstrates Mid Chile having the highest level of variation above expectation, Aramburu having the lowest, and

Aguazuque falling close to expectation. The left orbit (Figure 5.101) shows Arica having the highest level of variation above expectation, Coyo Oriente having the lowest, and 256

Cerro del Oro near expectation. For the nasal (Figure 5.100), Cerro del Oro and

Aguazuque are nearly tied for having the highest level above expectation. Patagonia falls far under the expected level of variation, and Ancon is very close to expectation.

Figure 5.99: RB Plot of the Right Masticatory Complex using both male and female samples.

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Figure 5.100: RB Plot of the Nasal using both male and female samples.

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Figure 5.101: RB Plot of the Left Orbit using both male and female samples.

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5.3.10 RB Plots: Male Samples

The male RB plots seem less consistent than for the pooled samples (Figures

5.102 – 5.111). While Arica has the highest levels above expectation sometimes, Cerro del Oro and Coyo Oriente seem equally as high for other modules. For the whole cranium

(Figure 5.102), Arica and Aguazuque each have substantially higher levels of variation above the expected. Aramburu falls the farthest below expectation, but Ancon, Patagonia, and Mid Chile have similar values. The basicranium (Figure 5.103) shows Arica having much higher levels of variance above expectation, with Cerro del Oro having the lowest.

The neurocranium (Figure 5.104) shows Cerro del Oro having the highest level above expectation and Ancon having the lowest. No populations fall near expectation and the populations are roughly equally higher and lower than expectation. For the face (Figure

5.105), Arica has the highest levels of variance above expectation, followed by Cerro del

Oro. No populations fall far below expectation and Mid Chile and Aguazuque seem equally below expectation. Coyo Oriente has variance levels close to expectation.

260

Figure 5.102: RB Plot of the Whole Cranium using male samples.

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Figure 5.103: RB Plot of the Basicranium using male samples.

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Figure 5.104: RB Plot of the Neurocranium using male samples.

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- Males

Figure 5.105: RB Plot of the Face using male samples.

Dividing the neurocranium into the smaller submodules each show a different pattern. The anterior neurocranium (Figure 5.106) shows Patagonia and Aguazuque having the highest levels of variance above expectation, while Mid Chile has the lowest.

Coyo Oriente falls relatively close to expectation. For the medial neurocranium (Figure

264

5.107), most populations fall below expectation, with Mid Chile and Patagonia being the lowest. Cerro del Oro has very high levels of variance. The posterior neurocranium

(Figure 5.108) shows Coyo Oriente having the highest levels of variance, while Cerro del

Oro has the lowest. No populations appear to be close to expectation.

Figure 5.106: RB Plot of the Anterior Neurocranium using male samples.

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Figure 5.107: RB Plot of the Medial Neurocranium using male samples.

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Figure 5.108: RB Plot of the Posterior Neurocranium using male samples.

The right orbit for the male samples (Figure 5.111) shows Mid Chile falling far below the expected levels of variance. Aguazuque and Arica are both the highest, but no population is substantially higher than expected. Patagonia lands very close to the expected levels of variance. The nasal (Figure 5.110) actually shows most of the

267 populations landing near the expected level of variation. Cerro del Oro is, however, relatively higher than expected than the other populations and Patagonia is the lowest.

For the right masticatory complex (Figure 5.109), Mid Chile falls very close to the expected level of variation, along with Aramburu. Arica has the highest level of variation above expectation and Aguazuque has the lowest.

Figure 5.109: RB Plot of the Right Masticatory Complex using male samples.

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Figure 5.110: RB Plot of the Nasal using male samples.

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Figure 5.111: RB Plot of the Right Orbit using male samples.

5.3.11 RB Plots: Female Samples

The RB plots for the female samples (Figures 5.112 – 5.221) show somewhat different patterns than either the pooled or male samples. While Arica does show higher than expected variation most of the time, it is not substantially higher than expected. Mid

270

Chile has the highest observed variance in many of the graphs. For the whole cranium

(Figure 5.112), Mid Chile has exceedingly high observed variance compared to the expected, while Cerro del Oro falls the farthest below expectation. Most of the populations for the whole cranium actually falls below expectation. On the basicranium

(Figure 5.113), Mid Chile again has substantially higher variation than is expected, while

Aguazuque is the farthest below. Aramburu’s variance is nearly equal to expectation.

Again, most of the populations fall below expectation. Mid Chile is also the highest level of variance above expectation for the neurocranium (Figure 5.114), while Aramburu is the lowest. Cerro del Oro and Patagonia seem equidistant to the expected. Unlike the other modules, the face (Figure 5.115) shows Mid Chile having the lowest level of variance, while Aguazuque has the highest. Note, however, that the line of expectation is nearly vertical.

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Figure 5.112: RB Plot of the Whole Cranium using female samples.

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Figure 5.113: RB Plot of the Basicranium using female samples.

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Figure 5.114: RB Plot of the Neurocranium using female samples.

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Figure 5.115: RB Plot of the Face using female samples.

Each submodule of the neurocranium demonstrates a different pattern. The anterior neurocranium (Figure 5.116) shows Cerro del Oro and Mid Chile having the highest levels of variance expectation, while Aguazuque has the lowest. Ancon,

Aramburu, and Patagonia all have levels of variance falling near expectation. The medial 275 neurocranium (Figure 5.117) shows Patagonia having the highest level of variation above expectation and Cerro del Oro falling far below expectation. Aramburu again falls close to expectation. The posterior neurocranium (Figure 5.118) has Mid Chile with substantially higher levels of variation above expectation. Ancon and Patagonia both fall around the same distance below expectation.

Figure 5.116: RB Plot of the Anterior Neurocranium using female samples.

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Figure 5.117: RB Plot of the Medial Neurocranium using female samples.

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Figure 5.118: RB Plot of the Posterior Neurocranium using female samples.

The left orbit for the female samples (Figure 5.221) shows many populations having higher levels of variance, but Mid Chile and Arica are both below, with Arica falling far below expected levels of variation. Ancon and Coyo Oriente both have variance that is close to expectation. The nasal (Figure 5.220) shows Patagonia with the

278 lowest levels of variance below expectation and Aguazuque with the highest. No population falls near expectation. The left masticatory complex (Figure 5.119) shows

Ancon and Arica approximately an equal distance above expectation. Cerro del Oro falls the farthest below expectation, with most populations having variance below expectation.

Figure 5.119: RB Plot of the Left Masticatory Complex using female samples.

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Figure 5.120: RB Plot of the Nasal using female samples.

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Figure 5.121: RB Plot of the Left Orbit using female samples.

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5.3.12 Mantel Tests

When comparing the climate variables to cranial regions there are a few patterns that emerge (Tables 5.6 - 5.15). The Mantel tests show that the only correlation to altitude is from the whole cranium, but the correlation itself is weak even after geographic distance is controlled for. Isothermality has the strongest correlation with the neurocranium, but this relationship weakens with the Partial Mantel. Precipitation shows no correlations with any particular cranial module through the Mantel test, and only weak correlations with the nasal and anterior neurocranium. The orbit and posterior neurocranium have strong correlations with temperature (the orbit having a negative correlation). Once Partial Mantel tests were run, the correlation only slightly weakened with the orbit but significantly decreased with the posterior neurocranium. Further, after the Partial Mantel test, the whole cranium, neurocranium, and anterior neurocranium had significant, but negative, correlations. For temperature range, the orbit and medial neurocranium had significant correlations both before and after the Partial Mantel test.

Lastly, only the posterior neurocranium shows a strong significant correlation to the geographic distance.

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Table 5.6: Mantel and Partial Mantel test results for the Whole Cranium, sorted by pooled male and female samples, male samples, and female samples.

Whole Cranium – Whole Cranium – Mantel Test: Whole Cranium1,2 Males1,2 Females1 Mantel r p r p r p Altitude 0.3779 0.0570 0.3680 0.1510 -0.1238 0.7150

Geodistance 0.2045 0.2090 0.2046 0.2350 0.4447 0.0320

Isothermal 0.6357 0.0800 0.7204 0.1190 0.1581 0.3930

Precipitation -0.0598 0.3830 0.1225 0.2130 0.2338 0.2560

Temperature -0.1848 0.7910 -0.2456 0.8830 0.2590 0.1350

Temperature 0.1016 0.2750 0.1134 0.2460 0.0408 0.4110 Range

Partial r p r p r p Mantel Altitude 0.3746 0.0560 0.3644 0.1430 -0.1671 0.8040

Isothermal 0.6150 0.0620 0.7060 0.1140 0.0225 0.4900

Precipitation -0.1740 0.7000 0.0346 0.3510 0.0417 0.4170

Temperature -0.4462 0.9970 -0.5303 0.9960 -0.0615 0.5990

Temperature 0.1007 0.2780 0.1128 0.2860 0.0382 0.4020 Range

1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

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Table 5.7: Mantel and Partial Mantel test results for the Basicranium, sorted by pooled male and female samples, male samples, and female samples.

Basicranium - Basicranium – Mantel Test: Basicranium Males Females1,2 Mantel r p r p r p Altitude 0.0556 0.4280 0.1018 0.2870 -0.2627 0.8920

Geodistance 0.0483 0.4290 0.0717 0.4080 -0.0136 0.4930

Isothermal 0.1390 0.4350 0.3069 0.2310 -0.3447 0.9620

Precipitation 0.1025 0.4190 0.0871 0.4000 -0.0704 0.5610

Temperature -0.1715 0.7030 -0.1953 0.7240 0.1292 0.2590

Temperature -0.1149 0.6690 -0.0220 0.5060 -0.2874 0.9450 Range

Partial r p r p r p Mantel Altitude 0.0529 0.4530 0.0980 0.2650 -0.2623 0.8770

Isothermal 0.1305 0.3940 0.3003 0.2560 -0.3585 0.9720

Precipitation 0.0905 0.3930 0.0615 0.4370 -0.0720 0.5630

Temperature -0.2765 0.8270 -0.3307 0.9260 0.1873 0.1890

Temperature -0.1158 0.6620 -0.0232 0.5280 -0.2873 0.9240 Range

1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

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Table 5.8: Mantel and Partial Mantel test results for the Neurocranium, sorted by pooled male and female samples, male samples, and female samples.

Neurocranium - Neurocranium - Mantel Test: Neurocranium1,2 Males Females Mantel r p r p r p Altitude 0.4120 0.1220 0.4094 0.1280 0.0643 0.3140

Geodistance 0.4654 0.0910 0.3759 0.1370 0.3394 0.1060

Isothermal 0.7873 0.0490 0.7540 0.0510 0.2420 0.2190

Precipitation 0.1249 0.1930 0.1959 0.1700 0.1195 0.3370

Temperature 0.0181 0.2730 0.0480 0.2780 0.0650 0.4190

Temperature 0.0625 0.3050 -0.0004 0.3610 -0.0506 0.5420 Range

Partial r p r p r p Mantel Altitude 0.4358 0.1230 0.4191 0.1020 0.0476 0.3500

Isothermal 0.7635 0.0610 0.7231 0.0520 0.1521 0.2350

Precipitation -0.1074 0.6070 0.0320 0.2870 -0.0399 0.4990

Temperature -0.4521 0.9960 -0.2999 0.9410 -0.2357 0.8270

Temperature 0.0628 0.3100 -0.0065 0.4370 -0.0590 0.5290 Range

1 Yellow shading represents statistically significant with p<0.05. 2 Negative correlations are statistically significant at p>0.95.

285

Table 5.9: Mantel and Partial Mantel test results for the Anterior Neurocranium, sorted by pooled male and female samples, male samples, and female samples.

Anterior Anterior Mantel Test: Anterior Neurocranium – Neurocranium – Neurocranium1,2 Males1,2 Females1,2 Mantel r p r p r p Altitude 0.3843 0.1350 0.4370 0.1230 0.1326 0.2020

Geodistance 0.3521 0.1510 0.3092 0.1810 0.2357 0.1930

Isothermal 0.8000 0.0700 0.8306 0.0800 0.4812 0.0850

Precipitation -0.0998 0.4680 0.0098 0.2670 -0.2683 0.8700

Temperature -0.0707 0.4620 -0.0771 0.4330 -0.0567 0.5750

Temperature 0.0160 0.3490 0.0330 0.3880 -0.0545 0.5170 Range

Partial r p r p r p Mantel Altitude 0.3895 0.1420 0.4415 0.1120 0.1227 0.2000

Isothermal 0.7760 0.0650 0.8125 0.0660 0.4414 0.0930

Precipitation -0.3097 0.9220 -0.1526 0.6550 -0.4317 0.9760

Temperature -0.4455 0.9980 -0.4063 0.9960 -0.3002 0.9260

Temperature 0.0116 0.3910 0.0299 0.3120 -0.0597 0.5640 Range

1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

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Table 5.10: Mantel and Partial Mantel test results for the Medial Neurocranium, sorted by pooled male and female samples, male samples, and female samples.

Medial Medial Mantel Test: Medial Neurocranium - Neurocranium - Neurocranium1 Males Females Mantel r p r p r p Altitude 0.3339 0.1350 0.0192 0.3930 0.0962 0.4720

Geodistance 0.1067 0.3500 -0.1736 0.7430 0.1547 0.3250

Isothermal 0.2655 0.1580 0.0135 0.3900 -0.1261 0.5030

Precipitation 0.1871 0.2570 -0.0407 0.4480 -0.0135 0.4220

Temperature -0.0548 0.6170 -0.1762 0.7820 0.2464 0.2940

Temperature 0.4091 0.0280 0.0562 0.3750 0.2467 0.1990 Range

Partial r p r p r p Mantel Altitude 0.3302 0.1350 0.0297 0.3410 0.8846 0.4870

Isothermal 0.2469 0.1660 0.0724 0.2940 -0.1859 0.6110

Precipitation 0.1566 0.2780 0.0428 0.3400 -0.0944 0.4930

Temperature -0.1725 0.7550 -0.0815 0.6320 0.1947 0.3400

Temperature 0.4099 0.0310 0.0597 0.3830 0.2474 0.2410 Range

1 Yellow shading represents statistically significant with p<0.05.

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Table 5.11: Mantel and Partial Mantel test results for the Posterior Neurocranium, sorted by pooled male and female samples, male samples, and female samples.

Posterior Posterior Mantel Test: Posterior Neurocranium – Neurocranium – Neurocranium1 Males1 Females1 Mantel r p r p r p Altitude 0.0736 0.3900 0.1244 0.1830 -0.0719 0.6640

Geodistance 0.5335 0.0490 0.5434 0.0250 0.3749 0.0830

Isothermal 0.4130 0.1220 0.4364 0.0580 0.2395 0.2020

Precipitation 0.2041 0.2650 0.3325 0.1090 0.0569 0.4080

Temperature 0.5546 0.0160 0.4726 0.0260 0.4552 0.0280

Temperature -0.1039 0.6400 -0.0239 0.4820 -0.1412 0.7370 Range

Partial r p r p r p Mantel Altitude 0.0507 0.3280 0.1110 0.9840 -0.1011 0.6520

Isothermal 0.3066 0.0630 0.3343 0.0720 0.1390 0.3240

Precipitation -0.0483 0.5590 0.1168 0.2850 -0.1354 0.6650

Temperature 0.3121 0.0710 0.1714 0.1720 0.2957 0.1090

Temperature -0.1321 0.7360 -0.0380 0.5270 -0.1583 0.7640 Range

1 Yellow shading represents statistically significant with p<0.05.

288

Table 5.12: Mantel and Partial Mantel test results for the Face, sorted by pooled male and female samples, male samples, and female samples.

Mantel Test: Face Face – Males1 Face – Females1 Mantel r p r p r p Altitude 0.1499 0.3150 0.0503 0.4440 0.4114 0.0590

Geodistance 0.2309 0.2120 0.4947 0.0210 0.09898 0.3200

Isothermal 0.2906 0.1980 0.2043 0.3520 0.5568 0.0480

Precipitation 0.0207 0.4140 0.3081 0.1620 -0.0681 0.4670

Temperature 0.1326 0.2920 0.2307 0.2180 0.1309 0.2510

Temperature 0.0958 0.3660 0.1372 0.3450 -0.0098 0.4520 Range

Partial r p r p r p Mantel Altitude 0.1406 0.3220 0.0250 0.4300 0.4084 0.0680

Isothermal 0.2363 0.2130 0.0602 0.4030 0.5564 0.0600

Precipitation -0.0961 0.5950 0.1097 0.3210 -0.1269 0.5880

Temperature -0.0321 0.4240 -0.1600 0.6680 0.0873 0.3240

Temperature 0.0950 0.3460 0.1495 0.2660 -0.0114 0.4490 Range

1 Yellow shading represents statistically significant with p<0.05.

289

Table 5.13: Mantel and Partial Mantel test results for the Right or Left Masticatory Complex, sorted by pooled male and female samples, male samples, and female samples.

Mantel Test: Right Right Masticatory Left Masticatory Masticatory Complex Complex - Males Complex - Females Mantel r p r p r p Altitude 0.2162 0.2110 -0.0588 0.6410 -0.3873 0.9140

Geodistance 0.3323 0.1280 0.0048 0.4940 0.1903 0.2530

Isothermal 0.4707 0.0790 -0.1531 0.7110 -0.3009 0.7720

Precipitation 0.1179 0.3020 0.1135 0.3550 0.2301 0.2810

Temperature 0.0782 0.3530 0.0905 0.3390 0.2842 0.1950

Temperature -0.0410 0.5110 -0.0219 0.5330 -0.2989 0.8770 Range

Partial r p r p r p Mantel Altitude 0.2092 0.2060 -0.0592 0.6370 -0.4064 0.9430

Isothermal 0.4094 0.1520 -0.1627 0.7070 -0.3865 0.9310

Precipitation -0.0378 0.4440 0.1248 0.3470 0.1647 0.3190

Temperature -0.2092 0.8100 0.1182 0.3020 0.2150 0.2670

Temperature -0.0487 0.5110 -0.0220 0.5410 -0.3073 0.8840 Range

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Table 5.14: Mantel and Partial Mantel test results for the Nasal, sorted by pooled male and female samples, male samples, and female samples.

Mantel Test: Nasal Nasal – Males1,2 Nasal - Females Mantel r p r p r p Altitude -0.0579 0.5130 -0.4724 0.9820 -0.0128 0.4520

Geodistance 0.1233 0.3060 -0.0820 0.6350 0.0175 0.4400

Isothermal 0.1107 0.4570 -0.5151 0.9900 -0.0416 0.4650

Precipitation -0.2263 0.7770 0.2730 0.1600 -0.2105 0.7570

Temperature 0.0375 0.4600 0.0996 0.4070 -0.1216 0.6700

Temperature -0.2052 0.8140 -0.1759 0.7540 -0.0563 0.5620 Range

Partial r p r p r p Mantel Altitude -0.0660 0.5430 -0.4700 0.9840 -0.0138 0.4670

Isothermal 0.0766 0.5190 -0.5173 0.9880 -0.0495 0.5000

Precipitation -0.3182 0.9030 0.3480 0.9000 -0.2447 0.8500

Temperature -0.0623 0.6050 0.2097 0.2310 -0.1806 0.7920

Temperature -0.2086 0.8240 -0.1753 0.7560 -0.0566 0.5910 Range

1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

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Table 5.15: Mantel and Partial Mantel test results for the Right or Left Orbit, sorted by pooled male and female samples, male samples, and female samples.

Right Orbit – Left Orbit – Mantel Test: Left Orbit1,2 Males1 Females1 Mantel r p r p r p Altitude 0.2944 0.2100 -0.0638 0.5250 -0.2673 0.9010

Geodistance -0.1508 0.6660 0.1603 0.2540 0.1029 0.3390

Isothermal 0.0664 0.2680 -0.0664 0.4950 -0.2584 0.8550

Precipitation -0.0718 0.5690 0.6310 0.0050 0.3292 0.1010

Temperature -0.3572 0.9930 -0.1021 0.5780 0.0522 0.3720

Temperature 0.6107 0.0050 0.3066 0.1080 0.3919 0.0460 Range

Partial r p r p r p Mantel Altitude 0.3071 0.1780 -0.0742 0.5300 -0.2752 0.9120

Isothermal 0.1209 0.2230 -0.1243 0.6380 -0.3076 0.8890

Precipitation -0.0043 0.4420 0.6341 0.0020 0.3185 0.1320

Temperature -0.3500 0.9780 -0.2882 0.9400 -0.0234 0.5070

Temperature 0.6201 0.0160 0.3083 0.1100 0.3925 0.0370 Range

1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

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There are some marked differences and similarities in the climate relationships when only male or female samples are considered (Tables 5.6 - 5.15). Males matched the results for the pooled samples more often, although this may be due to the larger sample size for males versus females. However, there were instances when the pooled, males, and females all gave very different results. For instance, the pooled samples showed no correlations to the climate when the face is considered. When only looking at males, though, the face is strongly correlated to geographic distance. Females only displayed weak correlations with altitude and isothermality. In some instances, such as for the medial neurocranium, males and females show no correlations, but the pooled samples are correlated to temperature range. It is worth noting that these results should be taken with caution as some populations had very small sample size when divided into males and females, and this can influence the results.

5.4 DISCUSSION

5.4.1 Fst: Pooled Cranial Series

Previous regional Fst estimates from crania have shown varied results. While some results have been similar to each other (0.1287 (Hubbe, Hanihara, et al., 2009);

0.1005 (Sardi et al., 2005)), others have shown a wider range of values. For instance,

Hubbe et al. (2015) found an Fst value of 0.275 when looking at the Americas, but only an Fst value of 0.068 for Early Americans. Our Fst results for the whole cranium were

0.3634, which are higher than these previous results. Even estimates using world-wide

293 samples have lower results than ours (approximately 0.276) (Hubbe et al., 2015). This dissertation is calculating Fst estimates based on 3D data, and thus more information, which could also explain the higher estimates, as more information tends to increase the degree of separation (A. Strauss & Hubbe, 2010).

On one hand, it is not unusual that our results are higher than previous estimates because our samples are confined to Western South America, which is known to have higher levels of variation (Pucciarelli et al., 2008; Pucciarelli et al., 2006). However, even other papers calculating Fst values for only Western South America (Pucciarelli et al.,

2006) had lower values than ours (approximately 0.19). Unlike some of these previous papers, our samples consist of samples dating in a range from ~5000 years BP – 500 years BP, which could be driving our Fst values to be higher.

When assessing Fst values in terms of cranial regions, it becomes apparent that the basicranium demonstrates less between group variation than either the neurocranium or the face. This supports the assertation that the basicranium is more conservative genetically and showing little effect from environmental variables (Lieberman, Ross, et al., 2000; McCarthy, 2001; Strait, 2001) (and as supported in Table 5.7). Interestingly, the face also did not show any correlation to environmental variables even though it is generally considered to be a very plastic region (Wood & Lieberman, 2001). Previous papers have shown correlations between the face and temperature, but mostly in the context of extreme cold environments (Harvati & Weaver, 2006a; Hubbe, Hanihara, et al., 2009; Roseman, 2004). While one population looked at in this series could be considered from an extreme cold environment (Patagonia), there was either too small of a

294 sample size relative to the total sample count for that pattern to be demonstrated, or the

Patagonian skulls do not demonstrate this trait.

Of note, the Patagonian crania were the largest crania of this series that was scanned. For most of the crania from this population, a third scan was required to cover the entirety of the surface. The Procrustes analysis performed for this analysis removes any effects of size, which is ideal for a series with a pooled male and female sample set.

However, the large size of the crania may have been due to the cold temperatures present in the Patagonian/Tierra del Fuego environment (Bernal, Perez, & Gonzalez, 2006; Perez et al., 2007).

5.4.2 Fst: Male Cranial Series

The Fst values for the male crania (Table 5.4) are relatively similar when compared to the pooled samples (Table 5.3). The main differences come in the order of the highest values to lowest values. The face has higher variation than the neurocranium, and the basicranium values for males is much higher than the pooled samples. As with the female samples, some of the population sizes for the males is small. Therefore, these

Fst values should be seen with caution.

5.4.3 Fst: Female Cranial Series

The Fst value of the face for the female samples is unusually high, even when unbiased values are considered (Table 5.5). This high value is likely due to the small

295 sample sizes present for some of the population, and all the Fst values for the females should be seen with caution.

5.4.4 Comparisons: Pooled Cranial Series with Climate Variables

Several regions of the cranium showed a correlation with temperature (the neurocranium, the orbit, the anterior neurocranium, and the whole cranium; Tables 5.8,

5.15, 5.9, and 5.6). However, these correlations are negative, meaning that the more similar the temperature in an environment is, the more different the crania are. Negative correlations with temperature have not been previously demonstrated (e.g., Hubbe,

Hanihara, et al., 2009) and we are not currently able to explain a correlation of this type.

The orbit, as well as the medial neurocranium, also show a significant positive correlation with temperature range. Hubbe, Hanihara, et al. (2009) had the whole cranium, the face, and the neurocranium related to temperature range, but these results were for worldwide populations and used linear measurements rather than 3D landmarks. For the Andes region, it seems that the correlations between biological distance and temperature range are correlated to only subsets of these larger cranial regions shown in Hubbe, Hanihara, et al. (2009) (face to orbit; neurocranium to medial neurocranium). Overall, these results demonstrate that the local environment likely has little effect on cranial morphological shape for populations living in the Andes. This is consistent with others showing a stronger correlation between local environment and size of the cranium (Perez, Lema,

Diniz‐Filho, et al., 2011).

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5.4.5 Comparisons: Male Cranial Series with Climate Variables

The results when comparing the male cranial series with climate variables is not straightforward when comparing to the results for the pooled series. While some results are similar, such as for the whole cranium, the base, the face, the masticatory complex, and the anterior neurocranium (Tables 5.6, 5.7, 5.12, and 5.13), there are some stark differences. The male posterior neurocranium is correlated with altitude while the pooled samples showed no differences. The male medial neurocranium did not retain its correlation with temperature range, while the male nasals were correlated with both altitude and isothermality. The male orbits were correlated with precipitation, but not temperature or temperature range like with the pooled samples. On the surface, it is interesting that the males would demonstrate differences in terms of what they are correlated with. However, without the ability to further test these relationships with a larger male sample size, I have to assume that these differences are caused by a difference in sample size. I feel that it is important to note that this may be an indication of how unstable the relationships are between crania and climatic variables, since small changes appear to have a large effect on the correlations.

5.4.6 Comparisons: Female Cranial Series with Climate Variables

The female crania series is even more different when compared to the pooled samples than the male series is. With the female series, there is an overall loss of correlations. The whole cranium, neurocranium, orbit, and medial neurocranium (Tables

5.6, 5.8, 5.15 and 5.10) lost either some or all of the correlations that were present. The

297 basicranium (Table 5.7) for the female series, however, is now correlated with isothermality, whereas the anterior neurocranium is correlated with precipitation but lost the correlation that was present with temperature. Given that the female series has the smallest samples sizes, it is not unexpected that many correlations would not be present.

It is hard to say whether the exact patterns seen with the female series are true patterns, or if they would be altered with a larger sample size. As with the male series, I have to currently assume that these differences are caused by a difference in sample size until further testing can take place.

5.4.7 MDS Analysis: Pooled Cranial Series

When looking at the MDS plots (Figures 5.62 – 5.71), one of the main patterns that occurs is Aguazuque and Patagonia separating from the rest of the populations, but generally remaining close to each other. While the populations themselves reside at opposite ends of the continent, they have shown similarities to each other in previous research (González-José et al., 2003; González-José, Van Der Molen, González-Pérez, &

Hernández, 2004; Neves, Hubbe, Correal, & Munford, 2006; Pucciarelli et al., 2006). In general, Patagonia has shown morphology that is more consistent with paleo-Americans

(González-José et al., 2003; González-José et al., 2004; Neves et al., 2006; Pucciarelli et al., 2006), which could explain their relationship with Aguazuque.

Cerro del Oro also consistently separates from the other populations. Most of the time it is not grouping closely with either Ancon or Aramburu, which are both located in

Peru as well. This seems to indicate that the relationships do not seem to be following a 298 pattern consistent with geographic distance, which is supported in Tables 5.6 – 5.10 and

5.12 – 5.15 because only the posterior neurocranium (Table 5.11) is significantly correlated with geographic distance. We know there are some genetic and cultural differences in populations living near each other in the Andes. Cabana et al. (2014) found that in Peru, highland populations demonstrated higher levels of female mediated gene flow and/or higher effective sizes than males. Lowlands showed the opposite pattern.

Additionally, there could be localized instances of gene flow or drift. These types of patterns could be altering the typical geographic distance – biological distance correlation that is seen when either the eastern or entire South American continent is analyzed.

Overall, the relationships and distances between each population also tend to change depending on which region, or module, of the cranium is being analyzed. For example, Figures 5.63 and 5.64 (Base, Neuro) show very different overall patterns in how each population is related to each other. Due to these differences, it is important to point out that comparing population relationships from different cranial analyses could be problematic if the landmarks being used are different from each other. In general, these results seem to indicate that altering the landmarks being used can lead to a different interpretation of how populations are related and interacted.

5.4.8 MDS Analysis: Male and Female Cranial Series

The MDS plots for males and females (Figures 5.72 – 5.91), some similarities exist with the pooled samples. For instance, there is still an overarching pattern that

Aguazuque and Cerro del Oro are more separated from the other populations. However, 299 the pooled samples typically saw both Aguazuque and Cerro del Oro separated whereas the males and females tend to show one or the other being further removed. This type of pattern makes sense, since the pooled samples are a combination of the male and female series. The male and female series shows one or another population separating, but the combination shows both.

There is also simply more division between the populations, and this is consistent throughout the male samples. The relationships seem clear and well defined, in spite of the somewhat smaller sample sizes. The MDS for females is also more varied, but in general there is less distinction between populations. This is likely due to smaller sample sizes when compared to the pooled samples. However, this pattern could also be indicative of higher levels of female mediated gene flow, as this would make the female samples appear less defined than the males. This type of cultural pattern has been seen by

Cabana et al. (2014) in highland Peruvian populations, but not lowland populations.

Further testing would be needed to demonstrate if pattern is consistent.

5.4.9 Contributions to the Hypotheses

This dissertation chapter is ultimately testing three cranial morphology related hypotheses (full descriptions can be found in section 1.2):

1. Basicranium variation is mostly the product of neutral evolutionary processes.

2. The neurocranium is the product of neutral evolutionary processes with some

influence of diversifying selection.

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3. The facial region will show stronger evidence of diversifying selection in

response to adaption to climate and diet variation.

The results presented in this chapter allow us to address these hypotheses as

follows:

Hypothesis 1: Cautiously supported

This hypothesis is supported in the sense that the basicranium was not correlated with any climate variables, including geographic distance. However, it is important to note that further testing needs to take place where the cranial regions are compared to

DNA variables, since the basicranium could be under stabilizing selection. I complete this portion of the hypothesis testing in Chapter 6.

Hypothesis 2: Cautiously supported

This hypothesis is supported in the sense that the neurocranium was only correlated with one climatic variable (temperature). Further analysis needs to occur where the neurocranium is compared to DNA variables to test if there are any patterns consistent with neutrality. I complete this portion of the hypothesis testing in Chapter 6.

Hypothesis 3: Rejected

This hypothesis can be rejected by these data because the face does not exhibit any significant correlations with any of the climate variables, including geographic distance. It is important to note that other variables not tested here could be influencing the face, such as diet.

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5.5 CONCLUSION

In this chapter, I used a variety of statistical tools to analyze cranial morphology of several populations found in the Andes region of South America. In terms of the various cranial regions, the basicranium showed the lowest levels of variation, while the neurocranium showed the highest (after the whole cranium). Breaking these regions down further demonstrated that the orbit actually had the lowest variation of any submodule. MDS plots consistently showed similarities between the Patagonian and

Aguazuque samples, with both of those being most different from Cerro del Oro.

However, the actual distances and orientations the different populations had in relation to each other differed significantly depending on the module of the cranium being analyzed.

These results indicate that caution should be taken when biological distances are used or compared. Slight alterations of the landmarks used in studies could alter the results.

Additionally, biological distances were compared to climatic variables, as well as geographic distance. The entire cranium, neurocranium, orbit, and anterior neurocranium held significant negative correlations to temperature while only the orbit and medial neurocranium were correlated with temperature range. Overall, these results demonstrate that the local environment likely has little effect on cranial morphological shape.

Moreover, only one region was significantly correlated with geographic distance. It has been established that nearby populations in the Andes sometimes have different patterns of subsistence and mobility patterns, which could influence how correlated populations are via geographic distance.

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This chapter generates a greater level of nuance to the previously established patterns of population relationships in the South American Andes. To better assess influences of neutral vs non-neutral effects on crania morphology, denser geographic coverage is ideal with populations from a greater variety of environments. This allows for temporal scales to be injected into the analysis as well as a deeper sense of any relationship to local environments. Comparing population distances to climate variables could also help elucidate the nature of the relationship between biological distances and non-neutral evolutionary effects.

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CHAPTER 6 : COMPARING CRANIAL MORPHOLOGY TO DNA

6.1 INTRODUCTION

In this chapter, I will test population affinity patterns between 3D cranial morphology and DNA data. Under the assumption that the DNA used in this dissertation is following neutral evolutionary patterns, this analysis will directly test how neutral different regions of the cranium are. It will also test if the population structure demonstrated by each of the different data types correspond with one another.

6.1.1 Concordance and Discrepancies Between DNA and Cranial Morphology

Cranial morphology has been widely used to estimate phylogenetic relationships among and between human populations (e.g., González-José et al., 2008; González-José,

Neves, et al., 2005; Harvati & Weaver, 2006a, 2006b; Herrera et al., 2014; Hubbe,

Harvati, et al., 2011; Hubbe et al., 2010; Hubbe et al., 2015; Perez et al., 2007; Perez et al., 2009; Perez, Lema, Diniz‐Filho, et al., 2011; Relethford, 2004a, 2004b; Roseman,

2004; Sardi et al., 2006; Sardi et al., 2005; Smith, 2009, 2011; von Cramon-Taubadel,

2014; von Cramon-Taubadel & Lycett, 2008). When compared against genetic data, discrepancies with morphology tend to arise in terms of population affinity and effects of microevolutionary processes. These discrepancies are clearly seen through research on human dispersion into the Americas, where genomic data tend to show a single migration 304 event (e.g., Raghavan et al., 2015; Schroeder et al., 2009; Wang et al., 2007; but see

Skoglund et al., 2015) and cranial data suggests multiple migrations (e.g., Hubbe, Neves, et al., 2011; Hubbe et al., 2010; Hubbe et al., 2015; but see de Azevedo et al., 2011). In spite of the discrepancies, research has thus far been limited in scope when analyzing the relationship of cranial morphology to genetics and the environment (e.g., Perez et al.,

2009; Roseman & Weaver, 2007; Smith, 2009), particularly when the Americas are considered, leaving uncertain the accuracy of each of these different data in reconstructing phylogenetic relationships.

One problem that arises when reconstructing phylogenetic relationships based on cranial morphology is quantifying the apportionment of variation due to neutral and non- neutral evolutionary processes and how this affects phylogenetic reconstructions of past populations. Recent research suggests there are different interpretations of phylogenetic relationships depending on which cranial region is being analyzed (e.g., de Azevedo et al., 2011; Hubbe et al., 2010). For example, some studies indicate the facial region as more strongly affected by environment pressures, particularly in response to colder climates (Betti et al., 2010; Hubbe, Hanihara, et al., 2009; Roseman, 2004). This problem is further complicated by issues that arise from only using genomic data, which have generated very distinct results in the past (e.g., Raghavan et al., 2015; Skoglund et al.,

2015). Most DNA data utilized in modern human phylogenetic studies are from modern populations, which limits the understanding of biological histories in the past. As both data types are independently problematic, reconciling the differences between them will lead to a clearer picture of the microevolutionary processes behind human cranial

305 morphological variation and will therefore permit us to create more reliable and comparable phylogenetic hypotheses based solely on skull morphology.

Many studies have shown that patterns of human cranial variation are consistent with global-scale evolutionary models (e.g., Betti et al., 2010; Hubbe, Hanihara, et al.,

2009; Smith, 2011; von Cramon-Taubadel, 2014). This includes crania being consistent with an out-of-Africa model followed by iterative founder effects (Betti et al., 2009;

Manica et al., 2007). Along with showing an over-arching pattern of neutrality, many of these studies demonstrate that local gene flow influences much of the patterns we see with cranial variation (Betti et al., 2010; Relethford, 2004b; Smith, Hulsey, West, &

Cabana, 2016).

Genetic correlations can create bias for both the rate and direction of evolutionary responses to selection (Lande, 1979). If these correlations are relatively strong, a population’s average phenotype can deviate from its optimal value, which can cause the selective pressures the population has undergone to be misinterpreted (Joganic et al.,

2018; Steppan, Phillips, & Houle, 2002). One example of this is in phylogenetic reconstructions of taxa within the papionin clade, which differ depending on whether genomic or morphological data is used (e.g., Joganic et al., 2018; Smith & von Cramon-

Taubadel, 2015).

One particular focus on studies looking at concordance between DNA and cranial morphology is the temporal bone. This focus seemed to begin with taxonomic and phylogenetic studies of various primate taxa (e.g., Harvati, 2003; Harvati & Weaver,

2006a, 2006b; Lockwood, Kimbel, & Lynch, 2004, 2005; Lockwood & Tobias, 2002; 306

Smith et al., 2007; Terhune, Kimbel, & Lockwood, 2007). For example, Lockwood et al.

(2005) tests the amount of disparity present among early hominin temporal bones and if the amount of within and between group variation matches that of modern primates and humans. Because some of these studies were successful in establishing phylogenies using the temporal bone, several papers focusing on modern human evolution were published

(e.g., Harvati & Weaver, 2006a, 2006b; Smith et al., 2007; von Cramon-Taubadel, 2009).

Many of these papers were able to demonstrate a pattern of neutrality for the temporal bone. The results from Harvati and Weaver (2006a, 2006b) suggest that the temporal bone may be tracking older evolutionary trends, while the cranial vault may be tracking more recent human evolutionary trends (von Cramon-Taubadel, 2009). Smith (2009) shows similar results, but also that the correlation with neutrality is not more significant for the temporal bone than for the basicranium, the upper face, or the whole cranium.

Following these studies, von Cramon-Taubadel (2009) tested whether the temporal bone demonstrates stronger correlations with neutrality than the other bones of the cranium, or the cranium as a whole. Her results showed that technically the temporal bone had the strongest correlation, but that it was not statistically different from the sphenoid, frontal, or parietal bones. She ultimately suggests that phylogenetic studies focus on using the whole cranium minus the regions that deviate from neutrality. These studies ultimately led to papers delving into the evolutionary relationships between various cranial modules and DNA.

According to population genetic theory, concordance between the data types is expected since researchers have mostly worked with neutral molecular data (Francalacci

307 et al., 2013; Kuruppumullage Don et al., 2013; Lohmueller et al., 2011; Pearson, 2013;

Weaver, 2014; Wilder et al., 2004) and cranial morphology has been shown to vary mostly according to the expectations of neutral evolutionary processes (e.g., Betti et al.,

2010; Harvati & Weaver, 2006a, 2006b; Hubbe, Hanihara, et al., 2009; Roseman, 2004;

Smith, 2011; von Cramon-Taubadel, 2014). However, as discussed above, many studies report discrepancies in the data.

Discrepancies are not only within and between the different DNA types, but also within morphological studies. As discussed above, there are incongruent estimates of genetic variation within the Americas, such as with Y-chromosomal studies (e.g., Cabana et al., 2014; Tarazona-Santos et al., 2001) and mtDNA studies (e.g., Fuselli et al., 2003;

Lewis & Long, 2008), but also between each DNA type. Furthermore, cranial variation in the Americas has been used to argue for one, two, three (or more) dispersal events (e.g.,

González-José et al., 2008; Hubbe et al., 2010; Pucciarelli et al., 2003).

Studies addressing these apparent discrepancies are typically rare, with only a small number of papers published in the past decade (e.g., Betti et al., 2010; Herrera et al., 2014; Hubbard, Guatelli-Steinberg, & Irish, 2015; Smith, 2009, 2011; Smith et al.,

2016; von Cramon-Taubadel, 2014). Most of these have focused on worldwide datasets of crania and genetic samples (e.g, Betti et al., 2010; Harvati & Weaver, 2006a;

Relethford, 2004b; Smith, 2011; von Cramon-Taubadel, 2014; von Cramon-Taubadel &

Lycett, 2008) while only a few have focused on a smaller geographic region (Herrera et al., 2014; Hubbard et al., 2015). With few studies addressing correspondences between the various data types, progress in this area has remained slow and limited.

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6.1.2 Chapter Goals

To achieve the goals of the chapter, the inclusion of multiple types of DNA and skeletal collections from the same geographic regions is required. The primary assumption in this chapter, and really all studies using cranial morphology for phylogenetic analysis, is that cranial variation is accumulated between geographic regions mostly due to neutral evolutionary processes, meaning the biological distances between them should be proportional to biological distances between molecular data that is not under selective pressures. This assumption mostly derives from the fact that gene- flow isolation due to geographic distance will affect the apportionment of variance in a similar way among different data types, even if they are not functionality related. With this in mind, this chapter is aimed at testing how different anatomical regions of the skull are accumulating differences among macro regions when compared to different genetic databases in South America and not which genes are associated with morphological phenotypes. Any deviations from this proportionality would indicate non-neutral forces acting on at least one of the data types, and allow us to formally test the hypotheses listed in Chapter 1.

The combination of these data types will allow for a more complete comparative analysis of microevolutionary processes and a more refined knowledge about the evolutionary processes behind the origins of variation in different anatomical regions of the skull, given that no previous research has incorporated all these data types into one comprehensive analysis. Correlations between these different data types will allow us to assess their relatedness (e.g., Herrera et al., 2014), while quantitatively testing

309 microevolutionary models will permit the determination of congruence of different patterns in these data types to climatic variables (e.g., Harvati & Weaver, 2006a, 2006b;

Hubbe, Hanihara, et al., 2009).

6.2 MATERIALS AND METHODS

6.2.1 Crania

The human crania utilized in this chapter are the same used in Chapter 4. There are 204 undeformed crania (122 males, 82 females), all from populations located along the Andes Mountains, from Colombia and extending down into the most southern extent of Chile. Demographic information for each population is provided in Table 5.1. This wide range of locations is ideal because it offers a variety of environments that can then be compared to the distance patterns seen in the cranial populations.

All measurements used were derived from 3D surface scans using a Next Engine

Scanner and the computer program ScanStudio. For a more detailed description of the process and requirements for scanning, see Chapter 5 (section 5.2.1 Crania: Samples).

Once a scan was complete, each scan needed to be cleaned and fused. For a complete description of cleaning and fusing the scanned images, see Chapter 5 (section 5.2.1

Crania: Samples). The final step is saving the files as both a .SCN and a .PLY type.

Type I and II landmarks (see section 5.2.1 Crania: Samples for a definition) were collected using the program Landmark (for a complete description, see section 5.2.1

Crania: Samples). In total, 78 different landmarks based on a combination of previous 310 work (Harvati & Weaver, 2006a, 2006b; Hubbe, Harvati, & Neves, 2009; Smith, 2009; von Cramon-Taubadel, 2009) were collected and are listed in Table 5.2 and shown in

Figure 5.1. The landmarks selected cover the entire skull and allow for the breakdown of the skull into distinct anatomical regions for testing the hypotheses. After each landmark on a skull was marked, the points were exported as a .PTS file and opened in Microsoft

Excel. They were converted from columns into a single row using a macro written by

Mark Hubbe.

Just as in Chapter 4, a total of 10 modules were used for this analysis. The first module is entire cranium, and includes all landmarks collected. Then the entire cranium is broken down into three primary modules: 1) face, 2) neurocranium, and 3) basicranium. These were again broken down into six more modules: 1) orbits, 2) nasal, 3) masticatory complex, 4) anterior neurocranium, 5) medial neurocranium, and 6) posterior neurocranium. Table 5.2 lists the landmarks associated with each module.

Each table of landmarks for each module then needs to be checked for outliers. To do this, a General Procrustes Analysis (GPA) is performed. For a complete description of this process, see section 5.2.2 Analytical Procedures. The GPA coordinates were plotted in a 3D scatterplot to show individual’s landmarks in this new coordinate system, with any point/s being “out of place” representing outliers. Once any outlier was found, I went back into Landmark to double-check the placement of the landmarks in questions for the specific individuals (see section 5.2.1 Crania: Samples for a complete description).

This analysis was performed three times. Once where all males and females were pooled together, and once with males and females separately. This allows us to determine 311 if there are any sex-linked patterns in the correlations we find. The first step of the analysis was to take the residuals from the GPA, which were projected into a linear space tangent to the curved shape space, and subject them to a Principal Component Analysis

(PCA).

To obtain morphological distances, Mahalanobis Squared Distances (D2) were calculated between all pairs of series (Mahalanobis, 1936) using R (Team, 2014). D2 matrices provide a measure of dissimilarity that considers differences observed between groups’ centroids, but also corrects the contribution of each variable to the final distance by their covariance so that the distance is not inflated by the correlation between them

(Hubbe et al., 2014a). This makes the Mahalanobis distance essentially a multidimensional version of measuring the number of standard deviations a point is from the mean. The morphological distances D2 were calculated using principal components representing 90-95% of the total variance (Harvati & Weaver, 2006a) so that the majority of the variance is fully represented by the distance matrix.

6.2.2 DNA

Mitochondrial haplogroup frequencies from 15 populations were collected from previously published literature (Table 4.1). The haplogroups included were A, B, C, and

D. The populations that were chosen are the ones that geographically provided the best match for the cranial populations. In some cases, because we do not have DNA and crania from matching populations, there was more than one population that could be a potential match. In these cases, the haplogroup frequencies from each population were 312 averaged. Table 6.1 lists the cranial populations, as well as the analogous mtDNA populations that were utilized. The analogous populations were chosen based on geographic location and population histories that show contact between the groups.

Perfect matches between crania and DNA were not possible because DNA has not been taken from these crania. Although the analogous populations are as close to a perfect match that is possible, there are potential problems with this approach. Confounding effects of local adaptation, population structure, and genetic drift are possible when the populations being compared are from different geographic areas or time periods (Ricaut et al., 2010).

Table 6.1: Table showing cranial populations and the analogous mtDNA populations.

Cranial Analogous MtDNA Population Population Aguazuque Embera, Antioquia Ancon Tupe, Tayacaja Aramburu Ancash, Trujillo Arica Aymara Cerro del Oro Tupe, Tayacaja Atacamenos, San Coyo Oriente Salvadore de Jujuy Huilliches, Mapuche, Mid Chile Peheunche Patagonia Yaghan, Fueguian

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Y-chromosome STR frequencies from 9 populations were also collected from previously published literature (Table 4.2). As with the mtDNA, we did not have DNA and crania from matching populations, so there was potentially more than one population that could be a match. In these cases, the STR frequencies from each population were averaged. Table 6.2 lists the cranial populations, as well as the analogous Y-chromosome

DNA populations that were used.

Table 6.2: Table showing cranial populations and the analogous Y-Chromosome populations.

Cranial Analogous Y- Population Chromosome Population Aguazuque Embera-Chami Chumbivilca, Ancon Huancavelica Shipibo-Conibo, Santiago Aramburu de Chuco Arica Chuquibamba Chumbivilca, Cerro del Oro Huancavelica Coyo Oriente Colla Mid Chile Mapuche Patagonia Tehuelche

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The distance matrices for both Y-chromosome STR frequencies and mtDNA haplogroup frequencies were based on Euclidean distances. A full description of

Euclidean distances can be found in section 4.2.1 DNA Data.

6.2.3 Climate

Due to the difficulty of finding direct genetic data to match prehistoric populations in the continent, this project uses geographic distance as a proxy for the expected biological distances resulting from neutral evolutionary processes, as many other studies have done in recent years (e.g., Betti et al., 2009; Hubbe, Hanihara, et al.,

2009; Hubbe, Neves, et al., 2011; Hubbe et al., 2014a; Relethford, 2004b; A. Strauss et al., 2015). For each population, geographic coordinates were established to the closest reference point or was based on previously published data about the population. Then, climatic distance matrices (based on climate variables such as average annual temperature, temperature range, altitude, rainfall, and isothermality) were built based on the squared differences in each of the climatic variables for all possible comparisons

(Harvati & Weaver, 2006a) based on present and past data from BIOCLIM (Hijmans et al., 2005). These initial matrices for each variable were accessed through ArcMap 10.4 and subsequently exported into R. Cranial anatomical regions that show a poor fit to the genetic distances between regions were correlated with these climatic variables to test if between-group differences in these regions can be explained by any environmental parameters considered in this chapter. Each of the climatic distance matrices are based on

Euclidean distances, which is the commonly used distance type for climatic data

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(Mimmack et al., 2001). A full description of Euclidean distances can be found in section

4.2.1 DNA Data.

6.2.4 Comparisons Between Data Types

Mantel test. The Mantel and Partial Mantel tests were performed in R using the vegan package (Oksanen et al., 2018).

In this chapter, the comparisons performed were: 1) pooled crania series with mtDNA, 2) pooled crania series with Y-chromosome DNA, 3) male crania series with mtDNA, 4) male crania series with Y-chromosome DNA, 5) female crania series with mtDNA, 6) female crania series with Y-chromosome DNA, 7) mtDNA with climate, and

8) Y-chromosome DNA with climate. Chapter 3 also contains comparisons between

DNA and climate variables, which are repeated here because the DNA series in this chapter are restricted to populations that match the cranial series.

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6.3 RESULTS

6.3.1 Comparisons: Mantel Tests of Pooled Cranial Series with DNA

The pooled crania series shows several statistically significant correlations when compared to mtDNA haplogroup frequencies and Y-chromosome STR frequencies

(Table 6.3). The entire neurocranium is very significantly correlated (r = 0.65, p = 0.003) with mtDNA. Breaking this module down shows that both the anterior and posterior portions of the neurocranium are still correlated with mtDNA, but not the medial neurocranium. In addition to the neurocranium, the masticatory module is also correlated with mtDNA (r = 0.49, p = 0.026).

The only significant correlation with the Y-chromosome is with the basicranium

(Table 6.3). However, this is a negative correlation (r = -0.39, p = 0.969) meaning that the more similar the populations are when using the basicranium, the more different the populations appear using the Y-chromosome STR frequencies. There are slight, but nonsignificant, correlations between the face, nasal, and posterior neurocranium with the

Y-chromosome data.

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Table 6.3: Mantel test results between the crania, mtDNA, and Y-Chromosome DNA for both male and female samples.

Pooled Crania – Pooled Crania – Y- MtDNA1 Chromosome1,2 r p r p Whole 0.4007 0.1020 -0.1698 0.7670 Cranium Basicranium 0.1619 0.2820 -0.3931 0.9690 Face 0.1547 0.2780 -0.3367 0.9310 Neurocranium 0.6507 0.0030 0.1025 0.3780 Right 0.4880 0.0260 -0.1493 0.7500 Masticatory Left Orbit -0.0746 0.5280 0.0363 0.4630 Nasal 0.0390 0.4060 -0.2824 0.9180 Anterior 0.5312 0.0330 0.0283 0.5230 Neurocranium Medial 0.1853 0.2080 -0.0100 0.5230 Neurocranium Posterior 0.5512 0.0180 0.3033 0.0610 Neurocranium 1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

6.3.2 Comparisons: Mantel Tests of Male Cranial Series with DNA

The male cranial series showed similar patterns as the pooled cranial series (see

Table 6.4). The male crania, when compared with mtDNA haplogroup frequencies, the neurocranium, anterior neurocranium, and posterior neurocranium all demonstrated statistically significant correlations. However, rather than the entire neurocranium

318 demonstrating the strongest correlation to mtDNA, as in Table 6.3 above, the posterior neurocranium from the male crania shows the strongest correlation (r = 0.59, p = 0.007).

When the male crania are compared with the Y-chromosome, both the basicranium and posterior neurocranium show significant correlations. The pooled samples above (Table 6.3) showed significant correlations with only the basicranium.

Similar to the pooled samples, the correlation with the basicranium is negative. However, the correlation with the posterior neurocranium is positive.

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Table 6.4: Mantel test results between the crania, mtDNA, and Y-Chromosome DNA for male samples.

Males - Y- Males – MtDNA1 Chromosome DNA1,2 r p r p Whole 0.3908 0.1260 -0.1553 0.7570 Cranium

Basicranium 0.1555 0.2770 -0.3434 0.9510

Face 0.2843 0.1730 -0.0872 0.6640 Neurocranium 0.5657 0.0180 0.0266 0.5160 Right 0.1177 0.3510 -0.2719 0.9000 Masticatory Right Orbit 0.2862 0.1210 0.1351 0.2520 Nasal -0.2232 0.8290 -0.1681 0.7700 Anterior 0.5319 0.0200 -0.0029 0.5840 Neurocranium Medial -0.1151 0.6100 -0.3074 0.9180 Neurocranium Posterior 0.5867 0.0070 0.3506 0.0430 Neurocranium 1 Yellow shading represents statistically significant with p<0.05.

2 Negative correlations are statistically significant at p>0.95.

6.3.3 Comparisons: Mantel Tests of Female Cranial Series with DNA

The female cranial series was only compared with mtDNA, since females do not carry the Y-chromosome. The comparisons showed no significant correlations from any cranial module with mtDNA (Table 6.5). The neurocranium and whole cranium show slight correlations, but both were nonsignificant.

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Table 6.5: Mantel test results between the crania and mtDNA for female samples

Females – MtDNA Cranial Module r p Whole Cranium 0.3385 0.082

Basicranium 0.02664 0.417 Face 0.183 0.255 Neurocranium 0.442 0.057

Left Masticatory 0.1752 0.241

Left Orbit -0.0593 0.564 Nasal -0.0578 0.583 Anterior 0.2896 0.148 Neurocranium Medial 0.2035 0.227 Neurocranium Posterior 0.3318 0.102 Neurocranium

6.3.4 Comparisons: DNA with Climate

Running Mantel and Partial Mantel tests between both mtDNA and Y- chromosome DNA to climate resulted in several significant correlations (Table 6.6). The

Mantel tests with mtDNA show significant correlations with geographic distance (r =

0.75, p = 0.002) and isothermality. There are only weaker correlations with altitude and temperature. Once geographic distance is controlled for, altitude becomes a significant correlation, and isothermality remains significant.

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The Mantel test for the Y-chromosome also shows a significant correlation with geographic distance (r = 0.36, p = 0.04), along with temperature. Unlike some of the results above, both of these correlations are positive instead of negative. However, once geographic distance is controlled for, there is only a weak, but nonsignificant, correlation with temperature.

Table 6.6: Mantel and Partial Mantel test results between climate, mtDNA, and Y-Chromosome DNA.

Y-Chromosome MtDNA Mantel DNA r p r p Altitude 0.3492 0.0780 0.1187 0.3590 GeoDistance 0.7518 0.0020 0.3630 0.0400 Isothermal 0.5613 0.0180 0.0783 0.4100 Precipitation 0.4042 0.1210 0.3042 0.0580 Temperature 0.3866 0.0780 0.4224 0.0200 TempRange -0.0204 0.4410 0.1283 0.2520

Partial Y-Chromosome MtDNA Mantel DNA r p r p Altitude 0.4646 0.0030 0.1051 0.3280 Isothermal 0.5209 0.0100 -0.0398 0.6110

Precipitation 0.1109 0.2660 0.1690 0.2190

Temperature -0.2465 0.9150 0.2583 0.0890

1 Yellow shading represents statistically significant with p<0.05.

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6.4 DISCUSSION

6.4.1 Comparisons: Pooled Cranial Series with DNA

One of the primary patterns emerging from this series of Mantel tests is that the neurocranium (and submodules of the neurocranium) are correlated with mtDNA (also seen in the male cranial series). This pattern has been seen before (Harvati & Weaver,

2006a), although it was expected that the basicranium would be the most neutral of the cranial modules (see section 1.2 Summary of Dissertation). A possible explanation for this is that the basicranium is evolutionarily constrained, or in other words, the basicranium is under stabilizing selection. This would mean that it is not strongly affected by environmental forces but is also not neutral because selection is acting in a way that causes less variation and does not allow drastic changes to occur over time.

The correlations with Y-chromosome STR frequencies does show a significant correlation with the basicranium, so it is possible that mtDNA haplogroups are not as neutral as is typically thought and the Y-chromosome may be more neutral. This is supported by the results in section 4.3.1 MtDNA (Tables 4.3, 4.7, and 4.8) where it is evident that mtDNA is at least regionally correlated with various environmental variables, while the Y-chromosome is consistently not correlated with these variables.

6.4.2 Comparisons: Male Cranial Series with DNA

The results for the male cranial series being compared with mtDNA and Y- chromosome DNA are very similar to the pooled cranial series (above). The primary

323 difference is that the posterior neurocranium, in addition to the basicranium, is also correlated with Y-chromosome DNA. Another difference is that rather than the entire neurocranium demonstrating the strongest correlation to mtDNA, the posterior neurocranium displayed the strongest correlation (although the entire neurocranium is still significantly correlated). The nuances between the pooled series and the male series could be a result of the male series being better matched to the Y-chromosome DNA since males are the only sex that carry the Y-chromosome. There could also be a selection bias with the samples, since 50% or more of the samples from Aguazuque and

Coyo Oriente were female and removed from this portion of the analysis.

6.4.3 Comparisons: Female Cranial Series with DNA

Comparing the female cranial series to mtDNA did not yield any significant correlations. One explanation for this is that the sample size for females was substantially lower than that for males (82 individuals compared to 122 individuals). This lower sample size includes small sample sizes (< 10) for four of the populations (Arica, Cerro del Oro, Mid Chile, and Patagonia). Given this, the results relating to the female cranial series should be seen with caution, as a higher female sample size is needed to confirm these results.

6.4.4 Comparisons: DNA with Climate

The mtDNA series used in this chapter is largely just a portion of the samples analyzed in Chapter 4. That being said, the results seen here are starkly different. This 324 series was found to be correlated with geographic distance, altitude, and isothermality.

The entire Andes series in Chapter 4 was correlated with every environmental variable until geographic distance was controlled for, which left no significant correlations with any variable (Table 4.5). Even once the Andes series was broken down, neither the results from the Northern or Southern Andes series (Tables 4.3 and 4.4, respectively) matches the results seen here. After the Partial Mantel test, the Northern Andes series was correlated with altitude, temperature, and temperature range. The Southern Andes series was correlated with temperature range. It should be stated that these correlations do not conclusively represent the occurrence of natural selection, and could be instead showing population movements.

The difference between the materials and methods used in Chapter 4 and this one is twofold. One difference is that two Peruvian populations were added from Cabana et al. (2014). The rest of the populations were present in Chapter 4, although only a subset of those populations were utilized. The second difference is that some of the frequencies were averaged to provide a reasonable approximation for the cranial series. This demonstrates how only slight alterations to the data can produce vastly different results.

Thus, caution should be warranted when drawing conclusions based on mtDNA haplogroup frequencies. More studies should be performed to elucidate the extent to which different environmental variables affect mtDNA and to understand the different levels of analysis.

The Y-chromosome results seen here are not different than in Chapter 4, in spite of the frequencies here being averaged as well. The primary difference is that the Andean

325 series from Chapter 4 did not show any correlations with geographic distance, whereas the series in this chapter did. It appears, based on this dissertation, that results from the

Y-chromosome is more consistent than the mtDNA. This could be because the mtDNA seems more influenced by regional or local environments than the Y-chromosome.

6.4.5 Implications to the Hypotheses

This dissertation is ultimately testing four hypotheses (full descriptions can be found in section 1.2 Summary of Dissertation):

1. Regional patterns of population structure will match global patterns.

2. Basicranium variation is mostly the product of neutral evolutionary

processes.

3. The neurocranium is the product of neutral evolutionary processes with

some influence of diversifying selection.

4. The facial region will show stronger evidence of diversifying selection in

response to adaption to climate and diet variation.

Hypothesis 1: Rejected.

Overall, world patterns show an isolation by distance, neutral evolution pattern for cranial morphology moving out of Africa (e.g., Betti et al., 2009). In this chapter, the populations presented in South America do not show patterns that match neutral evolution (with DNA acting as the proxy for neutral evolution). While some regions of the cranium appear to correspond with some of the DNA variables, such as the

326 neurocranium, the whole cranium does not. When the whole cranium is broken down, there are differences between which regions correspond to mtDNA versus Y- chromosome DNA.

Hypothesis 2: Rejected.

Overall, the results concerning the basicranium indicate that it is under stabilizing selection. This would mean that it is not neutrally evolving since selection is acting on it.

It was not correlated with any of the environmental variables, and not correlated at all with mtDNA. It was correlated with the Y-chromosome STRs, but this was a weak, negative correlation. The Fst analysis also showed that the basicranium was the least variable of any of the major modules of the cranium. Together, these results more strongly indicated stabilizing selection rather than neutrality.

Hypothesis 3: Supported.

Although we expected some degree of neutrality because the shape of the neurocranium is somewhat restricted by the basicranium, the neurocranium has also been known to co-vary with some environmental forces (namely temperature) (e.g., Hubbe,

Hanihara, et al., 2009). This effect is primarily discussed in reference to populations living in extreme cold, such as Siberia. The results in this dissertation shown in section

5.3.12 Mantel Tests (Table 5.7) demonstrates that after geographic distance is controlled for, the neurocranium is correlated with temperature. This correlation is seen even without populations being from extreme cold environments, meaning that the neurocranium likely has some influence from diversifying selection. This chapter also

327 demonstrated a correlation between the neurocranium and mtDNA, meaning that the neurocranium is likely largely following neutral evolution.

Hypothesis 4: Cautiously supported.

While this hypothesis was primarily addressed in Chapter 5, the correlations performed for this chapter cautiously reject the hypothesis. There are no correlations between the face and any of the DNA frequencies chosen for the dissertation. Based solely on these results, there is no evidence that the face is evolving neutrally, but more analyses would need to be performed to understand they type of influences acting on the facial region.

6.5 CONCLUSION

Comparisons between mtDNA and cranial modules fairly consistently demonstrated significant correlations with the neurocranium and two submodules of the neurocranium, the anterior and posterior neurocranium. Both the pooled and male cranial series showed this correlation, but the female cranial series did not. This was likely due to the smaller sample size of the female series, but further testing will need to be performed to confirm this. These results, in conjunction with the results from Chapter 5 (significant correlation between the neurocranium and temperature), support the hypothesis that the neurocranium is the product of neutral evolutionary forces with some influence of diversifying selection.

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The Y-chromosome demonstrated a weak but significant negative correlation with the basicranium in both the pooled and male cranial series. Additionally, the posterior neurocranium from the male series showed a significant correlation with the Y- chromosome STRs. Given that the basicranium was not correlated with mtDNA haplogroups, the hypothesis that the basicranium is the product of neutral evolutionary forces was not rejected or supported. These results do demonstrate, however, that Y- chromosome STR and mtDNA haplogroup frequencies are not both representing neutrality, as there are starkly different patterns between the two DNA types. Caution should be warranted when using either data type to estimate neutrality until their level of neutrality is more understood.

Further compounding these genetic results is that it appears that slight alterations in which populations are used in analysis produce very different results. This chapter found correlations between Y-chromosome STR and mtDNA haplogroup frequencies with climatic variables, just as was done in Chapter 4. In Chapter 4, one subset of the populations were made to represent the Andes. This series showed correlations with every variable until geographic distance was controlled, which left no significant correlations (when analyzing mtDNA). This chapter also ran an Andean series, but with less populations than in Chapter 4. In this case, both altitude and isothermality were significantly correlated. Again, caution is warranted when using mtDNA haplogroups until their interactions with the local environment are more understood.

In general, the affinity patterns seen in the populations are not consistent between the cranial morphology and the DNA data. There are three primary possibilities for this:

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1. The cranial morphological populations are a poor match for the DNA

populations chosen to be a match. This is quite possibly because the DNA

frequencies are from modern populations, while the cranial morphological

data is mostly dated to around AD 1000-1500. Furthermore, the DNA and

crania are not from the exact same individuals. This situation is unavoidable

until more aDNA can be collected from the South American continent.

2. MtDNA haplogroups and Y-chromosome STR frequencies are not under

neutral evolution. A discussion of this in relation to these results can be seen

in section 4.4.3 Implications to the Hypotheses.

3. The cranium does not show a neutrally evolving pattern on a regional scale. It

has been shown that the cranium follows neutral evolutionary patterns when

studied on a global scale (e.g., Betti et al., 2009; Manica et al., 2007; von

Cramon-Taubadel & Lycett, 2008), but it is possible that this pattern is only

true on a worldwide scale.

Given the patterns of data seen in this dissertation, the third option seems to best fit. The isolation by distance pattern seen on a worldwide scale has been shown on numerous occasions. However, on a regional scale, the patterns differ. Researchers cannot usually agree on how neutral the cranium is or the evolutionary effects governing the shape of the cranium (e.g., González-José et al., 2008; González-José, Ramírez-Rozzi, et al., 2005;

Hubbe, Hanihara, et al., 2009; Konigsberg, 1990; Perez & Monteiro, 2009; Roseman &

Weaver, 2007; von Cramon-Taubadel, 2011b; von Cramon-Taubadel & Weaver, 2009).

This could be from researchers using different cranial samples from around the world.

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The results in this dissertation also show that each region of the cranium shows a different relationship to the climatic variables. Most of the previous research just mentioned uses cranial measurements that span regions that could differentially be affected, further compounding the results.

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CHAPTER 7 : CONCLUSION

This final chapter presents a brief summary of the different conclusions that were developed throughout the entire dissertation, and places them in a broader context.

7.1 OVERALL CONCLUSIONS

The primary goal of the dissertation was to address two research questions: (1)

How consistent are the patterns of population affinity when comparing different regions of the crania to geographic distances for populations in South America? (2) If they are not consistent, what are the possible causes of the observed differences? To achieve this goal, comparisons were made between the whole cranium, as well as different functional modules, to geographic distance, climatic variables, mtDNA haplogroup frequencies, and

Y-chromosome STR frequencies. Both of the DNA frequencies (mitochondrial and Y- chromosome) acted as proxies for neutral evolution, while the climate variables tested the extent to which natural selection was acting on the different regions of the cranium. The hypotheses developed for this dissertation are fully addressed in section 1.2 Summary of the Dissertation. Table 7.1 summarizes the hypotheses and the results from each chapter as they relate to them.

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Table 7.1: Table summarizing the hypotheses, with the results from Chapters 4, 5, and 6.

Hypotheses Chapter 4 Chapter 5 Chapter 6

Regional patterns Rejected: For Not addressed in Rejected: For of population DNA, each region this chapter crania, some structure will of South America patterns seem to match global shows a different correspond with patterns. relationship with neutral evolution, climate variables. but this is variable

between comparisons with mtDNA and Y- chromosome DNA. Basicranium Not addressed in Cautiously Rejected: The variation is mostly this chapter supported: The basicranium the product of basicranium was appears to be under neutral not correlated with stabilizing evolutionary climate variables, selection. processes. but needs to be compared to DNA

(Chapter 6).

The neurocranium Not addressed in Cautiously Supported: The is the product of this chapter supported: neurocranium was neutral Neurocranium was correlated with evolutionary correlated with only mtDNA, but also processes with one climate temperature. some influence of variable, but needs diversifying to be compared to selection. DNA (Chapter 6). The facial region Not addressed in Rejected: The face Cautiously will show stronger this chapter was not correlated supported: Face evidence of with climate was not correlated diversifying variables. with DNA selection in frequencies so it response to does not seem to be adaptation to evolving neutrally. climate and diet variation.

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In general, when considering the cranium, this dissertation demonstrates that the neurocranium is likely evolving neutrally, but with some influence from diversifying selection. The face, however, was not directly correlated with either climate or DNA frequencies, making it unclear whether it is following neutral evolutionary patterns. The basicranium was found to be highly conservative. This makes sense anatomically and developmentally because it is the first region of the skull to form in utero (e.g.,

Lieberman, Ross, et al., 2000). It is also developed through endochondral ossification, and is therefore thought to be under a stronger genetic control than other regions (e.g.,

Lieberman, 1996; Wood & Lieberman, 2001). Variance/covariance matrices of the basicranium have also previously been shown to be consistent with neutral evolution

(Smith, 2011). In the case of my dissertation work, the basicranium was more conservative than what we would expect under neutral evolution. This indicates that it is likely under stabilizing selection.

Both cranial morphology and DNA frequencies show different patterns on a regional scale versus a global one. Globally, both data types demonstrate an isolation by distance pattern in their population structure, but this breaks down once the continent is divided into different regions. Analyzing the DNA evidence (Chapter 4) demonstrates that gene flow likely has barriers that follow the ecogeographic regions of South

America. The exception to this is with the Amazon, which is probably lacking structure based on cultural patterns instead. Chapter 5 shows that cranial morphology also differs from global patterns, but are not consistent with the patterns seen with the DNA frequencies. A possibility for this is that the cranium only shows neutrally evolving

334 patterns on a global scale, but non-neutral patterns show a stronger signal once there are only regional samples. Another possibility is that the populations utilized for the DNA analysis does not completely match the populations for the cranial analysis (although the populations were highly related or located in the same general geographic area).

When looking at previous studies that have been published, my results show that there is no universal model for the evolution of modern humans on a regional scale.

While cranial morphology and genetic studies show consistency at the global level (e.g.,

Betti et al., 2009; Prugnolle et al., 2005) via isolation by distance, this consistency breaks down regionally within the continent of South America and demonstrates that isolation patterns between populations may be regionally structured based on ecology and/or climate. Importantly, this dissertation does not disprove or question the validity of studies such as A. Strauss et al. (2015) or Nicholson and Harvati (2006). Instead, it supplies a further understanding of how variation is distributed across a continent and demonstrates that this is not as straight-forward as previously suggested.

7.2 LIMITATIONS

Limitations in this dissertation are primarily based around availability of data. I was unable to use perfect matches between the crania and DNA, since DNA has not been taken directly from the individuals used in the cranial analysis, nor has DNA been taken from the same populations. Therefore, I needed to rely on analogous populations to complete the analysis.

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Y-Chromosome data is also missing for the entire country of Chile. This made some analyses impossible because several ecological zones specified in this dissertation encompassed various parts of Chile. This also made finding analogous populations difficult since I needed to find populations possibly interacting from a further geographic distance.

To complete the analysis for this dissertation, crania that were not modified were required to have the most accurate correlations to evolutionary or ecological models and variables. Cranial modification, however, is not unusual in the Andean region, which reduced my sample sizes. I also was unable to collect cranial samples from locations east of the Andes. Without having cranial samples encompassing the entirety of the continent, placing the results of this dissertation in the wider context of the peopling of the New

World becomes much more difficult.

7.3 FUTURE DIRECTIONS

Incorporating more DNA data, particularly from autosomal DNA, could allow for better estimates of neutral evolution. There is also the potential to further understand if population patterns from any genetic data type matches the patterns seen with cranial morphology, since this has yet to be extensively studied.

Moving forward, the addition of 3D scans of crania from the eastern side of the

South American continent would allow for a broader pattern to be established. It would also enable a closer comparison with DNA to see if the genetic patterns seen across the

336 continent resemble the patterns from craniometrics. Additional samples from the populations analyzed here would also be preferable, because some of these groups had small sample sizes and may not be wholly representative of populations in the Andes.

However, this may not be possible as there are limited museum samples for human skeletal collections in general. While this dissertation was partly inspired by questions relating to the human dispersion to the Americas, little could be related back to this topic due the limitation of the data. Additional crania would also create an opportunity for placing these patterns in the broader context of the human dispersion to the Americas.

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