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Ontogenetic Variation in Three Native North American Populations

Permanent Link: http://ufdc.ufl.edu/UFE0018740/00001

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Title: Ontogenetic Variation in Three Native North American Populations Eco-Geographic Effects on Human Growth and Development
Physical Description: 1 online resource (249 p.)
Language: english
Creator: Waxenbaum, Erin B
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: eco, limb, skeleton
Anthropology -- Dissertations, Academic -- UF
Genre: Anthropology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: My study represents an analysis of postcranial growth and development among three Native North American populations: Native Alaskan groups, South Dakota Arikara, and New Mexico Puebloan populations. These eco-geographically distinct populations were chosen to investigate (1) whether groups vary in postcranial limb measurements as adults and if these patterns correlate to eco-geographic condition and (2) how and when do these patterns of variation arise during the juvenile period. Analyses of the adult component of each population demonstrate statistically significant deviation of the group means for postcranial elements with significant variation manifest in the distal segments of both the upper and lower limbs. Similarly, this morphological size variation shows strong evidence for patterning along geographic boundaries and climatic conditions. These morphological differences conform to Bergmann's and Allen's biological 'rules' for homeothermic animals; individuals inhabiting colder climates exhibit shorter extremities while those occupying warmer clines display longer limbs. Patterns of growth among the juvenile components of each population were appreciable different that those seen in the adult sample. However, the adolescent period served as the transitional period linking juvenile and adult patterns of variation. The null hypothesis of isometry was rejected favoring an alternative hypothesis of ontogenetic scaling and divergent growth trajectories dependent upon the individual skeletal element and population compared. Unique growth origins were found for the ulnae, radii, femur and fibulae but not for the humeri and tibiae when the Native Alaskan sample was compared to both the South Dakota Arikara and New Mexico Puebloan groups. This discrepancy cannot be fully accounted for at this time, but has been reported in prior analyses of human skeletal growth. While Native North American populations share a common evolutionary history, they do conform to previously examined morphological patterns of human variation and are distinguished by certain components of their local environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Erin B Waxenbaum.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Falsetti, Anthony B.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0018740:00001

Permanent Link: http://ufdc.ufl.edu/UFE0018740/00001

Material Information

Title: Ontogenetic Variation in Three Native North American Populations Eco-Geographic Effects on Human Growth and Development
Physical Description: 1 online resource (249 p.)
Language: english
Creator: Waxenbaum, Erin B
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: eco, limb, skeleton
Anthropology -- Dissertations, Academic -- UF
Genre: Anthropology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: My study represents an analysis of postcranial growth and development among three Native North American populations: Native Alaskan groups, South Dakota Arikara, and New Mexico Puebloan populations. These eco-geographically distinct populations were chosen to investigate (1) whether groups vary in postcranial limb measurements as adults and if these patterns correlate to eco-geographic condition and (2) how and when do these patterns of variation arise during the juvenile period. Analyses of the adult component of each population demonstrate statistically significant deviation of the group means for postcranial elements with significant variation manifest in the distal segments of both the upper and lower limbs. Similarly, this morphological size variation shows strong evidence for patterning along geographic boundaries and climatic conditions. These morphological differences conform to Bergmann's and Allen's biological 'rules' for homeothermic animals; individuals inhabiting colder climates exhibit shorter extremities while those occupying warmer clines display longer limbs. Patterns of growth among the juvenile components of each population were appreciable different that those seen in the adult sample. However, the adolescent period served as the transitional period linking juvenile and adult patterns of variation. The null hypothesis of isometry was rejected favoring an alternative hypothesis of ontogenetic scaling and divergent growth trajectories dependent upon the individual skeletal element and population compared. Unique growth origins were found for the ulnae, radii, femur and fibulae but not for the humeri and tibiae when the Native Alaskan sample was compared to both the South Dakota Arikara and New Mexico Puebloan groups. This discrepancy cannot be fully accounted for at this time, but has been reported in prior analyses of human skeletal growth. While Native North American populations share a common evolutionary history, they do conform to previously examined morphological patterns of human variation and are distinguished by certain components of their local environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Erin B Waxenbaum.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Falsetti, Anthony B.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0018740:00001


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1 ONTOGENETIC VARIATION IN THREE NATIVE NORTH AMERICAN POPULATIONS: ECO-GEOGRAPHIC EFFECTS ON HUMAN GROWTH AND DEVELOPMENT By ERIN B. WAXENBAUM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Erin B. Waxenbaum

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3 ACKNOWLEDGMENTS A dissertation is accomplished by no single individual; and at many points it seems like it takes a small army. I take this opportunity to acknowledge and thank just a few of the many people that made this possible and contributed to my sanity over the past four years. I gratefully acknowledge Dr. David R. Hunt of the National Museum of Natural History unknowingly at the time, introducing me to my dissertation. He has always opened his door (and office) to me (for extended periods of time) and I will ever be grateful for his guidance, support and confidence. Finally, I thank you for granting me access to these skeletal remains over the course of my research. I also thank Dr. Steve Ousley and Ms. Erica Jones for allowing me to constantly invade the Repatriation Office at the National Museum of Natural History, Smithsonian Institution and putting up with my incessant questions and penchant for highlighting inconsistencies. I thank Dr. Heath Edgar of the Maxwell Museum of Anthropology, University of New Mexico for granting me access to the Ancestral Puebloan skeletal remains housed there. I also thank Dr. Adam Sylvester, University of Tennessee, Knoxville, for his statistical assistance in the final phases of my data analysis. I extend my utmost appreciation and gratitude to my doctoral committee Drs. Anthony B. Falsetti (chair), Michael W. Warren, Connie J. Mulligan and Marty J. Cohn. Dr. Warren showed me, by example, that a project of this nature is possible. I thank him for always being available for advice, a last minute proofread, or even a last minute pelvis. I thank Dr. Mulligan for constantly challenging me to make my project better and consequently remind me that a dissertation should be appreciated and available to academics of varied backgrounds. I thank Dr. Cohn for coming to UF coincident with my arrival! You have opened my eyes to a field I may

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4 have never appreciated otherwise and ingrained a new perspective in my mind that I know will permeate my academic future. Papa, what can I say, thank you for everything: for always being my sounding board when I need to talk through an idea at you, for supporting my mini-second-masters, for your patience in allowing me to challenge or debate a statistical concept until we are both blue in the face, and make our work our own. You have opened so many doors to all of us for which I will be forever grateful. Pound pups Laura (my comrade in arms, graduated but certainly not forgotten), Shanna, Shiela, Laurel, Carlos, Heather and the rest of the gang I thank them for their constant teasing as proof of their friendship (if anyone ever heard how we talk to each other they would never know how much we depend on each other for camaraderie and sanity!). I thank them for every lunchtime conversation, quick fix for an Excel catastrophe, corrected preposition and the annual debauchery that are AAFS meetings. I thank all those who love both the cheese and the egg particularly the Washington, DC contingent. You represent some of my oldest and closest friends and I know you will remain that way for a long, long time to come. For those who have housed me (or have just tolerated me spending an inordinate amount of time on your couch), I could not have gotten through the last few years without your love, support and silliness.

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5 put up with my twists and -realization for almost a decade (yes, I know, we have been friends for far too long). I guess I officially owe you a fancy dinner (or two) now. Brad, your support and confidence in me is ever absolute and unwavering. You constantly reaffirm my belief in myself and are always available to brag about my accomplishments when the appropriate family event provides. I am so grateful to have you as part of my life. Last by not least I thank my family. They have supported me for the last 26 years in every way. I would like to thank my parents in particular for their love and unconditional support, well without his big sister around to tease and torture him as thoroughly as he should have been. My graduate education and doctoral research would not have been possible without the financial support provided by: the C.A. Pound Human Identification Lab, Department of Anthropology, Honors College, National Museum of Natural History, Smithsonian Institution, John M. Goggin award, William R. Maples award, Express (sadly), my family and, of course, my savings account.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................3 LIST OF TABLES ...........................................................................................................................9 LIST OF FIGURES .......................................................................................................................12 ABSTRACT ...................................................................................................................................14 CHAPTER 1 INTRODUCTION ..................................................................................................................16 Archaeological Juvenile Skeletal Remains .............................................................................18 The Critics .......................................................................................................................18 The Proponents ................................................................................................................19 A Deeper Appreciation for Human Variation ........................................................................20 2 RELEVANT BACKGROUND LITERATURE ....................................................................21 Study Limitations....................................................................................................................21 Dental Development ...............................................................................................................23 Methodological Concerns ................................................................................................24 Dental Calcification .........................................................................................................25 Dental Eruption ...............................................................................................................27 Sequence of Calcification and Eruption ..........................................................................28 Canalization of Dentition ................................................................................................29 Population Variation ........................................................................................................30 Influence of Sex ...............................................................................................................31 Effects of Varied Nutrition ..............................................................................................33 Skeletal Development .............................................................................................................34 Methodological Concerns ................................................................................................35 Growth Velocity ..............................................................................................................35 Population Variation ........................................................................................................37 Proportionality in Growth ................................................................................................38 Influence of Sex ...............................................................................................................39 Nutritional Considerations ...............................................................................................40 Dental Development vs. Skeletal Maturation .........................................................................42 Evolutionary-Genetic Relatedness of Populations .................................................................43 Ecological Considerations ......................................................................................................46

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7 3 MATERIALS .........................................................................................................................51 Native Alaskan ........................................................................................................................51 Spatial Distribution ..........................................................................................................51 Temporal Distribution .....................................................................................................53 Excavation and Principal Investigators ...........................................................................54 Life History Conditions ...................................................................................................54 Repatriation Status ...........................................................................................................55 South Dakota Arikara .............................................................................................................56 Spatial Distribution ..........................................................................................................56 Temporal Distribution .....................................................................................................57 Excavation and Principal Investigators ...........................................................................58 Life History Conditions ...................................................................................................59 Repatriation Status ...........................................................................................................60 Ancestral Puebloan .................................................................................................................61 Spatial Distribution ..........................................................................................................61 Temporal Distribution .....................................................................................................62 Excavation and Principal Investigators ...........................................................................63 Life History Conditions ...................................................................................................64 Repatriation Status ...........................................................................................................65 Eco-Geographic Data ..............................................................................................................65 4 ASSUMPTIONS, METHODS AND HYPOTHESES ...........................................................67 Theoretical and Methodological Assumptions .......................................................................67 Methods ..................................................................................................................................68 Data Collection .......................................................................................................................69 Dental Data Collection and Dental Age Determination ..................................................69 Postcranial Data Collection .............................................................................................69 Data Description and Considerations .....................................................................................70 Logarithmic Transformation ...........................................................................................70 Descriptive Statistics and Age Categories .......................................................................70 Analysis of Adult Skeletal Remains .......................................................................................71 Demonstration of Population Variation: TukeyP ......................................... 71 Principal Components Analysis ......................................................................................72 Canonical Discriminant Analysis ....................................................................................74 Examination of Skeletal Indices ......................................................................................75 Canonical Correlation Analysis .......................................................................................76 Analysis of Juvenile Skeletal Remains ...................................................................................77 Principal Components Analysis ......................................................................................77 Reduced Major Axis Regression: Multiple Comparison of Growth Trajectories ...........78 Test of Isometry and Allometry ......................................................................................80 Interpretive Framework ..........................................................................................................84

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8 5 RESULTS AND DISCUSSION .............................................................................................87 Results of Adult Skeletal Analysis .........................................................................................87 P ..........................................87 Principal Components Analysis ......................................................................................91 Canonical Discriminant Analysis ....................................................................................94 Examination of Skeletal Indices ......................................................................................98 Canonical Correlation Analysis .....................................................................................103 Results of Juvenile Skeletal Analysis ...................................................................................108 Principal Components Analysis ....................................................................................108 Reduced Major Axis Regression: Multiple Comparison of Growth Trajectories .........113 Test of Isometry .............................................................................................................134 The Origins of Variation: Which Came First, the Chicken or the Egg? ...............................139 Limb Development ........................................................................................................140 Broad Mechanism of Change: Hox Genes ....................................................................141 Patterns of Limb Variation: Forelimb and Hindlimb Variability ..................................143 Patterns of Limb Variation: Proximal Stability and Distal Variability .........................143 Long Bone Development ...............................................................................................145 Local Mechanisms of Change .......................................................................................147 Indian hedgehog (Ihh) and parathyroid-hormone-related protein (PTHrP) ...........147 Fibroblast growth factors (FGFs) ...........................................................................148 Bone morphogenetic proteins (BMPs) ...................................................................148 Insulin-like growth factors (IGF) ...........................................................................149 Growth hormone (GH) ...........................................................................................149 Final Thoughts ......................................................................................................................150 6 SUMMARY ..........................................................................................................................151 APPENDIX A POSTCRANIAL DATA .......................................................................................................157 B DENTAL DATA ..................................................................................................................181 Adult Dental Data .................................................................................................................182 Juvenile Dental Data .............................................................................................................182 C DESCRIPTIVE STATISTICS..............................................................................................214 D POSTCRANIAL MEASUREMENTS .................................................................................223 REFERENCES ............................................................................................................................227 BIOGRAPHICAL SKETCH .......................................................................................................249

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9 LIST OF TABLES Table page 3-6 Spatial distribution of Native Alaskan archaeological sites. .............................................51 3-7 Temporal distribution of Native Alaskan sites. .................................................................53 3-12 Spatial distribution of South Dakota Arikara archaeological sites ....................................56 3-13 Temporal distribution of South Dakota Arikara sites. .......................................................57 3-18 Spatial Distribution of Ancestral Puebloan archaeological sites .......................................61 3-19 Temporal Distribution of Ancestral Puebloan sites. ..........................................................62 3-20 Period of recorded climatic data ........................................................................................65 3-21 Climatological conditions ..................................................................................................66 4-1 Age categories. ..................................................................................................................71 4-2 Heterochrony: paedomorphosis vs. peramorphosis. ..........................................................85 5-1 Significance of differences among adult humeri (n=408) .................................................88 5-2 Significance of differences among adult ulnae (n=330) ....................................................88 5-3 Significance of differences among adult radii (n=358) .....................................................88 5-4 Significance of differences among adult femora (n=421) .................................................89 5-5 Significance of differences among adult tibiae (n=403) ...................................................89 5-6 Significance of differences among adult fibulae (n=316) .................................................89 5-7 Population means (mm) by element ..................................................................................89 5-8 Principal components analysis of adult size variables.......................................................93 5-9 Principal components analysis of adult shape variables ....................................................94 5-10 Canonical discriminant analysis test of significance for adult postcrania within population ..........................................................................................................................95 5-11 Statistical results of canonical discriminant analysis for adult postcrania within population ..........................................................................................................................95 5-12 Total canonical structure of adult postcranial variables within population .......................96

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10 5-13 Class means on canonical variables within population......................................................96 5-14 Significance of differences among brachial indices ........................................................102 5-15 Significance of differences among crural indices ............................................................102 5-16 Canonical correlation among postcranial measurements ................................................104 5-17 Canonical correlation among eco-geographic variables ..................................................105 5-18 Canonical correlation between postcranial measurements and eco-geographic variables ...........................................................................................................................106 5-19 Principal components analysis of juvenile size variables ................................................109 5-20 Principal components analysis of juvenile shape variables .............................................110 5-21 Principal components analysis of juveniles Birth-2 estimated years of age (n=41) .....111 5-22 Principal components analysis of juveniles 3-7 estimated years of age (n=37) ...........112 5-23 Principal components analysis of juveniles 8-11 estimated years of age (n=19) .........112 5-24 Principal components analysis of juveniles 12-21 estimated years of age (n=76) .......113 5-25 Slope by element within population ................................................................................131 5-26 Intercept values by element within population ................................................................132 5-27 Multiple comparisons of slopes and intercepts: p-values for humeri ..............................132 5-28 Multiple comparisons of slopes and intercepts: p-values for ulnae .................................132 5-29 Multiple comparisons of slopes and intercepts: p-values for radii ..................................132 5-30 Multiple comparisons of slopes and intercepts: p-values for femora ..............................133 5-31 Multiple comparisons of slopes and intercepts: p-values for tibiae .................................133 5-32 Multiple comparisons of slopes and intercepts: p-values for fibulae ..............................133 5-33 Principal components analysis of Native Alaskan juveniles (n=52) ...............................135 5-34 Principal components analysis of Native Alaskan juveniles Covariance matrix .........135 5-35 Principal components analysis of South Dakota Arikara juveniles (n=76) .....................135 5-36 Principal components analysis of South Dakota Arikara juveniles Covariance matrix ...............................................................................................................................135

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11 5-37 Principal components analysis of New Mexico Puebloan juveniles (n=44) ...................136 5-38 Principal components analysis of New Mexico Puebloan juveniles Covariance matrix ...............................................................................................................................136 5-39 Results for the test of isometry ........................................................................................137 5-40 Deviation from isometry ..................................................................................................138 A-1 Code for population assessment ......................................................................................157 A-2 Code for sex assessment ..................................................................................................157 A-3 Postcranial data by population, estimated dental age and sex .........................................157 B-1 Stages of crown and root formation .................................................................................181 B-2 Additional code for dental assessment .............................................................................182 B-3 Permanent dentition .........................................................................................................182 B-4 Juvenile dentition .............................................................................................................182 B-5 Adult dental data ..............................................................................................................183 B-6 Juvenile dental data ..........................................................................................................205 C-1 Descriptive statistics Native Alaskan juveniles .............................................................214 C-2 Descriptive Statistics Native Alaskan males .................................................................215 C-3 Descriptive Statistics Native Alaskan females ..............................................................215 C-4 Descriptive statistics South Dakota Arikara juveniles ..................................................216 C-5 Descriptive statistics South Dakota Arikara males ........................................................218 C-6 Descriptive statistics South Dakota Arikara females ....................................................218 C-7 Descriptive statistics. Ancestral Puebloan juveniles. .......................................................219 C-8 Descriptive statistics Ancestral Puebloan males ...........................................................220 C-9 Descriptive statistics Ancestral Puebloan females ........................................................221

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12 LIST OF FIGURES Figure page 3-1 Geographic distribution of Native Alaskan sites ...............................................................53 3-2 Geographic distribution of South Dakota Arikara sites .....................................................57 3-3 Geographic distribution of Ancestral Puebloan sites ........................................................62 5-1 Canonical discriminant analysis of three populations: Plot of class means ......................97 5-2 Box plot of adult brachial index against sex by population ..............................................99 5-3 Box plot of adult crural index against sex by population ................................................100 5-4 Box plot of adult intermembral index against sex by population ....................................101 5-5 Reduced major axis regression for Native Alaskan (n=52) log(humeri) on log(size) ....114 5-6 Reduced major axis regression for Native Alaskan (n=52) log(ulnae) on log(size) .......115 5-7 Reduced major axis regression for Native Alaskan (n=52) log(radii) on log(size).........116 5-8 Reduced major axis regression for Native Alaskan (n=52) log(femora) on log(size).....117 5-9 Reduced major axis regression for Native Alaskan (n=52) log(tibiae) on log(size) .......118 5-10 Reduced major axis regression for Native Alaskan (n=52) log(fibulae) on log(size) .....119 5-11 Reduced major axis regression for South Dakota Arikara (n=76) log(humeri) on log(size)............................................................................................................................120 5-12 Reduced major axis regression for South Dakota Arikara (n=76) log(ulnae) on log(size)............................................................................................................................121 5-13 Reduced major axis regression for South Dakota Arikara (n=76) log(radii) on log(size)............................................................................................................................122 5-14 Reduced major axis regression for South Dakota Arikara (n=76) log(femora) on log(size)............................................................................................................................123 5-15 Reduced major axis regression for South Dakota Arikara (n=76) log(tibiae) on log(size)............................................................................................................................124 5-16 Reduced major axis regression for South Dakota Arikara (n=76) log(fibulae) on log(size)............................................................................................................................125 5-17 Reduced major axis regression for N.M. Puebloan (n=44) log(humeri) on log(size) .....126

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13 5-18 Reduced major axis regression for N.M. Puebloan (n=44) log(ulnae) on log(size) ........127 5-19 Reduced major axis regression for N.M. Puebloan (n=44) log(radii) on log(size) ........128 5-20 Reduced major axis regression for N.M. Puebloan (n=44) log(femora) on log(size) .....129 5-21 Reduced major axis regression for N.M. Puebloan (n=44) log(tibiae) on log(size) .......130 5-22 Reduced major axis regression for N.M. Puebloan (n=44) log(fibulae) on log(size) .....131 D-1 Juvenile and adult humeri measurement ..........................................................................223 D-2 Juvenile and adult ulnae measurement ............................................................................224 D-3 Juvenile and adult radii measurement ..............................................................................224 D-4 Juvenile and adult femora measurement ..........................................................................225 D-5 Juvenile and adult tibiae measurement ............................................................................225 D-6 Juvenile and adult fibulae measurement ..........................................................................226

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14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ONTOGENETIC VARIATION IN THREE NATIVE NORTH AMERICAN POPULATIONS: ECO-GEOGRAPHIC EFFECTS ON HUMAN GROWTH AND DEVELOPMENT By Erin B. Waxenbaum Deember 2007 Chair: Anthony B. Falsetti Major: Anthropology My study represents an analysis of postcranial growth and development among three Native North American populations: Native Alaskan groups, South Dakota Arikara, and New Mexico Puebloan populations. These eco-geographically distinct populations were chosen to investigate (1) whether groups vary in postcranial limb measurements as adults and if these patterns correlate to eco-geographic condition and (2) how and when do these patterns of variation arise during the juvenile period. Analyses of the adult component of each population demonstrate statistically significant deviation of the group means for postcranial elements with significant variation manifest in the distal segments of both the upper and lower limbs. Similarly, this morphological size variation shows strong evidence for patterning along geographic boundaries and climatic conditions. These morphological differhomeothermic animals; individuals inhabiting colder climates exhibit shorter extremities while those occupying warmer clines display longer limbs. Patterns of growth among the juvenile components of each population were appreciable different that those seen in the adult sample. However, the adolescent period served as the transitional period linking juvenile and adult patterns of variation. The null hypothesis of

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15 isometry was rejected favoring an alternative hypothesis of ontogenetic scaling and divergent growth trajectories dependent upon the individual skeletal element and population compared. Unique growth origins were found for the ulnae, radii, femur and fibulae but not for the humeri and tibiae when the Native Alaskan sample was compared to both the South Dakota Arikara and New Mexico Puebloan groups. This discrepancy cannot be fully accounted for at this time, but has been reported in prior analyses of human skeletal growth. While Native North American populations share a common evolutionary history, they do conform to previously examined morphological patterns of human variation and are distinguished by certain components of their local environment.

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16 CHAPTER 1 INTRODUCTION It is through a comprehensive understanding of the history, origins, and biological change within human populations of the past that we come to appreciate the morphological variation expressed within modern human groups. This project looks to secure a greater appreciation and understanding of the subtle and dramatic variations of human growth and development through an eco-geographic analysis of skeletal biology in three Native North American populations. Ontogeny refers to the growth of an individual throughout their life history through embryonic and/or post-natal development (e.g., Gould, 1977; McKinney and McNamara, 1991). A broad, comparative and quantitative analysis of the rate, timing and trajectory of human skeletal maturation and ontogeny has not been accounted for in the literature. This study will allow human biologists a greater understanding of the fundamental questions relating to the morphological variation observed in the skeletal record. Studies on preand post-contact Native North American human remains have been hindered by the incomplete nature of juvenile specimens in most archaeological assemblages. The lack of empirical evidence on growth and developmental plasticity of comparative populations has allowed for only theoretical discussions of ontogenetic human variation. The introduction of developmental biology, population genetics and overall integrative approaches to f early development and how the internal timing of developmental events and environmental cues affect developmental processes on a population level. This discipline has enormous promise for the field of skeletal biology. This study will take a more comprehensive and quantitative approach than previously applied in the anthropological literature to better understand the origins of variation and timing of growth events between three eco-geographically distinct populations:

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17 the Arikara of South Dakota, Ancestral Puebloan populations of New Mexico and Native Alaskan groups. These populations were specifically chosen given their eco-geographic distinction, temporal proximity and relatively conserved evolutionary history in order to investigate whether these conditions affect final adult morphology and how climatic and geographic variation is manifest in the growing, juvenile skeleton. This author is unaware of alternate skeletal populations that share the degree of temporal and evolutionary relatedness exhibited in these three groups. On an evolutionary scale, variation in skeletal morphology in many species has been attributed to variations in eco-geographic condition; therefore this variable will be the primary focus of this investigation. Concepts derived from skeletal biology, evolution, genetics and ecology will be integrated in order to more fully appreciate, from a broad perspective, how eco-geographic differences affect human skeletal development. This project will address the following questions: Do significant differences exist between the adults of these three populations? If so, does this variation correlate with eco-geographic conditions? Given the genetic and evolutionary history of Native Americans, are members of these three populations the same physical size at birth? By identifying whether these populations begin postnatal growth at the same stage, I will discern whether differences in adult form can be attributed to heterochronic differences in the growth process and/or how these changes correlate with eco-geographic factors. Do these geographically distinct populations share the same developmental rates? Do they have isometric, parallel or divergent allometric growth trajectories? Do overall shorter groups merely begin development at a small size, grow more slowly throughout development or stop growing earlier? By defining critical periods of growth and variation in growth trajectory of postcranial elements, I will further define the potential influences that might critically impact overall population growth patterns. A greater understanding of growth in Native American juvenile remains obtained in the late 19th and early 20th centuries has been hindered by the forceful treatment and handling of

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18 Native American peoples. The harsh introduction of contact groups with Native North Americans has greatly shaped the relations between Native Americans and present-day Americans (Prince, 1998; Zimmerman, 1998). Many of the skeletal populations housed at academic institutions, including all groups in the present analysis, are in the process of repatriation in an attempt to ameliorate this history. Although these wrongs may never be suitably righted, further investigation into the biological development of the unique features that distinguish these native groups may provide the keys to an understanding of their origins, dispersal and modern day association and may add a novel and unique understanding to the repatriation effort. Archaeological Juvenile Skeletal Remains Numerous critics have disagreed with the analysis of juvenile, archaeological skeletal remains due the inherent limitations of these samples. This author joins many others (Kronfeld, 1935; Jantz and Owsley, 1984b; Johnston, 1968; Steyn and Henneberg, 1996; Sundick, 1978; fronting both the strengths and weaknesses of this type of data sample. The Critics Francis Johnston (1962, 1968) has been one of the more vocal critics of employing juvenile skeletal samples in the analysis of growth and development, despite his use of juvenile skeletal assemblages throughout his own research. Johnston (1968) draws attention to the inability to definitively determine accurate chronological age (similarly noted by Steyn and Henneberg, 1996; Sundick, 1978) and the cross-sectional nature of the juvenile samples determined solely by the mortality distribution of archaeological populations: No matter how impressive any skeletal information pertaining to immature individuals may appear, and particularly when incremental growth is the frame of reference, some degree of error is introduced by the very fact that the sample is skeletal. It does not represent the normal, healthy, population from which it was drawn. The fact that a person died young presupposed illness, injury, or other deficiency which prevented his reaching

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19 adulthood. These factors, limiting as they are, are present in all studies of non-living material, and, if such material is to be of use at all, they must be borne graciously and realized analytically (Johnston 1962: 249). The most frequently illustrated argument is that juvenile skeletal samples represent the portion of the population that did not survive to adulthood. Thus this population of juveniles could be representing potentially retarded or pathologically altered growth patterns (Jantz and McKern (1970) and Sundick (1978) highlight that when dealing with remains of unknown chronological age there is no way for investigators to determine which individuals followed influences affecting skeletal maturation. Beyond aging criteria, juvenile archaeological samples are plagued by other potential roadblocks. Studies of juvenile skeletal growth are generally hindered by small sample size among juvenile skeletal remains, forcing analysts to pool both male and females together despite their variation in growth and development, particularly toward puberty. In addition Sundick (1978) highlights the frequent incompleteness of juvenile skeletons, scarcity of individuals from the adolescent period and the effect of secular trends that may affect maturation stages making comparisons between different time periods inappropriate. The Proponents Many authors and academics cite juvenile skeletal remains as an important and necessary tool in appreciating variation among past populations. Lovejoy et al. (1990) comment that children are more likely, particularly among archaeological assemblages, to die of acute disease leaving little or no skeletal evidence. As far as physical size is concerned, Sundick (1978)

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20 concludes that children frequently ill are comparable in overall growth to those less prone to illness. More importantly, Kronfeld (1935) highlights what could be lost to the literature if analyses of archaeological skeletal remains were not conducted. If juvenile human remains are would never produce the understanding that these fundamental disciplines have offered to science thus far. If growth analyses were to be conducted solely on animal instead of human (Kronfeld, 1935: 1139). A Deeper Appreciation for Human Variation Despite the limitations of analyzing juvenile skeletal remains for growth research, it has been noted by many authors (e.g. Hunt, 1958; Jantz and Jantz, 1999) that younger individuals, undergoing rapid growth and development, are most susceptible to ecological change which manifest in population variation. It is through such analyses that a deeper understanding of the mystery behind this variation can be appreciated. Johnston (1969: 335) finds: The major component of variation among adults is due to variations in the growth process. In other words, the first step in understanding differences among groups, whether at the level of genus or of population, lies in the understanding of the processes of growth of each. The evolving and exploding discipline developmental biology has revealed that through a developmental focus a greater degree of information can be assembled concerning evolution and genes, theorists have omitted what selection really acts upon: ontogeny. Ontogenies evolve, not three Native North American populations to further appreciate the variation that exists among these groups.

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21 CHAPTER 2 RELEVANT BACKGROUND LITERATURE Study Limitations Naturally, the very existence of skeletons implies death from one cause or another, including pathological states. This means that in any collection of skeletons, some are included because as living individuals they succumbed to illness. -Stewart, The Effects of Pathology on Skeletal Populations. Numerous authors have highlighted the inherent difficulty of analyzing archaeological juvenile human remains given their unknown causes of death. McKern (1970) and Sundick (1978) note that when dealing with remains of unknown chronological age there is no way for population and which may have been subject to outside influences affecting skeletal maturation. The most frequently highlighted argument is the undeniable fact that juvenile skeletal samples represent the portion of the population that did not survive to adulthood. When the elderly pass away, it is unfortunate yet anticipated; when a child dies it is always premature. Because of this, juveniles represented in archaeological populations could be indicative of those adversely affected by illness hindering skeletal growth (Jantz and Owsley, 1984b; Johnston, 1962, 1968; A population may be defined as a group of interbreeding individuals sharing a similar environment, culture and often an evolutionary history. Goodman and Armelagos (1989) note that environment is the predominant force responsible for the stressors adversely affecting past human populations. In this case, environment includes pathogens, culture and nutrition. Lampl and Johnston (1996: 346) highlight the shared environmental condition of a population as a

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22 ses of the present investigation, New Mexico, environmental, and pathological influences. Goodman and Armelagos (1989) highlight that there are many pathogens, such as viruses, that leave no appreciable physical manifestation on the human skeleton. Such pathogens harm manifest a substantial, lasting response. Given the fragile immune system of very young children Lovejoy and co-workers (1990) have similarly found that in most anthropological populations juvenile deaths are the result of acute conditions that did not affect dental or osteological maturation. Diet and nutrition are known to be influential variables in skeletal development. However, while the potential for nutritional and dietary variation between these populations is probable, the ability to accurately account for that in prehistoric, archaeological populations of skeletal remains is difficult. The advent of isotopic analysis could lead to a more accurate assessment of dietary variation between these groups, however given the repatriation guidelines affecting all populations in the present investigation, destructive analysis is prohibited. Despite any negatives on the analysis of archaeological juvenile skeletal remains, it has been noted by many authors (e.g. Hunt, 1958; Jantz and Jantz, 1999) that younger individuals, undergoing rapid growth and development, are most susceptible to ecological change manifest in human variation. It is through such analyses that greater appreciation of the mystery of this variation can be best appreciated. Despite the inherent difficulties posed by an analysis of

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23 archaeological juvenile remains, the potential of a more robust understanding of the origin of variation in past and present human populations outweighs the difficulties of the sample. Skeletal growth is a complex phenomenon and is sensitive to a wide variety of influences. Endocrine titers, nutrition, genome, and disease are among factors directly bearing on the rate and pattern of growth. Final adult stature is a result of all these factors, and there exists no method by which they can easily be partitioned into their relative contribution to its achievement (Lovejoy et al., 1990: 534). Understanding the patterns of growth across archaeological populations through analysis of juvenile skeletal remains is an important component of understanding the variation seen in human populations today. While it is an ideal scientific and statistical scenario to analyze growth patterning of a population of known age, such studies are not conducive to archaeological assemblages. All scientific investigations have some degree of inherent limitation. What is y influence the results and conclusions drawn from their data. Dental Development Studies of physical growth have historically centered on long bone and dental development. The present investigation upholds this tradition. Historically, investigators of growth and development have used either dental or skeletal estimated age as the proxy for true chronological age. However, it has become clear that dental age provides a more accurate and less variable representation of chronological age when the individual is of an archaeological context, forensic nature, or circumstance where exact birth dates are unknown (Albert and Greene, 1999; Anderson et al., 1975, 1976; Dahlberg and Menegaz-Boch, 1958; Delgado et al., 1975; Garn et al., 1965; Gustafson and Koch, 1985; Haavikko, 1974; Hagg and Matsson, 1985; Hedge and Sood, 2002; Hummert and Van Gerven, 1983; Johnston, 1961; Lampl and Johnston, 1996; Liversidge et al., 1998; McGregor et al., 1968; McKenna et al., 2002; Merchant and Ubelaker, 1977; Moorrees et al., 1963a, b; Nolla, 1960; Robinow et al., 1942; Smith, 1991;

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24 Sundick, 1977, 1978; Walker, 1969). These studies have demonstrated that, as compared to skeletal development, dental calcification and eruption is less effected by local environment, nutritional modification and is highly genetically conserved. aging has many advantages as compared with other biological indices. Garn et al. (1965) examined tooth formation and eruption throughout the growth of participants in the Fels Longitudinal Growth Study. It was concluded that dental development is less affected by nutritional or hormonal variations than were other developing systems such as osseous development (Garn et al., 1965; Smith, 1991). Lewis and Garn (1960) similarly highlight that dental development is more highly correlated to chronological age than skeletal development. Methodological Concerns In the absence of definitive birth or medical records dental age assessment has been shown to be the best available method of chronological age estimation (Demirjian et al., 1973; Demirjian and Goldstein, 1976; Gustafson and Koch, 1985; Hunt and Gleiser, 1955; Moorrees et al., 1963a, b, 1965; Schour and Massler, 1941). Dental age estimates are varied however and may be hindered by the many methods created and employed as well as the method-sample application (Liversidge, 1994; Merchant and Ubelaker, 1977; Smith, 1991; Sundick, 1978). Maturity scales, providing a broad developmental scheme, best approximate chronological age in archaeological assemblages (Moorrees et al., 1963a, b; Schour and Massler, 1941; Smith, 1991; Ubelaker, 1978). Schour and Mto also be most highly and severely criticized (Merchant and Ubelaker, 1977; Smith, 1991; Sundick, 1978) given the small sample size employed, unknown samples per age group, lack of differential s

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25 been cited as overestimating chronological age as compared to the Moorrees et al. (1963a, b) standards (Merchant and Ubelaker, 1977; Smith, 1991). Moorrees et al. (1963a: 205) note that while an estimate of dental age can be assessed with precision for a given tooth in a particular population, variability still exists and averaging l. (1963a) highlight that in developing dental standards, chronological age as well as sex are integral factors for the most precise age estimation. Finally, and most importantly, it is noted t be taken into consideration, al., 1963a: 212). Merchant and Ubelaker (1977) highlight Moorrees et al. (1963a, b) as the most suitable methodological dental standard for estimating age of proto-historic Arikara from South Dakota. The large, longitudinal sample, objectively defined dental stage, and the variability presented in their sex-more appropriate (Merchant and Ubelaker, 1977; Sundick, 1978) than alternative studies. Dental Calcification Dental development has two primary stages: (1) calcification and formation of crowns and roots, and (2) eruption. Dental calcification has the distinct advantage of being able to be after death (Hunt and Gleiser, 1955; Moorrees et al., 1963a, b; Saunders, 1992). Through the numerous studies of formation versus eruption, tooth formation proves to be a more reliable correlate to chronological age (Albert and Greene, 1999; Anderson et al., 1976; Hunt and Gleiser, 1955; McKenna et al., 2002; Moorrees et al., 1963a, b; Smith, 1991; Sundick, 1977; Thompson et al., 1975).

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26 Dental formation begins prenatally. The first stages of calcification begin in the second semester of pregnancy as early as 12-16 weeks (Smith, 1991). The calcification process takes approximately 2-3 years from initial calcification to root completion and is a standard used by many investigators to highlight variability in sex, population and dental development (Christensen and Kraus, 1965; Garn and Lewis, 1956, 1957; Garn et al., 1959, 1965; Gilster et al., 1964; Gleiser and Hunt, 1955; Haavikko, 1974; Hagg and Matsson, 1985; Kronfeld, 1935; Lunt and Law, 1974; Nystrom and Ranta, 2003; Smith, 1991). Tooth formation is a process that can be observed for a confined period of time and is less affected by nutritional, hormonal or environmental extremes than other somatic systems (Garn et al., 1965; Haavikko, 1974; Liversidge et al., 1998; Sundick, 1978). readily documented in both longitudinal and cross-sectional studies. Christensen and Kraus (1965) examined 73 human fetuses to determine the time and location of the calcification of the human permanent first molar. The first stages of calcification were noted in all permanent first molars prior to birth occurring as early as 28-32 fetal weeks. It was concluded that, overall, initial calcification of the permanent first molars and deciduous molars are strikingly consistent with little to no inter-individual variation (Christensen and Kraus, 1965; Hunt and Gleiser, 1955). Nystrom and Ranta (2003) sought to test the consistency of dental formation among a group of 29 Finns as seen through panoramic tomograms taken at autopsy. In 14 tooth pairs, 5.5% displayed differences in calcification stage; among 30 tooth

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27 pairs of permanent first molars, differences in calcification were noted in only 3 or 10%. formation and calcification. These studies highlight the highly conserved nature of human dental calcification and support the use of calcification standards for chronological age estimation. Dental Eruption first seen in assessment (Saunders, 1837 cited in Demirjian, 1978). Since then, eruption has been noted as one of the many possible indicators of chronological age assessment. Dental eruption or gingival emergence (Demirjian et al., 1973), as defined by numerous authors (Bailey, 1964; Bambach et al., 1973; Billewicz et al., 1973; Brown, 1978; Demirjian, 1978; Eleveth, 1960; Gustafson and Koch, 1985; Helm, 1969; Meredith, 1946; Nanda, 1960; Saunders, 1992; Ulijaszek, 1996), is penetration of the surface of the gum by the dental enamel. Liversidge et al. (1998) and Demirjian et al. (1973) define eruption as two distinct stages: (1) alveolar eruption, when the tooth emerges from the alveolar bones, and (2) clinical, or gingival emergence, when the tooth penetrates the gingiva. Other authors, as cited above, combine these processes into a single event. Tooth eruption patterns and sequence vary among individuals and populations (Holman and Yamaguchi, 2005; Lee et al., 1965; Nanda, 1960; Steggerda and Hill, 1942; Watts, 1985; period of dental eruption as taking place between 9-18 months of age. Robinow et al. (1942) examined eruption of the deciduous dentition among 64 white children and noted significant difference in the eruption time of central and lateral incisors between the maxilla and mandible (Meredith, 1946). It was also concluded

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28 that boys were less variable in eruption time and were advanced in eruption of all deciduous developmental indices which are relatively unaffected by factors which delay the development of more by stage of root formation than chronological, somatic or skeletal age. Sequence of Calcification and Eruption Knowledge of the sequence of dental calcification and eruption is generally variable yet influential in age assessment of individuals of different ancestry and sex (Garn et al., 1956, 1959; Garn and Lewis, 1957; Gilster et al., 1964; Gleiser and Hunt, 1955; McGregor et al., 1968; Meredith, 1946; Steggerda and Hill, 1942; Sundick, 1978). Garn et al. (1956) investigated the sequence of calcification in mandibular molars and premolars among 359 American children. It was concluded that mandibular M1, P1, and M3 showed first, second and last evidence of calcification respectively; M2 and P2 varied in timing of calcification, if not occurring at the same time. Siblings tended to display more consistently identical calcification sequences than would be expected by chance, thus it was concluded that developmental calcification sequences were likely genetically determined (Garn et al., 1956). Sex has also been sited as an influential factor in the sequence of dental calcification (Garn et al., 1956; Gilster et al., 1964; Gleiser and Hunt, 1955; Steggerda and Hill, 1942). Gilster et al. (1964) concluded that development and calcification of the mandibular second deciduous molars were advanced in females over males. Gleionset of dental calcification of the mandibular first permanent molar in girls. Children of African descent appear to be slightly advanced in formation and calcification over Caucasian children (Gilster et al., 1964). Garn et al. (1956) found that in females, P2 tended to calcify before M2 and in boys, the (P2 M2) sequence, or coincidental calcification, was more apparent. Overall,

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29 Garn et al. (1956) concluded that 40.3% of the children examined display M1 P1 P2 M2 M3 sequence, 37.7% displayed M1 P1 (P2 M2) M3 sequence, and finally 21.9% displayed M1 P1 M2 P2 M3 sequence. These analyses highlight that dentition that calcifies and/or erupts earlier in the dental sequence is more deeply canalized than later calcifying/erupting teeth. Canalization of Dentition While variation in the timing and sequence of dental formation and eruption has been documented, the process has come to be considered highly genetically canalized and less influenced by environmental condition (Albert and Greene, 1999; Brown, 1978; Butler, 1967a; Dahlberg and Menegaz-Bock, 1958; Delgado et al., 1975; Demirjian, 1978; Garn et al., 1965; Gleiser and Hunt, 1955; Gustafson and Koch, 1985; Harris and McKee, 1990; Holman and Yamaguchi, 2005; Lewis and Garn, 1960; Liversidge et al., 1998; Mays, 1995; McKay and Martin, 1952; Moorrees, 1965; Nystrom and Ranta, 2003; Robinow et al., 1942; Smith, 1991; Sundick, 1977; Tanner, 1962; Thompson et al., 1975; Turner, 1963; Walker, 1969). Garn et al. (19-favored Garn, Lewis and associates, in 1959, studied overall variability in tooth formation in an analysis of 255 white, Ohio-born, middle-class children. It was concluded that variability increases steadily as age progresses and later formed teeth showed more variability than earlier forming ones (Garn et al., 1959; Gleiser and Hunt, 1955). Liversidge et al. (1998: 421) similarly note that while genetic foundations guide early development, environmental influences increase earlier developing teeth are more

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30 variation, disease, population variability, sex, etc. Population Variation Investigators of population discrepancies in dental eruption have found that some groups are more precocious, while others display consistent emergence patterns (Bambach et al., 1973; Banerjee and Mukherjee, 1967; Brown, 1978; Chagula, 1960; Dahlberg and Menegaz-Bock, 1958; Friedlaender and Bailit, 1969; Garn et al., 1965; Harris and McKee, 1990; Holman and Yamaguchi, 2005; Johnston, 1961; Malcolm and Bue, 1970; Meredith, 1946; McGregor et al., 1968; McKay and Martin, 1952; McKenna et al., 2002; Moorrees et al., 1963a, b; Nanda, 1960; Owsley and Jantz, 1983; Saunders, 1992; Steggerda and Hill, 1942; Sundick, 1977, 1978; the archaeological context, Owsley and Jantz (1983) highlight the problems of applying White standards to Native American groups, particularly archaeological Arikara of South Dakota. when compared with White standards (Hurme, 1948; Robinow et al., 1942) for dental eruption. Garn et al. (1973) concluded that population differences exceeded variation based on nutrition or socio-economic status by a factor of two or more. Bambach et al. (1973) found that, that up to 18 months of age, European and American children were dentally precocious as compared to the Tunisian children examined. Banerjee and Mukherjee (1967) found that among 588 Bengalee children, the initiation of dental eruption was considerably later when compared with Japanese, Koreans, European Whites and Americans. Bengalee children exhibited an average eruption time of 10-13 months for their first tooth compared to 6-8 months for Whites and 7-9 months seen in Japanese and Korean samples (Banerjee and Mukherjee, 1967).

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31 Friedlaender and Bailit (1969) examined a Melanesians juveniles living in New Guinea. It was concluded that both males and females examined in this analysis displayed permanent dental eruption precocious to Europeans and Asiatic populations but later than Africans. Due to the lack of modern medical care and a lower nutrition level than surrounding areas, these results further the notion that the differences in dental eruption are of genetic origin rather than nutritional or environmental (Friedlaender and Bailit, 1969). Friedlaender and Bailit (1969) do not deny environmental effects on development, only posit that they may be less operative than genetic canalization. Influence of Sex Studies have shown a significant difference between the sexes in terms of dental calcification (Anderson et al., 1975, 1976; Demirjian, 1978; Eleveth, 1960; Gustafson and Koch, 1985; Hagg and Matsson, 1985; Harris and McKee, 1990; Helm, 1969; Hunt and Gleiser, 1955; Lewis and Garn, 1960; Maj et al., 1964; Malcolm and Bue, 1970; McKenna et al., 2002; Moorrees et al., 1963a, b; Moorrees, 1965; Owsley and Jantz, 1983; Schour and Massler, 1941; Sundick, 1977; Thompson et al., 1975). This difference increases with age and successive dental stages of calcification (Anderson et al., 1976; 1976, 1977; Thompson et al., 1975) studies of children of the Burlington Growth Centre found that molars displayed the greatest sex difference as compared to skeletal maturity and canines displayed the greatest sex variation in calcification. Thompson et al. (1975) found that male calcification of the canine lagged behind females by almost 20%. These results are confirmed by Brown (1978) where the mandibular canines of girls erupted over 10 months earlier than in boys. analysis. Moorrees (1965) noted that while variation in dental formation and development is a complex issue, sexual dimorphism among dentition has been shown to be less variable in

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32 females over males, leading researches to posit that variability may be an X-chromosome linked feature. Research has also commented on the effects of sex on dental eruption (Brook and Barker, 1972; Brown, 1978; Gleiser and Hunt, 1955; Hurme, 1949; Lewis and Garn, 1960; McKay and Martin, 1952; Meredith, 1946; Moorrees et al., 1963a, b; Nanda, 1960; Steggerda and Hill, 1942). Brook and Barker (1972) similarly found that among 4873 eastern New Guinean children little difference waAustralian Aborigines concluded that for most teeth, girls displayed precocious eruption of 6 months with maxillary I1 and mandibular I2 emerging earlier in boys. These results are consistent with Gleiser and Hunt (1955) and Hurme (1949) results on sex differences in dental eruption (Lewis and Garn, 1960). However, among permanent teeth, females display a given developmental stage earlier than males (Brook and Barker, 1972; Demirjian, 1978; Steggerda and Hill, 1942; Thompson et al., 1975). Lewis and Garn (1960) examined three stages of tooth formation (calcification of the crown, crown completion, and apical closure) and two stages of dental movement (alveolar eruption and attainment of occlusal level). Their analysis found that female children were advanced over males by and average of 0.32 years and as high as 0.92 years in the permanent dentition. While numerous authors have sited sex as a significant discriminator between male and female dental eruption and calcification, other investigators found evidence to the contrary (Anderson and Popovich, 1981; Bailey, 1964; Bambach et al., 1973; Billewicz et al., 1973; Brook and Barker, 1972; Falkner, 1957; Friedlander and Bailit, 1969; Gron, 1962; Hurme, 1948, 1949; Lee et al., 1965; McGregor et al., 1968; Nanda, 1960; Nolla, 1960; Scott and Ferguson,

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33 1956). As compared to dental formation, Anderson et al. (1981) found that emergence sequences were not significantly different between male and female children of the Burlington Growth male and female dental patterning. Effects of Varied Nutrition The effect of malnutrition on dental development is an important consideration as the eruption of the deciduous and permanent dentition is often used for chronological age estimation. While malnutrition may be expected to adversely affect dental maturation, several comparative studies have not been able to produce definitive results as to the effects of moderate protein deficiencies on dental development (Anderson et al., 1975, 1976; Bailey, 1964; Delgado et al., 1975; Holman and Yamaguchi, 2005; Tisdall, 1937). There is debate as to the consistency of dental emergence as compared to calcification. Anderson et al. (1975, 1976) found that malnutrition affects skeletal maturation significantly dental eruption rates in healthy Chimbu infants as compared to malnutrition apparent in other New Guinean groups studied indicated that protein deficiencies do not greatly affect dental eruption rates. Holman and Yamaguchi (2005) found conflicting evidence of the effects of severe malnutrition on dental eruption; much of this variation was attributed to the study design employed. However, they conclude that nutritional status may be a significant factor leading to variation in dental eruption among varied human populations (Holman and Yamaguchi, 2005).

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34 Skeletal Development Growth is an easily measured and well-defined characteristic that has the potential to tell us a great deal about the past and present of our species. -Tanner, Human Growth and Development. While dental formation and eruption timing has been shown to be less variable, long bone growth variation is an important source of information concerning not only development and regional variation but nutrition, health and environmental influence (e.g. Armelagos et al., 1972; Blanco et al., 1974; Deutsch et al., 1981; Hewitt et al., 1955; Hummert and Van Gerven, 1983; Humphrey, 1998; Jantz and Owsley 1984b; Owsley and Jantz, 1985). Johnston and Snow (1961) highlight that the entire skeleton, both dentition and postcranial long bones, are a better judge of age and maturation at death than any single element. Recent studies have attempted to promote an understanding of the relationship between dental and postcranial age assessment and broaden our appreciation for the study of juvenile development and what it may provide our knowledge of geographically and temporally distinct populations (Jantz and Owsley, 1984a). However, further examination of multiple comparative populations is necessary to expand our understanding of how growth in the past may influence our understanding of the present. Studies of long bone growth and maturation cited one of its earliest experiments in 1747 by Stephen Hales (Bisgard and Bisgard, 1935). Hales drilled two holes in the tibial shaft of a growing chicken and found that when the animal was examined two months later, while the long bone had grown 1 inch in length, the distance between the two holes remained the same. Hale concluded that increase in long bone length occurred at the proximal and distal extremities and growth was not equally distributed between these two ends (Bisgard and Bisgard, 1935). As rudimentary as this growth experiment may seem, some of its central conclusions hold true today. Longitudinal growth of long bones takes place at the ends through deposition of new

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35 bone between the diaphysis and epiphyseal cartilage. Similarly, bone growth does not occur proportionally over time (Bisgard and Bisgard, 1935). Through further analysis of long bone growth and development an even deeper understanding of its intricacies can be exposed. Investigations of growth on skeletal remains provide advantages not seen in growth studies on living children. Jantz and Owsley (1984a) have noted that direct measurement on juvenile skeletal remains provides easier and more direct methods of data on proportional change. Skeletal samples similarly extended the temporal, geographic and ecological range of conditions in which human populations have been found (Jantz and Owsley, 1984a). Methodological Concerns Methodological concerns in estimating chronological age are of utmost importance in dealing with juvenile, archaeological skeletal remains (Johnston, 1969; Lampl and Johnston, 1996; Lovejoy et al., 1990; Merchant and Ubelaker, 1977). Numerous authors (e.g. Johnston, 1969; Lampl and Johnston, 1996; Lovejoy et al., 1990) have looked to alternate theories of estimating juvenile chronological age through the postcranial remains. Lampl and Johnston (1996) highlight two primary sources of error in aging skeletal remains: human variability, not knowing the consistency of maturation rates in a given society and the use of reference standards to age remains when such standards may not be population appropriate. Growth Velocity Growth has been defined by Needham (1933) as an increase in spatial dimensions, not necessarily taking place in all dimensions equally. Growth velocity is term that this author takes to connote span of time within which significant periods of size increase and/or maturation occur. Timing and intensity of growth and development are of particular importance when studying variation and particularly when comparing distinct human groups (Armelagos et al., 1972; Hummert and Van Gerven, 1983; Lovejoy et al., 1990; Roberts, 1960; Schultz, 1960;

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36 Armelagos et al. (1972) highlight this problem in the analysis of growth velocities among archaeological populations; sufficient sample size of individuals of all developmental stages may onsideration in order to understand how populations come to vary as adults (Cameron and Demerath, 2002). Armelagos et al. (1972) concluded that growth velocity in their prehistoric Sudanese Nubian sample was more irregular, yet similar to that of the growth velocity seen in the longitudinal study of American boys. Lovejoy et al. (1990; Maresh, 1955) compared the growth velocity of juveniles of the Libben population with healthy Euroamerican children from luded that growth patterns were quite comparable between these two populations with a significant lag observed in the Libben Eskimo and Aleutian juvenile skeletal remains, it was to the femur, with increasing age. Smith and Buschang (2004) analyze growth velocity among children 3 to 10 years of age noting a consistent decelerating pattern of growth over the juvenile period. Growth velocities also were found to display a proximal-distal gradient with proximal elements (e.g. humeri, femora) displaying a greater growth velocity than distal elements (e.g. radii, fibulae). Similarly, the lower limb was shown to have a greater growth velocity than the upper limb (Smith and Buschang, 2004).

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37 Population Variation Numerous studies of human post-cranial remains were carried out in the pursuit of population specific standards or in the appreciation of human variation (Armelagos et al., 1972; Blanco et al., 1974; Jantz and Owsley, 1984b; Johnston, 1962; Lovejoy et al., 1990; Nyati et al., 2006; Roberts, 1976; Saunders, 1992; Steyn and Henneberg, 1996; Todd, 1931; Toselli et al., development of rural Guatemalan children lagged behind that of both British and American discovered that these children had greater diaphyseal long bone lengths than those of Libben, Altenerding, Eskimo and Native American sample groups. The description of these patterns of growth velocity, symmetry and overall variation impart important morphological characteristics used to better understand prehistoric populations (Armelagos et al., 1972; Garn, 1957; Lovejoy et al., 1990). Studies of variation among the growth trajectories of closely related groups have also been pursued in nonhuman models. Falsetti and Cole (1992) investigated relative growth in three species of callitrichines. The models Falsetti and Cole (1992) explore in this inter-specific analysis are comparable to human population variation in adult shape and size: Model I, divergent growth trajectories: no shape differences apparent at birth, adult differences accumulated through divergent growth patterns through life Model II, parallel allometry: interspecific/interpopulation differences are present at birth and are maintained through similar allometric growth Model III, parallel isometry: common, isometric growth patterns produce adult shape differences Model IV, ontogenetic scaling: adult size and shape discrepancies are produced through different and changing rates or duration of growth along similar allometric trajectories (Falsetti and Cole, 1992).

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38 While human variation is encompassed within a single species and is not directly comparable to trends of inter-specific analysis, the models discussed above open discussion as to what factors may influence the variation seen among adults. Proportionality in Growth Growth symmetry and proportionality are an integral component of the analysis of human variation across the globe and the span of human evolution (Armelagos et al., 1972; Buschang, 1982; Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 2001; Humphrey, 1998; Jantz and Owsley, 1984a; Jungers et al., 1988; Moss et al., 1955; Roberts, 1978; Tanner et al., 1982; functional morphology, and the relationship of climatic factors (Holliday and Ruff, 2001). Population axial indices have also been found to have a large genetic component being less affected by nutritional insult (Holliday and Ruff, 2001; Meadows and Jantz, 1995). adult form. These proportional differences over time also reflect population and ancestral variations (Hiernaux, 1968; Jantz and Owsley, 1984a). Different regions of the body have their own rate/velocity of growth that is constantly changing with age and maturity (Schultz, 1960). established early in childhood; this is also reflected in Aleutian and Eskimo growth velocity. These early patterns presuppose adult Eskimo and Aleutian adult body proportions, which conform to ecogeographic rules of proportionality. Such sensitive variations detected in growth can provide insight to the patterns of intermembral (and intramembral) and physical variation noted across populations today. upper-middle class white children showed that through age 11, diaphyseal long bone lengths

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39 sustain positive allometric growth with stature. It is also noted that allometric growth within each extremity is consistently similar and intralimb variation is small, the humerus maintaining greater allometric growth in the upper limb and tibia displaying greater relative growth over the femur (Buschang, 1982). Jantz and Owsley (1984a) note that within both the upper and lower limbs, proximal elements increase more rapidly than distal elements. Influence of Sex Many authors have noted the discrepancy between skeletal growth of males and females over their lifetime (Acheson, 1954; Buschang, 1982; Gasser et al., 2000, 2001a, b; Gindhart, 1973; Hewitt et al., 1955; Humphrey, 1998; Johnston, 1961; Liliequist and Lundberg, 1971; Lovejoy et al., 1990; Maresh, 1955; Nyati et al., 2006; Roche, 1978; Sheehy et al., 2000; Smith and Buschang, 2004; Todd, 1931; Watts, 1985). Humphrey (1998) refers to differential Roche (1978) and Gasser et al. (2000) find that typically, girls achieve adult stages of skeletal development at earlier chronological ages than their male counterparts. Watts (1985) notes that this trend of female advanced development over males is evident across not only humans, but similarly among chimp and macaque development. Humphrey (1998) highlights that sexual from the differential rate and duration of growth during different periods of development between males and females, particularly around the time of puberty (Gasser et al., 2000, 2001a, b; Lovejoy et al., 1990; Maresh, 1955). males are consistently favored over females. Gasser and co-workers (2000) find that males tend to exhibit greater height than females based upon a variety of variables active during juvenile development (i.e. early increase in testosterone in prepubescent boys, beginning as early as

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40 infancy) that do not manifest in greater physical size until puberty. Gindhart (1973) observed that male and female white North American children have comparably sized tibiae until age 13 with the males being slightly, yet not significantly, ahead of females. While males and females tend to remain comparable in size through the juvenile period, boys tend to have a later onset of puberty by approximately 1.5 years accounting for the substantial gain in physical size, relative to females (Gasser et al., 2000). Nutritional Considerations Nutritional considerations are always discussed when considering the somatic and skeletal growth of juveniles (Acheson, 1960; Blanco et al., 1974; Hewitt et al., 1955; Holliday and Ruff, 2001; Humphrey, 1998; Jantz and Owsley, 1984a, b; Lovejoy et al., 1990; Martorell et al., 1979; Owsley and Jantz, 1985; Roberts, 1978; Ruff, 1993; Sellen, 1999; Toselli et al., 2005). Childhood growth has often been seen as a marker of health and nutrition in society (Lampl and Johnston, 1996). Humphrey (1998) highlights that significant nutritional resources are required to support the energetic constraints imposed particularly during adolescent growth. Historically, the most noted nutritional effect manifest in the juvenile skeleton are lines of arrested growth, called Harris lines. Harris lines are dense calcium deposits, visible in radiography, laid down in the metaphysis when grow recommences after a period of growth cessation. Harris lines have been attributed to illness, acute infection, inoculation or any somatic insult such as malnutrition (Acheson, 1960; Blanco et al., 1974; Clarke, 1982; Gindhart, 1969; Survey attributed the incidence of Harris lines to acute, abrupt illness such as measles. These researchers similarly note that apparent severe, long term illness left no trace on the bone radiographically and conversely some films displayed prominent Harris lines when no illness was recorded (Hewitt et al., 1955).

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41 While evidence of Harris lines have been associated with illness and malnutrition, these periods of arrested growth do not appear to affect overall skeletal development. Mays (1995) found no significant relationship between long bone growth and the appearance of Harris lines. individuals display evidence of Harris lines at some point in time, these arrested periods of development do not appear to affect final adult stature. Similarly, discussions on the affects of nutritional insults during childhood are often -Eckhardt et al., 2005; Martorell et al., 1979; Sellen, 1999). This term refers to a confined period of exceptionally rapid growth following an identified episode of constraint. Cameron and Catch-up growth has even been found to account for resolving constrained growth throughout the developmental period in developed and developing countries. Martorell et al. (1979) concluded that among four rural Guatemalan villages chronic malnutrition affected overall body size to a significantly greater extent than skeletal maturation. Eckhardt et al. (2005) note that extending growth beyond the adolescent state may be an adaptive response to growth insults affecting early maturation. It was concluded that those individuals with the greatest -related developmental constraints throughout childhood (Eckhardt et al., 2005). Nutritional factors, though significant ito affect patterns of variation between geographically distributed populations (Ruff 1993:55; Schreider, 1964). Ruff (1993) finds that although secular trends and improved nutrition may

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42 account for increases in height, evolutionary trends in limb proportionality are not affected. nutritional variations tend to be short-lived relative to eco-geographic variation (Ruff 1993:56). Dental Development vs. Skeletal Maturation There is much debate in the literature as to the relationship between dental and skeletal maturation and their comparability (Anderson et al., 1975; Billewicz et al., 1973; Demirjian and Goldstein, 1976; Demirjian, 1978; Demisch and Wartmann, 1956; Falkner, 1957; Malcolm and Bue, 1970; McGregor et al., 1968; McKay and Martin, 1952; Todd, 1931). Anderson et al. (1975) found that dental calcification was significantly related to skeletal maturation. However, other researchers have indicated that the skeletal system, as well as other maturation factors such as puberty, remains fundamentally independent of dental development (Demirjian, 1978; Falkner, 1957; McGregor et al., 1968). Demirjian (1978) cites the distinct developmental origins of skeletal versus dental development. Bone is derived from mesoderm and teeth are, in part, epithelial (Falkner, 1957). significant association between dental and skeletal maturity at any given age or developmental stage. Demisch and Wartmann (1956) found the mean difference between skeletal age and chronological age among 151 American white children from Boston was 0 months. While, as discussed above, nutritional, environmental, population and sex difference may affect the dentition as well as the postcrania, dental maturation appears to be more resistant to such extrinsic perturbations. Malcolm and Bue (1970) noted a trend toward later eruption times in individuals with slower overall physiological growth. Such findings link dental and postcranial growth based upon the extrinsic factors influential in their acceleration or retardation. McGregor et al. (1968)

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43 concluded that among young children in the rural villages of Gambia, children taller or heavier for their age displayed precocious dental eruption. While they noted no evidence to the contrary (slow growth related to a prolonged period of eruption), it was highlighted that children smaller for their age may be assessed as chronological younger than they truly are as compared to the (1952) found that among Zulu and Bantu, who displayed precocious dental development of 1 year as compared to Americans, exhibited carpal ossification which was delayed 18 months to 2 years as compared to these same Americans. McKay and Martin (1952) attribute this discrepancy to the genetically predetermination of tooth bud formation and eruption, while later physical development is more subject to harsh environmental influences. Evolutionary-Genetic Relatedness of Populations Relethford (2004) described global patterns of human genetic and morphological diversity by the isolation-by-distance model citing geographic distance as a significant predictor of genetic variation. This model posits that the genetic distinction between populations will increase as the physical, geographic distance between groups increase. Many studies have been conducted, employing a wide range of genetic, morphological, linguistic and dental evidence, to determine the origin of and relationship between Native American groups (Crawford, 1998; Greenberg et al., 1986; Kolman et al., 1995, 1996; Malhi et al., 2003; Mulligan et al., 2004; Relethford, 2004; Schillaci and Stojanowski, 2003; Torroni et al., 1992, 1994; Zlojutro et al., 2006). One of the longest lived and widespread explanations for the distribution of Native Americans throughout thThe three wave model is represented by the three main linguistic families of New World populations: Na-Dene, Aleut-Eskimo and Amerind. Amerind, representing those individuals from North and South American being the oldest, based upon its rate and range of geographic

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44 Na-Dene, individuals residing on the Pacific Northwest, central Alaska and northwest Canada, represent a second migration with deep internal linguistic divisions among speakers, dates to approximately 9,000 B.P. Greenberg et al. (1986: 470) note that Aleut-Eskimo speakers are most Na-Dene and correlate to an entrance into the New World between 2,900 and 5,600 B.P. Support for the three wave hypothesis has also been drawn from preliminary analyses of mitochondrial DNA (mtDNA) (Torroni et al., 1992, 1994). However, further analysis finds little support for the separation of these three main New World groups based on mtDNA (Kolman et al., 1995). Kolman and colleagues (1995) compared mitochondrial DNA sequences between three Amerind populations representing the geographic extremes of Amerind settlements and one Na-Dene group from the Pacific Northwest. It was concluded that the average genetic distance among the mtDNA haplotypes between the major groups is equivalent between Amerind and Na-Dene populations compared. The reduced genetic diversity displayed between these groups -Dene and Amerind groups (Kolman et al., 1995: 281). Kolman et al. (1995) conclude that the diversity presented here could have been accumulated after entry into the New World. model, recent analyses point toward a single migration, approximately 17,000 years ago, with Mongoloid origin (Mulligan et al., 2004). With the advent of comparative analyses on indigenous genetic data, investigations became directed toward Native American origins based on the distribution of New World haplotypes in Asia. Native American mtDNA has been attributed to one of four main haplotypes labeled A, B, C and D. These haplotypes populations

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45 are nonrandomly distributed throughout Native North Americans (Kolman et al., 1996; Malhi et al., 2003; Mulligan et al., 2004). Kolman et al. (1996) note general clines of the four haplogroup distributions throughout the Americas; a north-to-south decrease in A haplotypes and an increase in haplogroups B, C and D. Haplogroup A is predominantly found in Eskimo-Aleut populations; B and C dominate in the Southwest (Mulligan et al., 2004; Zlojutro et al., 2006). It was found that the four Native American haplotypes are not frequent or widespread among Asian populations, particularly in Siberia, the once thought origin of New World populations (Kolman et al., 1996). Kolman et al. (1996) note that this disparity of haplotypes dispersion in Asia genetic markers would be reproduced in the New World on three, distinct occasions. Comparable to Native American diversity, Mongolian populations occupy a wide geographic range yet appear to have low levels of genetic variation (Kolman et al., 1996; Malhi et al., 2003). Lending further credence to their ancestral connection, Kolman et al. (1996) attribute this homogeneity to life history and cultural factors of Mongolian life. Malhi et al. (2003) similarly noted the high frequency of haplogroup B and overall genetic homogeneity of Ancestral Puebloan and Zuni populations of the Southwest which they similarly attributed to cultural factors of the region and shared evolutionary history. The lower level of overall genetic diversity among Native American groups promotes a single migration of Native peoples from Asia into the New World with subsequent bottleneck effects and gene flow (appreciated in Native American populations today is most probably a product of their shared

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46 genetic and evolutionary history influenced by eco-geographic destinations, cultural development and life history factors. Given the three populations of Native North American populations compared in the present analysis, an appreciation of their potential evolutionary relatedness and/or genetic distinction is important in evaluating the conclusions drawn from their comparison. Numerous authors (e.g. Eveleth and Tanner, 1990; Holliday and Ruff, 2001) have made similar comparisons to those proposed here on human populations that span the farthest extremes of the world. This project explores population variation on three human populations that display a more highly conserved evolutionary history. Ecological Considerations It is clear that genes per se wepossibility realization of its potential is ecologically driven. -Carroll, Endless Forms Most Beautiful. Numerous authors have identified eco-geographic influences on the morphology of human populations (Abouheif, 2004; Blackburn et al., 1999; Blanco et al., 1974; Carroll, 2005; Corruccini, 1974; Franciscus and Long, 1991; Freitas et al., 2004; Hall, 2004; Holliday, 1997a, b; Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 2001; Hunt, 1958; Jantz and Owsley, 1984a, b; Jantz and Jantz, 1999; Johnston, 1962; Larsen, 2004; Lovejoy et al., 1990; Mayr, 1956; McHenry, 1994; Moorrees et al., 1963a, b; Moorrees, 1965; Nanda, 1960; Newman, 1956; Newman and Munro, 1955; Owsley et al., 1982; Owsley and Jantz, 1985; Pearson, 2004; Ravosa et al., 1995; Reid, 2004; Roberts, 1960, 1976, 1978; Ruff, 1993; Ruff et al., 2006; Tanner, 1960, 1992; Todd, 1931; Trinkaus, 1978, 1981; Van Valen, 1962; Warren, 1997; Weaver and Ingram, 1969; West-Eberhard, 2004). Blanco et al. (1974), Freitas et al. (2004) and others note that population and individual human variation in physical development results from the interaction

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47 genetically predetermined potential for the velocity at which he will grow and mature and this in highlighting existing genetic variability or bringing about genetic change in a population (Hall, 2004). An understanding of the evolutionary and current environmental pressures affecting population variation is critical to evaluating adult morphological variation (Ruff et al., 2006). Holliday and Falsetti, 1995, 1999; Newman and Munro, 1955; Roberts, 1978: 34; Ruff, 1993; Schreider, 1964; Trinkaus, 1981; Warren, 1997; Weaver and Ingram, 1969). Extremity lengths 7) asserts that lower latitude populations display longer extremities while those of higher latitudes have shorter limbs. This stems from gene flow from warmer geographic regions during the transition to modern Homo sapienshypothesis is posited by Bergmann (1847): body mass increases as a population inhabits cooler or more polar regions resulting in shorter extremities and an overall decrease in body surface area. These rules of morphological patterning to geographic variation are attributed to methods of heat dissipation. In warmer climates, linear individuals with longer extremities have a greater surface area/volume ratio than those in cooler climates with shorter limbs (Blackburn et al., 1999; Holliday and Falsetti, 1995; Holliday, 1997a, b; Schreider, 1964; Weaver and Ingram, 1969). In an analysis of modern human populations, Warren (1997) finds that these rules of human proportionality manifest early in fetal development.

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48 Significant evidence for eco-geographic patterning of skeletal morphology and proportionality has been found among evolutionary study of hominids (Holliday 1997a, b; Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 1997, 2001; McHenry, 1994; Ruff, 1993; Trinkaus, 1981). Many authors cite the strong genmodern populations which stems from evolutionary climatic adaptations (Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 2001; Vrba, 1996). Holliday (1997b) and Trinkaus (1981) note that Neandertal postcrania-to skeletal trunk height and size. These characteristics are attributed to the extremely cold tempEvidence of regional variation has also been identified among mammals (e.g. Riesenfeld, 1973; Weaver and Ingram, 1969). Riesenfeld (1973), in experimental studies on the effects of extreme heat and cold on rats, found that when subject to extreme cold growth was retarded in the distal segments (radius, ulnae, tibiae) of the limbs while the proximal limb (humeri/femora) were less significantly affected. When subject to heat exposure, all limb elements were found have elongated relative to trunk length (Riesenfeld, 1973). Similar results were found by Weaver and Ingram (1969) in an experimental study of pigs. Littermates were separated and raised in two extremities than those littermates raised in warmed conditions (Weaver and Ingram, 1969:710).

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49 rules has not been identified, many theoretical mechanisms have been proposed (Holliday 1997a, b; Holliday and Falsetti, 1995; Trinkaus, 1981; Weaver and Ingram, 1969). One of possible conservation of heat in colder regions by minimizing surface area/volume ratio. Heat loss in any animal is directly proportional to body surface area (Holliday, 1997b). An increase in body size Similarly, incrsubsequently increases surface area/volume ratio making it easier for organisms in warmer climate to dissipate heat (Holliday, 1997a, b; Holliday and Falsetti, 1995). An alternate mechanism for changing in body form resulting from eco-geographic condition involves blood flow (Trinkaus, 1981; Weaver and Ingram, 1969). Animals subject to cold stress are habitually vasoconstricted which could reduce oxygenation and nutrient supply to the extremities (Trinkaus, 1981; Weaver and Ingram, 1969). Following this hypothesis Trinkaus (1981) proposed that under cold stress distal segments would be more affected by this vasoconstriction than proximal components accounting for the pattern of shorter distal limb segments evidenced in cold stressed, homeothermic animals. The theory and practice of eco-geographic patterning among human populations has its critics and exceptions (Geist, 1987; Mayr, 1956; McNab, 1971; Schreider, 1964; Vrba, 1996). variables that may not be properly associated with geographic variation. McNab (1971:852) argues that body size is determined by the food available and associated competition for

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50 conclusions are based upon correlations of head-body lengths to latitude without regard to relative extremity length to body size. Similarly, Geist (correlating body mass, calculated from skull data, to latitude among deer and wolves. While neither an empirical nor a theoretibody size with increasing latitude was not as large a magnitude as predicted. Unfortunately, craniometric data might not be the best proxy for body size and extremity length. These numerous studies have set the stage for this analysis to confirm whether climatic patterns of variation are preserved among these three eco-geographically distinct populations.

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51 CHAPTER 3 MATERIALS Native Alaskan The 242 individuals representing the Native Alaskan sample are all currently housed at the National Museum of Natural History, Smithsonian Institution. Dental and postcranial data collected on all individuals is found in Appendix A. All materials were analyzed between December 2004 and May 2005. Spatial distribution The Native Alaskan skeletal remains were recovered from a series of archaeological sites distributed throughout the Aleutian Islands and the north and west coast of mainland Alaska. Table 3-6. Spatial distribution of Native Alaskan archaeological sites. Quad/Region Longitude Latitude N Locale n Longitude Latitude Afognak ----3 Hog Isl. 3 53 54'20.38" 166 33'42.88" Atka 52 12'40.36" 174 39'40.68" 1 Atka Isl. 1 52 12'40.36" 174 39'40.68" Attu 52 26'05.55" 173 33'48.26" 10 Agattu Isl. 10 52 26'05.55" 173 33'48.26" Baird Inlet 61 20'25.01" 149 34'33.18" 11 Kuskogamut 11 ----Barrow 71 17'24.61" 156 47'19.61" 4 Pt. Barrow 4 71 17'24.61" 156 47'19.61" Bethel 60 47'23.05" 161 45'31.10" 15 Akiachak 1 60 54'20.77" 161 25'26.57" Akiak 1 60 54'35.02" 161 13'24.46" Kwishluk 1 ----Napaskiak 11 60 42'18.07" 161 45'58.85" Old Bethel 1 ----Dillingham 59 02'24.22" 158 27'57.75" 13 First Wood Lk. 2 ----Kaskanak 5 59 16'59.92" 156 10'59.99 Kokwak 3 Nushagak R. 3 59 31'28.08" 157 47'22.76" Dixon Entrance ----1 Dall Isl. 1 54 57'58.33" 132 57'19.35" Fairbanks 64 50'42.33" 147 43'17.81" 1 Fairbanks 1 64 50'42.33" 147 43'17.81" Goodnews 59 07'09.85" 161 34'53.59" 6 Mumtrak 5 ----Togiak 1 59 03'35.91" 160 22'38.85" Holy Cross 62 12'06.00" 159 46'14.32" 3 4 Anvik 3 62 39'11.06" 160 11'56.29" Bonasila 8 Ghost Creek 4 62 12'30.68" 159 46'45.51" Holochacat 3 ----Holy Cross 6 ----Shageluk 9 62 39'16.94" 159 31'28.08"

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52 Table 3-6. Continued Quad/Region Longitude Latitude N Locale n Longitude Latitude Unknown 1 ----Hooper Bay 61 31'54.97" 166 05'53.16" 14 Napareyaramiut 3 ----Unknown 11 ----Kwiguk 62 45'23.20" 164 30'52.39" 28 Kwiguk Pass 12 ----New Fort Hamilton 10 62 43'36.40" 164 51'18.92" Old Andreivsky 2 ----Old Hamilton 4 ----Marshall 61 52'45.94" 162 05'16.51" 20 Pilot Station 13 61 56'10.31" 162 52'53.50" Ingrehak 7 ----Naknek 58 43'43.63" 157 00' 56.53" 10 Coffee Pt. 1 ----Egegik 4 58 13'01.33" 157 21'18.54" Pawik 5 ----Nome 64 29'59.73" 165 24'20.59" 5 Cape Nome 1 64 27'43.40" 164 57'46.08" Sledge Isl. 4 64 29'08.73" 166 12'51.33" Nushagak Bay 58 56'58.76" 158 29'10.16" 3 Kulukak Bay 3 ----Petersburg 56 48'22.00" 132 58'15.35" 2 Ft. Wrangell 1 56 29'03.45" 132 22'11.38" Prince of Wales Isl. 1 55 31'39.88" 132 48'20.22" Rat Isl. 51 48'03.69" 178 17'36.43" 6 Amchitka Isl. 6 51 22'44.01" 179 15'29.88" Russian Mission 61 47'07.86" 161 19'27.77" 18 Bogus Creek 2 ----Okahamute 5 ----Paimute 11 ----Samalga Isl 52 47'08.29" 169 12'04.52" 10 Kagamil Isl. 10 52 59'27.12" 169 42'41. 31" Shishmaref 66 15'24.30" 166 03'59.69" 1 Sarichef Isl. 1 66 14'19.98" 166 05'37.02" Sleetmute 61 42'04.44" 157 10'09.08" 3 Old Napaimiut 3 ----Teller 65 15'51.45" 166 21'47.95" 10 Mitliktavik 4 ----Wales 6 65 36'40.5 8" 168 05'51.25" Umnak 53 22'00.22" 167 54'00.00" 2 Umnak Isl. 2 53 22'00.22" 167 54'00.00" Unalaska 53 37'33.97" 167 01'57.97" 5 Amaknak Isl. 1 58 53'49.24" 166 31'49.40" Shiprock Isl. 4 ----Unknown 6 Yukon River 6 62 34'55.55" 164 28'18.18"

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53 Figure 3-1. Geographic distribution of Native Alaskan sites Temporal Distribution Table 3-7. Temporal distribution of Native Alaskan sites. Quad N Excavator Occupation Reference Afognak 3 Hrdlicka 1600 1800 Dumond, 2002; Hrdlicka, 1945 ; Hunt 2002 Atka 1 Attu 10 Baird Inlet 11 Bethel 15 Dillingham 13 Dixon Entrance 1 Fairbanks 1 Goodnews 6 Holy Cross 34 Hooper Bay 14 Kwiguk 28 Marshall 20 Naknek 10 Dumond, 1981

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54 Table 3-7. Continued Quad N Excavator Occupation Reference Nome 5 Hrdlicka 1600 1800 Dumond, 2002; Hrdlicka, 1945 ; Hunt 2002 Nushagak 3 Petersburg 2 Rat Islands 6 Russian Mission 18 Samalga Island 10 Shishmaref 1 Sleetmute 3 Teller 10 Umnak 2 Unalaska 5 Barrow 4 Collins 1600 1750 Dumond, 2000 Unknown 6 ------s period through personal communication with Dr. David Hunt (May 2007). Excavation and Principal Investigators Nearly all Native Alaskan human remains housed at the Smithsonian Institution were acquired and collected through expeditions by Ales Hrdlicka between 1926 and 1938 and one 1945; Hunt, 2002, 2007). Life History Conditions Some of the first explorers into the New World through the Bering Strait were Russian traders and hunters looking to expand and establish trade relations throughout Alaska in the early to mid-18th century (VanStone, 1984). Though the Alaska mainland and Aleutian Islands had a a majority of these populations occupied, European contact played a significant role in the life history of the Native Alaskan populations in this study. European contact with native peoples in Alaska is evidenced by a relatively more amenable introduction than other regions of the New World. Russians sought out native

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55 community leaders who were given silver medallions in recognition of their status and to encourage open trade. Communities were paid for furs, fish and work on whaling ships in items including flour, tobacco, molasses, lead, firearms and ammunition (VanStone, 1984). Subsistence strategies in southwest Alaska and the Aleutians centered on fishing and game hunting (Dumond, 2000; VanStone, 1984). Arctic and sub-arctic populations were particularly adept in their hunting/fishing strategies as opposed to many other hunting and gathering societies at this time who derived a considerable portion of their energy sources from vegetable resources unavailable in Alaska (Freeman, 1984). Coastal and island communities subsisted primarily on fishing (predominantly salmon and cod) and hunting sea mammals, seal and walrus being the most important maritime game (Freeman, 1984; VanStone 1984). Among coastal sites the remains of cetaceans, rodents, sea invertebrates and carnivores were also found (Savinetsky, 2002). Mainland-coastal groups often ventured further inland hunting caribou and reindeer to supplement their diet (Dumond, 2000; VanStone, 1984). At Wales, Collins documented a significant quantity of bird remains found among refuse including Passerines, shore birds and gulls (Dumond, 2000; Savinetsky, 2002). Repatriation Status Native Alaskan skeletal remains are currently housed at the U.S National Museum of Natural History. Portions of the skeletal remains examined in the present investigation housed at repatriation and are unavailable for further research; other sites have already been repatriated (e.g. Kagamil Island) (personal communication with Dr. Bill Billeck, April 2007, Repatriation Department, National Museum of Natural History, Smithsonian Institution).

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56 South Dakota Arikara The 379 individuals representing the Arikara sample are all currently housed at the National Museum of Natural History, Smithsonian Institution. Dental and postcranial data collected on all individuals is found in Appendix A. All materials were analyzed between May 2005 and August 2005 supported by a Graduate Fellowship from the National Museum of Natural History, Smithsonian Institution. Spatial Distribution The South Dakota Arikara skeletal remains were recovered from a series of archaeological sites distributed throughout northern/central South Dakota. Table 3-12. Spatial distribution of South Dakota Arikara archaeological sites County N Longitude Latitude Site n Buffalo 3 43 51'01.95" 99 30'17.34" 39BF2A Medicine Crow 3 Campbell 11 45 35'31.56" 99 49'14.29" 39CA4 Rygh 11 Charles Mix 3 43 26'30.03" 98 42'47.23" 39CH45 Hitchel l 1 39CH7 Oldham 2 Corson 55 45 29'29.66" 101 35'24.86" 39CO31 Nordvold I 4 39CO32/33 Nordvold Cemetery 32 39CO9 Leavenworth 19 Lyman 2 43 35'44.57" 99 51'47.94" 39LM218 Black Partizan 2 Sully 104 44 43'14.32 100 06'23.14" 39SL29 C.B. Smith 1 39SL38 1 39SL4 Sully 102 Stanley 64 44 12'56.73" 100 30'35.84" 39ST1 Cheyenne River 43 39ST16 Breeden 2 39ST203 Black Widow Ridge 2 39ST215 Leavitt 11 39ST216 Buffalo Pasture 3 39ST224 1 39ST23 1 39ST3 BLACK WIDOW 1 Walworth 134 45 19'53.31" 99 57'15.50" 39WW1 Mobridge 133 39WW7 Swan Creek 1 Unknown 3

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57 Figure 3-2. Geographic distribution of South Dakota Arikara sites Temporal Distribution The temporal range for the occupation of these archaeological sites was determined based on tree ring dating as well as ceramic and archaeological artifacts (Jantz and Owsley, 1984b; Owsley and Jantz, 1985). Table 3-13. Temporal distribution of South Dakota Arikara sites. Site Period Time Reference 39BF2A Medicine Crow Plains woodland 700 900 Ahler and Toom, 1995; Key, 1983; Lehmer, 1971 39CH7 Oldham 39CA4 Rygh Extended coalescent 1550 1675 Jantz and Owsley, 1984a; Key, 1 983; Lehmer, 1971; Owsley et al., 1982 39CO32/33 Nordvold Cemetery

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58 Table 3-13. Continued Site Period Time Reference 39SL29 C.B. Smith Extended Post contact coalescent 1550 1780 Jantz and Owsley, 1984a; Key, 1983; Lehmer, 1971 39SL38 39S L4 Sully 39CO31 Nordvold I PC coalescent 1675 1792 Jantz and Owsley, 1984a; Key, 1983; Lehmer, 1971; Owsley et al., 1982 39ST1 Cheyenne R. 39ST16 Breeden 39ST203 Black Widow Ridge 39ST215 Leavitt 39ST216 Buffalo Pasture 39ST224 39ST23 39WW1 Mobridge 39WW7 Swan Creek Hurt, 1957; Jantz and Owsley, 1984a; Key, 1983; Lehmer, 1971; Owsley et al., 1982 39CO9 Leavenworth Disorganized coalescent 1780 1862 Jantz and Owsley, 1984a; Key, 1983; Lehme r, 1971; Owsley et al., 1982 39CH45 Hitchell Unknown Unknown Unknown 39LM218 Black Partizan 39ST3 Black Widow Excavation and Principal Investigators The skeletal remains in the present analysis were excavated by multiple individuals and institutions beginning in the 1920s and continuing until the early 1970s (Bass et al., 1971; Lehmer, 1971; Owsley and Jantz, 1994; Wedel, 1961). Skeletal remains were first recovered and curated in South Dakota in the late 1920s by two notable archaeologists who were interested in uncovering the states prehistory: M.W. Stirling of the U.S. National Museum and W.H. Over of the University of South Dakota and the Over Museum later named in his honor (Ahler and Toom, 1995; Bass et al., 1971; Krause, 1971; Owsley and Jantz, 1994; Zimmerman, 1985).

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59 Another series of skeletal remains were recovered through the Missouri Basin Project undertaken by the Smithsonian Institution, Bureau of American Ethnology and the National Park Service in conjunction with local universities and historic societies (Bass et al., 1971; Lehmer, 1971; Wedel, 1961). This endeavor sought the construction of four large reservoirs in North and South Dakota along the Missouri River. Four large cemeteries were excavated under the direction of William Bass of the University of Kansas (later University of Tennessee, Knoxville) including a majority of the present sample: Leavenworth (39CO9), Mobridge (39WW1) (the final season in 1971 under the direction of D.H. Ubelaker of the U.S. National Museum) and Sully (39SL4) (Bass et al., 1971; Owsley et al., 1982; Owsley and Jantz, 1994). Finally, W.R. Hurt partially excavated the Swan Creek (39WW7) cemetery which was added to the original Over Collection (Hurt, 1957; Owsley and Jantz, 1994). Much of the Over Museum Collection and Bass excavations of South Dakota Arikara skeletal remains were loaned to the University of Tennessee, Knoxville and the U.S. National Museum for osteological analysis. A large portion of the remaining Over Museum human skeletal collection was repatriated and re-interred in 1986 (Owsley and Jantz, 1994). Life History Conditions Those few individuals dated to the Late Plains Woodland period are considered pre-Arikara and characterized by a transition from hunting-gathering and small scale horticultural practices to becoming more sedentary with an increasingly intensified, full-time agricultural foundation and introduction of unique pottery styles to the region (Key, 1983; Lehmer, 1971; Wedel, 1961; Zimmerman, 1985). Paleobiotic, archaeological and zooarchaeological analyses of the Medicine Crow site, a major representative for the Plains Woodland period, revealed cultivated corn, beans, squash, sunflowers and tobacco. Native fauna predominantly exploited by the inhabitants of this period included bison, deer, and waterfowl as well as fish native to the

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60 Missouri River including catfish and bass (Ahler and Toom, 1995; Bass et al., 1971; Lehmer, 1971; Wedel, 1961). The Coalescent Tradition is a broad category in which the South Dakota Arikara become the group as recognized today. This period is divided into four categories of which the latter three comprise the present sample: Extended Coalescent, Post-contact Coalescent and Disorganized Coalescent (Key, 1983). Each of these phases is marked by distinctive cultural features and transitions affecting the South Dakota Arikara. The Extended Coalescent period is marked by an explosion of fortified settlements and communities (Krause, 1971; Zimmerman, 1985). The subsequent Post-contact Coalescent period is distinguished from the Extended by the introduction of horses into the Plains from Spanish settlements in the southwest U.S. and the advent of the fur trade by European settlers moving through the central Plains (Key, 1983; Wedel, 1961). The fur trade had a significant impact on the Native American regions they encountered and this was similarly the case with the South Dakota Arikara. European contact introduced cultural artifacts, furs, trade and technology and similarly wrought disease and oppression on native groups (Key, 1983; Wedel, 1961; Zimmerman, 1985). Finally, the Disorganized Coalescent period is the reconstitution of the South Dakota and rebuilt themselves into banded settlements (Key, 1983: 23). Repatriation Status South Dakota Arikara skeletal remains are currently housed at the U.S National Museum of Natural History and the University of Tennessee, Knoxville. The skeletal material examined Americans groups of South Dakota for repatriation and are unavailable for further research in its

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61 entirety. The Three Affiliated Tribes have not made a decision at this time and the remains are presently held by the Smithsonian (personal communication with Dr. Bill Billeck, April 2007, Repatriation Department, National Museum of Natural History, Smithsonian Institution). Ancestral Puebloan The 250 individuals representing the Ancestral Puebloan sample are from two collections: 197 of the individuals are currently housed at the National Museum of Natural History, Smithsonian Institution, analyzed between December 2003 and May 2005; 53 individuals are housed at the Maxwell Museum of Anthropology, University of New Mexico, analyzed during September 2006 supported, in part, by the John M. Goggin Award, University of Florida. Spatial Distribution The Ancestral Puebloan skeletal remains were recovered from a series of archaeological sites distributed throughout northern New Mexico. Table 3-18. Spatial Distribution of Ancestral Puebloan archaeological sites County N Longitude Latitude Region Affiliation n Sandoval 80 35 47'40.57" 106 48'02.10" Jemez Amoxiumqua 24 Unshagi 11 Unknown 27 Giusewa 18 Cibola 101 35 13'14.18" 107 57'33.66" Zuni Hawikku 101 McKinley 12 35 48'17.89" 108 11'47.82" Heshatauthla 12 San Juan 25 36 20'03.20" 108 14'19.97" Chaco Canyon Pueblo Bonito 10 Pueblo del Ar royo 4 Unknown 11 Rio Arriba 21 36 06'53.86" 106 46'24.66" Pajarito Plateau Puye 21 Santa Fe 1 35 30'05.14" 106 06'44.36" Tsankawi 1 Torrance 10 34 44'49.95" 105 52'47.20" Salinas Pueblo Quarai 10

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62 Figure 3-3. Geographic distribution of Ancestral Puebloan sites Temporal Distribution Table 3-19. Temporal Distribution of Ancestral Puebloan sites. Region Affiliation Period Occupation Dating Method Excavator Reference Jemez Amoxiumqua Pueblo V 1540 1670 archaeological remains Hodge Noble 1981; Parsons, 1925 ; Reiter, 1938 Unshagi Giusewa Hewett Reiter, 1938

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63 Table 3-19. Continued Region Affiliation Period Occupation Dating Method Excavator Reference Zuni Hawikku Pueblo V 1540 1670 archaeological remains, pottery Hodge Corruccini, 1972; Smith et al., 1966 Heshatauthla Chaco Canyon Pueblo Bonito Pueblo III 919 1130 tree ring Judd Corruccini, 1972 Pueblo del Arroyo Unknown Salinas Pueblo Quarai Pueblo IV V 1315 1677 archaeolog ical remains, pottery Hewett, Hurt Hewett, 1913; Hurt, 1990 Pajarito Plateau Puye Pueblo IV 1507 1565 tree ring Hewett Barnes, 1994; Corruccini, 1972 Tsankawi Excavation and Principal Investigators The Pajarito Plateau region (comprising the Puye and Tsankawi populations) was excavated and so named by Edgar L. Hewett as part of a Bureau of American Ethnology-sponsored expedition in 1909 (Barnes, 1994; Corruccini, 1972). Hewett excavated the Puye, Tsankawi and Otowi communities and sent the human skeletal remains to the Smithsonian National Museum of Natural History where they continue to be curated today (Barnes, 1994; Corruccini, 1972). The first archaeological projects at Quarai were similarly made by E.L. Hewett in 1913 (Hurt, 1990). Expeditions to this site continued by Hewett and his team until 1936. In 1939 W.R. Hurt, sponsored by the Museum of New Mexico and the Works Project Administration, began excavation of the region that continued until 1940. Excavations made by Hewett were brought to the National Museum of Natural History, those under the direction of Hurt are housed at the Maxwell Museum, University of New Mexico (Hurt, 1990).

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64 F.W. Hodge excavated the Zuni and Jemez regions between 1910 and 1923 with the gross majority of the human skeletal remains being sent to the Smithsonian. The Jemez site of Giusewa was also excavated in 1935 by students of the University of New Mexico Field School under the direction of E.L. Hewett (Reiter, 1938). Part of the Chaco Canyon complex (Pueblo Bonito) was excavated and human remains collected by Neil Judd in the 1920s are also housed at the National Museum (Corruccini, 1972; Schroeder, 1979; Smith et al., 1966). Life History Conditions The Pueblo IV Rio Grande Classic cultural stage saw enlarging communities. The Ancestral Puebloan peoples began to colonizing areas with abundant water supplies along the Rio Grande and away from the intruding Spaniards. The Zuni, during the Pueblo V period, were subject to strong influence and devastation by the Spanish expeditions (Cordell, 1984; Hewett, 1937; Hooton, 1930). Europeans were lured to the area by the promise of reported riches and gold in the region. Legions of armed horsemen came into the Zuni region and settled. Many Puebloan and Zuni populations revolted in 1680 killing hundreds of Spanish settlers and managed to stave off their return for over a decade (Cordell, 1984; Hewett, 1937). The Pueblos of northern New Mexico were rich in volcanic soil which provided for an abundance of crops when water supplies were sufficient (Barnes, 1994; Hewett, 1913). The region is home to abundant native plants including corn, yucca fruit and seeds, prickly pear, pinon nuts, wild plumes, grapes and chokecherries (Barnes, 1994; Cordell, 1984; Hewett, 1937). Puebloan populations during this period were communally involved with agriculture employing a highly developed irrigation system (Hewett 1937). Wild game hunted in the area included mule deer, cottontail rabbits, black bears, squirrels, mountain lions, raccoons and beavers (Barnes, 1994).

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65 Repatriation Status The Ancestral Puebloan skeletal remains examined in the present analysis are currently housed at the U.S National Museum of Natural History and at the Maxwell Museum, University of New Mexico. The Jemez and Salinas Pueblo skeletal material housed at the National Museum unavailable for further research. The appropriate modern tribal groups have not made a decision at this time and the remains are being held by the Smithsonian (personal communication with Dr. Bill Billeck, April 2007, Repatriation Department, National Museum of Natural History, Smithsonian Institution). Eco-Geographic Data For each population, seven eco-geographic variables were obtained from the National Climatic and Data Center, National Oceanic and Atmospheric Administration, U.S. Department of Commerce (2004). This source compiles monthly and annual averages of climatic data from between 1931-2001. Earliest date of recorded data varies by location. Six variables were chosen for each population that best reflect the general climatological conditions of that region. Ideally, climatic information would match the temporal period of each population, however that data was not available at this time. The period of recorded data for each population is found in Table 3-20. Table 3-20. Period of recorded climatic data State Years Alaska 1948 2001 South Dakota 1949 2001 New Mexico 1931 2001 Climatic data is represented by three variables relating to temperature and three related to precipitation. Annual mean temperature is the average temperature recorded over the period of

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66 record for each station. Highest and lowest monthly means reflect the seasonal highs and lows. Annual mean precipitation reflects the average precipitation recorded over the period of record by recording station. Monthly high and low values reflect the seasonal extremes of precipitation (NCDC, 2004). Table 3-21. Climatological conditions Temperature (F) Precipitation (inches) State Latitude Longitude Annual mean Highest month mean Lowest month mean Annual mean Monthly high Monthly low Alaska 60 47N 161 50W 29.9 60.0 13.2 16.18 5.49 0.00 South Dakota 45 01N 99 58W 44.0 77.7 1.0 1 8.94 7.00 0.00 New Mexico 35 03N 106 36W 56.8 82.7 29.8 9.47 3.29 0.00 The single recording stations/climatic values for each population reflect a weighted average of the intra-population longitude and latitude weighted by the number of individuals from that location.

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67 CHAPTER 4 ASSUMPTIONS, METHODS AND HYPOTHESES Theoretical and Methodological Assumptions Previous research on human skeletal remains documents variation between eco-geographically distinct groups. This project looks to more closely examine the origin and subsequent distribution of variation in growth and determine whether this early variability can be attributed to eco-geographic origin. This project presupposes the following theoretical and methodological assumptions (modeled after Schillaci and Stojanowski, 2003): Given the skeletal remains available for each individual, the most appropriate methods for both age and sex estimations were employed. The literature has provided systematic, experimentally tested methods for accurate age and sex determination. The sex of each individual in the present analysis was determined without error. This assumption is supported by the comparison of the present data sample to those analyses Museum of Natural History. Any discrepancies were re-evaluated in conjunction with Dr. David Hunt and Ms. Erica Jones. The age of each individual in the present analysis was estimated to be as accurate as possible given the remains available. This assumption is supported by the comparison of the present data sample to those analyses conducted by the Repatriation Department at the -evaluated in conjunction with Dr. David Hunt and Ms. Erica Jones. For the purposes of this analysis, growth for all individuals is considered to begin at birth. diaphysis. The individual remains recovered within the three burial areas were members of the populations that these individuals represent: individuals recovered from the specified Alaskan sites were Native Alaskans; individuals recovered from the specified sites in New Mexico were Ancestral Puebloan; individuals recovered from the specified sites in South Dakota were members of Arikara groups. It is difficult to say with certainty that individuals buried at a particular site were members of that cultural group. However, it is improbable that the deceased were brought in from unrelated groups for burial. These groups represent three distinct samples which are continuous within each group. Human skeletal remains are often used to account for processes that have gone on in the

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68 past. The remains available for each eco-geographic group represent the population from each specific region and time period. Members of each population and their recent ancestors are assumed to have lived in that region for the entirety of their lives; sharing similar life history and cultural factors. The eco-geographic conditions for each area have not drastically changed over the course While the diet and life history conditions between these groups are assumed to be varied, it will not be considered an influential factor in the present analysis. The cause of death for these individuals did not significantly affect their growth. -descendant relationships, heterochronic perspectives will be used in the present analysis as an interpretative framework to discuss the analytical results. No ancestor-descendant relationship is strictly inferred by these associations and conclusions. Given these theoretical and methodological assumptions, these three groups are appropriate for comparative analysis. Methods This project has two analytical objectives: (1) do these three populations have significantly different postcranial long bones lengths as adults, if so does this variation correlate to eco-geographic conditions (2) how and where does this variation manifest in the juvenile skeleton. Once these two areas are explored the question of why they differ can be addressed. These analyses are conducted through examination of a mixed, cross-sectional sample of three archaeological populations of Native North Americans. The methods presented here represent the most appropriate statistical tools appreciated from the literature. All statistical analyses in this investigation were conducted through the statistical program SAS Institute Inc., Version 9.1 (2004) except for the reduced major axis regression which was graphed and completed, in part, through PAST Version 1.68 (Hammer et al., 2001).

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69 Data Collection Dental Data Collection and Dental Age Determination Dental age determinations are provided for each individual as a secondary variable for data analysis. Dental age estimation has been shown to be the most reliable surrogate for chronological age in the absence of definitive birth or medical records (e.g. Demirjian et al., 1973; Demirjian and Goldstein, 1976; Gustafson and Koch, 1985; Hunt and Gleiser, 1955; Moorrees et al., 1963a, b, 1965; Schour and Massler, 1941; Smith, 1991). Lewis and Garn (1960) highlight the strong correlation of dental development to chronological age compared to skeletal development. For evaluation of juvenile dentition, dental maturity scales and calcification stages following Moorrees et al. (1963a, b), Ubelaker (1978) and Buikstra and Ubelaker (1994) were chosen due to the mixed, cross-sectional nature of these data as well as its population specificity. For adults, dental wear standards were used for age estimation, as well as a combination of postcranial aging criteria including features of the pubic symphysis, auricular surface change, cranial suture closure and osteoarthritis (Brooks and Suchey, 1990; Buikstra and Ubelaker, 1994; Meindl and Lovejoy, 1985; Ubelaker, 1978). Postcranial Data Collection Linear measurements from the postcranial skeleton include maximum diaphyseal long most point to the distal most point for each element. Both right and left sides will be measured when available however the left elements will be used for all analyses; the right element was used if the left side was absent or fragmented. Measurements will be recorded to the nearest millimeter using an osteometric board. An individual icranial long bone epiphyses.

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70 Data Description and Considerations Logarithmic Transformation Numerous authors (e.g. Blackith and Reyment, 1971; Cochard, 1985; Corruccini, 1983, 1987; Falsetti and Cole, 1992; Holliday, 1997b; Holliday and Ruff, 2001; Jolicoeur, 1963; Jungers and German, 1981; Jungers, 1985; Jungers et al., 1988, 1995; McKinney and McNamara, 1991; Reyment et al., 1984; Shea, 1985a, b; Sokal and Rohlf, 1995) have required logarithmic transformation prior to applying statistically meaningful tests on growth data. Log-transformation corrects for outliers, skewedness and unequal variation that may exist among the data. The log-transformed maximum length measure of each element is regressed against the geometric mean for that individual. Geometric mean [(ln(humeri)+ln(radii)+ln(ulnae)+ln(femora)+ln(tibiae)+ln(fibulae))/6)] will serve as a proxy for overall body size (Gould, 1971; Holliday, 1997b; Jungers et al., 1988, 1995). Descriptive Statistics and Age Categories Descriptive statistics are a preliminary analytical method of presenting a summary of the data. These methods reduce an immense wealth of information to a few summary measures pulations. For each population, the post-cranial measurements will be separated by element and subjected to the following manipulations by age category (description of age categories is found in Table 4-1): number of observations, mean, standard deviation, coefficient of variation, range, minimum and maximum value. The mean represents the arithmetic average of a particular element in an age category. The standard deviation highlights how far the data points in a sample range as compared to the mean. The coefficient of variation is the standard deviation divided by the mean. This value represents the variation in a given pool of values. It is hypothesized that the

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71 coefficient of variation will be relatively consistent across age groups, increasing as age categories approach maturity as sexual dimorphism will have a greater effect on the sample mean. Table 4-1. Age categories. Estimated dental age Category Birth 1 yr 1 Birth 6 months 1.1 6 months 1 yr 1.2 1 yr 2 yr 2 2 yr 3 yr 3 3 yr 5 yr 4 5 yr 7 yr 5 7 yr 9 yr 6 9 yr 11 yr 7 12 yr 15yr 8 15 yr 17 yr 9 17 yr 25 yr 10 20 yr 40 yr 11 40 yr + 12 Age categories are employed for the descriptive statistics of each group to highlight the distribution of data across discrete segments of development. These categories are arbitrary and are not intended to connote analytical separation of the data. Categories were determined based on the data; as sample sizes varied, not all populations were subject to the same categorizations at this time. The remainder of the methods utilized in this investigation will view the juvenile data as a continuous sample. The descriptive statistics for all populations are found in Appendix C. Analysis of Adult Skeletal Remains DemonstratioP This investigation is based on the premise that populations of Native Alaskans, South Dakota Arikara, and Ancestral Puebloan groups of New Mexico vary significantly in long bone lengths. To demonstrate whether postcranial long bones vary significantly among the adults

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72 -test (also called the T-conservative test of significance comparing group means designed to reduce the possibility of committing a Type I error; rejecting a true null hypothesis (Sokal and Rohlf, 1995). For this analysis the following hypotheses will be tested: Ho: The null hypothesis will be accepted if no significant differences (p>0.05) are appreciated among the group means of any element between the three populations compared. Ha: The null hypothesis will be rejected if significant differences (p<0.05) are appreciated between any element between the three groups compared. Given the eco-geographic (1847) it is predicted that these populations will vary significantly. o Ha1edicted that the population inhabiting the lowest latitude (New Mexico Puebloan) will display the greatest extremity mean length than those occupying higher latitudes (Native Alaskan). o Ha2ations occupying the coldest (temperature-wise) climate (Native Alaskan) will display the smallest mean extremity length. PROC GLM procedure in SAS (SAS Institute, Inc., 2004). Principal Components Analysis Principal components analysis (PCA) is a tool designed to reduce the dimensionality of the data without any a priori assumptions. When analyzing the degree of dissimilarity in a set of variables, PCA is used to focus on those variables which account for the largest portion of the -transformed variance-covariance -related shape changes in growing elements (Corruccini, 1987; Falsetti, 1989; Jolicoeur, 1963; Jungers et al., 1988, 1995; Reyment et al., 1984; Sprent, 1972). Relative growth can be interpreted from the first principal component

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73 and when all loadings are of the same sign (Jungers et al., 1988: 103). The second principal component which often accounts for a large portion of the variation describes size-related shape variation. PCA is implemented here to provide the clearest picture and description of the variation within and between groups. Many authors have cited not only physical size as an integral factor in population variation, but also size-related shape variation; i.e. populations and/or species do not only vary in overall body size, but in the shape of their elements (Blackith and Reyment, 1971; Corruccini, 1987; Darroch and Mosimann, 1985; Falsetti, 1989; Jungers and German, 1981; Jungers et al. 1988, 1995; Reyment et al., 1984; Sprent, 1972; Waxenbaum et al., 2007). Size or relative growth can 289). Shape, also referred to as size-altern-space defined by many Falsetti, 1989: 57). Shape variables can be analyzed individually and are calculated by subtracting the geometric mean from each log-transformed variable, e.g. shape(humeri) = ln(humeri)-ln(geometric mean). Principal components analysis will be conduced on both size and shape data of the adult and juvenile samples to determine whether shape and/or size represents a significant source of variation. PCA does not result in a statistical test of significance. However null and alternative hypotheses concerning the variation within the data can be explored. For this analysis the following hypotheses will be tested: Ho: The null hypothesis will be accepted if the variation across all six variables is equal for both the analyses of size and shape.

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74 Ha: The null hypothesis will be rejected if the variation among the postcranial variables differs for the analyses of size and shape. o Ha1: Patterns of variation will exist within the upper and/or lower limb. o Ha2: Patterns of variation will exist between the upper and lower limb. The calculations of the principal components were performed by the PROC PRINCOMP procedure in SAS (SAS Institute, Inc., 2004). Canonical Discriminant Analysis Canonical discriminant analysis is a multidimensional, data-reduction technique, similar to principal components analysis. As compared to PCA, in canonical discriminant analysis a priori assumptions concerning the data are considered (SAS Institute Inc., 2004). Canonical discriminant analysis is primarily used to highlight the interrelationships between multiple groups (populations) in morphometric studies (Blackith and Reyment, 1971; Falsetti, 1989). The canonical loadings measure the linear correlation between the original variables and the canonical variates and reflect the relative contribution of each variation to the separation across each axis (Reyment et al., 1984). For this analysis the following hypotheses will be tested: Ho: The null hypothesis will be accepted if there is no significant (p>0.05) variation between these three populations. Ha: The null hypothesis will be rejected if significant variation (p<0.05) is appreciated within and between these three populations. o Ha1: All three populations will vary significantly from one another. o Ha2: When the total canonical structure is appreciated, patterns of variation will exist within the upper and/or lower limb. o Ha3: When the total canonical structure is appreciated, patterns of variation will exist between the upper and lower limb.

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75 employed to test whether significant variation exists between the three populations of adults sampled. Significant patterns of variation would allow further investigation to determine (1) if similar variation is found among the juvenile sample and (2) if this variation is correlated to eco-geographic variation. The calculations of the canonical discriminate variates were performed by the PROC CANDISC procedure in SAS (SAS Institute, Inc., 2004). Examination of Skeletal Indices Patterns of population variation have also been explored through an investigation of skeletal indices. Skeletal indices serve as an additional dimension for the analysis of population, regional and eco-geographic variation (Falsetti and Cole 1992; Holliday 1997a, b; Holliday and Ruff, 2001; Jantz and Owsley, 1984a; Jantz and Jantz, 1999; Warren, 1997). Three skeletal indices will be explored in the present analysis: brachial (radius/humerus), crural (tibiae/femora), and intermembral (humeri + radius)/(femur + tibia). Comparisons of index variation will be visually assessed through the use of box-plot analysis and tests of significant differences in these For this analysis the following hypotheses will be tested: Ho: The null hypothesis will be accepted if there is no significant (p>0.05) variation exhibited between each population index compared. Ha: The null hypothesis will be rejected if significant variation (p<0.05) is appreciated among any of the three population indices compared. o Ha1: All three populations will exhibit significant variation among the brachial indices compared. o Ha2: All three populations will exhibit significant variation among the crural indices compared.

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76 o Ha3: All three populations will exhibit significant variation among the intermembral indices compared. PROC GLM procedure in SAS (SAS Institute, Inc., 2004). Canonical Correlation Analysis Comparisons between geographic/climatic and growth/population variation can be best investigated through canonical correlations analysis (Blackith and Reyment, 1971; Falsetti, 1989; Jantz et al., 1992; Reyment et al., 1984). This mode of statistical inquiry is often seen in the course of morphological and morphometrics studies when one set of variables, as a whole, is compared to another complete set of variables with no a priori assumptions concerning the nature of the data (Blackith and Reyment, 1971; Reyment et al., 1984). Such is the case in the present analysis comparing population long bone lengths to eco-geographic variables. 4: 80). This method determines the maximum correlation between linear functions of two distinct sets of variables. The components of the canonical vectors are determined through the coefficients defining these linear combinations. Successive linear combinations are explored so that the resultant variables display the strongest correlation between the two sets of data (Blackith and Reyment, 1971; Reyment et al., 1984). A significant or high correlation between linear postcranial dimensions and eco-geographic variables would indicate that variation in population postcranial dimensions is related to certain aspects of eco-geography. For this analysis the following hypotheses will be tested: Ho: The null hypothesis will be accepted if the multivariate tests of the canonical correlation model are not significant (p>0.05).

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77 Ha: The null hypothesis will be rejected if the multivariate tests of the canonical correlation model are significant (p<0.05). The following alternative hypotheses will be explored: o Ha1: Postcranial elements will be highly correlated with latitude as compared to the other ecoo Ha2: Postcranial elements will be highly correlated with temperature variations as compared to the other eco-geographic con o Ha3: Postcranial elements will be highly correlated with precipitation variation as compared to the other eco-geographic conditions considered (serving as outgroup). The calculations of the canonical correlation variates were performed by the PROC CANCORR procedure in SAS (SAS Institute, Inc., 2004). Analysis of Juvenile Skeletal Remains Principal Components Analysis As mentioned previously, a principal components analysis will be conducted on adult as well as juvenile sample to appreciate the degree of variation in the data accounted for by both he allometric equation is to utilize the first principal component of the covariance matrix of log-transformed data. In looking at growth data, physical size is assumed to be the primary variable of change, however, shape variation will also be explored to determine whether this feature accounts for a significant degree of variation within the sample. As mentioned previously, PCA does not result in a statistical test of significance. However null and alternative hypotheses concerning the variation within the data can be explored. The null and alternative hypotheses for PCA on the juvenile data set are the same as those for the adult analysis.

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78 Reduced Major Axis Regression: Multiple comparison of growth trajectories Scientists examine a phenomenon, study its variation, and successively reduce its unexplained variation as more and more of its causes are understood -Sokal and Rohlf, Biometry: The principles and practice of statistics in biological research Regression is a statistical tool using an independent variable to predict the value of a dependent variable. This analysis is used to determine the nature of a statistical relationship (linear or curved), the variability of a predicted value, whether variability is constant over a range of predictions, how informative an independent variable is in predicting the dependent variable and helps to predict the range of sampling error (Ott and Longnecker, 2001). However, numerous regression methods exist and are not all equally appropriate to analyze all data sets. regression is based on the following assumptions: an independent variable X can be measured without error, expected value Y is a linear function, for a given value of Xi of X, Y values are independently and normally distributed and finally samples along the regression line have a common variance independent of the magnitude of X or Y. Model II regression scenarios are found when both variables display or have potential for random variation. In Model II regressions both variables are subject to error and the regression line varies dependent upon the goal of the investigator, functional relationship or prediction. In this family of regression methods, both variables are normally distributed and the association between the two is determined by the magnitude of the parameter. In biology, these regression models are used when both variables deviate naturally, such as in the case of individual genetic differences or environmentally induced variation (Sokal and Rohlf, 1995). Model II regression is also employed in situations where the sample does not follow a bivariate normal distribution. Sokal and Rohlf (1995) explain this through an example of an

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79 investigation of relating chest width in men to their height. Classes of height may be arranged for the total height distribution and sample frequencies of men from each height class to gain an appreciation of this relation over the range of the data. Many biological examples fall into this class of regression models (Sokal and Rohlf, 1995). Generally, if both variables have the same unit of measure, determining the slope of the major axis, or principal axis, overcomes of the issue of the X and Y error. If the two variables have different units of measure, as they often do in growth studies, major axis regression is not suitable. This unit dependence can be overcome through a standardization of the variables through logarithmic transformation of the data. The new principal axis of the standardized variables is known as the reduced major axis and is generally seen in allometric analyses. Because the measures of the various postcranial elements differ in their dimensions throughout development, simple linear regression is no longer an appropriate statistical tool. Reduced major axis regression is specifically used when the dimensions or sizes being compared vary (McKinney and McNamara, 1991; Sokal and Rohlf, 1995; Vrba, 1998). This function, as stated, reduces the dimensionality or size component as a compounding element and standardizes the variables by minimizing their deviation from the regression line. Since different populations are being compared throughout development where physical size is constantly in flux, reduced major axis regression is the most appropriate statistical method for this analysis (Sokal and Rohlf, 1995). The calculations and images used to perform a multiple comparison of slopes and intercepts through reduced major axis regression were performed in PAST, Version 1.68 experimentwise Type I error rate induced through multiple comparisons/permutations of a given

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80 set of data; reducing the possibility of incorrectly rejecting a true null hypothesis (Ott and Longnecker, 2001). This correction factor is calculated by dividing the number of multiple comparisons by the alpha value selected; e.g. the number of multiple comparisons in this analysis is 18, hence the corrected alpha value = 0.05/18 = 0.002777 = 0.003. For this analysis the following hypotheses will be tested: Ho1: The null hypothesis will be accepted if the multiple comparison of slopes for comparison of each element between populations shows no significant variation (p>0.003). Ho2: The null hypothesis will be accepted if the multiple comparison of intercepts for comparison of each element between populations shows no significant variation (p>0.003). Ha1: The null hypothesis will be rejected if the multiple comparison of slopes for any comparison of elements is found to be significant (p<0.003). Ha1: The null hypothesis will be rejected if the multiple comparison of intercepts for any comparison of elements is found to be significant (p<0.003). Test of Isometry and Allometry Allometry is a study of size and shape (e.g. Cheverud, 1982; Godfrey and Sutherland, 1995; McKinney and McNamara, 1991; Shea, 1983a, 1985a; Vinicius and Lahr, 2003; Wu et al., 2003). Mathematics and biology were not always as well acquainted as they are in the physical Huxley (1932), have been praised for their roles in focusing our attention on a new perspective and new questions in biological thought. Thomps it is obvious that the form of an organism is determined by its rate of growth in various directions; hence rate of growth deserves to be studied as a necessary preliminary to the theoretical study of form, and organic form itself is found, mathematically speaking, to be a function of time. Every growing organism, and every part of such a growing organism, has its own specific rate of growth, referred to this or that particular direction; and it is by the ratio between these rates in different directions that we must account for the external forms of all (Thompson, 1943: 82).

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81 factor that can be quantified, given the appropriate variables and mathematical tools. Allometry, for the present analysis, is defined as the relative ratio between body parts within a particular individual or between populations. Ontogenetic or growth allometry has become an integral component i176; Sprent, 1972). Ontogenetic allometry, often termed relative growth, quantifies changing or differential growth within or between populations. The slightest variation in allometric growth during development can have significant morphological affects on final adult form (Gould, 1977; McNamara, 1986). Such an analysis of relative growth is employed to determine the extent of variation in the pAmerican populations. For this analysis the following hypotheses will be tested: Ho: The null hypothesis suggests that these three groups grow at a constant rate of development indicated by isometry (slope not significantly different from 1.0). Ha: Rejection of the null hypothesis indicates that any of these three populations do differ significantly from isometry (slopes significantly different from 1.0). Five alternative models, modeled after those of Falsetti and Cole (1992) are presented: o Ha1: The three populations have a universal origin at birth and display ontogenetic scaling. o Ha2: The three populations have a universal size origin at birth and each group displays a unique growth trajectory. o Ha3: One or more groups display size differences at birth which are maintained through parallel isometry. o Ha4: One or more groups display size differences at birth and display parallel allometry. o Ha5: One or more groups display size differences at birth and one or more groups display a unique growth trajectory.

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82 These five alternative models take two distinct forms: two having a universal postnatal origin and three having one or more groups beginning postnatal growth at a unique size. Model a1 predicts ontogenetic scaling. All three groups have a universal size origin at birth and similarly allometric growth trajectories, yet differences between the groups are a result of heterochrony and/or differences in duration of growth events. Model a2 hypothesizes that the three populations will display divergent growth trajectories. No appreciable size difference at birth, however final adult form is distinctly different through deviating patterns of growth over the course of development. Models a3-a5 suggests that interpopulation differences will be apparent at birth. Model a3, parallel isometry, shows each population beginning postnatal development at a unique size and maintaining common isometric growth trajectories to maturity. Model a4 posits parallel allometry where the differences seen at birth are preserved through similar allometric growth trajectories. Finally, Model a5 predicts that adult variation is a result of not only unique postnatal development, but unique growth trajectories from one or more of the compared groups. An example of how these methods are used in comparative limb growth in physical anthropology is demonstrated in the analysis conducted by Falsetti and Cole (1992). In a study of relative growth in the postcrania of callitrichines, Falsetti and Cole (1992) investigate whether significant differences exist in proportions of the postcranial skeleton of the three species (Saguinus oedipus, Saguinus fuscicollis and Callithrix jacchus), the ontogenetic histories of these differences, and whether these proportional differences reflect variation in functional behavior. These patterns of postcranial growth, or ontogenetic trajectories, were analyzed through the multivariate generalization of the bivariate allometric equation.

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83 Similar discern growth patterns as a means to analyze and reconstruct heterochronic models of evolutionary/genetic selective forces. The genetic and environmentally induced variation in the data as well as the need to reduce the dimensionality for comparative purposes through reduced major axis regression, logarithmic transformation and principal components analysis make the riate allometric equation the most appropriate method to analyze the data of the present analysis (e.g. Blackith and Reyment, 1971; Falsetti and Cole, 1992; Gould, 1977; Ravosa et al., 1995; Reyment et al., 1984). lize the allometry equation is to use the first infamous quote, Jolicoeur (1963) describes the test of significance for the determination of allometry or deviation from isometry. Jolicoeur (1963) remarks that others have assessed allometric relationships using a single variate (an individual element) through a multiple linear regression as seen in the reduced major axis regression conducted presently. It is noted that this procedure sets a single variable apart from all others and subsequently does not express the allometric relationships that exist between all possible pairs as a single biological unit (Jolicoeur, ation from isometry evaluates all compared biological variates equally and holistically instead of isolating a single variable for all others to be considered against. While nature readily demonstrates that humans grow and vary through time, it is expected that the null hypothesis will be easily rejected in favor of one of the alternate hypotheses previously discussed. However, discovering which alternate hypothesis or model the growth

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84 trajectories these populations will follow is truly the goal of this analysis and will help direct what conclusions may be drawn concerning the differential development these three populations. The statistical procedure to test for deviation from isometry was performed by the PROC IML procedure in SAS (SAS Institute, Inc., 2004). A comparable test for deviation from isometry, similarly following Jolicoeur (1963), was conducting in Microsoft Office Excel (2007) under the direction of Dr. Adam Sylvester, University of Tennessee, Knoxville (personal communication). The final test determining the degree of deviation from isometry was conducted following Jolicoeur (1963) in Microsoft Office Excel (2007) under the direction of Dr. Adam Sylvester (personal communication). Interpretive Framework Heterochrony and allometry are two similar and related terms in that they are often used to explain mechanisms of ontogeny, phylogeny, evolution and comparative or functional anatomy. Allometry relates to physical properties of size and shape which is tested for experimentally, whereas heterochrony is a developmental and theoretical mechanism used to explain phylogenetic, evolutionary or temporal changes in physical form. Allometry speaks to physical, mathematical principles of shape and size, whereas heterochrony is used to explain how form changes throughout evolution. In a sense, allometry is a mathematical representation of heterochrony. Despite their convoluted history and the debates that still reign today, allometry and heterochrony serve as integral and complementary tools in the study of development, biology and evolution. Heterochrony is a change in relative timing of developmental events that has been used to explain population and evolution variation (e.g. Alberch et al., 1979; Bogin, 1997; de Beer, 1958; Godfrey and Sutherland, 1995, 1996; Gould, 1977; Haeckel, 1905; Hall and Miyake, 1995; Matsuda, 1987; McKinney, 1999; McKinney and McNamara, 1991; McNamara, 1986, 1995,

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85 1997; Raff and Wray, 1989; Ravosa et al., 1995; Shea, 1981, 1983b, 1989; Smith, 2001; Stone, 2004; Vinicius and Lahr, has been intensely debated and constantly modified since its inception yet is repeatedly employed to interpret biological variation. Heterochrony describes modifications of the relative timing of growth events as well as how the rate of growth is modified over time. The most simplified model of heterochronic variation is presented by McKinney and McNamara (1991; modified below). Heterochrony is 977; McKinney and McNamara, 1991). Table 4-2. Heterochrony: paedomorphosis vs. peramorphosis. Paedomorphosis Neoteny Reduced rate of growth Post displacement Delayed onset of growth Progenesis Early offset of growth Peramorphosis Acceleration Increase d rate of growth Pre displacement Early onset of growth Hypermorphosis Delayed offset of growth then patterns of pre-/post-will be considered the point where all epiphyses of each element are completely fused to the

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86 diaphysis. However, no evidence has been found to indicate that the age of physiological/hormonal maturity varies between these populations. Through the implementation of allometric analysis, variation in rate of growth (neoteny/acceleration) will be comparable between these populations through an analysis of their growth trajectories or the slope value analyzed in the reduced major axis regression. Population variation in biological anthropology has often focused on variation in adult phenotypes. While historically focusing on ancestor-descendant relationships, heterochrony provides the present study a structured, theoretical framework to discuss how phenotypes evolve: through ontogeny. If variation is found to manifest during the developmental period, mechanisms of variation, from advances in developmental biology, may provide further insight into the true

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87 CHAPTER 5 RESULTS AND DISCUSSION Results of Adult Skeletal Analysis P The premise of my project is that if variation exists within and between the adults of these populations then an analysis of how juveniles differ can be explored. To this end, a post hoc comparison of the means for each of the six postcranial elements analyzed (humeri, ulnae, radii, femora, tibiae, -test (also called the T-method) is a highly conservative test of significance designed to reduce the possibility of committing a Type I error; rejecting a true null hypothesis (Sokal and Rohlf, 1995). This analysis will allow for a statistical assessment of hypotheses relating to population variation among archaeological human groups. In this case, the null hypothesis states that no significant differences (p<0.05) will be found among the comparison of mean long bone lengths between these three groups. If the null hypothesis is rejected, alternative hypotheses of how and dictate that these populations should vary to a degree based upon their eco-geographic (New Mexico Puebloan) will display longer extremities as compared to those occupying higher latitude (Native Alaskan). Once it is determined whether these groups truly differ significantly, the correlation of these differences to their geographic condition will be investigated. These data were initially separated by sex to account for the sexual dimorphism inherent in

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88 populations produced comparable results to those presented here which pool the sexes within each of the three groups. Given the objective of determining whether these populations, as a whole, vary significantly pooling of males and females within each group is appropriate. Results of this analysis highlight significant differences between populations for most elements compared. South Dakota Arikara were found to have population humeral means significantly different (p<0.0001) than from all other groups. However, some analyses were found to be nonsignificant at the alpha = 0.05 level. Among humeri of all adults sampled New Mexico (N.M.) Puebloan individuals and Native Alaskans sampled could not be statistically distinguished. Table 5-1. Significance of differences among adult humeri (n=408) Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan 0.5903 <0.0001 All comparisons were found to be significantly different (p<0.05) among the ulnae and radii of all adults sampled. Table 5-2. Significance of differences among adult ulnae (n=330) Native Alaskan S.D. Arikara S .D. Arikara <0.0001 N.M. Puebloan 0.0026 <0.0001 Table 5-3. Significance of differences among adult radii (n=358) Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan <0.0001 <0.0001

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89 Among the femora for the adults sampled, South Dakota Arikara were found to be significantly different (p<0.0001) from both Native Alaskan and New Mexico Puebloan groups. However, population means could not be distinguished between the Native Alaskan and New Mexico Puebloan populations as seen previously in the comparison of population mean for the humeri. Table 5-4. Significance of differences among adult femora (n=421) Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan 0.7485 <0.0001 Among the tibiae and fibulae of all adults sampled, all groups displayed statistically significant separation (p<0.0001). Table 5-5. Significance of differences among adult tibiae (n=403) Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan <0.0001 <0.0001 Table 5-6. Significance of differences among adult fibulae (n=316) Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan <0.0001 <0.0001 The are displayed in Table 5-7. Table 5-7. Population means (mm) by element Humeri Ulnae Radii Femora Tibiae Fibulae Native Alaskan 294.362 235.600 216.137 406.373 325.868 318.880 S.D. Arikara 310.982 264.286 245.469 434.788 368.124 359.592 N.M. Puebloan 292.088 243.183 225.333 408.528 343.491 334.744

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90 Given the results displayed in Tables 5-1 through 5-6, the null hypothesis of finding no significant differences among the six individual postcranial long bones compared between populations is rejected. Alternatively, these populations differ significantly, particularly the South Dakota Arikara which were found to be significantly different from all other groups among all postcranial elements compared. These results imply that Native Alaskan groups were ting in their foreshortened limbs relative to the New Mexico Puebloan and South Dakota Arikara groups. Results for the South Dakota Arikara, with the largest population means for all postcranial elements compared, imply a lesser degree of cold stress with New Mexico Puebloan displaying a degree of cold stress intermediate suppose that the South Dakota Arikara lie at a lower latitude compared with the Native Alaskans which would occupy the higher latitude. Canonical correlation analysis of the eco-geographic condition among these groups will further evaluate these implied patterns of variation. Interestingly, New Mexico Puebloan and Native Alaskans were found to significantly differ among the distal components of both the upper and lower limb (ulnae/radii, tibiae/fibulae), however the proximal limb elements (humeri and femora) could not be distinguished. Similarly patterns of distal vs. proximal limb segment variation have been identified in primate, hominid and modern human studies (Falsetti and Cole, 1992; Holliday 1997a, b; Holliday and Falsetti, 1995; Holliday and Ruff, 2001; Trinkaus, 1981). These patterns of variation will be analyzed for further confirmation of these results and to determine whether they are correlated to the distinct eco-geographic conditions of these three groups. have a threshold. Given the strict interpretation of these rules in light of the eco-geographic and

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91 morphological data presented here it would be expected that New Mexico Puebloan groups with the lowest latitude and highest temperature variables would display the greatest extremity length, however this is not the case. There may be other factors not explored in the present analysis that may account for this deviation including growth potential and plasticity or other genetic and morphological restrictions (Garruto, 1995; Eveleth and Tanner, 1990; McNamara, 1986; Pritchard, 1995). Investigation into the source of variation within and between these groups and how they correlate to eco-geographic variables will provide further insight into how the rules of Bergmann (1847) and Allen (1877) may affect these populations. Principal Components Analysis Principal components analysis (PCA) is a statistical method which highlights that portion of the data, without a priori assumptions, accounting for the largest components of sample variation (Blackith and Reyment, 1971; Jolicoeur, 1963; Reyment et al., 1984). By pooling all individuals from each of the three populations, PCA will allow for a focus specifically on which variability. In growth studies, physical size is assumed to be the most variable character. However, compared, PCA analysis of log-transformed data can help determine whether this variation is a product of size alone or whether the shape accounts for the variation noted (Blackith and Reyment, 1971; Corruccini, 1987; Darroch and Mosimann, 1985; Falsetti, 1989; Reyment et al., 1984; Sprent, 1972). Log-transformation is imposed on the data to correct for unequal variation in the sample based on the known discrepancy of physical size between and within each population (e.g. Blackith and Reyment, 1971; Corruccini, 1983, 1987; Falsetti and Cole, 1992; Jolicoeur, 1963; Jungers et al., 1988, 1995; Reyment et al., 1984; Sokal and Rohlf, 1995). This

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92 correction is important when comparing growth data as well as among those samples where variation in physical size variation is a known to be significant. calculated by subtracting the geometric mean from each log-transformed variable: e.g. shape(humeri) = ln(humeri) ln(geometric mean) (Corruccini, 1987; Falsetti, 1989). In the absence of a true age or body size of an individual, geometric mean is often employed as a holistic estimate of overall body size (Gould, 1971; Holliday, 1997b; Jungers et al., 1988, 1995; Reno et al. in prep). The geometric mean is calculated by: [(ln(humeri)+ln(ulnae)+ln(radii)+ln(femora)+ln(tibiae)+ln(fibulae))/6)]. Subsequently the adult sample as all individuals for this analysis require a complement of all six postcranial elements which is not consistent among archaeological assemblages. While there is no explicit test of statistical significance in PCA, null and alternative hypotheses of the data can still be appreciated. The null hypothesis for this investigation states that the variation among the variables will be equal across all six postcranial elements for both the analysis of size and shape. The alternative hypothesis of divergent patterns of variation among the data allows for an investigation into the causation of whether variation is patterned within each limb or between the upper and lower extremities. If patterns of variation occur how and why certain elements would vary in size and/or shape over others will be explored. This analysis will provide the clearest picture of the variation that exists among these data. The first six principal components from the analysis of adult size and shape (n=227) including eigenvectors, eigenvalues, percentage of variation and total variance presented in Tables 5-8 and 5-9. In the principal components analysis of the adult size variables, the first principal component (PC1) accounts for 92.2% of the total within-group variation with all

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93 positive eigenvector loadings. The second principal accounts (PC2) for only 3.3% of the variation indicating a significantly reduced component of size-related shape variation within the adult sample. Table 5-8. Principal components analysis of adult size variables Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3169 0.7054 0.3088 0.5531 0.0067 0.0201 L(ulnae) 0.4231 0.2666 0.4488 0.1353 0.3874 0.6163 L(radii) 0.4656 0.4074 0.4089 0.0089 0.3763 0.5551 L(femora) 0.3234 0.4952 0.0572 0.7837 0.1784 0.0260 L(tibiae) 0.4443 0.0493 0.4728 0.0505 0.6541 0.3821 L(fibulae) 0.4490 0.1320 0.5558 0.2424 0.4983 0.4058 Eigenv alue 0.0339 0.0012 0.0011 0.0004 0.0001 0.0001 % variation 92.2 3.3 2.9 1.0 0.4 0.3 These results further highlight that variation in physical size plays a significant role in the arison of group means) but within the sample as a whole. From an analysis of the loadings from PC1 a pattern emerges. As seen in the previous analysis, the proximal vs. distal segments of both the upper and lower limbs display a unique pattern of variation. The loadings for distal segments (ulnae, radii, tibiae, fibulae) exhibit consistent and larger values than the proximal components (humeri, femora) indicating that size plays a larger role in the variation among distal segments. These results are statistically confirmed in the previous analysis where group separation was imposed on the data. For the Native Alaskan and New Mexico Puebloan samples, group means for the proximal limb segments, humeri and femora, could not be statistically distinguished. These results in upper and lower limb is more highly variable than the proximal elements of each limb.

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94 Table 5-9. Principal components analysis of adult shape variables Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 S(humeri) 0.6477 0.1633 0.6210 0.0095 0.0350 0.4082 S(ulnae) 0.2019 0.4916 0.1575 0.3693 0.6242 0.4082 S(radii) 0.4089 0.4743 0.0073 0.3608 0.5574 0.4082 S(femora) 0.4839 0.1464 0.736 4 0.1696 0.0806 0.4082 S(tibiae) 0.2244 0.4594 0.0562 0.6644 0.3567 0.4082 S(fibulae) 0.2964 0.5235 0.2093 0.5128 0.4056 0.4082 Eigenvalue 0.0018 0.0010 0.0003 0.0001 0.0001 0.0000 % variation 52.6 29.4 10.2 4.0 3.5 0.0 In the principal components analysis of adult shape variation, the first principal axis (PC1) accounts for 52.6% of within-sample variation; just over half of the variation appreciated in the sample. In this case, all eigenvector loadings on the first principal component are not of the same sign. All loadings are negative expect for the humeri and femora. The second principal component from the analysis on adult size variation shows comparable results to the first principal component of the adult shape analysis. Both PC2 from the size analysis and PC1 from the shape investigation indicate that the proximal segments of the upper and lower limb share a larger degree of shape conservation than evidenced in the distal portion of each limb. The second principal component (PC2) for within-sample shape accounts for 29.4% of the total variation. These results indicate that while shape does play some role in the variation apparent within this Canonical Discriminant Analysis Canonical discriminant analysis is a multidimensional, data-reduction technique used to highlight the relationship between multiple groups (Blackith and Reyment, 1971; Falsetti, 1989). As compared to PCA, canonical discriminant analysis allows for the a priori designations of group separation. The canonical loadings in this analysis reflect the degree of variation among the elements compared which contributes the greatest degree to overall group separation. The

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95 null hypothesis for this analysis would find that there is no significant separation among these that the null hypothesis will easily be rejected in support for one of the alternative hypotheses. Alternative hypotheses explored for this analysis will examine whether significant variation (p<0.05) is appreciated between these three population. Similarly an analysis of overall canonical structure will determine whether any specific patterns of variation can be appreciated among the variation will vary between proximal and distal components within both the upper and lower limbs. The canonical discriminant analysis of three populations shows that significant differences exist within and between populations for the measures used in this study. All tests of multivariate significance provide a p-value of <0.0001 as seen in Table 5-10. The first two canonical variates are both significant and account for 100% of the morphological variation among the adult sample. Table 5-10. Canonical discriminant analysis test of significance for adult postcrania within population Statistics Value F value p value Wilks' Lambda 0.3363 26.44 <0.0001 Pillai's Trace 0.7546 22.22 <0.0001 Hotelling Lawley Trace 1.7028 30.98 <0.0001 Roy's Greatest Root 1.5255 55.94 <0.0001 Table 5-11. Statistical results of canonical discriminant analysis for adult postcrania within population Canonical Variate Eigenvalue % of Variation F value P value 1 1.5225 89.59 26.44 <0.0001 2 0.1774 10.41 7.80 <0.0001 The total canonical structure found in Table 5-12 represents the re-scaled canonical discriminant scores. A pattern of variation can be appreciated among the first canonical scores.

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96 Distal limb segments exhibit appreciably higher loadings (0.6 or greater) than those of the proximal limb elements. This lends further support toward the conclusion that a large majority of the variation among the adult sample is accounted for in the distal elements among both the upper and lower extremities. Table 5-12. Total canonical structure of adult postcranial variables within population Variable Can1 Can2 humeri 0.4821 0.6019 ulnae 0.7900 0.5252 radii 0.8333 0.3860 femora 0.5899 0.5890 tibiae 0.8390 0.3704 fibulae 0.8439 0.4025 Table 5-13.Class means on canonical variables within population Population Can1 Can2 Native Alaskan 1.4599 0.1692 South Dakota Arikara 1.3326 0.3024 New Mex ico Puebloan 0.3005 0.7326 Table 5-13 displays the class means within and between the three populations examined. Between-group separation by class means are visually appreciated in Figure 5-1. The Native esented by the red circle, the South Dakota represented in Figure 5-1 in green. The dotted circles surrounding each group mean represent no designation of significance, however provide visual appreciation of the separation of the scatter for each population and their degree of overlap.

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97 Figure 5-1. Canonical discriminant analysis of three populations Plot of class means

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98 Examination of Skeletal Indices Patterns of population variation have also been examined through proportionality or limb indices (Falsetti and Cole 1992; Holliday 1997a, b; Holliday and Ruff, 2001; Jantz and Owsley, 1984a; Jantz and Jantz, 1999; Warren, 1997). Intermembral (humeri + radius)/(femur + tibia), brachial (radius/humeri), and crural (tibia/femur) indices are highlighted in this investigation as they appreciate variation within upper and lower limb as well as between the extremities. These indices serve as an additional dimension for the analysis of population, regional and eco-geographic variation. These patterns of variation are appreciated in the box-plots of index means by population found in Figures 5-2 through 5-4. Box-plots are a method of visually comparing two or more groups without assumptions of the statistical distribution between them (substantiated here -14 and 5-15). Another invention of John Tukey, box-plots are a useful tool used to display the dispersion of the data (parameters of box as well as median line within), skewness (asymmetry of upper and lower portions within the box), the lower and upper quartile of the data (displayed in the lines extending from each box) and highlight outliers (Sokal and Rohlf, 1995). The samples are separated by sex to appreciate how sexual dimorphism accounts for the variation in the pattern of population variation. post hoc comparison of index means between these three populations. For this analysis, the null hypothesis dictates that no significant variation will be appreciated among the brachial, crural and intermembral indices compared. Given the results of the previous analyses it is predicted that the null hypothesis will be rejected in favor of one or more of the alternative hypotheses indicating that significant variation will exist among one or all of the indices compared. With the

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99 icularly among the comparisons between Native Alaskan and New Mexico Puebloan populations, it is suspected that significant variation will be appreciated in the crural and brachial index comparisons given the nonsignificant results for the proximal limb elements (humeri and femora) and the significant differences appreciated between the distal limb components (ulnae, radii, tibiae, fibulae). If so, this alternative method of postcranial analysis will lend further statistical support those previous results and hypotheses. Figure 5-2. Box plot of adult brachial index against sex by population The results of the box-plot comparison of brachial indices among the adults of the three populations are seen in Figure 5-2. South Dakota Arikara were found to have higher brachial indices (radius/humeri) than any other population, followed by New Mexico Ancestral Puebloan groups and with Native Alaskans displaying the lowest brachial index. In other words, the South Dakota Arikara have longer distal upper limb segments relative to their proximal upper limbs;

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100 Native Alaskans have relatively shorter distal limb components relative to proximal upper limb. Comparable patterns are appreciated between both the males and females of each group indicating that although males and females within each group differ in physical overall size the pattern of variation between populations remains the same. Figure 5-3. Box plot of adult crural index against sex by population A similar pattern to the brachial index is found among the crural index (tibia/femora) comparison found in Figure 5-3. The South Dakota Arikara display overall higher crural indices than those found among the Native Alaskan or Ancestral Puebloan populations. This indicates that the South Dakota Arikara possess a relatively longer tibia as compared to their femora with Native Alaskans displaying a relatively short tibia compared to their femoral length. Once again, separation of the sexes had no effect on the pattern of variation between populations.

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101 These two comparisons of indices across populations fit the previously appreciated pattern of morphological population variation among the distal components of both the upper and lower limb. This hypothesis of variation being accounted for primarily among distal limb components is further appreciated by the box plot of the adult intermembral index [(humeri+radii)/(femora+tibia)] found in Figure 5-4. The plots for each sex within each population highlight that among intermembral index patterns of sexual dimorphism are quite similar as compared to other indices. These results show an overall higher range for the Native Alaskan intermembral index than the other two populations as well as a greater spread between males and females. These results indicate that the Native Alaskans analyzed here have a larger upper limb relative to their lower limb and display a greater degree of sexual dimorphism in intermembral index than South Dakota Arikara and New Mexico Puebloan groups. Figure 5-4. Box plot of adult intermembral index against sex by population

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102 comparison of the means or T-method. This highly conservative test accounts for the multiple comparisons these data have been subject to by limiting the potential of making a Type I error, rejecting a true null hypothesis (Sokal and Rohlf, 1995). For this test of significance, the sexes were once again pooled within each of the three populations to highlight the overall structure of each group. significant (p<0.0001), however the model of intermembral comparisons was nonsignificant; i.e. not separation could be achieved among the data. Given the box-plot of intermembral comparison displayed in Figure 5-analysis of statistical significant separation of brachial and crural index means are displayed in Tables 5-14 and 5-15. Table 5-14. Significance of differences among brachial indices Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan <0.0001 0.0036 Table 5-15. Significance of differences among crural indices Population Native Alaskan S.D. Arikara S.D. Arikara <0.0001 N.M. Puebloan <0.000 1 0.0027 populations show significant separation in terms of both brachial and crural indices. Given the visual separation of the groups by sex displayed in Figures 5-3 and 5-4, these results are not unexpected. Now that the population variation between these groups have been established and patterned through three separation methods of analysis, correlation of this variation to eco-geographic condition can be explored.

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103 Canonical Correlation Analysis Canonical correlations analysis is a statistical method used to analyze the interaction between two sets of data. This mode of analysis allows for one set of variables, as a whole, to be compared to another distinct set of data without a priori assumptions concerning the nature of either data set. Comparisons between environmental features and morphological characters are best appreciated through canonical correlations analysis (Blackith and Reyment, 1971; Falsetti, 1989; Jantz et al., 1992; Ralph et al., 1998; Reyment et al. 1984; Wilson et al. 2005). In this case, adult postcranial long bone lengths are compared to eco-geographic variables (descriptions of these variables are found in Table 3-21). For this comparison the six postcranial elements (humeri, ulnae, radii, femora, tibiae, fibulae) are canonically correlated against six eco-geographic variables (latitude, mean annual temperature, month high mean temperature, month low mean temperature, average precipitation, month high mean precipitation; the lowest month precipitation variable was identical for all populations in pilot research and was removed from subsequent analysis). The null hypothesis for this investigation states that the correlation of postcranial measurements and eco-geographic conditions will not show any significant pattern of correlation. If a significant correlation is found and the null hypothesis is rejected, it can be determined what pattern of variation exists among the eco-geographic variables chosen and whether variation in these populations is rules. In the canonical correlation analysis between postcranial linear dimensions and eco-geographic variablTrace, Hotelling-(p<0.0001) for all adult samples (n=227). These results demonstrate a significant relationship

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104 between the six postcranial elements examined and six measures of eco-geography. Given the first canonical variable accounting for 89.59% of sample variation and overall model significance these results indicate that the eco-geographic variables explored here are a strong predictor of linear limb measurements for the populations in question. The second canonical variable accounts for an additional 10.41% of the shared variance between postcranial measurements and eco-geographic variables. The total canonical correlation structure used to describe the relationship between postcranial measurements and eco-geographic variables are found in Table 5-16, 5-17, 5-18. Table 5-16 displays the correlation among postcranial measurements. Unsurprisingly there is an overall high degree of correlation displayed among the variables. The length of a single element is highly correlated to the length of other elements within a given individual. However, there is a trend for slightly higher loadings within the distal limb segments (ulnae correlation values range from 0.8496 to 0.9808; radii correlation values range from 0.8352 to 0.9808; tibiae correlation values range from 0.8443 to 9802; fibulae correlation values range from 0.8293 to 0.9802) than proximal limb (humeri correlation values range from 0.8293 to 0.8975; femora correlation values range from 0.8760 to 0.9079). Though the pattern displayed here is slight, this may be a further indication that there is a greater degree of variation accounted for by the distal segments of each limb than the proximal elements. Table 5-16. Canonical correlation among postcranial measurements humeri ulnae radii femora tibiae ulnae 0.8496 radii 0.8352 0.9808 femora 0.8975 0.8760 0.8636 tibiae 0.8443 0.9197 0.9251 0.9079 fi bulae 0.8293 0.9107 0.9220 0.8889 0.9802

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105 Table 5-17 displays the canonical structure among the eco-geographic variables analyzed. A strong negative correlation is found between geographic latitude and all temperature variables. This is expected as the father north (increasing latitude) one travels, the cooler (decreased annual mean temperature) the overall temperature becomes. An appreciably lower correlation is found between latitude or temperature and precipitation. These results indicate that temperature or latitude alone is not necessarily a strong indicator of precipitation. Table 5-17. Canonical correlation among eco-geographic variables Temperature Precipitation Latitude Annual mean High month mean Low month mean Annual mean Annual mean 0.995 9 High month mean 0.9809 0.9593 Low month mean 0.9255 0.9559 0.8341 Annual mean 0.5010 0.5770 0.3230 0.7915 High month mean 0.3694 0.4517 0.1815 0.6939 0.9893 Table 5-18 displays the correlation values between postcranial measurements and eco-geographic variables. While the correlation loadings in Table 5-18 are moderate as compared to the within-variable variation, patterns of significance are appreciated. The most obvious trend in the canonical correlation between postcranial measurements and eco-geographic variables is the overall negative correlation of latitude with all elements examined. This indicates that as latitude increases, all elements decrease in physical size. However, as previously noted, a unique pattern of distinction is found between distal and proximal limb segments for both the upper and lower extremities. Higher correlation loadings are found among ulnae (-0.3269), radii (-0.3879), tibiae (-0.3952) and fibulae (-0.3898) than the humeri (-0.1275) and femora (-0.1937). These results indicate a stronger correlation of the distal limb segments with changing latitude. This, along

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106 with the higher variation found within the distal segment reflected in the canonical discriminant analysis, suggests that the distal segments of the upper and lower limbs is a main feature of interest in the analysis of population variation. Table 5-18. Canonical correlation between postcranial measurements and eco-geographic variables Temperature Precipitation Latitude Annual mean Hi gh month mean Low month mean Annual mean High month mean humeri 0.1275 0.0888 0.2073 0.0422 0.3021 0.3458 ulnae 0.3269 0.2752 0.4293 0.0910 0.3194 0.3981 radii 0.3879 0.3376 0.4855 0.1544 0.2730 0.3584 femora 0.1937 0.1501 0.2823 0.0004 0.3134 0.3692 tibiae 0.3952 0.3450 0.4924 0.1618 0.2679 0.3542 fibulae 0.3898 0.3386 0.4894 0.1523 0.2809 0.3673 A similar correlation of variation among the proximal vs. distal limb components is found in the temperature variables examined. Given the high correlation between temperature and latitude, these similar patterns of variation are not unexpected. Annual mean temperature correlation loadings are comparably higher for the ulnae (0.2752), radii (0.3376), tibiae (0.3450) and fibulae (0.3386) than for the humeri (0.0888) and femora (0.1501). Comparable findings are seen among the high and low month mean temperature for the humeri and femora loadings with both variables displaying a negative value (-0.0422 and -0.0004 respectively) while all other temperature variable loadings are positive. This overall positive correlation indicates a general trend in my data: as temperature increases, postcranial long bone measurements lengthen. Little information is gained from the correlation of postcranial measurements to the precipitation variables selected. No specific patterns of variation are appreciated as annual mean precipitation displays a loadings range of 0.2679 to 0.3194 and high month precipitation mean ranging from 0.3458 to 0.3981. However, given the statistical significance of this correlation

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107 model and positive loadings for all precipitation variables, a positive correlation between increased precipitation and longer postcranial elements is concluded. As mentioned previously patterns of variation between proximal vs. distal segments within the limbs are aligned with eco-geographic condition and have been explored previously varied organisms as well as specifically on primates, hominids and modern human groups (Holliday, 1997a, b; Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 1997, 2001; Lee et al. 1969; Mayr, 1956; Murphy, 1985; Newman and Munro, 1955; Roberts, 1978; Ruff, 1993; Trinkaus, 1981; Vrba, 1996; Warren, 1997; Weaver and Ingram, 1969). These experiments have been most notably tested ageographic variation and seasonality on House Sparrow body size found that annual temperature range is of greater importance on predicting physical size than another other climatic variable; displayed a relative foreshortening of the distal limb segments particularly in the lower limb. This pattern of variation is considered a reflection of cold adaptation to the periglacial climate of Europe during the last glacial period (Trinkaus, 1981). Similarly, Trinkaus (1981) documented a significant, positive relationship between brachial and crural indices among modern human populations and mean annual temperature. Lee and colleagues (1969) found that distal limb segments were more variable than proximal elements with tibia lengths reduced in animals raised in experimentally cooler climates as compared to those reared under warm conditions. Holliday and Ruff (2001) analyzed human populations from varied groups across four continents to determine whether these previously reported patterns of distal limb segment variation held and whether it was correlated to environmental condition. It was concluded that variation among

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108 widely dispersed human populations was largely due to differences in distal limb segments and this variation was a result of both environmental and genetic factors (Holliday and Ruff, 2001). The present analysis finds that these conclusions are true not only of widely varied human groups but are similarly true of three Native North American groups of temporally conserved, spatially distinct individuals with a relative common evolutionary history. Results of Juvenile Skeletal Analysis Principal Components Analysis Among growth analyses, physical size is presumed to be the primary variable of change. The first principal component of the log-transformed covariance matrix is used to account for 1989; Jolicoeur, 1963; Jungers and German, 1981; Jungers et al., 1988). However, many authors have focused not only on size variation between inter-/intraspecific groups but on shape variation of individual elements (Blackith and Reyment, 1971; Corruccini, 1987; Darroch and Mosimann, 1985; Falsetti, 1989; Jungers and German, 1981; Jungers et al. 1988, 1995; Reyment et al., 1984; Sprent, 1972; Waxenbaum et al., 2007). Shape variables are calculated by subtracting the geometric mean from each log-transformed variable, e.g. shape(humeri) = ln(humeri)-ln(geometric mean). Principal components analysis will confirm whether size or shape accounts for a greater percent of variation in the given sample. By employing the geometric mean for an analysis of shape variation, the sample size of the overall analysis greatly reduced due to the need for each individual included to have data for each of the six element variables. Given the archaeological nature of this investigation, these requirements excluded many individuals from analysis. However, a second PCA analysis for each population will be conducted subsequently for the purposes of testing deviations from isometry. The patterns of these results (Table 5-29, 5-31, 5-33) are comparable to those found in Table 5-19. The first six principal component scores

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109 from the analysis of juvenile size and shape (n=172) including eigenvectors, eigenvalues, percentage of variation and total variance presented in Tables 5-19 and 5-20. In the principal components analysis of the juvenile size variables, the first principal component (PC1) accounts for 99.6% of the total within-sample variation with all positive eigenvector loadings. The second principal component (PC2), defining size-related shape variation accounts for only 0.2% of the within-group variation. This demonstrates that a majority of the variation in the juvenile sample is purely a result of size alone. Table 5-19. Principal components analysis of juvenile size variables Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3967 0.2921 0.5874 0.1428 0.6221 0.0683 L(ulnae) 0.3510 0.5776 0.2366 0.0633 0.1153 0.6853 L(radii) 0.3632 0.5370 0.1586 0.1465 0.1735 0.7091 L(femora) 0.4575 0.5362 0.1725 0.0919 0.6797 0.0522 L(tibiae) 0.4367 0.0684 0.4940 0.6652 0.3217 0.1201 L(fibulae) 0.4327 0.0154 0.5476 0.7093 0.0627 0.0745 Eigenvalue 1.2733 0.0026 0.0011 0.0005 0.0003 0.0001 % variation 9 9.6 0.2 0.0 0.0 0.0 0.0 A pattern of variation exists between the upper and lower limb with the upper limb elements (humeri, ulnae, radii) displays comparable, lower loading coefficients (ranging from 0.3510 to 0.3967) than the lower limb (ranging from 0.4327 to 0.4575). This indicates that a larger majority of the variation among the juvenile analysis is accounted for in the lower limb as compared to the upper limb. The pattern of variation between proximal and distal segments seen in the adult sample is not substantiated in the juvenile analysis. From this analysis the alternative hypothesis that variation will be patterned between the upper and lower limb as opposed to patterns of variation within the upper and lower extremity. These results indicate that the lower limb increases to a greater extent over the course of juvenile development as compared to the upper limb.

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110 In the principal components analysis of the juvenile shape variables, the first principal components accounts for 84.5% of the variation among the shape variable for the elements within the juvenile sample. As similarly seen in the adult analysis, the eigenvector coefficients are do not all share the same loading signs. The upper limb elements (humeri, ulnae, radii) are positive and the lower limb elements (femora, tibiae, fibulae) are negative. This variation in eigenvector size indicates that there is a greater degree of shape conservation within the upper limb which is distinct from that of the lower limb. Table 5-20. Principal components analysis of juvenile shape variables Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 S(humeri) 0.0510 0.7129 0.1460 0.5413 0.0896 0.4082 S(ulnae) 0.5802 0.0555 0.0295 0.1852 0.6769 0.4082 S(radii) 0.4635 0.1353 0.1818 0.2423 0.7129 0.4 082 S(femora) 0.5386 0.3281 0.1213 0.6447 0.0713 0.4082 S(tibiae) 0.3000 0.4008 0.6150 0.4347 0.1238 0.4082 S(fibulae) 0.2561 0.4493 0.7427 0.0962 0.0696 0.4082 Eigenvalue 0.0140 0.0014 0.0005 0.0003 0.0001 0.0000 % variation 84.5 8.8 3.4 2.3 0.7 0.0 The principal components analysis of the within-juvenile population between log-size and log-shape indicates that less variation is demonstrated when shape is the main focus as compared to size. However, both size and shape vary greatly over the course of the juvenile growth period. Among the juvenile sample, as anticipated given the range of juvenile data, size accounts for the gross majority of variation. Initially, it was unusual to find that the juvenile sample did not display a similar pattern of differential variation between the proximal and distal components of each limb as seen in the adult sample instead for both shape and size the juvenile sample was separated between the upper vs. lower limb. To determine the origins of adult variation, it was expected that the juvenile component of the data would mirror the adult sample. However, it seems likely that this

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111 variation is a product of the natural flux of growth in conjunction with the wide age range examined. The human lower limb tends to be physically longer in length as compared to the upper limb (this can be appreciated in the descriptive statistics outlined in Appendix C). Hence, it would be expected that during development, the lower limb across populations would display a higher degree of variation than the upper limb. Potential developmental mechanisms for why and how the patterns differ between the adult and juvenile sample will be explored in subsequent sections of this chapter. To investigate these results further, additional principal component analyses were performed separating the data into four discrete growth segments to discern whether patterns of variation can be appreciated. These growth periods highlight some of major phases of human growth: birth 2 years of estimated age, the postnatal growth spurt; 3-7 years of estimated age, childhood period; 8-11 early pubescent period; 12-21 pubescent period (adopted from Gasser et al., 2000; Gasser et al., 2001a, b; Smith and Buschang, 2004). Table 5-21. Principal components analysis of juveniles Birth-2 estimated years of age (n=41) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3973 0.3109 0.3198 0.7603 0.2472 0.0623 L(ulnae) 0.3410 0.5534 0.3114 0.0205 0.0171 0.6925 L(radii) 0.3526 0.5045 0.2923 0. 1645 0.0157 0.7129 L(femora) 0.4689 0.5829 0.3103 0.5695 0.1181 0.0755 L(tibiae) 0.4284 0.0241 0.4696 0.2211 0.7385 0.0309 L(fibulae) 0.4448 0.0445 0.6311 0.1456 0.6156 0.0400 Eigenvalue 0.2527 0.0010 0.0005 0.0002 0.0001 0.0000 % variation 9 9.24 0.41 0.20 0.08 0.04 0.00 The PCA results for both the birth-2 and 3-7 estimate age ranges display comparable results as seen in Tables 5-21 and 5-22. The patterns of variation among the first principal components scores match those of the previous PCA on the total juvenile sample with a

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112 separation between the upper and lower limb; an overall higher variation displayed among the lower limb when compared to the upper limb. Table 5-22. Principal components analysis of juveniles 3-7 estimated years of age (n=37) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3723 0.5185 0.4892 0.2650 0.5259 0.0782 L(ulnae) 0.3751 0.3828 0.3649 0.0549 0.3033 0.6960 L(radii) 0.3893 0.4396 0.3615 0.3464 0.0897 0.6295 L(femora) 0.4363 0.5862 0.1587 0.3913 0.5285 0.0907 L(tibiae) 0.4477 0.1577 0.6415 0.2240 0.5161 0.2155 L(fibulae) 0.4221 0.1502 0.2449 0.7767 0.2783 0.2417 Eigenvalue 0.1252 0.0020 0.0009 0.0005 0.0003 0.0000 % variation 96.9 1.56 0.77 0.46 0.23 0.06 A unique pattern of variation is appreciated when look at early pubescent (Table 5-23; 8-11 years of estimated age) and pubescent (Table 5-24; 12-21 estimated years of age) stages of development. The 12-21 estimated age category mirrors the adult pattern of differential variation between the proximal and distal elements within both the upper and lower limb. Table 5-23. Principal components analysis of juveniles 8-11 estimated years of age (n=19) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3875 0.4342 0.4934 0 .0648 0.6391 0.0710 L(ulnae) 0.3292 0.4824 0.3849 0.0591 0.0843 0.7071 L(radii) 0.3603 0.4893 0.3008 0.1379 0.1818 0.6986 L(femora) 0.4465 0.5150 0.0726 0.2152 0.6943 0.0391 L(tibiae) 0.5102 0.0755 0.5419 0.6465 0.1310 0.0705 L(fibulae) 0.3 895 0.2610 0.4678 0.7133 0.2280 0.0179 Eigenvalue 0.0860 0.0037 0.0014 0.0002 0.0002 0.0000 % variation 93.71 4.06 1.59 0.3 0.23 0.00 An unusual pattern of variation is found in the 8-11 estimated age category for the PCA results. A consistent trend for the younger juvenile sample is revisited in the consistent first principal component loadings for the upper limb elements. However, no consistent pattern of variation is appreciated among the lower limb elements. These results may be a product of the

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113 small sample size available for this age category, but it may also be indicative of a transitional period between early development and adolescence marked by a transition from juvenile to adult skeletal characteristics. Table 5-24. Principal components analysis of juveniles 12-21 estimated years of age (n=76) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3721 0.5032 0.5059 0.4574 0.3521 0.1375 L(ulnae) 0.4102 0.3713 0.3703 0.2468 0.1540 0.6869 L(radii) 0.4296 0.4818 0.2527 0.1752 0.16 60 0.6790 L(femora) 0.3552 0.5568 0.0045 0.3921 0.6342 0.0876 L(tibiae) 0.4232 0.1571 0.5794 0.3490 0.5715 0.1090 L(fibulae) 0.4508 0.2046 0.4551 0.6507 0.3094 0.1687 Eigenvalue 0.0945 0.0022 0.0015 0.0006 0.0003 0.0002 % variation 95.06 2.24 1 .51 0.60 0.35 0.23 Reduced Major Axis Regression: Multiple comparison of growth trajectories Regression is a statistical tool using an independent variable to predict a dependent variable (Ott and Longnecker, 2001). Reduced major axis regression is a specific regression format used when both variables are subject to error and vary naturally, such as genetic differences, allometric investigations or when individuals are subject to environment-induced variation. Most biological comparisons are examined through this method (Sokal and Rohlf, 1995). Reduced major axis regressions on log-transformed data were executed on the juvenile sample (birth to 21 estimated years of age) for each element against log(size) for each population. The slopes and intercepts for each element were subject to t-tests to determine whether they differ significantly. A Bonferroni correction was applied to the traditional alpha value of 0.05 to determine significance given the multiple comparisons being employed on the data (Sokal and Rohlf, 1995). This highly conservative correction is performed by dividing the

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114 standard alpha value (0.05) by the number of comparisons imposed on the data (3) to produce a new alpha value of 0.0167. The sample sizes for this analysis are greatly reduced given the restrictions of the size variable. Figure 5-5. Reduced major axis regression for Native Alaskan (n=52) log(humeri) on log(size)

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115 Figure 5-6. Reduced major axis regression for Native Alaskan (n=52) log(ulnae) on log(size)

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116 Figure 5-7. Reduced major axis regression for Native Alaskan (n=52) log(radii) on log(size)

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117 Figure 5-8. Reduced major axis regression for Native Alaskan (n=52) log(femora) on log(size)

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118 Figure 5-9. Reduced major axis regression for Native Alaskan (n=52) log(tibiae) on log(size)

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119 Figure 5-10. Reduced major axis regression for Native Alaskan (n=52) log(fibulae) on log(size)

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120 Figure 5-11. Reduced major axis regression for South Dakota Arikara (n=76) log(humeri) on log(size)

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121 Figure 5-12. Reduced major axis regression for South Dakota Arikara (n=76) log(ulnae) on log(size)

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122 Figure 5-13. Reduced major axis regression for South Dakota Arikara (n=76) log(radii) on log(size)

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123 Figure 5-14. Reduced major axis regression for South Dakota Arikara (n=76) log(femora) on log(size)

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124 Figure 5-15. Reduced major axis regression for South Dakota Arikara (n=76) log(tibiae) on log(size)

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125 Figure 5-16. Reduced major axis regression for South Dakota Arikara (n=76) log(fibulae) on log(size)

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126 Figure 5-17. Reduced major axis regression for N.M. Puebloan (n=44) log(humeri) on log(size)

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127 Figure 5-18. Reduced major axis regression for N.M. Puebloan (n=44) log(ulnae) on log(size)

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128 Figure 5-19. Reduced major axis regression for N.M. Puebloan (N=44) log(radii) on log(size)

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129 Figure 5-20. Reduced major axis regression for N.M. Puebloan (n=44) log(femora) on log(size)

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130 Figure 5-21. Reduced major axis regression for N.M. Puebloan (n=44) log(tibiae) on log(size)

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131 Figure 5-22. Reduced major axis regression for N.M. Puebloan (n=44) log(fibulae) on log(size) Table 5-25. Slope by element within population Native Alaskan South Dakota Arikara N.M. Puebloan Humeri 0.9629 0.9548 0.9637 Ulnae 0.9304 0.8856 0.8774 Radii 0.9501 0.9149 0.9132 Femora 1.0432 1.0720 1.0721 Tibiae 1.0655 1.060 6 1.0603 Fibulae 1.0201 1.0575 1.0532

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132 Table 5-26. Intercept values by element within population Native Alaskan South Dakota Arikara N.M. Puebloan Humeri 0.0838 0.0858 0.0677 Ulnae 0.0626 0.1838 0.1911 Radii 0.0272 0.0737 0.0682 Femora 0 .0334 0.0530 0.0467 Tibiae 0.1186 0.1001 0.0950 Fibulae 0.0275 0.1081 0.0962 These slopes and intercepts of each element by population will be analyzed to determine whether they differ significantly from each other (alpha = 0.0167). The slope represents the growth trajectory for each element within a population and the intercept representing the origin or size at birth. Table 5-27. Multiple comparisons of slopes and intercepts: p-values for humeri Slope Native Alaskan South Dakota Arikara Inte rcept Native Alaskan South Dakota Arikara South Dakota Arikara 0.6062 South Dakota Arikara 0.9999 N.M. Puebloan 0.9655 0.5220 N.M. Puebloan 0.5599 0.3785 Table 5-28. Multiple comparisons of slopes and intercepts: p-values for ulnae Slope Native Al askan South Dakota Arikara Intercept Native Alaskan South Dakota Arikara South Dakota Arikara 0.0162 South Dakota Arikara 0.0000 N.M. Puebloan 0.0122 0.5930 N.M. Puebloan 0.0001 0.7455 Table 5-29. Multiple comparisons of slopes and intercepts: p-values for radii Slope Native Alaskan South Dakota Arikara Intercept Native Alaskan South Dakota Arikara South Dakota Arikara 0.0571 South Dakota Arikara 0.0005 N.M. Puebloan 0.0776 0.9087 N.M. Puebloan 0.0027 0.8019

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133 Table 5-30. Multiple comparisons of slopes and intercepts: p-values for femora Slope Native Alaskan South Dakota Arikara Intercept Native Alaskan South Dakota Arikara South Dakota Arikara 0.0920 South Dakota Arikara 0.0010 N.M. Puebloan 0.1262 0.9948 N.M. Puebloan 0.0051 0.77 90 Table 5-31. Multiple comparisons of slopes and intercepts: p-values for tibiae Slope Native Alaskan South Dakota Arikara Intercept Native Alaskan South Dakota Arikara South Dakota Arikara 0.7508 South Dakota Arikara 0.4285 N.M. Puebloan 0.767 2 0.9821 N.M. Puebloan 0.3705 0.7976 Table 5-32. Multiple comparisons of slopes and intercepts: p-values for fibulae Slope Native Alaskan South Dakota Arikara Intercept Native Alaskan South Dakota Arikara South Dakota Arikara 0.0262 South Dakota A rikara 0.0018 N.M. Puebloan 0.0837 0.7631 N.M. Puebloan 0.0169 0.5703 The results of this comparative analysis of slopes and intercepts for each element by populations find significant difference between the slopes (growth trajectories) only for the ulnae when Native Alaskan juveniles are compared to both South Dakota Arikara and New Mexico Puebloan populations. However, the null hypothesis cannot be rejected for the remaining elements compared. Significant differences were appreciated among the intercepts (size at birth) between multiple comparisons. The intercepts of Native Alaskans were distinguished from those of both South Dakota Arikara and New Mexico Puebloan among the radii, ulnae and femora. Similarly, the Native Alaskans and South Dakota Arikara were found to have significantly distinct intercepts for the fibulae compared.

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134 These individual comparisons of slopes cannot provide a comprehensive analysis of allometry for each population. A test of overall isometry will provide a more holistic interpretation of whether these populations display allometric or isometric trajectories. Test of Isometry To provide a more comprehensive statistical assessment of individual or population cedure will evaluate whether the null hypothesis of isometry (slope = 1.0) holds or whether the alternative hypotheses of positive (slope > 1.0) or negative (slope < 1.0) allometry better fits the data (Corruccini, 1983; Falsetti and Cole, 1992; Jolicoeur, 1963, 1984; Jungers and German, 1981; Jungers et al., 1988; log-transformation covariance matrix for all variables in the given data set. All loadings for the fiof the variance (Jungers and German, 1981; Sprent, 1972). Multivariate isometry exists when all p only be true if the vector of direction cosines of the first principal component of the logarithmic covariance matrix is eqevaluated dictates isometry. In this case, 6 postcranial dimensions are evaluated making multivariate isometry equal to 0.408. A principal components analysis was also conducted on the juvenile sample for each population. The covariance matrix for each population is displayed in Tables 5-34, 5-36, and 5-38 as they will be an integral component of the isometry analysis.

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135 Table 5-33. Principal components analysis of Native Alaskan juveniles (n=52) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3946 0.0950 0.6004 0.5885 0.3535 0.0586 L(ulnae) 0.3806 0.5593 0.1165 0.1718 0.2931 0.6429 L(radii) 0.3887 0.5744 0.0537 0.1486 0.1719 0.6815 L(femora) 0.4274 0.4590 0.3020 0.2770 0.6091 0.2601 L(tibiae) 0.4369 0.3521 0.2290 0.4898 0.5850 0.2249 L(fibulae) 0.4180 0.1165 0.6924 0.5342 0.2152 0.0240 Eigenvalue 0.7851 0.0029 0.0012 0.0006 0.0039 0.0002 % variation 99.32 0.37 0.16 0.08 0.05 0.03 Table 5-34. Principal components analysis of Native Alaskan juveniles Covariance matrix L(humeri) L(ulnae) L(radii) L(femora) L(tibiae) L(fibulae) L(humeri) 0.1230 0.1176 0.1202 0.1326 0.1352 0.1292 L(ulnae) 0.1178 0.1148 0.1170 0.1271 0.1300 0.1246 L (radii) 0.1202 0.1170 0.1197 0.1297 0.1328 0.1274 L(femora) 0.1326 0.1271 0.1297 0.1444 0.1469 0.1401 L(tibiae) 0.1352 0.1300 0.1328 0.1469 0.1506 0.1435 L(fibulae) 0.1292 0.1246 0.1274 0.1401 0.1435 0.1380 Table 5-35. Principal components analysis of South Dakota Arikara juveniles (n=76) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3923 0.0701 0.4546 0.1190 0.7843 0.0726 L(ulnae) 0.3637 0.4932 0.2376 0.1237 0.1903 0.7187 L(radii) 0.3758 0.4323 0.2511 0.1622 0.3338 0.686 3 L(femora) 0.4401 0.7188 0.2686 0.1014 0.4480 0.0805 L(tibiae) 0.4358 0.1528 0.5546 0.6653 0.1910 0.0037 L(fibulae) 0.4346 0.1579 0.5424 0.7009 0.0021 0.0277 Eigenvalue 2.1528 0.0020 0.0008 0.0004 0.0003 0.0001 % variation 99.83 0 .09 0.04 0.02 0.02 0.01 Table 5-36. Principal components analysis of South Dakota Arikara juveniles Covariance matrix L(humeri) L(ulnae) L(radii) L(femora) L(tibiae) L(fibulae) L(humeri) 0.3318 0.3072 0.3174 0.3718 0.3680 0.3669 L(ulnae) 0.3072 0.2 854 0.2947 0.3440 0.3410 0.3403 L(radii) 0.3174 0.2947 0.3046 0.3556 0.3524 0.3516 L(femora) 0.3718 0.3440 0.3556 0.4183 0.4130 0.4114 L(tibiae) 0.3680 0.3410 0.3524 0.4130 0.4094 0.4077 L(fibulae) 0.3669 0.3403 0.3516 0.4114 0.4077 0.4070

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136 Table 5-37. Principal components analysis of New Mexico Puebloan juveniles (n=44) Eigenvectors Element PC1 PC2 PC3 PC4 PC5 PC6 L(humeri) 0.3962 0.2042 0.6773 0.3755 0.4232 0.1894 L(ulnae) 0.3606 0.5476 0.0169 0.1224 0.1391 0.7318 L(radii) 0.3753 0. 4918 0.2604 0.1681 0.3187 0.6477 L(femora) 0.4408 0.5023 0.2406 0.1773 0.6781 0.0650 L(tibiae) 0.4361 0.3486 0.0377 0.7470 0.3588 0.0099 L(fibulae) 0.4331 0.2067 0.6536 0.4757 0.3337 0.0692 Eigenvalue 1.9707 0.0020 0.0008 0.0003 0.0002 0.0001 % variation 99.82 0.10 0.04 0.02 0.01 0.01 Table 5-38. Principal components analysis of New Mexico Puebloan juveniles Covariance matrix L(humeri) L(ulnae) L(radii) L(femora) L(tibiae) L(fibulae) L(humeri) 0.3100 0.2818 0.2931 0.3441 0.3403 0.3378 L(ulnae) 0.2818 0.2569 0.2673 0.3128 0.3095 0.3075 L(radii) 0.2931 0.2673 0.2783 0.3256 0.3223 0.3203 L(femora) 0.3441 0.3128 0.3256 0.3836 0.3791 0.3763 L(tibiae) 0.3403 0.3095 0.3223 0.3791 0.3752 0.3722 L(fibulae) 0.3378 0.3075 0.3203 0.3763 0.3722 0.3702 Results of this analysis for each population including eigenvector, eigenvalues, percent of variation and covariance matrix for the first six principal components are found in Tables 5-33 through 5-38. For each of the populations examined, the first principal component (PC1) displays all positive loadings and accounts for over 99% of the variation of the within-sample variation. These results find that a gross majority of the variation within each sample is accounted for through size variation. Since this analysis focus solely on juvenile remains beginning at birth and throughout development, it is not surprising to find size would account for a majority of the variation even after the data is normalized through log-transformation. The pattern among the loadings for the first principal component for each population analyzed shows comparable trends to those seen in Table 5-19. For each population loadings among the upper limb elements (humeri, ulnae, radii) display a maximum range of 0.3669-0.3946 and the lower limb components (femora, tibiae, fibulae) display a maximum PC1

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137 loadings of 0.4180-0.4408. This pattern distinguishes the upper and lower limbs finding a consistently greater increase in the lower limb as compared to the upper limb. Though variation between the adult components of these three populations is undeniable, the patterns of developmental variation are comparable between these groups. Table 5-39 displays the p-values for the test of the hypothesis of whether any of these three populations deviate significantly from isometry. Despite the results of the individual elements examined, when appreciated together these results show that all three populations examined deviate significantly from isometry. These results are not surprising for anyone who has watched a child grow. Rates of growth and change throughout the human developmental period are often in flux with a high growth velocity immediately after birth which decreases significantly from ages 3-10 years and then increasing again during the prepubertal growth spurt (Gasser et al., 2000; Gasser et al., 2001a, b; Smith and Buschang, 2004). Table 5-39. Results for the test of isometry Population p value Native Alaskan <0.0001 South Dakota Arikara <0.0001 New Mexico Pueblo an <0.0001 While these results are not surprising when appreciated in conjunction with the previous analysis depicting unique origins for certain elements it provides a clearer picture of how and why these groups vary as adults. The extent or degree of deviation from isometry is calculated using the isometry vector in conjunction with the first principal component vector for all six postcranial elements. Table 5-40 displays the deviation from isometry in degrees. These results confirm the significant deviation from isometry demonstrated above and reflect the pattern of variation previously appreciated in these populations. The South Dakota Arikara and New Mexico Puebloan are comparable in their

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138 deviation from isometry while Native Alaskans are appreciably different. Given the permutations this data have already been subject to, determination of the separation of these values is statistically inappropriate. Table 5-40. Deviation from isometry Population De viation (degrees) Native Alaskan 2.94 South Dakot a Arikara 4.44 New Mexico Puebloan 4.42 When all the results of the juvenile analysis are taken together, these results do not exactly match any of the five alternative hypotheses posed. In this case, the juvenile results best match a combination of ontogenetic scaling and divergent growth trajectories. The South Dakota Arikara and New Mexico Puebloan groups displayed no significant variation of slope or growth trajectory for any of the individual elements compared. These two groups also share a comparabhighlight that the Arikara and Puebloan groups do display significant size differences in all postcranial elements compared. Hence given the comparable origin and slope for all elements, ontogenetic scaling best fits the juvenile results. The Native Alaskan sample displayed significant intercept differences in four out of the six postcranial elements compared and present a separation in deviation from isometry as compared to both the Arikara and Puebloan populations. Given these results a combination of ontogenetic scaling and divergent growth trajectories is concluded for the juvenile comparison between these three populations. The results for the juvenile component of this data set find that heterochronic, interspecific growth models presented here may not be appropriate for an intraspecific analysis of human

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139 growth. Growth and development are multifaceted processes with eco-geographic, evolutionary factors highlighting only a small factor of its complexity. The Origins of Variation: Which Came First, the Chicken or the Egg? The goal of this investigation was to refocus an analysis of human variation on what I proposed to be the most influential and informative medium to determine how and why these specific groups might differ: variation in ontogeny. The results of my investigation find that the morphology of Native Alaskans, South Dakota Arikara, and New Mexico Puebloan adult populations examined vary significantly and these differences are highly correlated to their eco-geographic condition. There is also a unique pattern to this variation: the distal segments of both the upper and lower limb account for a majority of the group distinction found among both analyses of the individual elements as well as through analysis of indices of the upper and lower limb components. An analysis of the juvenile segment of each population finds that the variation between these three populations is maintained in the developmental period and may even manifest before birth; beyond the scope of the present data. However, advances in developmental biology have addressed questions similar to those posed in anthropology: How and where do variation between groups manifest? What can this tell us about the evolution of novel characters? With a particular focus on limb patterning and development, analysis of Hox gene expression, answers. (McKinney and Gittleman, 1995: 21). McKinney and Gittleman (1995) pose this question as the quintessential example of way questions in evolution have historically been framed. Physical anthropologists and evolutionary theorists tend to examine trends among adult population ot genes or

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140 population variation on the growth and development of the individuals within populations to estigation did not focus specifically on the developmental history of these groups, it can help to bridge the gap between studies of human variability and developmental biology to speculate as to which factors may be the probable source of variation. Limb Development hich limit the amount and kind of insight into mechanisms responsible for the vast diversity that is created among the highly conserved patterns of limb morphogenesis (Gilbert, 2003; Oster et al., 1988; Shubin and Alberch, 1986; Shubin et al., 1997; Vorobyeva and Hinchliffe, 1996; Wolpert, 1991, 2007). Despite the great range of specialization among vertebrate appendages, the same underlying pattern is maintained (Wolpert, 2007). experimental embryology that not only reassessed the traditional understanding of developmental biology but also allowing for an appreciation of discrete units of morphological variation and plasticity (Akam, 1998; Gilbert et al., 1996; Gilbert, 2003; Lovejoy et al., 2000; Raff 1996; Reno et al. in submission). Distinction of the autonomous developmental components in the embryo has allowed for a better appreciation of phenotypic plasticity and variation in isolated body segments such as the limb (Gilbert and Bolker, 2003; Lewontin, 2001; Nijhout, 2003; Pritchard,

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141 1995; Raff and Raff, 2000; Raff, 1996; Roberts, 1995; Schlichting and Pigliucci, 1998; Schlichting, 2003; Schmalhausen, 1949; Shubin et al., 1997). Given the wide range of variation and specializations seen in tetrapods, it is fitting that the limb would be modulated in even smaller components (Reno et al., in prep). Limb development is separated into three distinct regions: the stylopod (humeri/femora), the zeugopod (radius/ulnae, tibiae/fibulae), and autopod (wrist elements and digits). Broad Mechanism of Change: Hox genes Transplantation and experimental studies have shown that the limb is specified long before limb bud outgrowth and the pattern is specified long before cartilage differentiation begins (e.g. Coates and Cohn, 1998; Gilbert et al., 1996; Lovejoy et al., 1999, 2000). These analyses provide potential mechanisms relevant to the present investigation. Hox genes are only just beginning to be fully appreciated. However given the known control of Hox genes during the earliest phases of limb initiation and development they served as a strong potential mechanism responsible for variation between and within population and species. The Hox complex is an ancient group of genes which encode for transcription factors fundamentally responsible for the development and patterning animal body plans (e.g. Akam, 1998a, b; Capecchi, 1997; Cohn, 2001; Gilbert, 2003; Reno et al., in submission; Spitz et al., 2007). Hox genes have been specifically highlighted for their integral role in limb initiation, patterning and development (Capecchi, 1997; Cohn, 2001; Cohn et al., 1995, 1997; Davis et al., 1995; Davis and Capecchi, 1996; Deschamps, 2004; Freitas and Cohn, 2006; Goff and Tabin, 1997; Kmita et al, 2005; Nelson et al., 1996; Reno et al., in submission; Sordino et al., 1995; Spitz et al., 2007; Tarchini and Duboule, 2006; Tarchini et al., 2006; Zakany and Duboule, 1999; Zakany et al., 2004). Investigation into Hox gene expression has drawn attention to not only the domains under Hox control but the importance of the temporal and spatial position of gene

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142 expression in the diversity of limb patterns (Cohn, 2001; Davis et al., 1995; Freitas and Cohn, 2006; Tarchini and Duboule, 2006). Hox genes are expressed in bounded domains which specify identity for different regions of the basic body plan and specifically within the limb field (e.g. Akam, 1998b, Wolpert, 2007). Morphology is determined by the time that a gene is activated in the embryo (termed temporal collinearity) and the positioning of the gene on the embryocollinearity) (Capecchi 1997; Coates and Cohn, 1998; Davis et al., 1995; Freitas and Cohn, 2006; Nelson et al., 1996; Spitz et al., 2007; Tarchini and Duboule, 2006; Tarchini et al., 2006; Zakany and Duboule, 1999). Hox genes were first identified in the Drosophila (detailed in Wolpert, 2007). This cluster of genes were subsequently discovered in mammals but was found to have undergone an evolutionary duplication event resulting in four Hox clusters labeled A-D. Clusters A and D have been experimentally shown to be integral to limb formation while clusters B and C are involved in patterning other body regions (Capecchi, 1997; Davis and Capecchi, 1996; Goff and Tabin, 1997; Kmita et al., 2006; Tarchini and Duboule, 2006; Wolpert, 2007). Duboule, Capecchi and colleagues, through experimental perturbations of Hox gene expression in mice limbs, found that Hox genes 9-13 are responsible for patterning the limb skeleton. Hox A and D genes belonging to groups 9 and 10 pattern stylopod formation, 10-12 control zeugopod development and 11-13 are responsible for autopod development (Carroll, 1997; Davis et al. 1995; Davis and Capecchi, 1996; Goff and Tabin; 1997; Kmita et al., 2006; Reno et al., in submission; Tarchini et al., 2006; Zakany and Duboule, 1999). These domains of Hox gene expression control length and shape of skeletal elements (Davis et al., 1995).

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143 Patterns of Limb Variation: Forelimb and hindlimb variability Forelimbs and hindlimbs in tetrapods (often referred to humans) evolved from the pectoral and pelvic fins of our aquatic ancestors (Gibson-Brown et al., 1996; Shubin et al., 1997). While unique adaptations have occurred simultaneously in the foreand hindlimbs linking their devein gene expression and interaction may have served as the basis for [their] independent Many transcription and genetic factors have been implicated in the variation between the forelimb and hindlimb (Gibson-Brown et al., 1996, 1998; Goff and Tabin, 1997; Nelson et al., 1996; Shubin et al., 1997). Hox genes have been found to exhibit specific foreor hindlimb expression patterns: Hoxc-6 is expressed in the mesenchyme of the forelimb while Hoxc-9, Hoxc-10 and Hoxc-11 are restricted to the hindlimb (Gibson-Brown et al., 1996, 1998; Nelson et al. 1996). Goff and Tabin (1997) note that while some Hox gene mutations affect both the foreand hindlimb, some show more specified effects: Hoxd-9 and Hoxd-12 mutations result in forelimb defects only while Hoxa-10 mutations only have implications in the hindlimb. Experimental analysis has determined that two T-box transcription factors, Tbx5 and Tbx4, are responsible and required for forelimb and hindlimb specification and limb axis determination respectively (Gibson-Brown et al., 1996, 1998; Wolpert, 2007). may be responsible for the pattern of variation found in the present analysis, specifically the differential variation of the upper vs. lower limb in the juvenile sample. Patterns of Limb Variation: Proximal stability and distal variability Experimental studies and analysis of evolutionary trends have found a differential pattern of variation within the limb (Capecchi, 1997; Davis and Capecchi, 1996; Freitas and Cohn, 2006;

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144 Hinchliffe, 1991; Kmita et al., 2006; Shubin et al., 1997; Tarchini and Duboule, 2006; Vorobyeva and Hinchliffepatterns among distal limb segments, notably the autopod, are prone to more significant experimental manipulation and natural variation than proximal limb structures (Capecchi, 1997; Davis and Capecchi, 1996; Kmita et al., 2006: 1116; Reno et al., in submission; Shubin et al., 1997; Sordino et al. 1995: 681; Tarchini and Duboule, 2006). Vorobyeva and Hinchliffe (1996) found that while microsurgery can readily produce either loss or addition of digits, the stylopod and zeugopod are more stable with the zeugopod displaying a slightly higher degree of experimental manipulation. These trends have similarly been revealed not only in the present investigation, but among the natural variation found in primate, hominid and human studies (e.g. Holliday, 1997a, b; Holliday and Falsetti, 1995, 1999; Holliday and Ruff, 1997, 2001; Roberts, 1978; Ruff, 1993; Trinkaus, 1981; Vrba, 1996; Weaver and Ingram, 1969). Given the theory and experimentation surrounding morphogenetic fields of development and Hox genes, mechanisms for independent modification within the limb can be appreciated through differential plasticity and constraint (Maynard Smith et al., 1985; Oster et al. 1988; Raff, 1996; Schlichting and Pigliucci, 1998; Shubin et al., 1997). Numerous authors have divided the 1997; Davis and Capecchi, 1996; Kmita et al., 2006: 1116; Sordino et al. 1995: 681; Tarchini and Duboule, 2006). Capecchi (1997: 280; David and Capecchi, 1996) notes that distal limb sensitivity to potential

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145 Freitas and Cohn (2006) highlight that this pattern of proximal stability/distal variability correlates to the temporal and spatial collinearity of Hox expression. This variation may results not only from collinearity, but separate regulatory control of limb development (Freitas and of limb regions under Hox control (Freitas and Cohn, 2006; Reno et al., in submission; Shubin et al., 1997: 640; Sordino et al., 1995). While not supplying all the answers yet, the exploding field of developmental biology has made significant contributions to our understanding of variation and development and having the greater potential to explain the underlying mechanisms for many of the observations made and questions asked by practitioners of biological anthropology. Despite the variation appreciated throughout molecular analyses of limb development, it is remarkable that many different animals, including humans, display comparable patterns of variation. The modules defined by Hox genes imply a variety of responses that correlate to not only the variation appreciated in distal limb components but to the creation of significant evolutionary novelties. In the future, developmental and evolutionary biology studies will provide even further insight into why these population and species level variations in limb development originate and how they become canalized in embryonic development. Long Bone Development Mesenchyme is a part of the embryonic mesoderm, consisting of unspecialized loosely packed cells from which connective tissue, cartilage and bone are formed. Bone formation begins when mesenchymal cells begin to cluster and condense. Most of the condensed cells become chondrocytes, the primary cell type of cartilage and precursor of bone. Chondrocytes have a characteristic shape and secrete a rich type II collagen, a fibrous protein matrix. While

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146 most of these condensation cells become chondrocytes, the cells surrounding the borders of the condensation form the perichondrium (Kronenberg, 2003). The cartilage cells continue to enlarge and multiply through a combination of matrix production and chondrocyte proliferation. Chondrocytes in the center are the first to stop proliferation and begin hypertrophy. This process alters the trajectory of their genetic program and initiates the synthesis of type X collagen. These hypertrophic chondrocytes in the center are (Kronenberg, 2003: 333). These cells attract blood vessels and chondroclasts and begin the mineralization process of the surrounding matrix. The hypertrophic chondrocytes adjacent to the perichondrium cease proliferation and become osteoblasts secreting a characteristic matrix which forms a bone collar forming scaffolding around the developing bone. This scaffolding supports the osteoblast activity that invades the cartilage as well as blood vessels and begins the initial laying down of the true bone matrix (Karsenty and Wagner, 2002; Kronenberg, 2003). As the chondrocytes continue to proliferate, the ends of the developing bone lengthens. Bone growth is perpetuated rapidly throughout fetal and postnatal life through this proliferation process. During this period of enlargement and elongation, secondary ossification centers are established as chondrocytes toward the ends stop proliferation, hypertrophy and begin increased vascularization. In long bones, proliferation continues at both the primary and secondary ossification sites. The cartilage between these cites is called the growth plate, forming a distinct layer of cells between ossification centers acting as the primary center of bone elongation (Wolpert, 2007). The columnar chondrocytes are flat and are termed resting or reserve chondrocytes as they serve as precursors for later, continued bone development between the primary and secondary ossification centers (Kronenberg, 2003). The growth plate disappears just

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147 after puberty in humans which marks the end of bone lengthening and formation (Karsenty and Wagner, 2002; Kronenberg, 2003; Wolpert, 2007). Local Mechanisms of Change Bones change, grow and are remodeled throughout ontogeny and throughout evolution. This change is orchestrated and regulated through many local and systemic, molecular and -out of [these] coskeletogenesis and bone growth is required to appreciate how the slightest variation in these cellular interactions can alter the adult phenotype. Some of the most integral components in the developmental process are the morphogenetic agents required for growth, maturation, proliferation and apoptosis. Some of which will be examined here as potential mechanisms responsible for the population-wise modifications within these three groups. For growth to be extended or retarded the rate of cellular proliferation and expansion of the hypertrophic zone needs to outweigh apoptosis or vice versa. There are a number of growth factors that have been attributed to the control and regulation of cell division, increase and/or death. A few of these integral players will be presented here as potential mechanisms responsible for variation in long bone length. Indian hedgehog (Ihh) and Parathyroid-hormone-related protein (PTHrP) as well as chondrocyte and osteoblast differentiation (Karsenty and Wagner, 2002; Kronenberg, 2003: 332; Liu et al. 2002). During development Ihh is secreted by preand early hypertrophic chondrocytes. Experimental studies of mice that have an inactivation of Ihh display pronounced abnormalities in bone growth marked by small cartilage elements due to decreased chondrocyte proliferation and an absence of osteoblast activity (Kronenberg, 2003).

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148 PTHrP is a protein similarly secreted embryonically by pre/early hypertrophic chondrocytes toward the ends of the developing bone (Kronenberg, 2003; Liu et al., 2002). This factor has been found to be primarily responsible for keeping proliferating chondrocytes in the proliferative pool thereby delaying their hypertrophy and subsequent mineralization. Experimental disruption of PTHrP results in premature maturation of chondrocytes and short-limbed dwarfism (Karsenty and Wagner, 2002; Liu et al., 2002). PTHrP and Ihh are found to work in concert to control extension at the ends of the developing bone. Mice who have an experimental inactivation of Ihh fail to produce PTHrP. Together Ihh and PTHrP are responsible for the determination of the extent and boarders of the proliferative zone of the developing bone (Kronenberg, 2003). Fibroblast growth factors (FGFs) FGFs are a family of polypeptides which serve multiple roles integral to cell growth and differentiation (Liu et al., 2002). Fgfr3 is expressed in proliferating and prehypertrophic chondrocytes inhibiting further proliferation (Karsenty and Wagner, 2002; Kronenberg, 2003). A null mutation of this gene results in an expanded growth plate and increased cell proliferation. A similar phenotype results from a null mutation in Fgf18. Fgfr1 and Fgfr2 are expressed in hypertrophic chondrocytes (Kronenberg, 2003; Liu et al. 2002). Bone morphogenetic proteins (BMPs) BMPs, also called growth and differentiation factors (GDFs), serve multiple roles in skeletal development (Karsenty and Wagner, 2002; Kronenberg, 2003; Sears et al., 2006). BMP2 and -6 are expressed in hypertrophic chondrocytes and BMP7 is expressed in proliferating chondrocytes. BMPs also are responsible for increasing the expression of Ihh by prehypertrophic chondrocytes and are subsequently responsible for chondrocyte proliferation (Kronenberg, 2003; Sears et al., 2006). A mutation in GDF5, in both mice and humans, causes multiple types of

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149 chondrodysplasia characterized by shortened limbs, abnormal joint development and digit reduction (Karsenty and Wagner, 2002). Sears and colleagues (2006) found that BMP signaling is responsible for the increased digit length in bats. Similarly, their results show that bat embryonic forelimbs display an upregulation of BMP production and activity relative to both mice as well as bat hindlimbs (Sears et al., 2006). As in bats, BMP may be a key mechanism responsible for an increase/decrease of limb length in other species. Insulin-like growth factors (IGF) IGF-1 and 2 are protein growth factors, closely resembling insulin (as they are so named), which play an integral role in embryonic and post-natal growth and development (Conlon and Raff, 1999; Lupu et al., 2001; Sutter et al., 2007; Wolpert, 2007). IGF-1 is considered a strong determinant of body size across mammals by controlling the rate of chondrocyte proliferation (Karsenty and Wagner, 2002; Sutter et al. 2007). Sutter and colleagues (2007) investigated whether IGF-1 was an integral factor responsible for the variation of body size among dog breeds. A specific IGF-1 polymorphism was found to be common to all small dog breeds by absent from large breeds. Sutter et al. (2007) concluded that this specific variant found only in small breeds was a probable mechanism responsible for small breed body size. Growth hormone (GH) Growth hormone is a post-embryonic protein hormone required for postnatal growth in humans and mammals (Conlon and Raff, 1999; Karsenty and Wagner, 2002; Lupu et al., 2001; Wolpert, 2007). Humans found to be deficient in GH become dwarfed while those with an excess of GH become giants (Conlon and Raff, 1999). Similarly, GH also acts by inducing the liver and other organs to produce IGF-1 (Conlon and Raff, 1999). Lupu and colleagues (2001) tested the roles of both IGF-1 and GH in the postnatal development of mice. It was found that

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150 GH and IGF-1 have independent and overlapping functions in chondrocyte development. Individuals with an inactivation of IGF-1 were small than individuals with an inactiviation of GH, however double mutants for both factors were the most affected (Lupu et al. 2001). Final Thoughts Small changes in molecular patterning can lead to dramatically different phenotypes. In the present analysis it is not possible to definitively determine what, if any, of the above morphogenetic agents are responsible for the variation between these three populations. However, it is probable that one or a combination of these factors may have been affected by the eco-geographic or their occupation and evolution. This physical isolation from one another and eco-geographic factors development and variation.

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151 CHAPTER 6 SUMMARY The objective of this analysis was to test whether three spatially distinct Native North American populations vary significantly by eco-geographic condition, with the particular goal of highlighting patterns of variation among juvenile skeletal remains. These data were derived from 871 individuals from three archaeological populations of skeletal remains: Native Alaskan groups, South Dakota Arikara and New Mexico Puebloan groups. These data are particularly compelling and serve as an integral contribution to population skeletal biology as approximately 50% of these individuals, at this time, have already been signed-off for repatriation from academic institutions (Public Law 101-601, 1990) and are currently off limits to future analysis. Within the next few years this figure will probably reach 100%. The results of this investigation have shown that significant variation can be appreciated among archaeological remains of juveniles and that population disparities in physical dimensions are significantly correlated to eco-geographic condition. Juvenile skeletal analyses based on archaeological assemblages have inherent limitations. The juvenile individuals of the present analysis represent the cross-sectional growth patterns of three groups. patterns are representative of their native groups. Many bacterial and viral conditions are acute, leaving no substantial or lasting manifestation on the developing skeleton. Similarly, children are incredibly resilient. After overcoming illness or growth arrest due to dietary constraint children vious growth trajectories. These limitations are outweighed by the probative value these data have for the understanding of human growth patterns and how this period may affect the morphology of a population.

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152 Given the nature of archaeological populations, numerous theoretical and methodological considerations were assumed. It is assumed that the sex and age of each individual was estimated as best as possible given the literature and methodology available at this time. The individuals from each population are considered to be members of that cultural group, sharing a common evolutionary history and immediate life history traits including eco-geographic condition. For the juvenile sample analyzed, it is assumed that all individuals display representative growth patterns for that specific population and that their growth was not adversely affected by their cause of death; all individuals displaying gross pathological conditions were excluded from these analyses. It is also assumed that, following the paradigms and models established in the literature, the most appropriate analytical methods for the data are being employed. Finally, it is assumed that given these theoretical and methodological considerations, these three groups were appropriate for comparative analysis against the selected eco-geographic variables. Many previous investigators have focused on geography (longitude and latitude) and homeothermic animals have been substantiated in analyses of hominids as well as other vertebrates. For this project, eco-geographic variables from three spatially distinct populations were compared. Eco-geographic variables consisted of a single geographic variable (latitude), three temperature variables (annual mean temperature, highest month mean, lowest month mean) and three climatic variables (annual mean precipitation, highest month mean, lowest month mean). These measures approximated the conditions of the region of the three populations examined at their time of occupation. These eco-geographic conditions were compared to the linear measurements of six postcranial long bones (humeri, ulnae, radii, femora, tibiae, fibulae)

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153 from each distinct population: Native Alaska groups (n=242), South Dakota Arikara (n=379) and Ancestral Puebloan groups from New Mexico (n=250). The principal components structure of the logged linear dimensions of both adults and juveniles provide measures of within population variation due to differences among both size and shape variables. For both adult and juvenile groups, the first principal component of log size and log shape accounted for a majority of the within group variation. This axis, representing size, was found to account for a majority of the variation among both adults and juveniles sampled. Shape variables described significantly less of the within population variation in the sample. The structure of the principal variates provided interesting results for both the adult and juvenile categories examined. The pattern of variation for the adult sample was separated between the proximal and distal components for both the upper and lower limbs. For both limbs the distal elements displayed a consistent and higher variate loading on the first principal axis indicating that the distal segments were responsible for a large portion of variation within the adult sample. Altneratively, the pattern of variation among the juvenile sample was separated between the upper versus lower limb with no further internal variation between the proximal and distal components within each limb. In this case, the lower limb displayed the higher loadings indicating that a larger portion of the juvenile sample variation is manifest in the lower limb. At this time the reason for this variation between the adult and juvenile sample is unclear. It is possible and highly likely that the juvenile sample variation is due to the natural variation in human development as well as cross-sectional nature of the data and the wide age range incorporated into the juvenile sample; this section of the data spans the entire postnatal growth period from infancy through adolescence.

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154 Further investigation into this quandary revealed adolescence as a period of juvenile development. Analysis of smaller, discrete juvenile age categories find that while birth-2 and 3-7 estimated years of age mirror the previous juvenile sample results, 8-11 and 12-21 age categories mark a transition to adult patterns of postcranial variation. The relationship between populations after accounting for within group differences was also examined through a canonical discriminant analysis. The patterns of between-group associations derived from linear postcranial dimensions suggest that these groups are readily distinguishable. The structure of the first canonical axis served to discern a unique pattern of variation among the elements examined. This analysis found that the distal limb segments (ulnae/radii, tibiae/fibulae) exhibited significantly higher canonical coefficients than found in the proximal limb components (humeri, femora). This pattern suggests that a majority of the variation between these populations is due to changes in the distal limb segments. Given a significant pattern of variation the correlation of these trends to environmental conditions were explored. Human morphological patterning of distal elements by climate/geography have been noted in the literature (e.g. Falsetti, 1989; Holliday and Falsetti, 1995; Holliday and Ruff, 2001; Trinkaus, 1981). In an analysis within and between 20 geographically distinct skeletal samples of modern humans Holliday and Ruff (2001) concluded that the relative variance of the distal limb was significantly great than those of the proximal; the tibia found to be the most variable. While not explicitly tested, climatic factors are highlighted as the probable factor responsible for distal segment variation among human populations (Holliday and Ruff, 2001). To determine whether eco-geographic variables in any way affect the morphological variation outlined from previous analyses, canonical correlations analysis was conducted.

PAGE 155

155 Canonical correlation between the six linear measurements and five eco-geographic conditions (low month mean precipitation was removed as it was the same value for all populations compared) were significant. The first canonical axis accounted for almost 90% of the sample variation and displayed a high coefficient loading of 0.77. This indicates that, overall, the eco-geographic variables selected for analysis are a relatively strong predictor of linear dimensions. The eco-geographic variables show a higher correlation to the distal limb segments than proximal ones; a trend similarly found among population variation in the canonical discriminant analysis. The most obvious pattern found in the canonical correlation analysis is the negative correlation of latitude against all linear dimensions compared; higher correlations are found for distal skeletal elements than proximal ones. Similarly, latitude exhibited the highest correlation coefficients for all eco-geographic conditions investigated. Precipitation variables showed a positive correlation with linear measurements however no specific patterns of variation were appreciated. These results support the cl The reduced major axis regression and test of isometry rejects the null hypothesis of isometry for all elements within the juvenile component of the three populations examined. While the slopes, or growth trajectories, for all elements were not found to differ significantly from each other across populations, collectively they were found to deviate significantly from multivariate isometry (for 6 variables, slope = 0.408). The intercepts for some elements compared did display a significant variation in intercept or size origin. The ulnae, radii, femora and fibulae were found to display common growth trajectories with unique size origins between populations with the humeri and tibiae exhibiting common allometric trajectory with common origin. It is uncertain why the tibiae did not follow previously identified trends among the distal

PAGE 156

156 limb seen here in the regressions of ulnae, radii and fibulae. However, for these three elements, the pattern of variation in the distal segments of the upper and lower limb found in previous analyses is revealed. The variation in adult form is a product of deviation not in rate of growth, but size at birth. Developmental biology has set the stage for a true understanding of the mechanisms responsible for population variation and evolutionary change. While a true understanding of why and how these populations came to vary is beyond the scope of this project, the identification of the extent and pattern of variation allows for a better understanding of evolutionary trends and opens the door to future investigations that will undoubtedly provide a deeper appreciation of morphological change. In conclusion, size of postcranial skeletal long bones is found to be significantly patterned by the eco-geographic variables outlined here. This picture of morphological variation is indicative of the genetic and developmental variation between these three populations related to their evolutionary relationships, separation and patterning by local conditions. Hopefully the results of this investigation will encourage further analysis into developmental patterning among archaeological and extant human populations as well as providing a platform for a deeper exploration into the mechanisms that give rise to skeletal and developmental variation.

PAGE 157

157 APPENDIX A POSTCRANIAL DATA Appendix A represents the raw post-cranial measurements taken on three populations of Native North Americans: Native Alaskans, South Dakota Arikara, and New Mexico Puebloan groups. All post-cranial long bone measurements are listed in millimeters. The code for each population is found in Table A-1. Table A-1. Code for population assessment Population Code Native Alaskan 1 South Dakota Arikara 2 New Mexico Puebloan 3 The sex is also coded into three categories found in Table A-2: male, female and undetermined. The group of undetermined individuals comprises the juvenile population. All adult individuals were assigned either a male or female sex assessment. Table A-2. Code for sex assessment Sex Code Male 1 Female 2 Undeterm ined 3 Table A-3. Post-cranial data by population, estimated dental age and sex Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 377826 1 6m +/ 3m 3 70 68 59 82 378385 1 6m +/ 3m 3 72 60 85 73 69 378660 1 9m +/ 3m 3 84 76 68 105 87 377827 1 1 yr +/ 4m 3 103 93 82 132 105 103 378434 1 1.5 yr +/ 6m 3 96 81 72 118 98 97 363601 1 1.5 yr +/ 6m 3 102 85 76 127 101 101 279211 1 2 yr +/ 8m 3 . 342513 1 2 yr +/ 8m 3 158 345339 1 2 yr +/ 8m 3 136 161 345386 1 2 yr +/ 8m 3 100 128

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158 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 378386 1 2 yr +/ 8m 3 91 82 134 106 105 378496 1 2 yr +/ 8m 3 110 92 85 135 111 378494 1 2.5 yr +/ 10m 3 123 97 90 156 125 122 351286 1 3 yr +/ 12m 3 130 107 96 170 138 135 351325 1 3 yr +/ 12m 3 155 117 106 201 149 150 363537 1 3 yr +/ 12m 3 182 363543 1 3 yr +/ 12m 3 118 93 83 150 115 363602 1 3 yr +/ 12m 3 125 82 164 129 377823 1 3 yr +/ 12m 3 133 102 91 169 130 127 378492 1 3 yr +/ 12m 3 109 91 82 134 114 379716 1 3.5 yr +/ 12m 3 132 105 94 172 135 139 345708 1 3.5 yr +/ 12m 3 . 162 345375 1 4 yr +/ 12m 3 . 163 363536 1 4 yr +/ 12m 3 150 187 154 363544 1 4 yr +/ 12m 3 135 109 98 186 139 144 363566 1 4 yr +/ 12m 3 140 111 100 180 145 377822 1 4 yr +/ 12m 3 133 108 96 173 140 137 378384 1 4.5 yr +/ 12m 3 136 115 103 183 144 143 345358 1 5 yr +/ 16m 3 147 126 112 196 1 55 154 345376 1 5 yr +/ 16m 3 145 116 103 195 149 147 345726 1 5 yr +/ 16m 3 157 123 108 219 171 174 351323 1 5 yr +/ 16m 3 159 126 115 212 164 165 377824 1 5 yr +/ 16m 3 150 122 195 154 151 345365 1 6 yr +/ 24m 3 153 208 351229 1 6 yr +/ 24m 3 157 127 113 216 169 169 378379 1 6 yr +/ 24m 3 135 112 139 378383 1 6 yr +/ 24m 3 187 145 143 351228 1 7 yr +/ 24m 3 173 121 232 180 180 378382 1 7 yr +/ 24m 3 145 123 . 345759x 1 7 yr +/ 24m 3 183 147 131 263 214 2 01 339051 1 8 yr +/ 24m 3 216 153 136 294 210 202 339754 1 8 yr +/ 24m 3 192 148 135 206 363542 1 8 yr +/ 24m 3 177 130 239 183 185 363546 1 8 yr +/ 24m 3 198 283 188 378394 1 8 yr +/ 24m 3 235 198 196 345729 1 9 yr +/ 24m 3 2 06 162 147 317 252 378381 1 9 yr +/ 24m 3 182 152 138 241 191 190 339749 1 10 yr +/ 30m 3 243 182 171 346 294 259

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159 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 363564 1 10 yr +/ 30m 3 134 2 73 342507 1 10 yr +/ 30m 3 312 241 240 345711 1 11 yr +/ 30m 3 149 323 268 241 363565 1 11 yr +/ 30m 3 206 331 247 363572 1 11 yr +/ 30m 3 224 137 291 228 214 379697 1 11 yr +/ 30m 3 180 153 139 254 199 198 378342 1 11 yr + / 30m 3 225 188 170 312 249 238 345725 1 12 yr +/ 36m 3 230 166 146 338 271 254 345753 1 12 yr +/ 36m 3 230 168 155 315 241 231 351285 1 12 yr +/ 36m 3 233 195 179 365 262 365907 1 12 yr +/ 36m 3 199 167 150 377912 1 12 yr +/ 36m 3 232 173 378380 1 12 yr +/ 36m 3 232 196 168 321 243 242 378650 1 12 yr +/ 36m 3 218 167 160 310 244 215 381108 1 12 yr +/ 36m 3 234 194 171 319 256 250 381105 1 12 yr +/ 36m 3 225 192 174 317 252 250 363539 1 13.5 yr +/ 36m 3 224 162 354 264 251 378489 1 13.5 yr +/ 36m 3 263 217 358 296 294 345368 1 13.5 yr +/ 36m 3 327 258 363563 1 13.5 yr +/ 36m 3 240 165 335 377918 1 13.5 yr +/ 36m 3 200 171 157 292 233 227 345736 1 15 yr +/ 36m 1 237 211 421 335 325 363581 1 1 5 yr +/ 36m 1 286 205 185 404 301 363562 1 15 yr +/ 36m 1 295 224 198 383 322 305 351299 1 18 yr +/ 36m 1 323 247 221 363525 1 18 yr +/ 36m 1 405 342416 1 18 yr +/ 36m 1 309 241 208 426 342415 1 15 yr +/ 36m 2 267 179 383 314 276 345735 1 15 yr +/ 36m 2 274 205 383 308 269 345750 1 15 yr +/ 36m 2 243 188 167 378 297 258 345359 1 18 yr +/ 36m 2 277 219 198 390 306 274 345361 1 18 yr +/ 36m 2 269 215 195 378 292 281 378696 1 18 yr +/ 36m 2 296 236 212 408 321 309 379173 1 18 yr +/ 36m 2 362 314 272 363571 1 18 yr +/ 36m 2 398 298 351198 1 18 yr +/ 36m 2 270 401 315 299 377923 1 17 25 yrs 1 288 210 393 351305 1 17 25 yrs 1 376

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160 Table A-3. Continued Individual Population De ntal Age Sex humeri ulnae radii femora tibiae fibulae 378694 1 17 25 yrs 1 293 241 214 408 298 345316 1 17 25 yrs 1 284 228 210 401 326 317 363577 1 17 25 yrs 1 308 217 403 295 379713 1 17 25 yrs 1 302 251 231 451 370 350 332611 1 17 25 yrs 1 30 6 251 230 435 348 340 339124 1 17 25 yrs 1 305 247 225 421 340 336 345303 1 17 25 yrs 1 323 247 222 438 348 330 351201 1 17 25 yrs 1 328 416 363568 1 17 25 yrs 1 303 247 219 429 357 340 339062 1 17 25 yrs 2 353 274 345357 1 17 25 yrs 2 278 206 198 383 319 314 345721 1 17 25 yrs 2 292 224 205 389 311 310 363603 1 17 25 yrs 2 . 316 378369 1 17 25 yrs 2 260 211 . 378695 1 17 25 yrs 2 289 224 196 392 308 273 381091 1 17 25 yrs 2 276 380 306 296 363587 1 17 25 yrs 2 286 228 205 405 315 309 377752 1 17 25 yrs 2 293 239 225 405 335 329 345331 1 17 25 yrs 2 300 238 223 427 343 336 345306 1 17 25 yrs 2 272 211 194 378 304 294 339059 1 17 25 yrs 2 297 236 220 404 324 314 339032 1 17 25 yrs 2 276 215 199 380 306 295 3 45728 1 17 25 yrs 2 337 345743 1 17 25 yrs 2 280 223 197 391 304 268 342504 1 17 25 yrs 2 222 199 383 345394 1 17 25 yrs 2 279 229 210 354 301 287 345751 1 17 25 yrs 2 285 237 212 408 317 315 345302 1 20 30 yrs 1 318 247 228 432 352 34 7 345701 1 20 30 yrs 1 293 242 220 410 336 325 339120 1 20 30 yrs 1 296 230 210 414 310 310 345349 1 20 30 yrs 1 378 475 376 332503 1 25 35 yrs 1 296 265 242 329 332511 1 25 35 yrs 1 287 225 417 351215 1 25 35 yrs 1 235 433 3 32514 1 25 35 yrs 1 309 250 227 424 349 329 345310 1 25 35 yrs 1 305 235 212 413 339 330 345347 1 25 35 yrs 1 317 240 433 353 346 351289 1 25 35 yrs 1 296 253 229 423 330 313

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161 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae rad ii femora tibiae fibulae 332501 1 30 40 yrs 1 300 238 222 418 343 339 339750 1 30 40 yrs 1 307 413 347 340 17479 1 30 40 yrs 1 318 267 244 445 358 340 332507 1 30 40 yrs 1 315 248 227 430 353 340 333467 1 30 40 yrs 1 308 260 239 410 357 341 33346 8 1 30 40 yrs 1 302 253 227 430 345 332 339034 1 30 40 yrs 1 299 238 220 404 315 310 339119 1 30 40 yrs 1 233 219 425 331 315 339121 1 30 40 yrs 1 313 248 230 423 333 328 339123 1 30 40 yrs 1 280 230 211 393 305 294 345378 1 30 40 yrs 1 336 268 250 450 360 345 345738 1 30 40 yrs 1 274 237 217 398 298 290 351224 1 30 40 yrs 1 298 245 222 411 328 339061 1 20 30 yrs 2 290 228 202 393 323 308 345360 1 20 30 yrs 2 321 435 332 345391 1 20 30 yrs 2 311 248 228 384 313 345710 1 20 30 yrs 2 29 6 228 206 397 320 351207 1 20 30 yrs 2 298 405 324 332506 1 20 30 yrs 2 268 216 199 373 295 286 333471 1 20 30 yrs 2 281 236 202 408 333 327 339125 1 20 30 yrs 2 290 231 212 401 310 305 345300 1 20 30 yrs 2 287 236 213 403 330 329 345307 1 20 30 yrs 2 297 414 342 332 345745 1 20 30 yrs 2 293 238 217 393 313 312 351217 1 20 30 yrs 2 297 220 414 324 304 333470 1 25 35 yrs 2 288 226 206 397 323 313 339060 1 25 35 yrs 2 274 224 203 371 302 291 339116 1 25 35 yrs 2 281 215 196 397 308 30 2 345333 1 25 35 yrs 2 295 231 212 413 326 324 345383 1 25 35 yrs 2 298 246 226 427 330 320 345397 1 25 35 yrs 2 276 216 388 317 311 345703 1 25 35 yrs 2 277 211 191 385 294 290 339033 1 25 35 yrs 2 324 246 230 419 346 332 339114 1 25 35 yrs 2 284 221 203 411 327 316 345301 1 25 35 yrs 2 220 203 392 345312 1 25 35 yrs 2 292 229 210 407 318 310 345733 1 25 35 yrs 2 275 220 204 388 312 311 351200 1 25 35 yrs 2 361 272

PAGE 162

162 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 351237 1 25 35 yrs 2 263 209 188 343 276 270 345330 1 25 35 yrs 2 289 222 406 334 332504 1 30 40 yrs 2 314 255 236 436 350 339 339031 1 30 40 yrs 2 292 232 213 407 316 311 342417 1 30 40 yrs 2 282 222 207 408 323 313 345309 1 30 40 yrs 2 295 231 211 399 317 311 345355 1 30 40 yrs 2 275 215 198 379 303 333472 1 30 40 yrs 2 292 210 415 334 318 342414 1 30 40 yrs 2 395 345304 1 30 40 yrs 2 319 257 234 433 343 335 345311 1 30 40 yrs 2 283 219 197 3 88 345343 1 30 40 yrs 2 275 216 195 390 305 296 345370 1 30 40 yrs 2 285 236 212 395 311 308 345702 1 30 40 yrs 2 310 229 209 439 360 345 345715 1 30 40 yrs 2 247 187 163 332 260 251 345754 1 30 40 yrs 2 290 235 213 417 318 308 351211 1 30 40 yrs 2 290 403 315 351225 1 30 40 yrs 2 374 292 289 351219 1 30 40 yrs 2 395 279202 1 40+ yrs 1 307 256 240 436 225255 1 40+ yrs 1 308 234 339058 1 40+ yrs 1 314 228 427 323 342482 1 40+ yrs 1 318 438 385 375 34 5334 1 40+ yrs 1 301 250 228 415 349 337 345348 1 40+ yrs 1 300 251 230 425 369 356 345731 1 40+ yrs 1 310 265 238 446 347 351204 1 40+ yrs 1 312 420 328 319 351226 1 40+ yrs 1 228 433 348 332502 1 40+ yrs 1 309 260 237 414 347 339 332512 1 40+ yrs 1 321 258 238 438 363 349 339118 1 40+ yrs 1 316 259 236 427 351 342 345400 1 40+ yrs 1 287 408 351220 1 40+ yrs 1 318 231 433 344 339 345388 1 40+ yrs 1 294 252 231 403 339 351202 1 40+ yrs 1 305 262 . 351199 1 40+ yrs 1 304 242 218 351203 1 40+ yrs 1 215 410 318 339115 1 40+ yrs 1 273 201 408 315 308

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163 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 345308 1 40+ yrs 1 326 267 255 446 355 338 345351 1 40 + yrs 1 291 228 212 380 311 307 345369 1 40+ yrs 1 333 280 256 455 383 368 345379 1 40+ yrs 1 320 243 229 440 354 345707 1 40+ yrs 1 301 228 426 348 333 345724 1 40+ yrs 1 280 240 219 397 315 307 345730 1 40+ yrs 1 292 234 216 391 313 305 345739 1 40+ yrs 1 338 246 225 467 376 352 345744 1 40+ yrs 1 279 244 227 402 344 333 351205 1 40+ yrs 1 420 336 351208 1 40+ yrs 1 290 234 213 409 322 310 351212 1 40+ yrs 1 291 246 222 398 331 318 332505 1 40+ yrs 2 287 240 219 406 331 324 345337 1 40+ yrs 2 304 407 329 323 345366 1 40+ yrs 2 306 250 225 425 344 338 345384 1 40+ yrs 2 252 353 294 345395 1 40+ yrs 2 279 404 306 351213 1 40+ yrs 2 291 208 426 327 351236 1 40+ yrs 2 273 231 403 304 351347 1 40+ yrs 2 296 415 334 315 351218 1 40+ yrs 2 279 227 205 396 317 279204 1 40+ yrs 2 290 344 332508 1 40+ yrs 2 287 226 212 410 324 312 333469 1 40+ yrs 2 292 213 200 408 339751 1 40+ yrs 2 299 238 215 415 326 323 345727 1 40+ yrs 2 270 218 199 40 0 329 325 351221 1 40+ yrs 2 283 235 212 400 310 300 345390 1 40+ yrs 2 280 216 199 376 302 287 345752 1 40+ yrs 2 277 244 394 315 345313 1 40+ yrs 2 280 218 313 351214 1 40+ yrs 2 270 221 195 382 310 301 339117 1 40+ yrs 2 273 212 187 373 281 345315 1 40+ yrs 2 277 218 190 397 308 301 345341 1 40+ yrs 2 265 382 345373 1 40+ yrs 2 283 223 202 395 318 299 345377 1 40+ yrs 2 305 259 234 430 354 336 345392 1 40+ yrs 2 282 217 196 381 308 300 345396 1 40+ yrs 2 235 210 392 322 315

PAGE 164

164 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 345719 1 40+ yrs 2 269 375 296 345742 1 40+ yrs 2 300 244 218 414 340 351197 1 40+ yrs 2 205 396 304 351234 1 40+ yrs 2 276 225 203 3 95 319 313 381403 2 birth +/ 2m 3 62 . 381447 2 birth +/ 2m 3 65 62 55 75 65 63 381448 2 birth +/ 2m 3 63 74 63 382733 2 birth +/ 2m 3 70 58 78 68 382957 2 birth +/ 2m 3 66 57 80 70 382963 2 birth +/ 2m 3 70 59 83 71 382966 2 birth +/ 2m 3 64 78 68 382987 2 birth +/ 2m 3 77 69 382989 2 birth +/ 2m 3 69 79 69 68 382992 2 birth +/ 2m 3 64 70 60 383059 2 birth +/ 2m 3 68 65 81 69 383109 2 birth +/ 2m 3 64 62 52 73 383110 2 birth +/ 2m 3 66 61 54 80 64 62 383134 2 birth +/ 2m 3 66 63 56 74 63 383168 2 birth +/ 2m 3 70 65 57 79 69 65 382908 2 birth +/ 2m 3 57 55 49 383169 2 birth +/ 2m 3 56 55 48 62 56 52 381461 2 birth +/ 2m 3 61 59 52 72 64 62 381639 2 birth + / 2m 3 66 61 55 78 381647 2 birth +/ 2m 3 64 62 73 64 381650 2 birth +/ 2m 3 66 64 56 74 381720 2 birth +/ 2m 3 65 . 382725 2 birth +/ 2m 3 60 58 51 69 60 382998 2 birth +/ 2m 3 59 75 64 61 383037 2 birth +/ 2m 3 65 80 383147 2 birth +/ 2m 3 69 61 382730 2 3m +/ 3m 3 64 57 383060 2 3m +/ 3m 3 55 383084 2 3m +/ 3m 3 75 65 383099 2 3m +/ 3m 3 67 58 80 383111 2 3m +/ 3m 3 66 55 75 67 383114 2 3m +/ 3m 3 69 66 58 80 71 67 381433 2 3m +/ 3m 3 68 65 56 67 381468 2 3m +/ 3m 3 65 61 53 77 66 65

PAGE 165

165 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 382952 2 3m +/ 3m 3 72 67 59 80 72 383028 2 3m +/ 3m 3 7 3 70 61 90 75 73 383083 2 3m +/ 3m 3 65 63 55 73 67 383105 2 3m +/ 3m 3 73 69 89 71 383115 2 3m +/ 3m 3 67 63 55 75 66 63 383140 2 3m +/ 3m 3 70 66 57 83 73 381409 2 6m +/ 3m 3 64 61 53 64 381361 2 6m +/ 3m 3 69 66 58 79 69 69 3813 24 2 6m +/ 3m 3 65 57 77 67 65 381331 2 6m +/ 3m 3 70 63 55 79 69 381364 2 6m +/ 3m 3 76 72 62 90 80 79 381416 2 6m +/ 3m 3 67 65 69 381424 2 6m +/ 3m 3 52 68 60 381426 2 6m +/ 3m 3 65 58 77 69 381427 2 6m +/ 3m 3 79 71 381460 2 6m +/ 3m 3 82 72 63 96 382695 2 6m +/ 3m 3 80 76 66 93 83 80 382697 2 6m +/ 3m 3 78 73 62 94 82 383024 2 6m +/ 3m 3 80 64 93 81 76 383042 2 6m +/ 3m 3 72 67 60 85 76 73 383081 2 6m +/ 3m 3 83 66 381406 2 6m +/ 3m 3 66 62 54 77 67 65 325413 2 9m +/ 3m 3 92 . 381436 2 9m +/ 3m 3 85 . 381446 2 9m +/ 3m 3 69 84 381466 2 9m +/ 3m 3 88 82 73 105 95 93 381467 2 9m +/ 3m 3 87 76 67 101 86 382902 2 9m +/ 3m 3 96 . 382970 2 9m +/ 3m 3 120 102 97 383031 2 9m +/ 3m 3 79 64 383064 2 9m +/ 3m 3 92 84 75 114 96 381325 2 1 yr +/ 4m 3 . 105 381335 2 1 yr +/ 4m 3 85 76 68 99 86 381368 2 1 yr +/ 4m 3 90 89 381419 2 1 yr +/ 4m 3 89 88 77 112 97 95 381439 2 1 yr +/ 4m 3 79 129 108 382927 2 1 yr +/ 4m 3 120 102 382930 2 1 yr +/ 4m 3 96 78

PAGE 166

166 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 382937 2 1 yr +/ 4m 3 124 382960 2 1 yr +/ 4m 3 101 124 382988 2 1 yr +/ 4m 3 93 87 112 385946 2 1 yr +/ 4m 3 94 75 112 96 382892 2 1 yr +/ 4m 3 107 96 85 111 381334 2 1.5 yr +/ 6m 3 115 92 381370 2 1.5 yr +/ 6m 3 109 96 87 3 81408 2 1.5 yr +/ 6m 3 100 . 381434 2 1.5 yr +/ 6m 3 99 89 80 124 104 103 382700 2 1.5 yr +/ 6m 3 110 96 85 139 114 112 382718 2 1.5 yr +/ 6m 3 106 87 131 112 382870 2 1.5 yr +/ 6m 3 104 95 84 130 111 107 382893 2 1.5 yr +/ 6m 3 116 100 90 382899 2 1.5 yr +/ 6m 3 100 . 382984 2 1.5 yr +/ 6m 3 87 111 111 383066 2 1.5 yr +/ 6m 3 93 75 98 383175 2 1.5 yr +/ 6m 3 105 94 84 136 114 111 381444 2 2 yr +/ 8m 3 105 95 83 135 109 108 381664 2 2 yr +/ 8m 3 1 34 116 104 173 144 382716 2 2 yr +/ 8m 3 119 106 148 382743 2 2 yr +/ 8m 3 123 104 98 154 382756 2 2 yr +/ 8m 3 105 93 151 130 126 382928 2 2 yr +/ 8m 3 105 92 134 130 383017 2 2 yr +/ 8m 3 108 95 85 138 113 110 381387 2 3 yr + / 12m 3 135 114 102 174 381659 2 3 yr +/ 12m 3 125 109 159 136 133 382671 2 3 yr +/ 12m 3 128 110 98 162 132 382685 2 3 yr +/ 12m 3 130 115 103 176 149 147 382727 2 3 yr +/ 12m 3 122 158 382936 2 3 yr +/ 12m 3 124 108 94 3 83043 2 3 yr +/ 12m 3 124 95 160 383091 2 3 yr +/ 12m 3 133 . 383096 2 3 yr +/ 12m 3 123 107 97 159 131 128 383139 2 3 yr +/ 12m 3 102 171 138 383142 2 3 yr +/ 12m 3 116 . 381181 2 3 yr +/ 12m 3 . 164 381462 2 4 yr +/ 12m 3 143 125 112 192 381340 2 4 yr +/ 12m 3 106 177 146 143

PAGE 167

167 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 381687 2 4 yr +/ 12m 3 137 119 106 192 158 147 381719 2 4 yr +/ 12m 3 116 104 382668 2 4 yr +/ 12m 3 183 151 382729 2 4 yr +/ 12m 3 124 109 193 163 162 383143 2 4 yr +/ 12m 3 157 118 385947 2 4 yr +/ 12m 3 143 125 112 194 160 385951 2 4 yr +/ 12m 3 120 212 174 381349 2 5 yr +/ 1 6m 3 236 188 381445 2 5 yr +/ 16m 3 147 124 113 189 157 157 382675 2 5 yr +/ 16m 3 155 135 123 203 172 382708 2 5 yr +/ 16m 3 161 138 125 218 183 181 382721 2 5 yr +/ 16m 3 161 139 124 190 187 381418 2 5 yr +/ 16m 3 150 171 3 81425 2 5.5 yr +/ 20m 3 148 127 115 155 153 381366 2 6 yr +/ 24m 3 145 133 119 165 160 381380 2 6 yr +/ 24m 3 225 187 185 381394 2 6 yr +/ 24m 3 150 . 381440 2 6 yr +/ 24m 3 138 239 203 200 382698 2 6 yr +/ 24m 3 162 135 12 5 210 182 382754 2 6 yr +/ 24m 3 155 132 119 234 169 163 382996 2 6 yr +/ 24m 3 182 153 138 247 201 201 383107 2 6 yr +/ 24m 3 179 145 132 200 193 381455 2 7 yr +/ 24m 3 158 142 255 206 209 381658 2 7 yr +/ 24m 3 172 148 138 251 207 187 38 2666 2 7 yr +/ 24m 3 184 143 143 247 210 382696 2 7 yr +/ 24m 3 173 133 233 201 200 382705 2 7 yr +/ 24m 3 178 240 197 196 382751 2 7 yr +/ 24m 3 183 140 133 261 210 185 382867 2 7 yr +/ 24m 3 176 151 138 243 199 196 383113 2 7 yr +/ 24m 3 183 158 143 240 197 198 384819 2 7 yr +/ 24m 3 185 153 140 249 212 203 382749 2 7.5 yr +/ 24m 3 209 166 150 292 239 214 381465 2 8 yr +/ 24m 3 203 180 165 285 230 223 381680 2 8 yr +/ 24m 3 203 157 258 381686 2 8 yr +/ 24m 3 188 150 137 260 382689 2 8 yr +/ 24m 3 199 173 152 267 220 219 382709 2 8 yr +/ 24m 3 186 166 148 257 214 207 382726 2 8 yr +/ 24m 3 184 157 140 263

PAGE 168

168 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 382 728 2 8 yr +/ 24m 3 273 381213 2 9 yr +/ 24m 3 285 382929 2 9 yr +/ 24m 3 216 299 251 325402 2 9 yr +/ 24m 3 281 236 231 325344 2 10 yr +/ 30m 3 202 287 243 325401 2 10 yr +/ 30m 3 199 177 162 275 247 243 3 85948 2 10 yr +/ 30m 3 191 171 300 246 244 382744 2 11 yr +/ 30m 3 233 195 346 293 264 382757 2 11 yr +/ 30m 3 245 198 180 361 303 266 382965 2 11 yr +/ 30m 3 222 194 177 318 260 255 382667 2 11 yr +/ 30m 3 201 172 155 278 229 227 382717 2 12 yr +/ 36m 3 210 187 169 315 250 233 325403 2 12 yr +/ 36m 3 224 200 181 327 271 269 381327 2 12 yr +/ 36m 3 226 193 176 311 252 255 381338 2 13.5 yr +/ 36m 3 . 319 325406 2 13.5 yr +/ 36m 3 347 298 325407 2 13.5 yr +/ 36m 3 220 315 258 325343 2 13.5 yr +/ 36m 3 220 319 262 255 382982 2 13.5 yr +/ 36m 3 265 218 196 346 291 291 382701 2 15 yr +/ 36m 1 307 277 252 412 363 359 382993 2 15 yr +/ 36m 1 311 266 241 418 389 375 383138 2 15 yr +/ 36m 1 250 215 202 346 294 287 382968 2 18 yr +/ 36m 1 309 258 237 443 367 350 381668 2 15 yr +/ 36m 2 361 332 319 382674 2 15 yr +/ 36m 2 287 245 224 400 345 383067 2 15 yr +/ 36m 2 298 249 221 413 363 351 381411 2 15 yr +/ 36m 2 359 325346 2 15 yr +/ 36m 2 290 254 228 426 384 342 325359 2 18 yr +/ 36m 2 295 252 226 408 336 324 381367A 2 18 yr +/ 36m 2 271 228 207 386 341 299 325405 2 18 yr +/ 36m 2 238 188 348 277 274 381663 2 18 yr +/ 36m 2 278 234 216 401 337 329 381351 2 17 25 yrs 1 3 16 425 382707 2 17 25 yrs 1 277 238 219 404 342 335 383035 2 17 25 yrs 1 325 280 266 381378 2 17 25 yrs 1 . 350 383098 2 17 25 yrs 1 257 248 380 368 325336 2 17 25 yrs 1 325 268 245 449 371

PAGE 169

169 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 325383 2 17 25 yrs 1 332 275 261 472 408 394 325385 2 17 25 yrs 1 328 267 248 458 401 385 382699 2 17 25 yrs 1 322 282 262 464 393 389 382746 2 17 25 yrs 1 340 275 262 472 399 394 382 926 2 17 25 yrs 1 305 263 245 428 366 354 382659 2 17 25 yrs 2 293 224 417 351 342 315532 2 17 25 yrs 2 323 254 229 442 348 337 325352 2 17 25 yrs 2 306 260 237 430 365 338 325356 2 17 25 yrs 2 271 231 215 371 325 325386 2 17 25 yrs 2 300 255 239 427 361 351 325389 2 17 25 yrs 2 302 408 344 325394 2 17 25 yrs 2 289 245 217 414 342 338 381369 2 17 25 yrs 2 287 216 317 381652 2 17 25 yrs 2 285 396 334 381661 2 17 25 yrs 2 299 242 227 408 327 317 381666 2 17 25 yrs 2 425 349 382706 2 17 25 yrs 2 321 231 420 357 339 382748 2 17 25 yrs 2 428 344 336 382951 2 17 25 yrs 2 289 257 236 401 345 343 382967 2 17 25 yrs 2 308 261 230 410 367 350 383045 2 17 25 yrs 2 295 240 225 340 383170 2 17 25 yrs 2 296 253 23 0 408 345 344 315531 2 17 25 yrs 2 320 268 248 453 377 362 325338 2 17 25 yrs 2 303 262 237 438 368 350 325367 2 17 25 yrs 2 441 380 369 325370 2 17 25 yrs 2 299 255 235 425 367 360 325371 2 17 25 yrs 2 324 246 439 354 344 325373 2 17 25 yrs 2 300 237 419 365 354 325379 2 17 25 yrs 2 299 250 234 424 353 339 325392 2 17 25 yrs 2 294 251 232 412 347 332 381328 2 17 25 yrs 2 304 263 238 381332 2 17 25 yrs 2 415 366 381353 2 17 25 yrs 2 . 317 382722 2 17 25 yrs 2 295 253 235 417 360 352 382732 2 17 25 yrs 2 280 246 232 399 347 334 382946 2 17 25 yrs 2 307 333 383019 2 17 25 yrs 2 318 270 254 384 383101 2 17 25 yrs 2 290 230 417 352 335

PAGE 170

170 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 325348 2 20 30 yrs 1 315 277 262 463 393 386 325363 2 20 30 yrs 1 318 273 256 437 380 372 325399 2 20 30 yrs 1 257 462 325425 2 20 30 yrs 1 329 . 380272 2 20 30 yrs 1 339 . 380273 2 20 30 yrs 1 336 265 . 381330 2 20 30 yrs 1 453 393 381341 2 20 30 yrs 1 307 265 243 436 372 381377 2 20 30 yrs 1 442 361 358 381442 2 20 30 yrs 1 302 235 415 345 332 382900 2 20 30 yrs 1 385 282 261 468 397 389 325351 2 25 35 yrs 1 316 259 239 439 368 359 325353 2 25 35 yrs 1 467 417 396 325375 2 25 35 yrs 1 332 276 258 457 376 375 381326 2 25 35 yrs 1 300 265 246 412 349 338 381354 2 25 35 yrs 1 316 270 254 428 389 366 381360 2 25 35 yrs 1 . 372 360 381397 2 25 35 yrs 1 306 428 381688 2 25 35 yrs 1 323 269 249 461 376 368 382686 2 25 35 yrs 1 325 275 254 448 388 374 383009 2 25 35 yrs 1 333 286 265 466 386 381 383026 2 25 35 yrs 1 323 272 260 383046 2 25 35 yrs 1 455 383128 2 25 35 yrs 1 3 30 . 383129 2 25 35 yrs 1 315 . 324739 2 30 40 yrs 1 346 301 276 476 419 399 325335 2 30 40 yrs 1 343 289 273 481 402 325341 2 30 40 yrs 1 337 288 270 454 413 407 325349 2 30 40 yrs 1 312 280 259 454 378 367 325364 2 30 40 yrs 1 305 242 457 368 358 325376 2 30 40 yrs 1 342 290 272 467 394 325395 2 30 40 yrs 1 310 288 259 425 343 381159 2 30 40 yrs 1 456 369 357 381339 2 30 40 yrs 1 325 253 463 385 372 381342 2 30 40 yrs 1 297 246 450 382 362 381344 2 30 40 yrs 1 317 266 248 441 375 371 381357 2 30 40 yrs 1 308 250 455 400 381 381379 2 30 40 yrs 1 326 269 254 371

PAGE 171

171 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 381388 2 30 40 yrs 1 321 264 442 390 370 381389 2 30 40 yrs 1 462 382 381390 2 30 40 yrs 1 460 394 385 381392 2 30 40 yrs 1 321 455 381400 2 30 40 yrs 1 326 269 253 439 390 388 381452 2 30 40 yrs 1 341 286 267 455 402 393 382662 2 30 40 yrs 1 329 287 273 459 407 392 3 82950 2 30 40 yrs 1 320 275 254 443 379 369 382969 2 30 40 yrs 1 321 265 240 445 375 358 383102 2 30 40 yrs 1 318 272 254 371 360 383119 2 30 40 yrs 1 334 275 253 431 363 325400 2 20 30 yrs 2 294 426 325417 2 20 30 yrs 2 457 367 3 81352 2 20 30 yrs 2 286 259 235 381401 2 20 30 yrs 2 278 240 222 400 332 320 381428 2 20 30 yrs 2 304 229 416 352 346 381453 2 20 30 yrs 2 321 . 382670 2 20 30 yrs 2 278 239 222 404 340 329 382911 2 20 30 yrs 2 324 . 382945 2 20 30 yrs 2 398 383023 2 20 30 yrs 2 304 250 233 420 356 347 383034 2 20 30 yrs 2 291 242 226 393 343 336 383150 2 20 30 yrs 2 248 231 325354 2 25 35 yrs 2 311 250 230 460 387 373 381348 2 25 35 yrs 2 294 410 341 330 381374 2 25 35 yrs 2 428 351 381398 2 25 35 yrs 2 293 249 398 381429 2 25 35 yrs 2 268 250 455 381435 2 25 35 yrs 2 306 270 252 442 380 370 381450 2 25 35 yrs 2 279 241 223 398 341 381657 2 25 35 yrs 2 316 262 239 434 374 357 382665 2 25 3 5 yrs 2 286 255 233 407 342 334 383032 2 25 35 yrs 2 240 420 383057 2 25 35 yrs 2 316 269 253 452 369 354 325339 2 30 40 yrs 2 318 266 249 437 374 369 325342 2 30 40 yrs 2 287 . 325357 2 30 40 yrs 2 305 269 246 438 370 325358 2 30 4 0 yrs 2 291 249 400 338 329

PAGE 172

172 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 325382 2 30 40 yrs 2 302 272 247 425 360 325393 2 30 40 yrs 2 308 265 243 417 358 355 381343 2 30 40 yrs 2 280 239 218 3 95 345 325 381383 2 30 40 yrs 2 293 . 381669 2 30 40 yrs 2 315 247 427 367 359 382661 2 30 40 yrs 2 289 247 228 411 343 340 382980 2 30 40 yrs 2 233 214 387 335 329 383086 2 30 40 yrs 2 281 247 223 409 336 330 383121 2 30 40 yrs 2 239 2 17 382 320 383141 2 30 40 yrs 2 284 259 237 419 349 342 384958 2 30 40 yrs 2 250 355 380642 2 40+ yrs 1 304 . 315533 2 40+ yrs 1 325 272 256 458 385 367 325334 2 40+ yrs 1 284 267 470 385 382 325362 2 40+ yrs 1 321 277 253 448 382 373 325366 2 40+ yrs 1 306 260 246 434 380 368 325369 2 40+ yrs 1 316 276 257 441 371 365 325372 2 40+ yrs 1 311 267 250 439 357 347 325377 2 40+ yrs 1 343 284 262 480 411 400 325378 2 40+ yrs 1 306 262 243 429 353 345 325381 2 40+ yrs 1 324 283 261 433 325388 2 40+ yrs 1 318 272 252 446 388 325390 2 40+ yrs 1 324 273 254 432 375 362 325391 2 40+ yrs 1 339 451 372 353 325396 2 40+ yrs 1 320 436 392 325397 2 40+ yrs 1 431 359 381384 2 40+ yrs 1 261 454 384 376 381404 2 40+ yrs 1 272 254 433 363 356 381407 2 40+ yrs 1 306 426 369 381410 2 40+ yrs 1 319 248 438 369 381417 2 40+ yrs 1 314 267 248 433 363 381422 2 40+ yrs 1 348 286 266 421 381423 2 40+ yrs 1 281 267 465 401 381438 2 40+ yrs 1 3 18 279 458 395 364 381443 2 40+ yrs 1 319 254 372 381451 2 40+ yrs 1 335 280 257 468 381454 2 40+ yrs 1 306 272 252 430 372 364 381469 2 40+ yrs 1 407 349 342

PAGE 173

173 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae ra dii femora tibiae fibulae 381651 2 40+ yrs 1 323 264 250 460 379 370 381689 2 40+ yrs 1 320 269 247 382669 2 40+ yrs 1 315 286 269 459 402 390 382673 2 40+ yrs 1 308 258 244 433 366 364 382678 2 40+ yrs 1 310 260 246 427 367 360 382710 2 40+ yr s 1 323 267 245 453 394 385 382719 2 40+ yrs 1 316 267 248 465 383 367 382745 2 40+ yrs 1 301 266 254 439 384 380 382747 2 40+ yrs 1 311 274 253 453 372 352 382901 2 40+ yrs 1 317 270 250 453 387 372 382913 2 40+ yrs 1 312 252 350 382990 2 40+ yrs 1 332 271 448 398 388 382991 2 40+ yrs 1 315 270 252 443 382 367 383027 2 40+ yrs 1 307 436 374 383033 2 40+ yrs 1 248 445 349 383058 2 40+ yrs 1 286 248 229 392 340 331 383108 2 40+ yrs 1 316 278 258 373 383116 2 40+ yrs 1 344 279 258 483 402 390 383127 2 40+ yrs 1 321 259 242 447 376 369 383144 2 40+ yrs 1 312 268 250 458 393 383151 2 40+ yrs 1 342 287 265 482 408 400 383171 2 40+ yrs 1 318 279 258 437 380 373 381449 2 40+ yrs 1 305 274 247 437 367 383069 2 40+ yrs 1 432 376 356 325360 2 40+ yrs 1 335 298 275 480 406 386 325365 2 40+ yrs 1 338 283 273 478 410 393 325374 2 40+ yrs 2 300 244 228 425 325387 2 40+ yrs 2 301 238 222 357 341 381160 2 40+ yrs 2 291 242 396 336 381386 2 40+ yrs 2 . 338 335 381430 2 40+ yrs 2 298 239 221 417 328 381431 2 40+ yrs 2 285 249 231 406 352 381656 2 40+ yrs 2 301 256 424 344 342 382723 2 40+ yrs 2 276 239 220 393 326 312 383011 2 40+ yrs 2 292 406 332 383016 2 40+ yrs 2 294 254 238 420 343 383118 2 40+ yrs 2 293 232 421 357 347 383145 2 40+ yrs 2 288 250 231 400 347 340

PAGE 174

174 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 387825 2 40+ yrs 2 285 225 385 321 381458 2 40+ yrs 2 305 256 372 325380 2 40+ yrs 2 304 253 235 419 363 357 269235 3 birth +/ 2m 3 68 61 53 79 66 63 271951 3 birth +/ 2m 3 68 88 75 70 314348 3 birth +/ 2m 3 69 65 57 81 69 66 311.2 3 birth +/ 2m 3 63 60 52 76 65 62 47b 3 birth +/ 2m 3 66 63 54 7 7 67 64 609BC57 3 birth +/ 2m 3 67 63 54 79 69 67 Bu8900561 3 birth +/ 2m 3 77 69 314347 3 birth +/ 2m 3 63 63 55 74 67 65 609BC53 3 3m +/ 3m 3 65 61 54 76 66 64 327132 3 3m +/ 3m 3 70 63 56 84 73 70 327133 3 6m +/ 3m 3 85 77 69 108 88 87 381259 3 6m +/ 3m 3 80 74 64 99 601.1 3 6m +/ 3m 3 81 72 62 98 82 77 269216 3 9m +/ 3m 3 86 . 269218 3 9m +/ 3m 3 88 115 95 89 271950 3 9m +/ 3m 3 77 70 63 96 81 77 308758 3 9m +/ 3m 3 71 . 308759 3 9m +/ 3m 3 85 74 66 104 89 84 334053 3 9m +/ 3m 3 99 88 79 325 3 9m +/ 3m 3 79 103 85 6012 3 9m +/ 3m 3 81 73 65 101 80 311.1 3 9m +/ 3m 3 88 72 271947 3 1 yr +/ 4m 3 93 99 381253 3 1 yr +/ 4m 3 83 74 66 104 87 381261 3 1 yr +/ 4m 3 98 77 97 94 608Bg 3 1 yr +/ 4m 3 91 81 71 116 h11179 3 1 yr +/ 4m 3 85 78 103 85 82 h1028 3 1 yr +/ 4m 3 89 78 69 115 89 314336 3 1 yr +/ 4m 3 97 87 75 120 103 99 St1950b 3 1 yr +/ 4m 3 85 76 107 84 327138 3 1.5 yr +/ 6m 3 65 67 90 269234 3 1.5 yr +/ 6m 3 117 314341 3 1.5 yr +/ 6m 3 95 104 98 269236 3 1.5 yr +/ 6m 3 97 . 271939 3 1.5 yr +/ 6m 3 86 106 90

PAGE 175

175 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae rad ii femora tibiae fibulae 271943 3 1.5 yr +/ 6m 3 87 112 91 308697 3 1.5 yr +/ 6m 3 101 87 77 125 107 105 308760 3 1.5 yr +/ 6m 3 103 91 83 132 108 308766 3 1.5 yr +/ 6m 3 88 77 68 89 381260 3 1.5 yr +/ 6m 3 97 86 76 125 103 101 38126 3 3 1.5 yr +/ 6m 3 107 90 80 135 112 3225 3 1.5 yr +/ 6m 3 77 68 105 90 2012 3 1.5 yr +/ 6m 3 88 . 3228 3 1.5 yr +/ 6m 3 105 93 83 133 110 106 47a 3 1.5 yr +/ 6m 3 89 79 70 90 6022 3 1.5 yr +/ 6m 3 90 110 603BC57 3 1.5 yr +/ 6m 3 102 93 81 h1013 3 1.5 yr +/ 6m 3 86 74 65 h1041 3 1.5 yr +/ 6m 3 88 80 72 109 228980 3 1.5 yr +/ 6m 3 100 . 308763 3 2 yr +/ 8m 3 89 . 413 3 2 yr +/ 8m 3 110 98 86 136 116 601.2g 3 2 yr +/ 8m 3 104 89 81 130 110 602.1 3 2 yr +/ 8m 3 104 90 80 h11165 3 2 yr +/ 8m 3 105 94 h11177 3 2 yr +/ 8m 3 92 112 308757 3 2 yr +/ 8m 3 . 271929 3 2 yr +/ 8m 3 99 89 78 130 107 314338 3 2 yr +/ 8m 3 82 146 120 118 381274 3 2 yr +/ 8m 3 128 100 90 169 134 327131 3 2 yr +/ 8m 3 93 82 136 113 105 3211 3 2.5 yr +/ 10m 3 105 93 . 308698 3 2.5 yr +/ 10m 3 113 95 85 143 119 308764 3 2.5 yr +/ 10m 3 103 130 102 308765 3 2.5 yr +/ 10m 3 109 97 8 5 144 417 3 3 yr +/ 12m 3 112 100 89 148 125 122 43 3 3 yr +/ 12m 3 122 161 136 130 3229 3 3 yr +/ 12m 3 119 100 90 158 603b 3 3 yr +/ 12m 3 100 271938 3 3 yr +/ 12m 3 . 271953 3 3 yr +/ 12m 3 118 3087 62 3 3 yr +/ 12m 3 111 88 147 120 114 327103 3 3.5 yr +/ 12m 3 137 115 105 190 157 152

PAGE 176

176 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 327104 3 3.5 yr +/ 12m 3 146 129 118 204 174 167 381258 3 3. 5 yr +/ 12m 3 137 123 185 3111 3 4 yr +/ 12m 3 157 133 119 271849 3 4 yr +/ 12m 3 142 118 105 196 421 3 4 yr +/ 12m 3 124 107 96 169 142 139 604 3 4 yr +/ 12m 3 120 103 93 158 134 609Bg3 3 4 yr +/ 12m 3 131 108 99 169 137 h1 1164 3 4 yr +/ 12m 3 112 h11172 3 4 yr +/ 12m 3 145 124 . h1015 3 4 yr +/ 12m 3 . 150 145 239505 3 4 yr +/ 12m 3 113 102 308696 3 4 yr +/ 12m 3 136 113 104 185 154 149 314339 3 4 yr +/ 12m 3 130 112 104 176 143 140 326194 3 4.5 yr +/ 12m 3 144 125 113 199 170 165 429c 3 5 yr +/ 16m 3 140 119 109 187 158 155 447 3 5 yr +/ 16m 3 212 173 h109 3 5 yr +/ 16m 3 151 115 207 172 169 269279 3 5 yr +/ 16m 3 170 139 3232 3 5.5 yr +/ 20m 3 162 . 327105 3 6 yr +/ 24m 3 177 151 139 253 206 205 432 3 6 yr +/ 24m 3 160 218 183 6048 3 6 yr +/ 24m 3 154 205 h11198 3 6 yr +/ 24m 3 159 . 269213 3 6 yr +/ 24m 3 182 . 327098 3 6 yr +/ 24m 3 157 137 215 186 18 1 262959 3 6.5 yr +/ 24m 3 142 122 110 190 156 158 308666 3 6.5 yr +/ 24m 3 167 147 131 235 Gal601 3 7 yr +/ 24m 3 186 154 140 205 271882 3 7 yr +/ 24m 3 158 221 308665 3 7.5 yr +/ 24m 3 189 159 142 264 217 209 263079 3 8 yr +/ 24m 3 170 246 h1056 3 8 yr +/ 24m 3 183 138 254 269221 3 8 yr +/ 24m 3 206 165 280 234 308686 3 8 yr +/ 24m 3 193 169 152 264 215 220 308694 3 8 yr +/ 24m 3 188 161 145 252 210 210 314299 3 8 yr +/ 24m 3 180 150 136 245 200 2 69251 3 8 yr +/ 24m 3 179 133 252 212 209 h103 3 8.5 yr +/ 24m 3 192 161 .

PAGE 177

177 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 308700 3 8.5 yr +/ 24m 3 222 184 171 262967 3 9 yr +/ 24m 3 222 322 262 259 262950 3 10 yr +/ 30m 3 195 172 280 314314 3 10 yr +/ 30m 3 220 178 164 330 272 259 327127 3 9 yr +/ 24m 3 184 155 137 263 215 210 308611 3 11 yr +/ 30m 3 281 225 206 394 337 296 308667 3 11 yr +/ 30m 3 335 286 26 2 327097 3 11 yr +/ 30m 3 227 336 308614 3 12 yr +/ 36m 3 303 239 216 395 325 316 308607 3 12 yr +/ 36m 3 259 203 190 360 308 286 308635 3 12 yr +/ 36m 3 279 225 203 407 331 291 327102 3 12 yr +/ 36m 3 233 201 184 355 291 308709 3 13.5 yr +/ 36m 3 220 201 406 351 309 308613 3 15 yr +/ 36m 1 306 255 233 410 364 354 327140 3 15 yr +/ 36m 1 234 345 262978 3 15 yr +/ 36m 2 263 324 304 271811 3 15 yr +/ 36m 2 385 330 310 308628 3 15 yr +/ 36m 2 286 219 203 36 5 312 269290 3 15 yr +/ 36m 2 199 295 308669 3 15 yr +/ 36m 2 199 169 154 291 259 229 327130 3 15 yr +/ 36m 2 274 230 216 382 324 381243 3 15 yr +/ 36m 2 281 233 208 406 334 318 381265 3 15 yr +/ 36m 2 277 319 308638 3 17 25 yrs 1 298 249 436 366 262947 3 17 25 yrs 1 281 410 359 338 262971 3 17 25 yrs 1 312 232 212 418 352 325 h11192 3 17 25 yrs 1 320 371 354 239259 3 17 25 yrs 1 304 258 244 425 361 355 314301 3 17 25 yrs 1 321 269 250 441 381 239216 3 17 25 yrs 1 294 242 413 353 334 271805 3 17 25 yrs 1 301 246 232 416 351 339 308606 3 17 25 yrs 1 218 404 330 314277 3 17 25 yrs 1 307 257 235 415 348 343 269227 3 17 25 yrs 2 291 215 411 336 271820 3 17 25 yrs 2 364 299 262911 3 17 25 yrs 2 280 392 320 271804 3 17 25 yrs 2 408 340 269281 3 17 25 yrs 2 228 389 323 318

PAGE 178

178 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 269299 3 17 25 yrs 2 218 385 324 316 2692 14 3 17 25 yrs 2 282 237 226 407 342 335 314309 3 17 25 yrs 2 284 224 208 400 318 308651 3 17 25 yrs 2 282 232 220 397 341 329 262912 3 20 30 yrs 1 305 240 436 372 262924 3 20 30 yrs 1 286 239 389 323 269232 3 20 30 yrs 1 285 369 27 1808 3 20 30 yrs 1 308 257 442 377 357 271815 3 20 30 yrs 1 305 248 225 419 340 330 308601 3 20 30 yrs 1 289 240 225 406 325 316 308626 3 20 30 yrs 1 301 265 251 453 376 358 314293 3 20 30 yrs 1 305 256 240 428 359 344 262923 3 25 35 yrs 1 312 263 2 43 432 377 362 308610 3 25 35 yrs 1 296 245 228 423 360 347 308619 3 25 35 yrs 1 303 248 231 413 351 347 308627 3 25 35 yrs 1 318 266 246 450 369 367 308634 3 25 35 yrs 1 320 268 249 445 380 375 314285 3 25 35 yrs 1 278 236 218 396 328 326 314319 3 2 5 35 yrs 1 308 257 245 428 357 356 308617 3 30 40 yrs 1 285 224 341 337 308620 3 30 40 yrs 1 295 249 232 308623 3 30 40 yrs 1 317 269 256 308676 3 30 40 yrs 1 257 414 363 352 308677 3 30 40 yrs 1 296 254 233 421 357 345 314286 3 3 0 40 yrs 1 299 266 243 431 371 357 314289 3 30 40 yrs 1 321 428 365 360 314300 3 30 40 yrs 1 309 426 364 350 314322 3 30 40 yrs 1 312 258 245 436 368 354 608BC57 3 30 40 yrs 1 325 280 257 452 381 357 J8388 3 30 40 yrs 1 420 355 239202 3 20 30 yrs 2 310 253 229 436 356 262910 3 20 30 yrs 2 215 386 327 316 262916 3 20 30 yrs 2 268 198 395 311 271812 3 20 30 yrs 2 285 345 271853 3 20 30 yrs 2 407 341 308615 3 20 30 yrs 2 278 244 225 397 343 336 308624 3 20 30 yrs 2 292 239 225 403 353 341 308642 3 20 30 yrs 2 283 241 224 415 343 331

PAGE 179

179 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 308672 3 20 30 yrs 2 297 243 229 343 308679 3 20 30 yrs 2 268 230 215 392 324 308685 3 20 30 yrs 2 276 223 210 391 340 330 314282 3 20 30 yrs 2 293 246 225 409 344 329 314295 3 20 30 yrs 2 279 216 394 330 239209 3 25 35 yrs 2 263 223 379 324 314 239215 3 25 35 yrs 2 275 239 223 388 334 323 262915 3 25 35 yrs 2 279 214 396 271809 3 25 35 yrs 2 278 230 210 404 329 320 271816 3 25 35 yrs 2 283 231 211 423 348 334 308612 3 25 35 yrs 2 290 253 233 418 343 330 308618 3 25 35 yrs 2 295 239 223 407 329 318 308621 3 25 35 yrs 2 279 231 218 387 332 321 308633 3 25 35 yrs 2 291 218 209 383 320 307 308707 3 25 35 yrs 2 299 244 227 409 345 348 308708 3 25 35 yrs 2 286 234 . 314287 3 25 35 yrs 2 267 222 205 392 310 304 314321 3 25 35 yrs 2 275 209 195 388 306 603QBq 3 25 35 yrs 2 421 350 344 271 806 3 25 35 yrs 2 274 227 216 308608 3 25 35 yrs 2 279 231 217 384 320 309 239203 3 30 40 yrs 2 290 238 222 404 336 324 239293 3 30 40 yrs 2 268 218 199 378 308 299 262909 3 30 40 yrs 2 261 201 384 320 302 269207 3 30 40 yrs 2 276 205 374 32 1 308654 3 30 40 yrs 2 281 237 217 393 338 308674 3 30 40 yrs 2 274 236 221 394 345 333 308675 3 30 40 yrs 2 280 221 212 386 308 288 308681 3 30 40 yrs 2 303 247 222 422 348 333 314283 3 30 40 yrs 2 290 244 225 405 336 326 314284 3 30 40 yrs 2 27 3 231 217 395 328 315 314302 3 30 40 yrs 2 281 230 209 387 323 314 314303 3 30 40 yrs 2 290 235 213 404 336 321 314305 3 30 40 yrs 2 272 203 377 310 314308 3 30 40 yrs 2 249 230 409 337 333 239207 3 40+ yrs 1 275 238 395 335 327 239208 3 40+ yrs 1 302 257 239 427 366 357 269210 3 40+ yrs 1 327 248 475 374 365

PAGE 180

180 Table A-3. Continued Individual Population Dental Age Sex humeri ulnae radii femora tibiae fibulae 271851 3 40+ yrs 1 . 366 355 308602 3 40+ yrs 1 301 259 241 427 364 3086 03 3 40+ yrs 1 312 262 241 422 349 338 308645 3 40+ yrs 1 328 264 250 445 376 366 308683 3 40+ yrs 1 294 . 314307 3 40+ yrs 1 331 280 262 450 387 371 h1010 3 40+ yrs 1 312 254 241 438 365 351 308609 3 40+ yrs 1 325 272 251 457 381 269223 3 40+ yrs 2 284 235 404 334 239204 3 40+ yrs 2 287 247 228 382 348 334 239251 3 40+ yrs 2 398 334 323 262921 3 40+ yrs 2 286 224 410 330 319 269211 3 40+ yrs 2 296 234 217 430 350 271810 3 40+ yrs 2 272 224 205 387 315 301 271860 3 40+ yr s 2 400 308604 3 40+ yrs 2 281 392 332 326 308622 3 40+ yrs 2 281 229 206 395 319 308629 3 40+ yrs 2 315 253 235 420 353 351 308649 3 40+ yrs 2 290 220 403 343 308691 3 40+ yrs 2 271 219 204 374 314294 3 40+ yrs 2 276 227 209 388 327 313 314317 3 40+ yrs 2 267 228 209 382 331 314318 3 40+ yrs 2 298 252 233 405 342 336 h1050 3 40+ yrs 2 297 406 351 314310 3 40+ yrs 2 269 388 323 308

PAGE 181

181 APPENDIX B DENTAL DATA Appendix B represents the raw dental inventories of each individual from which post-cranial data was also recorded. In the interest of space, this section is separated into two tables: Table B-5 and B-6; adult and juvenile dental data respectively. Given the progressive and fluid nature of human development, many individuals have dental data represented in both Table B-5 and B-6. For both adults and juveniles, dentition was often found at varied stages of crown calcification, root formation and eruption. Stage of crown and root formation (indicate in text by described in Table B-1. Table B-1. Stages of crown and root formation Code Stage 1 I nitial cusp formation 2 Fusion of cusps 3 Cusp outline complete 4 Crown 1/2 complete 5 Crown 3/4 complete 6 Crown complete 7 Initial root formation 8 Initial cleft formation (for multi root dentition) 9 Root 1/4 complete 10 Root 1/2 complete 11 R oot 3/4 complete 12 Root length complete 13 Apex 1/2 closed 14 Apex closed tooth/root complete Throughout the course of data collection addition code became necessary to more fully describe dental progression or when crown/root assessment was not possible. These additional codes are illustrated in Table B-2.

PAGE 182

182 Table B-2. Additional code for dental assessment Code Description X Present EAS Erupting at alveolar surface/plane ER Erupting 1/2 of crown visible above alveolar plane AFE Almost fully erupted 3/4 crown visible above alveolar plane U Unerupted visible below alveolar plane R Tooth not present socket resorbed L Loose dentition Adult Dental Data The adult complement of dentition is represented by 32 permanent teeth. For both clinical and practical purposes teeth are assigned a numeric (1-position as seen in Table B-3 (e.g. Buikstra and Ubelaker, 1994). The adult dental data collected for this project, found in Table B-5, displays the tooth number (1-32) across the horizontal axis with the individual catalogue number down the left, vertical axis. Table B-3. Permanent dentition Right M3 M2 M1 P2 P1 C I2 I1 I1 I2 C P1 P2 M1 M2 M3 Left Maxillae 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Mandible 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 Juvenile Dental Data The juvenile complement of dentition is represented by 20 deciduous teeth. As seen in the permanent, adult dentition the deciduous teeth are assigned a numeric (51-7) to designate tooth identity and position following Table B-4 (e.g. Buikstra and Ubelaker, 1994). The juvenile dental data collected for this project, found in Table B-6, displays the tooth number (51-70) across the horizontal axis with the individual catalogue number down the left, vertical axis. Table B-4. Juvenile dentition Right m2 m1 c i2 i1 i1 i2 c m1 m2 Left Maxillae 51 52 53 54 55 56 57 58 59 60 Mandible 70 69 68 67 66 65 64 63 62 61

PAGE 183

183 Table B-5. Adult dental data

PAGE 184

184 Table B-5. Continued

PAGE 185

185 Table B-5. Continued

PAGE 186

186 Table B-5. Continued

PAGE 187

187 Table B-5. Continued

PAGE 188

188 Table B-5. Continued

PAGE 189

189 Table B-5. Continued

PAGE 190

190 Table B-5. Continued

PAGE 191

191 Table B-5. Continued

PAGE 192

192 Table B-5. Continued

PAGE 193

193 Table B-5. Continued

PAGE 194

194 Table B-5. Continued

PAGE 195

195 Table B-5. Continued

PAGE 196

196 Table B-5. Continued

PAGE 197

197 Table B-5. Continued

PAGE 198

198 Table B-5. Continued

PAGE 199

199 Table B-5. Continued

PAGE 200

200 Table B-5. Continued

PAGE 201

201 Table B-5. Continued

PAGE 202

202 Table B-5. Continued

PAGE 203

203 Table B-5. Continued

PAGE 204

204 Table B-5. Continued

PAGE 205

205 Table B-6. Juvenile dental data

PAGE 206

206 Table B-6. Continued

PAGE 207

207 Table B-6. Continued

PAGE 208

208 Table B-6. Continued

PAGE 209

209 Table B-6. Continued

PAGE 210

210 Table B-6. Continued

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211 Table B-6. Continued

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212 Table B-6. Continued

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213 Table B-6. Continued

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214 APPENDIX C DESCRIPTIVE STATISTICS Table C-1. Descriptive statistics Native Alaskan juveniles DA category N Element Mean n Std Dev COV Range Min Max 1 4 humeri 82.250 4 15.152 18.422 33.000 70.000 103.000 radii 79.000 3 12.767 16.161 25.000 68.000 9 3.000 ulnae 67.250 4 10.626 15.801 23.000 59.000 82.000 femora 101.000 4 23.051 22.822 50.000 82.000 132.000 tibiae 88.333 3 16.042 18.160 32.000 73.000 105.000 fibulae 86.000 2 24.042 27.955 34.000 69.000 103.000 2 8 humeri 108.800 5 16.037 14.740 40.000 96.000 136.000 radii 87.250 4 5.188 5.946 11.000 81.000 92.000 ulnae 78.750 4 5.852 7.432 13.000 72.000 85.000 femora 133.333 6 13.53 10.148 40.000 118.000 158.000 tibiae 116.500 4 29.85 25.622 63.000 98.000 161.0 00 fibulae 103.500 4 5.972 5.770 14.000 97.000 111.000 3 8 humeri 127.571 7 14.444 11.322 46.000 109.000 155.000 radii 101.167 6 9.725 9.612 26.000 91.000 117.000 ulnae 90.000 7 8.851 9.834 24.000 82.000 106.000 femora 165.750 8 20.317 12.258 67.000 134.000 201.000 tibiae 128.571 7 12.367 9.619 35.000 114.000 149.000 fibulae 133.500 4 12.234 9.164 28.000 122.000 150.000 4 13 humeri 144.000 11 9.497 6.595 27.000 132.000 159.000 radii 116.100 10 7.781 6.702 21.000 105.000 126.000 ulnae 103.222 9 7.190 6.954 21.000 94.000 115.000 femora 190.727 11 14.806 7.763 47.000 172.000 219.000 tibiae 151.923 13 11.056 7.278 36.000 135.000 171.000 fibulae 150.444 9 12.249 8.142 37.000 137.000 174.000 5 7 humeri 15 7.667 6 17.739 11.251 48.000 135.000 183.000 radii 127.250 4 14.614 11.485 35.000 112.000 147.000 ulnae 121.667 3 9.018 7.412 18.000 113.000 131.000 femora 221.200 5 28.438 12.856 76.000 187.000 263.000 tibiae 169.400 5 30.088 17.762 7 5.000 139.000 214.000 fibulae 173.250 4 24.144 13.936 58.000 143.000 201.000 6 7 humeri 195.167 6 14.648 7.505 39.000 177.000 216.000 radii 153.750 4 5.909 3.843 14.000 148.000 162.000 ulnae 137.200 5 6.221 4.534 17.000 130.000 147.000 femora 268.167 6 34.528 12.875 82.000 235.000 317.000 tibiae 204.000 7 23.259 11.402 69.000 183.000 252.000 fibulae 193.250 4 7.365 3.811 17.000 185.000 202.000 7 8 humeri 215.600 5 23.818 11.047 63.000 180.000 342.000 radii 174.333 3 18. 717 10.736 35.000 153.000 188.000 ulnae 150.000 6 16.661 11.108 37.000 134.000 171.000

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215 Table C-1. Continued DA category N Element Mean n Std Dev COV Range Min Max 7 8 femora 305.250 8 30.705 10.059 92.000 254.000 346.000 tibiae 246.571 7 2 9.871 12.115 95.000 199.000 294.000 fibulae 231.667 6 21.86 9.436 61.000 198.000 259.000 8 14 humeri 227.692 13 16.378 7.193 64.000 199.000 263.000 radii 183.300 10 17.764 9.691 51.000 166.000 217.000 ulnae 163.333 12 10.165 6.224 33.000 14 6.000 179.000 femora 329.250 12 21.566 6.550 73.000 292.000 365.000 tibiae 256.364 11 17.351 6.768 63.000 233.000 296.000 fibulae 246.000 9 22.327 9.076 79.000 215.000 294.000 Table C-2. Descriptive Statistics Native Alaskan males DA ca tegory N Element Mean n Std Dev COV Range Min Max 9 6 humeri 303.250 4 16.215 5.347 37.000 286.000 323.000 radii 230.800 5 16.709 7.240 42.000 205.000 247.000 ulnae 204.600 5 13.686 6.689 36.000 185.000 221.000 femora 407.800 5 16.903 4.145 43 .000 383.000 426.000 tibiae 319.333 3 17.156 5.372 34.000 301.000 335.000 fibulae 315.000 2 14.142 4.490 20.000 305.000 325.000 10 11 humeri 304.000 10 13.904 4.574 44.000 284.000 328.000 radii 240.250 8 14.310 5.956 41.000 210.000 251.000 ulnae 221.000 8 7.445 3.369 21.000 210.000 231.000 femora 415.545 11 22.020 5.299 75.000 376.000 451.000 tibiae 335.250 8 27.049 8.068 77.000 295.000 370.000 fibulae 335.500 6 11.167 3.328 33.000 317.000 350.000 11 24 humeri 306.591 22 21.0 73 6.873 104.000 274.000 378.000 radii 246.684 19 12.074 4.895 38.000 230.000 268.000 ulnae 227.091 22 10.954 4.824 40.000 210.000 250.000 femora 422.783 23 17.850 4.222 82.000 393.000 475.000 tibiae 338.952 21 19.795 5.840 78.000 298.000 376.000 fibulae 327.150 20 16.787 5.131 57.000 290.000 347.000 12 31 humeri 304.929 28 16.161 5.300 65.000 273.000 338.000 radii 250.850 20 12.787 5.097 52.000 228.000 280.000 ulnae 228.269 26 12.344 5.407 55.000 201.000 256.000 femora 421 .857 28 20.301 4.812 87.000 380.000 467.000 tibiae 342.680 25 21.748 6.346 74.000 311.000 385.000 fibulae 334.100 20 20.789 6.223 70.000 305.000 375.000 Table C-3. Descriptive Statistics Native Alaskan females DA category N Element Mean n Std Dev COV Range Min Max 9 9 humeri 270.857 7 15.678 5.788 53.000 243.000 296.000

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216 Table C-3. Continued DA category N Element Mean n Std Dev COV Range Min Max 9 9 radii 212.600 5 17.729 8.339 48.000 188.000 236.000 ulnae 190.200 5 17.484 9.193 45.0 00 167.000 212.000 femora 386.778 9 14.078 3.640 46.000 362.000 408.000 tibiae 307.222 9 9.782 3.184 29.000 292.000 321.000 fibulae 279.750 8 16.577 5.926 51.000 258.000 309.000 10 18 humeri 283.071 14 10.788 3.811 40.000 260.000 300.000 radii 224.500 14 10.811 4.816 33.000 206.000 239.000 ulnae 206.385 13 10.744 5.206 31.000 194.000 225.000 femora 385.563 16 23.016 5.969 90.000 337.000 427.000 tibiae 312.200 15 15.844 5.075 69.000 274.000 343.000 fibulae 303.077 1 3 20.023 6.606 68.000 268.000 336.000 11 44 humeri 289.590 39 15.736 5.434 77.000 247.000 324.000 radii 227.727 33 14.041 6.166 70.000 187.000 257.000 ulnae 208.143 35 13.931 6.693 73.000 163.000 236.000 femora 398.409 44 22.084 5.543 107.0 00 332.000 439.000 tibiae 316.897 39 20.542 6.482 100.000 260.000 360.000 fibulae 310.576 33 19.682 6.337 94.000 251.000 345.000 12 30 humeri 283.036 28 13.031 4.604 54.000 252.000 306.000 radii 229.048 21 13.075 5.708 47.000 212.000 259.0 00 ulnae 206.700 20 11.743 5.681 47.000 187.000 234.000 femora 398.214 28 17.608 4.422 77.000 353.000 430.000 tibiae 318.179 28 16.755 5.266 73.000 281.000 354.000 fibulae 313.250 16 14.434 4.608 51.000 287.000 338.000 Table C-4. Descriptive statistics South Dakota Arikara juveniles DA category N Element Mean n Std Dev COV Range Min Max 1.1 40 humeri 66.138 29 4.189 6.334 17.000 56.000 73.000 radii 63.069 29 3.605 5.716 15.000 55.000 70.000 ulnae 55.308 26 3.069 5.550 13. 000 48.000 61.000 femora 76.765 34 5.466 7.121 28.000 62.000 90.000 tibiae 67.077 26 4.251 6.338 19.000 56.000 75.000 fibulae 63.600 15 4.595 7.225 21.000 52.000 73.000 1.2 37 humeri 83.214 28 11.305 13.585 43.000 64.000 107.000 radii 73.474 19 10.052 13.681 35.000 61.000 96.000 ulnae 65.458 24 9.156 13.987 33.000 52.000 85.000 femora 97.808 26 17.875 18.276 61.000 68.000 129.000 tibiae 84.640 25 14.877 17.577 51.000 60.000 111.000 fibulae 77.818 11 12.344 15.862 3 3.000 64.000 97.000 2 19 humeri 109.733 15 10.361 9.442 41.000 93.000 134.000 radii 99.714 14 6.911 6.931 27.000 89.000 116.000 ulnae 87.875 16 6.965 7.926 29.000 75.000 104.000

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217 Table C-4. Continued DA category N Element Mean n Std Dev COV Ran ge Min Max 2 19 femora 141.727 11 13.857 9.777 49.000 124.000 173.000 tibiae 116.167 12 13.155 11.324 46.000 98.000 144.000 fibulae 113.111 9 8.923 7.888 27.000 103.000 130.000 3 12 humeri 126.000 10 5.617 4.458 19.000 116.000 135.000 r adii 110.500 6 3.271 2.960 8.000 107.000 115.000 ulnae 98.714 7 3.638 3.686 9.000 94.000 103.000 femora 164.875 8 7.492 4.544 18.000 158.000 176.000 tibiae 137.200 5 7.190 5.241 18.000 131.000 149.000 fibulae 143.000 4 16.145 11.290 36 .000 128.000 164.000 4 15 humeri 150.444 9 8.589 5.709 24.000 137.000 161.000 radii 127.222 9 8.212 6.455 23.000 116.000 139.000 ulnae 114.333 12 7.475 6.538 21.000 104.000 125.000 femora 199.000 11 17.070 8.578 59.000 177.000 236.000 tibiae 168.455 11 14.788 8.778 44.000 146.000 190.000 fibulae 162.429 7 16.328 10.053 44.000 143.000 187.000 5 18 humeri 170.867 15 13.700 8.018 40.000 145.000 185.000 radii 143.571 14 9.255 6.446 31.000 127.000 158.000 ulnae 132.429 14 9. 304 7.026 28.000 115.000 143.000 femora 241.000 14 12.926 5.363 51.000 210.000 261.000 tibiae 193.188 16 16.971 8.785 57.000 155.000 212.000 fibulae 189.938 16 17.230 9.071 57.000 153.000 210.000 6 10 humeri 198.500 8 11.551 5.819 32.000 1 84.000 216.000 radii 165.333 6 10.764 6.511 30.000 150.000 180.000 ulnae 149.857 7 9.582 6.394 28.000 137.000 165.000 femora 273.900 10 15.286 5.581 42.000 257.000 299.000 tibiae 230.800 5 14.789 6.407 37.000 214.000 251.000 fibul ae 215.750 4 6.898 3.197 16.000 207.000 223.000 7 8 humeri 217.000 6 19.339 8.912 46.000 199.000 245.000 radii 187.833 6 10.685 5.688 26.000 172.000 198.000 ulnae 169.000 5 10.416 6.164 25.000 155.000 180.000 femora 305.750 8 32.810 10.731 86.000 275.000 361.000 tibiae 257.125 8 26.894 10.459 74.000 229.000 303.000 fibulae 247.143 7 15.247 6.169 39.000 227.000 266.000 8 8 humeri 227.500 6 19.181 8.431 55.000 210.000 265.000 radii 199.500 4 13.429 6.731 31.000 187.000 218.000 ulnae 180.500 4 11.446 6.341 27.000 169.000 196.000 femora 325.714 7 15.041 4.618 36.000 311.000 347.000 tibiae 270.667 6 20.057 7.410 48.000 250.000 298.000 fibulae 268.571 7 28.260 10.522 86.000 233.000 319.000

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218 Table C-5. Descriptive statistics South Dakota Arikara males DA category N Element Mean n Std Dev COV Range Min Max 9 4 humeri 294.250 4 29.545 10.041 61.000 250.000 311.000 radii 254.000 4 27.142 10.686 62.000 215.000 277.000 ulnae 233.000 4 21.618 9.278 50.00 0 202.000 252.000 femora 404.750 4 41.404 10.229 97.000 346.000 443.000 tibiae 353.250 4 41.121 11.641 95.000 294.000 389.000 fibulae 342.750 4 38.578 11.255 88.000 287.000 375.000 10 11 humeri 318.889 9 18.510 5.804 63.000 277.000 340.000 radii 267.222 9 13.599 5.089 44.000 238.000 282.000 ulnae 250.667 9 14.491 5.781 47.000 219.000 266.000 femora 446.500 8 24.946 5.587 68.000 404.000 472.000 tibiae 378.889 9 23.358 6.156 66.000 342.000 408.000 fibulae 374.143 7 2 2.741 6.078 59.000 335.000 394.000 11 49 humeri 323.725 40 15.908 4.914 88.000 297.000 385.000 radii 276.214 28 10.093 3.654 42.000 259.000 301.000 ulnae 255.853 34 10.361 4.049 41.000 235.000 276.000 femora 450.175 40 15.619 3.469 69.000 4 12.000 481.000 tibiae 383.784 37 17.095 4.454 74.000 345.000 419.000 fibulae 371.424 33 17.234 4.640 75.000 332.000 407.000 12 53 humeri 318.867 45 12.892 4.043 62.000 286.000 348.000 radii 273.103 39 9.888 3.621 50.000 248.000 298.000 ulnae 254.364 44 9.236 3.631 46.000 229.000 275.000 femora 446.383 47 18.972 4.250 91.000 392.000 483.000 tibiae 380.622 45 18.038 4.739 81.000 340.000 421.000 fibulae 368.816 38 15.971 4.330 69.000 331.000 400.000 Table C-6. Descriptive statistics South Dakota Arikara females DA category N Element Mean n Std Dev COV Range Min Max 9 9 humeri 279.571 7 20.598 7.368 60.000 238.000 298.000 radii 243.667 6 10.443 4.286 26.000 228.000 254.000 ulnae 215.714 7 14.127 6.549 40.000 1 88.000 228.000 femora 389.111 9 27.260 7.006 78.000 348.000 426.000 tibiae 339.375 8 30.580 9.011 107.000 277.000 384.000 fibulae 319.714 7 26.139 8.176 77.000 274.000 351.000 10 33 humeri 299.893 28 13.022 4.342 53.000 271.000 324.000 radii 253.474 19 9.800 3.866 36.000 231.000 270.000 ulnae 232.560 25 9.256 3.980 36.000 215.000 254.000 femora 418.667 27 16.864 4.028 82.000 371.000 453.000 tibiae 351.000 30 15.091 4.299 63.000 317.000 380.000 fibulae 344.458 24 14 .903 4.326 67.000 317.000 384.000 11 38 humeri 297.724 29 14.205 4.771 46.000 278.000 324.000 radii 252.741 27 11.551 4.570 39.000 233.000 272.000

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219 Table C-6. Continued DA category N Element Mean n Std Dev COV Range Min Max 11 38 ulnae 233.600 25 11.644 4.985 39.000 214.000 253.000 femora 418.226 31 21.197 5.068 78.000 382.000 460.000 tibiae 353.231 26 16.585 4.695 67.000 320.000 387.000 fibulae 343.571 21 15.753 4.585 53.000 320.000 373.000 12 15 humeri 293.786 14 8.350 2.842 29. 000 276.000 305.000 radii 247.273 11 7.086 2.866 18.000 238.000 256.000 ulnae 228.300 10 6.147 2.693 18.000 220.000 238.000 femora 409.333 12 13.513 3.301 40.000 385.000 425.000 tibiae 344.000 14 14.961 4.349 51.000 321.000 372.000 fibulae 339.143 7 13.825 4.077 45.000 312.000 357.000 Table C-7. Descriptive statistics. Ancestral Puebloan juveniles. DA category N Element Mean n Std Dev COV Range Min Max 1.1 10 humeri 66.375 8 2.615 3.94 7.000 63.000 70.000 radii 63.000 9 2. 398 3.806 8.000 60.000 68.000 ulnae 54.375 8 1.598 2.939 5.000 52.000 57.000 femora 79.100 10 4.228 5.345 14.000 74.000 88.000 tibiae 68.600 10 3.204 4.671 10.000 65.000 75.000 fibulae 65.667 9 2.872 4.374 8.000 62.000 70.000 1.2 20 h umeri 86.842 19 6.414 7.386 22.000 77.000 99.000 radii 76.643 14 5.486 7.158 18.000 70.000 88.000 ulnae 69.077 13 5.454 7.895 17.000 62.000 79.000 femora 106.357 14 7.469 7.023 24.000 96.000 120.000 tibiae 89.250 12 4.545 8.454 23.000 80.000 103.000 fibulae 86.200 10 6.973 8.089 22.000 77.000 99.000 2 31 humeri 97.292 24 9.937 10.213 42.000 86.000 128.000 radii 87.158 19 9.805 11.25 40.000 65.000 105.000 ulnae 78.150 20 7.836 10.026 29.000 65.000 94.000 femora 126.0 00 18 16.284 12.924 64.000 405.000 169.000 tibiae 106.500 16 12.215 11.469 45.000 89.000 134.000 fibulae 101.625 8 9.211 9.064 28.000 90.000 118.000 3 11 humeri 111.750 8 6.431 5.755 19.000 103.000 122.000 radii 97.000 5 3.082 3.178 7.000 93.000 100.000 ulnae 87.000 5 2.302 2.634 5.000 85.000 90.000 femora 138.778 9 19.601 14.124 61.000 100.000 161.000 tibiae 125.000 4 7.789 6.231 17.000 119.000 136.000 fibulae 117.000 4 11.944 10.209 28.000 102.000 130.000 4 19 humeri 138.571 14 10.128 7.309 37.000 120.000 157.000

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220 Table C-7. Continued DA category N Element Mean n Std Dev COV Range Min Max 4 19 radii 117.286 14 8.730 7.444 30.000 103.000 133.000 ulnae 106.714 14 7.966 7.464 26.000 93.000 119.000 femora 1 86.214 14 16.282 8.744 54.000 158.000 212.000 tibiae 154.077 13 14.609 9.481 40.000 134.000 174.000 fibulae 153.444 9 11.425 7.446 30.000 139.000 169.000 5 11 humeri 164.000 11 13.038 7.950 44.000 142.000 186.000 radii 142.200 5 12.988 9.1 34 32.000 122.000 154.000 ulnae 130.000 4 13.928 10.714 30.000 110.000 140.000 femora 219.571 7 20.28 9.236 63.000 190.000 253.000 tibiae 182.750 4 20.549 11.244 50.000 156.000 206.000 fibulae 187.250 4 22.544 12.040 47.000 158.000 205 .000 6 12 humeri 194.364 11 15.552 8.001 43.000 179.000 222.000 radii 163.000 8 10.268 6.299 34.000 150.000 184.000 ulnae 147.111 9 14.391 9.783 38.000 133.000 171.000 femora 264.200 10 22.837 8.644 77.000 245.000 322.000 tibiae 220.6 25 8 19.198 8.702 62.000 200.000 262.000 fibulae 219.000 6 19.807 9.023 50.000 209.000 259.000 7 5 humeri 230.750 4 36.207 15.691 86.000 195.000 281.000 radii 191.667 3 29.023 15.142 53.000 172.000 225.000 ulnae 185.000 2 29.698 16.053 42.0 00 164.000 206.000 femora 335.000 5 40.410 12.063 114.000 280.000 394.000 tibiae 298.333 3 34.210 11.467 65.000 272.000 337.000 fibulae 272.333 3 20.551 7.546 37.000 259.000 296.000 8 5 humeri 268.500 4 29.727 11.071 70.000 233.000 303.000 radii 217.600 5 15.868 7.292 38.000 201.000 239.000 ulnae 198.800 5 12.398 6.236 32.000 184.000 216.000 femora 384.600 5 25.245 6.564 52.000 355.000 407.000 tibiae 321.200 5 22.830 7.108 60.000 291.000 351.000 fibulae 300.500 4 1 4.295 4.757 30.000 286.000 316.000 Table C-8. Descriptive statistics Ancestral Puebloan males DA category N Element Mean n Std Dev COV Range Min Max 9 2 humeri 306.000 1 0.000 306.000 306.000 radii 255.000 1 0.000 255.000 255.000 uln ae 233.500 2 0.707 0.303 1.000 233.000 234.000 femora 410.000 1 0.000 410.000 410.000 tibiae 364.000 1 0.000 364.000 364.000

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221 Table C-8. Continued DA category N Element Mean n Std Dev COV Range Min Max 9 2 fibulae 349.500 2 6.364 1.8 21 9.000 345.000 354.000 10 10 humeri 304.222 9 12.706 4.177 40.000 281.000 321.000 radii 251.833 6 12.608 5.007 37.000 232.000 269.000 ulnae 233.286 7 13.913 5.964 38.000 212.000 250.000 femora 419.778 9 12.101 2.883 37.000 404.000 441.000 tibiae 357.200 10 13.983 3.914 51.000 330.000 381.000 fibulae 341.143 7 10.699 3.136 30.000 325.000 355.000 11 26 humeri 303.250 24 12.712 4.192 47.000 278.000 325.000 radii 256.050 20 11.409 4.456 44.000 236.000 280.000 ulnae 238.47 4 19 11.389 4.776 39.000 218.000 257.000 femora 424.217 23 20.392 4.807 84.000 369.000 453.000 tibiae 359.087 23 17.347 4.831 58.000 323.000 381.000 fibulae 349.850 20 14.151 4.045 59.000 316.000 375.000 12 11 humeri 310.700 10 17.976 5.78 6 56.000 275.000 331.000 radii 260.750 8 12.476 4.785 42.000 238.000 280.000 ulnae 246.625 8 7.763 3.148 23.000 239.000 262.000 femora 437.333 9 23.092 5.28 80.000 395.000 475.000 tibiae 366.300 10 15.217 4.154 52.000 335.000 387.000 fibulae 353.750 8 14.916 4.217 44.000 327.000 371.000 Table C-9. Descriptive statistics Ancestral Puebloan females DA category N Element Mean n Std Dev COV Range Min Max 9 8 humeri 254.143 7 38.325 15.08 87.000 199.000 286.000 radii 212.750 4 29.781 13.998 64.000 169.000 233.000 ulnae 195.250 4 28.016 14.349 62.000 154.000 216.000 femora 354.000 6 49.031 13.85 115.000 291.000 406.000 tibiae 314.571 7 25.520 8.113 75.000 259.000 334.000 fibulae 290.250 4 41.234 14.206 89.00 0 229.000 318.000 10 9 humeri 283.800 5 4.266 1.503 11.000 280.000 291.000 radii 227.800 5 7.294 3.202 19.000 218.000 237.000 ulnae 217.250 4 7.632 3.513 18.000 208.000 226.000 femora 394.778 9 14.593 3.696 47.000 364.000 411.000 tibia e 327.000 9 14.186 4.338 43.000 299.000 342.000 fibulae 324.500 4 9.037 2.785 19.000 316.000 335.000 11 43 humeri 281.564 39 11.066 3.930 49.000 261.000 310.000 radii 234.424 33 10.627 4.533 44.000 209.000 253.000 ulnae 216.000 38 9.656 4. 470 38.000 195.000 233.000 femora 397.846 39 14.414 3.623 62.000 374.000 436.000 tibiae 331.821 39 13.751 4.144 50.000 306.000 356.000 fibulae 322.867 30 14.335 4.440 60.000 288.000 348.000 12 17 humeri 284.667 15 13.151 4.620 48.000 267.0 00 315.000

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222 Table C-9. Continued DA category N Element Mean n Std Dev COV Range Min Max 12 17 radii 234.800 10 11.942 5.086 34.000 219.000 253.000 ulnae 217.273 11 11.490 5.288 31.000 204.000 235.000 femora 397.882 17 14.326 3.601 56.000 374.0 00 430.000 tibiae 335.467 15 11.951 3.563 38.000 315.000 353.000 fibulae 323.444 9 15.436 4.772 50.000 301.000 351.000

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223 APPENDIX D POSTCRANIAL MEASUREMENTS Appendix D depicts the maximum long bone measurements of the humeri, ulnae, radii, greatest distance from the proximal most point to the distal most point for each element. Measurements were recorded to the nearest millimeter using an osteometric board. All illustrative figures present the anterior view of the right post-cranial element. In each image the juvenile element is on the left, adult element on the right. Figure D-1. Juvenile and adult humeri measurement

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224 Figure D-2. Juvenile and adult ulnae measurement Figure D-3. Juvenile and adult radii measurement

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225 Figure D-4. Juvenile and adult femora measurement Figure D-5. Juvenile and adult tibiae measurement

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226 Figure D-6. Juvenile and adult fibulae measurement

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249 BIOGRAPHICAL SKETCH Erin B. Waxenbaum received her bachelor of arts and master of arts in anthropology from Brandeis University in May 2002. From 2003-2007, she pursued a doctorate in anthropology specializing in skeletal biology, growth and development, and forensic anthropology at the University of Florida. During this period, she taught for the Department of Anthropology and Honors College and was involved in casework at the C.A. Pound Human Identification lab, University of Florida. This work resulted with a Ph.D. project entitled -geographic effects on