An Ecogeographic Study of Body Proportion Development in the Sadlermiut Inuit of Southampton Island, Nunavut

Home , Dorset culture, Sadlermiut, The Extreme (novel)

Natalie Symchych

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Anthropology University of Toronto

An Ecogeographic Study of Body Proportion Development in the Sadlermiut Inuit of Southampton Island, Nunavut

Natalie Symchych Doctor of Philosophy Graduate Department of Anthropology University of Toronto 2016

This thesis explores growth status and body proportion development in past North Amer- ican Arctic populations. Living in one of the most extreme environments on the planet, Arctic foragers provide an opportunity to explore how human morphological variation is shaped by growth and climate.

The study focuses on the Sadlermiut Inuit, who lived on Southampton Island in Hudson Bay, Nunavut. This main sample is comprised of 111 juveniles and 160 adults (62 F, 52 M, 46 und). Comparative samples are derived from Northwest Hudson Bay, Point Hope (Alaska), and Greenland, and are comprised of 106 juveniles and 151 adults (76 F, 75 M). Growth status in four long bones is assessed by comparing the samples’ tempo of growth to normative values from a modern North American sample. Body proportion development is assessed by calculating brachial index, crural index, and limb length relative to skeletal trunk height. Plots of index values versus dental age are assessed visually, and compared to results from the literature.

Sadlermiut individuals who died as juveniles show a predominant pattern of growth faltering as compared to the North American tempo of growth. Most Sadlermiut juveniles who died in infancy either began life lagging in proportional growth, or fell behind quickly after birth. Most Sadlermiut juveniles who died later in childhood/adolescence had continued to falter in growth, indicating either insuﬃcient, or a lack of, catch-up growth.

ii This contrasts with Point Hope, which showed a broad range of growth outcomes: individuals with growth in line with the North American tempo, as well as individuals with lagging and accelerated growth. The analysis of body proportion development demonstrates that Sadlermiut infants exhibit a wide range of index values, and adult-type body proportions appear by early- to mid-childhood. Adult proportionality is achieved by most older Sadlermiut juveniles, despite a lag in linear growth. This demonstrates that linear growth is more environmentally labile, while body shape is more conserved. Moreover, the timing of body proportion development is consistent across samples from this study and the literature, suggesting a consistent pattern of growth with regard to body proportionality, regardless of latitude/climate and ultimate adult proportionality.

iii Acknowledgements

I owe thanks to many people for their support and assistance over the course of my doctoral degree. First and foremost is my supervisor, Susan Pfeiffer. She has been continually supportive of me and this project, and I am deeply grateful for her guidance these past five years. I am grateful to the members of my examination committee for their excellent input and suggestions, which helped me to improve this thesis. I benefited greatly from the guidance and perspective offered by Tracey Galloway, Tracy Prowse, Michael Schillaci, and Daniel Sellen. I thank the curators and curatorial staff who gave me the opportunity to study the skeletal remains under their care: Janet Young and Jerry Cybulski of the Canadian Museum of History; Gisselle Garcia of the American Museum of Natural History; and Niels Lynnerup of the University of Copenhagen. I am grateful to the Inuit Heritage Trust and the Greenland National Museum and Archives for granting permission to study the Canadian and Greenlandic skeletal collections. My research travel was supported by funding from the University of Toronto Department of Anthropology, and the School of Graduate Studies. My degree has been supported by scholarships from the Social Sciences and Humanities Research Council, the Ontario Graduate Scholarship program, and the University of Toronto. A big thank you to Janet Young, for her continued mentorship and support. Thank you for hiring me ten years ago, and for encouraging me to explore my interests. And thank you to Megan Gardiner of the CMH for all her help from afar. I wish to thank my family and friends for their love and support. To my parents, who have been outstandingly supportive, and generous with their proof-reading skills. And finally, thank you to my partner Brent Pym — for the abundant love and encouragement, for the tech support, and for the (as always) excellent food.

iv Contents

1 Introduction1

1.1 Hypotheses...... 3

2 Background5

2.1 Body form and ecogeographic expectations...... 5

2.1.1 Non-human ecogeographic patterning...... 6

2.1.2 Classical approaches to ecogeographic patterning in humans...7

Adult body proportions in living populations...... 7

Adult body proportions in past populations...... 9

Juvenile body proportions in modern (Arctic) populations.... 15

Juvenile body proportions in past populations...... 17

2.1.3 Emerging perspectives on human ecogeographic patterning.... 20

Tissue economy...... 20

Childhood growth trade-oﬀs...... 21

Allometry...... 23

Population structure and history...... 25

2.1.4 Methodological considerations...... 28

2.2 Linear growth...... 29

2.2.1 General pattern and normal variability of linear growth...... 29

Longitudinal and cross-sectional growth studies...... 32

v 2.2.2 Eﬀects of growth on body proportion development...... 33

2.2.3 Growth studies in past populations...... 36

Humphrey methodology and the Denver Growth Study...... 37

2.2.4 Considerations for the study of growth in the Arctic...... 39

2.3 Main sample: the Sadlermiut Inuit...... 43

2.3.1 Sadlermiut introduction and ethnographic contact...... 43

2.3.2 Sadlermiut within Arctic population history...... 44

2.3.3 Sadlermiut as a single population...... 46

2.4 Arctic contextual information...... 47

2.4.1 Ethnographic information...... 47

2.4.2 Inuit diet and the health ‘paradox’...... 51

2.4.3 Infant mortality data in recent historical Inuit populations.... 53

3 Materials and Methods 55

3.1 Materials...... 55

3.1.1 Sadlermiut...... 55

3.1.2 Point Hope...... 57

3.1.3 Kamarvik and Silumiut (Northwest Hudson Bay)...... 59

3.1.4 Greenland Thule and Inuit...... 60

3.2 Sample selection...... 60

3.3 Sex determination and age estimation...... 61

3.3.1 Dental age intra-observer error...... 62

3.3.2 Regression-based age estimation...... 62

3.4 Osteometric variables...... 64

3.5 Investigation of linear growth...... 67

3.6 Investigation of body proportion development...... 68

vi 4 Results 71

4.1 Sample composition...... 71

4.2 Investigation of linear growth...... 75

4.2.1 Sadlermiut linear growth...... 82

Summary...... 88

4.2.2 Point Hope linear growth...... 88

4.2.3 NW Hudson Bay and Greenland linear growth...... 91

4.2.4 Summary of growth analysis...... 92

4.3 Investigation of body proportion development...... 95

4.3.1 Adult body proportions...... 95

4.3.2 Sadlermiut body proportions...... 106

4.3.3 Point Hope body proportions...... 111

4.3.4 NW Hudson Bay and Greenland body proportions...... 115

4.3.5 Body proportion development summary and comparison..... 116

5 Discussion and Conclusions 120

5.1 Hypotheses...... 120

5.2 Linear growth...... 121

5.3 Body proportion development...... 125

5.4 Broader Implications...... 126

5.5 Notes on limitations and unexplored data...... 129

5.6 Conclusions...... 131

Bibliography 160

Appendix A Greenland Sample Information 161

Appendix B Dental Age Estimation 164

B.1 Dental age estimation examples...... 164

vii B.2 Intra-observer error...... 164 B.3 Dental age regression...... 165

Appendix C Additional Plots — Sample Distributions 171

Appendix D Additional Plots — Investigation of Growth 174

Appendix E Additional Plots — Investigation of Body Proportion Devel- opment 178

Appendix F Dataset 186

viii List of Tables

2.1 Historical Inuit infant mortality rates (IMR)...... 54

3.1 Osteometric variables...... 65

3.2 Adult long bone end points (mm)...... 67

3.3 Denver regression equations...... 68

3.4 Body proportion indices...... 69

3.5 Summary of comparative studies...... 70

4.1 Number of juveniles per sample...... 72

4.2 Number of adults per sample, by sex...... 74

4.3 Adult brachial and crural index values...... 95

4.4 Comparative northern hemisphere body proportion samples, with latitude, brachial and crural index values...... 100

4.5 Adult relative upper limb length (no sacrum) index values...... 101

4.6 Adult relative lower limb length (no sacrum) index values...... 101

A.1 List of Greenland sites...... 161

B.1 Example 1: XIV-C:120 (infant)...... 165

B.2 Example 2: XIV-C:281 (perinate)...... 165

B.3 Repeated Sadlermiut dental age estimates (yrs)...... 166

B.4 Ordinary least squares regression equations...... 166

B.5 Sadlermiut individuals with regression-based age estimations...... 170

ix F.1 Juvenile data...... 186 F.2 Adult data...... 192

x List of Figures

2.1 Body proportion indices from Denver Growth Study...... 35

3.1 Sample locations...... 56

3.2 Sadlermiut site locations...... 57

4.1 Juvenile sample distribution by dental age...... 72

4.2 Juvenile sample distribution by dental age and population...... 73

4.3 Humerus length by dental age, all samples...... 75

4.4 Radius length by dental age, all samples...... 76

4.5 Femur length by dental age, all samples...... 76

4.6 Tibia length by dental age, all samples...... 77

4.7 Growth of humerus, all samples...... 78

4.8 Growth of radius, all samples...... 79

4.9 Growth of femur, all samples...... 80

4.10 Growth of tibia, all samples...... 81

4.11 Sadlermiut upper limb growth, % length attained residuals from Denver mean...... 84

4.12 Sadlermiut lower limb growth, % length attained residuals from Denver mean...... 85

4.13 Sadlermiut upper limb growth, % length attained residuals from Denver mean, individuals under two years...... 86

xi 4.14 Sadlermiut lower limb growth, % length attained residuals from Denver mean, individuals under two years...... 87

4.15 Point Hope upper limb growth, % length attained residuals from Denver mean...... 89

4.16 Point Hope lower limb growth, % length attained residuals from Denver mean...... 90

4.17 NW Hudson Bay and Greenland upper limb growth, % length attained residuals from Denver mean...... 93

4.18 NW Hudson Bay and Greenland lower limb growth, % length attained residuals from Denver mean...... 94

4.19 Brachial Index in adults and juveniles, all samples...... 96

4.20 Crural Index in adults and juveniles, all samples...... 97

4.21 Body proportion indices by latitude...... 99

4.22 Relative humeral diaphyseal length (no sacrum), all samples...... 102

4.23 Relative radial diaphyseal length (no sacrum), all samples...... 103

4.24 Relative femoral diaphyseal length (no sacrum), all samples...... 104

4.25 Relative tibial diaphyseal length (no sacrum), all samples...... 105

4.26 Sadlermiut juvenile indices with adult reference values...... 107

4.27 Sadlermiut juvenile indices, under two years of age...... 108

4.28 Sadlermiut juvenile relative upper limb length indices with adult reference values...... 109

4.29 Sadlermiut juvenile relative lower limb length indices with adult reference values...... 110

4.30 Tigara juvenile indices with adult reference values...... 112

4.31 Tigara juvenile relative upper limb length indices with adult reference values113

4.32 Tigara juvenile relative lower limb length indices with adult reference values114

4.33 Sadlermiut and Denver index values by age...... 118

xii A.1 Greenland site locations...... 163

B.1 Cubic polynomial regression equation (all iliac breadths)...... 167

B.2 Cubic model evaluation of ﬁt...... 167

B.3 OLS regression of dental age on iliac breadth...... 168

B.4 <60 mm equation evaluation of ﬁt...... 168

B.5 >80 mm equation evaluation of ﬁt...... 169

C.1 Juvenile sample distribution by dental age (regression-aged individuals removed)...... 171

C.2 Juvenile sample distribution by dental age and population (regression-aged individuals removed)...... 172

C.3 Point Hope sub-distributions by dental age...... 173

D.1 Upper limb bone lengths by dental age, all samples, Sadlermiut regression- aged individuals excluded...... 174

D.2 Lower limb bone lengths by dental age, all samples, Sadlermiut regression- aged individuals excluded...... 175

D.3 Growth of humerus, all samples, Sadlermiut regression-aged individuals excluded...... 175

D.4 Growth of radius, all samples, Sadlermiut regression-aged individuals excluded...... 176

D.5 Growth of femur, all samples, Sadlermiut regression-aged individuals excluded...... 176

D.6 Growth of tibia, all samples, Sadlermiut regression-aged individuals excluded177

E.1 Sadlermiut adult limb proportion indices, using maximum lengths.... 178

E.2 Point Hope adult limb proportion indices, using maximum lengths.... 179

E.3 NW Hudson Bay adult limb proportion indices, using maximum lengths. 179

xiii E.4 Greenland adult limb proportion indices, using maximum lengths.... 180 E.5 Adult limb proportion indices, using maximum lengths...... 180 E.6 Adult relative humeral lengths, all samples...... 181 E.7 Adult relative radial lengths, all samples...... 181 E.8 Adult relative femoral lengths, all samples...... 182 E.9 Adult relative tibial lengths, all samples...... 182 E.10 Ipiutak limb index values...... 183 E.11 Ipiutak juvenile relative upper limb length indices with adult reference values183 E.12 Ipiutak juvenile relative lower limb length indices with adult reference values184 E.13 NW Hudson Bay limb index values...... 184 E.14 Greenland limb index values...... 185

xiv Chapter 1

Introduction

Anthropologists have long endeavoured to understand human morphological variation as a reflection of climate. The ecogeographic ‘rules’ of Bergmann(1847) and Allen(1877) suggest that humans at higher latitudes will have larger bodies and/or shorter extremities than those at lower latitudes. A ‘cold-adapted’ body may confer an advantage in situa- tions involving extreme or prolonged environmental cold stress. Environmental stresses may contribute to differential mortality and fertility, thus driving adaptive change in a population (Frisancho, 2009). Periods of cold have been linked to major speciation events in the hominid lineage; as such, cold-related fertility and mortality are issues ripe for investigation (Steegmann, 2007). Motivated by hominin morphological variation and evolution, studies of human skeletal ecogeographic patterning flourished in the 1990s (e.g. Ruff, 1991, 1994; Holliday, 1997a,b). The topic has experienced a resurgence of interest more recently, reignited by Holliday and Hilton’s (2010) study of the Point Hope Inuit skeletal collections from Alaska. Recent research has also picked up on the study of juvenile body proportions (e.g. Temple et al., 2011; Cowgill et al., 2012; Bleuze et al., 2014). Several researchers have suggested that cold would have acted selectively on children in the past, and thus children are important research subjects (Newman, 1975; Steegmann, 2007; Cowgill et al., 2012). It is reasonable to think that extreme environmental pressure

1 Chapter 1. Introduction 2 will select for patterns in juvenile body proportions similar to those of adults, but it is not clear how ecogeographic variation among juveniles interacts with changes in body shape during growth (Cowgill et al., 2012). Moreover, there is a paucity of studies on children from extreme environments.

Further to this idea, it is not clear how growth insufficiency may interact with body proportion development — and this is a confounding factor in the study of body proportion development. The study of linear skeletal growth and that of body proportion development have measurements in common, namely long bone lengths. This overlap allows for a combination of the two topics into one comprehensive study. The health status of past populations is commonly explored through linear skeletal growth. This is based on two foundational concepts: that the health and nutritional status of a nation’s citizens are accurately reflected by its children’s average heights and weights (Eveleth and Tanner, 1976; Lampl and Mummert, 2014); and, that the growth of a child is the single best indicator of his or her health and development (Johnston, 1969; de Onis et al., 2004). In growth studies of past populations, linear growth is often used as a non-specific indicator of nutritional status. Malnutrition and poor health can lead to reduced growth and stunting, reflected by reduced stature or long bone length-for-age (Saunders and Hoppa, 1993; Johnston, 1998). This, in turn, can lead to lower limb to trunk proportions and foreshortened limb segments (Tanner et al., 1982; Jantz and Jantz, 1999).

The research presented in this thesis explores growth status and body proportion development in past North American Arctic populations. Arctic foragers experience extreme and prolonged cold temperatures, marked seasonal variation in daylight hours, and a dependence on uncultivated resources with limited plant availability (So, 1980; Hilton et al., 2014). North American Arctic forager groups traditionally employed a varied and broad range of sophisticated technology, social organization, and residential mobility — cultural systems which eﬀectively buﬀered against the physical elements of the Arctic. These strategies also allowed forager groups to expand rapidly across the Chapter 1. Introduction 3

North American Arctic, from the Bering Strait to Greenland. Since Arctic foragers exist at the extreme end of the foraging spectrum, in terms of latitude and resource availability, they present special insight into human adaptability — from both cultural and physical standpoints (Hilton et al., 2014).

Living in one of the most extreme environments on the planet, Arctic foragers provide an opportunity to explore the effects of this environment on human adaptability — specifically, to understand how human morphological variation is shaped by growth and climate. This study represents the first exploration of growth outcomes in past populations from across the North American Arctic, and attempts to explore how the harsh environment may have affected growth. This study also extends the analysis of body proportion development to a well-preserved sample with a large number of infant individuals. By studying four different Arctic samples, a broader picture of the range of variation in growth and body proportion development can be obtained.

The main study sample, the Sadlermiut Inuit, lived on Southampton Island in northwestern Hudson Bay, Nunavut. Comparative samples are derived from the northwest coast of Hudson Bay, Alaska, and Greenland. Taken together, the samples span approximately 1600 to 100 years before present (BP). The sample is generally well-preserved, and covers infancy to late adolescence.

1.1 Hypotheses

This study intends to test two hypotheses, as follows.

1. Sadlermiut adult skeletons will show proportionality consistent with adaptation to low ambient temperatures

2. Sadlermiut juveniles will exhibit characteristic skeletal proportions from early life onwards Chapter 1. Introduction 4

Beyond these hypotheses, this study will explore more generally the interaction between growth suﬃciency and body proportion development. Relevant background information is presented in Chapter 2. Topics covered include ecogeographic body proportionality, linear growth, and the Sadlermiut Inuit. Chapter 3 details the skeletal samples studied, and the methods employed in data collection and analysis. Chapter 4 presents the results of the linear growth and body proportion development analyses, while Chap- ter 5 addresses the research hypotheses and discusses the results in the context of relevant literature. Chapter 2

Background

2.1 Body form and ecogeographic expectations

Widely distributed animal species exhibit morphological patterns related to climate. The most well known of these patterns are described by the ‘rules’ of Bergmann(1847) and Allen(1877). These generalizations state that within a species or subspecies of geographically dispersed homeothermic animals, those at higher latitudes will tend to have higher body mass (Bergmann) and/or shorter extremities (Allen) than their conspeciﬁcs at lower latitudes. Both rules describe shifts of the body surface area to volume ratio that allow animals to conserve or disperse body heat (Scholander, 1955; Holliday, 1997a; Pearson, 2000). These relationships have generally been interpreted as the eﬀects of natural selection acting on environmental stress-related adaptive change (Newman, 1953). The literature on human ecogeographic body patterning has traditionally fallen into this ‘classical’ camp of environment acting on the body.

Mayr(1956) emphasized, however, that the establishment of a pattern, and the physiological interpretation given to that pattern, are two separate steps. Moreover, he noted that the phenotype of an animal is the result of compromise between many competing selection pressures, of which thermoregulation is only one (Mayr, 1956). Several ‘emerging’

5 Chapter 2. Background 6 perspectives in the literature have attempted to take this into consideration, although Stock(2013) has observed that recent ecogeographic analyses have continued to interpret variation in human limb proportions as being adaptive to environmental stress, and as being relatively stable through development. This, despite evidence of plasticity in human body size and shape as demonstrated by Tanner et al.(1982) and Bogin et al. (2002). The recognition that human body size and shape can be aﬀected by a number of factors has been discussed in this emerging dialogue on ecogeographic patterning. These factors include tissue economy, growth trade-oﬀs, allometry, and population history and movement.

The following sections will explore the literature human ecogeographic body patterning, by looking ﬁrst at the historic or ‘classical’ literature, and then moving on to the newer, ‘emerging’ perspectives. Additionally, non-human ecogeographic patterning will be brieﬂy touched on.

2.1.1 Non-human ecogeographic patterning

Support for ecogeographic patterning in animals is well established. In a review of data compiled from the literature, Ashton and colleagues (2000) found that Bergmann’s rule holds for species in most families and all orders of mammals. Moreover, it holds for species of mammals of very different sizes, from various parts of the world, and it holds for mammals with very different ecologies. In a later study, Millien et al.(2006) reviewed evidence from contemporary, historical, and evolutionary studies to determine how pow- erful ecogeographic rules could be in interpreting patterns of organismal response to climate change. Bergmann’s rule was found to hold for most (62% to 83%) vertebrate species, excepting squamates and fishes. Recent research on ecogeographic patterning in animals is greatly motivated by climate warming, and seeks to investigate body size/shape changes associated with climate change (Smith et al., 2009; Gardner et al., 2011; Teplit- sky and Millien, 2014). In light of the increased rate of global warming, Millien and Chapter 2. Background 7 colleagues (2006) argue that there is an urgent need for an understanding of how climate changes can impact living organisms. Researchers have evaluated both past populations of animals (e.g. Smith et al., 2009; Secord et al., 2012; Rosvold et al., 2014) and living populations (e.g. Rodr´ıguezet al., 2008; Clauss et al., 2013; Gutirrez-Pinto et al., 2014).

2.1.2 Classical approaches to ecogeographic patterning in hu-

mans

The eﬀects of climate on the human physique, both in living and past populations, have been subject to study. In an early study, Hrdliˇcka(1930) described modern Alaskan Inuit as having short limbs and long bodies, with relatively short forearms. It wasn’t until the 1950’s, however, that interest in ecogeographic patterns in humans really ﬂourished. Two types of approaches have been used to study temperature-related adaptation in humans: distributional studies and physiological/functional studies. Distributional studies test human physical characteristics for an association with natural environmental variables, and typically analyze a large human dataset over a wide range of geographical zones. Physiological studies place human subjects in actual or simulated cold environments, and assess one or more indices of physiological response (Steegmann, 1975). Anthropologists have largely focused on distributional studies.

Adult body proportions in living populations

Adult human variation in dimensions including stature, body weight, and relative sitting height has been investigated through distributional studies in populations from diﬀerent climates around the world (Pearson, 2000). Schreider’s (1950) study of skin surface area to body weight in sixteen male populations found an association between temperate cold and a heat-conserving body build. Roberts (1953) found an inverse relationship between mean weight and mean annual temperature in a sample of 116 male indigenous groups, and a follow up study (Roberts, 1973) found that inhabitants of colder regions exhibited Chapter 2. Background 8 relatively longer torsos, larger chests, and shorter arms than inhabitants of warmer regions. Studies on New World aboriginal populations by Newman(1953, 1960) found a strong correlation between body weight and mean coldest month temperature, as well as evidence of reduced leg length (and thus Allen’s rule) in North American Arctic samples. In a study of United States Army inductees, Newman and Munro(1955) found a correlation of greater weights, surface areas, and weight per unit surface area in inductees from colder climates. Schreider(1964) again conﬁrmed the existence of relationships between temperature, weight, body surface area, and limb length. Importantly, he cautioned against envisioning a single gradient of body morphology, and suggested that two separate ecogeographic gradients must be considered: one involving temperature and the surface area to mass ratio, and one involving temperature and relative limb length.

Katzmarzyk and Leonard(1998) re-evaluated the influence of climate on human body size and proportions by analyzing data published subsequent to Roberts’ initial 1953 study. The authors expected that the relationship between climate and body size would have changed due to secular trends in human growth. The data included mean stature and body mass for 223 male and 195 female adult samples from studies published since 1953, drawn largely from the physical anthropology literature. A subsample of studies also included sitting height. Body mass index, surface area to mass ratio, and relative sitting height were calculated. In keeping with Roberts’ methodology, the relationship between body size/shape and mean annual temperature was examined. The results confirmed those found by Roberts(1953, 1973), that a significant negative association exists between body mass and mean annual temperature. Katzmarzyk and Leonard found much lower correlations and significantly shallower regression slopes than those obtained by Roberts, however, and attributed these differences to positive secular trends in body mass resulting from changing lifestyle and dietary patterns. These differences were most notable among tropical populations. The decline in strength of this association suggested that the strong relationship found by Roberts was attributable partly to differences in Chapter 2. Background 9 diet and nutrition, as well as partly to differences in thermal stress (Katzmarzyk and Leonard, 1998).

More recently, Foster and Collard(2013) re-examined Bergmann’s rule in modern humans. They highlighted the fact that many studies have employed samples with a dis- proportionately large number of warm-climate and northern hemisphere groups. Rather than reflecting the relationship between temperature and body shape in Homo sapiens as a whole, the authors argued that these studies reflected this relationship in warm-climate and/or northern hemisphere groups only. Foster and Collard retested the hypothesis that modern humans conform to Bergmann’s rule, while controlling for sample biases. Three sets of analyses were employed: first, they replicated the approach of past studies; second, they used stratified sampling to control for warm-climate bias in the sample; and third, to investigate northern hemisphere sample bias, they investigated the relationship separately in the northern and southern hemispheres. Their sample was comprised of male data from 263 groups. Bergmann’s rule was supported when they analyzed the entire sample and when they controlled for warm-climate bias, but it was only partially supported when they controlled for northern hemisphere bias. Further analysis showed that the temperature range in the habitable regions of the southern hemisphere was insufficient for thermoregulation-related natural selection to become a dominant influence on variation in modern human body size. The authors concluded that modern male humans do therefore conform to Bergmann’s rule, but only when there are major differences in latitude and temperature among groups.

Adult body proportions in past populations

Adult body proportions have also been studied through skeletal dimensions, especially linear long bone measurements and proportions of limb segments (Trinkaus, 1981; Holli- day, 1997a; Pearson, 2000). The study of ecogeographic body proportions has important implications for hominin morphological variation and evolution. By studying the natural Chapter 2. Background 10 variability among modern human populations within a physiological framework, the significance of morphological variation in earlier hominins can be better understood (Ruff, 1993). Climatic variation may have had a greater effect on early human evolution through less, or less efficient, cultural buffering (Ruff, 1993). The analysis of ecogeographic body proportion variation in adult fossil hominids has been used to investigate thermoregulatory adaptation in the past, as well as patterns of human migration and evolution (Trinkaus, 1981; Ruff, 1991, 1994; Holliday, 1997a,b, 2000; Pearson, 2000).

Studies of body proportions in past populations have heavily focused on samples derived from cold climates. Trinkaus(1981) investigated limb proportions in Neanderthal, early anatomically modern, and very recent skeletal samples. The author found strong support for Allen’s rule, with samples from warmer climates having relatively longer distal limb segments. Neanderthals displayed short distal limb segments, comparable to modern high latitude populations. Early anatomically modern (Upper Paleolithic) samples, on the other hand, were found to be more similar to modern tropical populations in distal limb length. Neanderthals also had relatively longer clavicles, suggesting wide trunks. Ruff(1991, 1993, 1994) demonstrated that bi-iliac breadth correlates with mean annual temperature, with populations from the coldest areas tending to have the widest absolute bi-iliac breadths. Ruff(1991) investigated the reduction in relative pelvic breadth from early small hominids to later larger hominids and argued that this reduction was at least partly due to thermoregulatory constraints on body shape, given an increase in body size. He proposed a cylindrical model for the human body, as the simplest geometric model to incorporate size and shape characteristics. In order to maintain the same surface area to mass ratio with a change in height, breadth must remain constant; conversely, a change in the surface area to mass ratio requires a change in absolute breadth. This cylindrical model can be used to predict variation in population proportions: variation in height will be accompanied by little or no variation in breadth within similar temperature zones; absolute breadth will vary between different temperature zones, with larger breadths Chapter 2. Background 11

(regardless of height) being found in colder climates. Bi-iliac breadth was used as an index of body breadth, chosen over other indicators (e.g. bi-acromial breadth, bi-trochanteric breadth). Bi-iliac breadth is measurable and is directly comparable in living humans and skeletal/fossil remains, and is relatively independent of variations in joint or limb morphology.

Ruff(1991) tested the validity of his cylindrical model by investigating bi-iliac breadth and stature in 71 living human populations samples. The populations were grouped into four broad regional/climatic groups: sub-Saharan African/tropical, southeastern Asian/subtropical, European/temperate, and northern Asian/Arctic-subarctic. Within similar temperature zones, populations of different average stature varied relatively little in average absolute bi-iliac breadth; in three of the four groups, body breadth did not change with changes in height. Ruff concluded that modern human populations living under the same general climatic conditions appeared to maintain similar body surface area to mass ratios by limiting variation in body breadth – despite variation in stature and body mass. Moreover, bi-iliac breadth was highly correlated with latitude, independent of changes in stature.

Holliday(1997a) examined cold-adaptation in European Neanderthals, specifically as reflected in body shape, by using a large comparative sample of recent humans and by employing multivariate analyses of size and shape. Six western European Neanderthals were compared with fifteen recent human samples grouped into four discrete regional categories: circumpolar, Europe, North Africa, and sub-Saharan Africa. Holliday found that increases in latitude were associated with decreases in brachial and crural indices. The European Neanderthal sample tended to fall at the extreme end of the modern high latitude group’s range, indicating a ‘hyper-polar’ morphology with a long trunk, large femoral heads and short distal limb segments. The hyper-polar morphology could be explained by the extremely low temperatures in glacial Europe, and by cultural buffers that were less effective in reducing selection pressure on body form. The author noted, Chapter 2. Background 12 however, that his circumpolar group was solely represented by the Koniag Inuit from Kodiak Island, Alaska. As this is not a High Arctic group, and may not approximate the current human extreme in cold-adapted morphology, Holliday suggested a need for comparison of Neanderthals to groups living in Greenland, the north shore of Alaska, or the Canadian archipelago.

In a series of papers, Temple and colleagues investigated ecogeographic patterning in prehistoric Japanese populations. Temple et al.(2008) investigated limb proportion variation between two groups: earlier Jomon period foragers, and subsequent Yayoi period agriculturalists. Data were collected from skeletal remains (N=133) excavated from four Jomon sites and three Yayoi sites, all located within temperate zones of Japan. Com- parative skeletal data were taken from sixteen geographically diverse groups (N=436). Signiﬁcant diﬀerences in relative limb length (i.e. crural and brachial indices) were found between the Jomon and Yayoi samples, and greater distal limb lengths were found among the earlier Jomon sample. These results were considered the outcome of one of two possible evolutionary scenarios. Either the Jomon sample’s Palaeolithic ancestors arrived from a temperate/tropical environment, and retained their limb proportions; or, they were similar in proportion to high latitude groups but changed after experiencing a warmer environment. The authors considered either scenario plausible, since Pleistocene Japan was climatically mild.

Subsequently, Temple and Matsumura(2011) added samples from the more northerly Japanese island of Hokkaido to their research on Jomon period foragers. The authors focused on relative body mass and relative limb segment length, with two hypotheses in mind. Relative body mass, a highly conserved feature, was expected to be enlarged in the Hokkaido Jomon sample, reﬂecting long-term exposure to cold environments. And despite evolution in a colder, high latitude environment, it was expected that the Hokkaido Jomon would express elongated distal relative to proximal appendage segments. With these two hypotheses regarding body proportions, the authors aimed to evaluate the ‘point of origin’ Chapter 2. Background 13 of the Jomon. The study sample was comprised of individuals from ten archaeological sites, and comparative skeletal data was derived from eighteen geographically diverse populations. The Hokkaido Jomon sample did indeed exhibit greater relative body mass, which the authors attributed to long-standing evolution in a colder climate, and possible association with migrations from Western Eurasia into Northeast Asia, and then into Japan. The Hokkaido Jomon sample also exhibited elongated distal limbs, similar to groups from warm, tropical environments. This result could either be a morphological response to the colonization of a temperate environment, or to a reduction in dietary stress (Temple and Matsumura, 2011). The authors also suggested that ‘cold-adapted’ intralimb indices may be associated with a threshold temperature, meaning that year- round exposure to the cold is required to express this trait. Finally, they pointed out that their interpretations were based on ‘adaptationist’ frameworks – neutral mutation and genetic drift must also be considered as contributing factors (Temple and Matsumura, 2011).

Holliday and Hilton(2010) investigated cold adaptation in the Point Hope skeletal collection from the northwest coast of Alaska. The sample represents two distinct pre- contact archaeological periods: 46 individuals from the Ipiutak Period, approximately 100 BC to AD 500; and 127 individuals from the Tigara Period, approximately 1200- 1600 AD. The Point Hope sample provided an opportunity to examine a large skeletal sample derived from circumpolar humans, and one which experienced much colder temperatures than the previously studied Kodiak Island sample. Holliday and Hilton hypothesized that the Point Hope samples would exhibit one of the most extreme human body shapes because of the extreme cold experienced by the population. Body shape would be characterized by extremely short limb segments, broad trunks, and heavy body mass. A second hypothesis suggested that the Point Hope Inuit would exhibit a more cold- adapted morphology than the Koniag Inuit from Kodiak Island, given the difference in winter temperatures between the two locations. The Point Hope samples were compared Chapter 2. Background 14 to samples of modern humans from Europe, Africa, and North America, ranging in date from over 3500 years ago to the second half of the twentieth century. While significant contrasts existed between the body proportions of the circumpolar groups and Africans, contrasts between the circumpolar groups and Europeans were decidedly less marked. The Ipiutak males were found to be more cold-adapted than Europeans in crural index and relative bi-iliac breadth only, while Ipiutak females also differed in relative femoral head size and tibial length:trunk height index. The Tigara were even less divergent from Europeans, being significantly different only in bi-iliac breadth and relative femoral head in males and females, respectively. Morphologically, the Tigara sample showed less extreme cold adaptation than either the Ipiutak or Koniag samples. The authors agreed with Ruff’s (1991) contention that bi-iliac breadth proportions are less developmentally labile than limb proportions, and concluded that relative bi-iliac breadth represents the most important reflection of cold adaptation.

Bleuze et al.(2014) investigated body shape and intralimb proportions in a sample of 163 adults from the Kellis 2 cemetery in the Dakhleh Oasis, Egypt. It was expected that brachial and crural indices, as well as body mass relative to stature, would be similar to other mid- and low-latitude groups. Given the Oasis’ geographic location, however, as well as Egypt’s complex socioeconomic and population histories, the authors theorized that the Kellis 2 sample would show greater variation than that expected based on climate. In addition to brachial and crural indices, a ratio of femoral head diameter to femoral length was employed as a proxy for body mass relative to stature. The intralimb indices of the Kellis 2 sample matched ecogeographic expectations — that is, they were not significantly different from comparative groups in Egypt, Upper and Lower Nubia, West Africa, and East Africa. Conversely, body shape (mass relative to stature) was found to be greater than expected given the geographic location. Since this pattern was not found in other desert groups included in the study, the authors argued it that it was likely not the result of a specific adaptation to desert conditions Chapter 2. Background 15

(i.e. dramatic ﬂuctuations in daily temperature). Rather, they argued that this pattern may be the result of population migration from higher latitude locations, or from some other socioeconomic condition. Interestingly, the authors found a range of local variation in intralimb proportions among their study sample. They cautioned against generalizing the physiques of groups from extensive geographic areas due to this variation.

Juvenile body proportions in modern (Arctic) populations

The evidence for characteristic body proportions in immature individuals is contradictory. There is support for a substantial genetic component to characteristic body proportions in fetal and young infant individuals (Holliday, 1997b). Diﬀerences between Alaskan Inuit and non-Inuit appear early in ontogeny (Y’Edynak, 1976); the low ratio of sitting height to stature found in Australian Aborigines is present early in development (Eveleth and Tanner, 1976); diﬀerences in brachial index, crural index, leg length relative to trunk, and relative pelvic width are found between African-American and Euro-American fetuses (Schultz, 1923, 1926). Interestingly, Eveleth and Tanner(1976) reported that the Alaskan Inuit body pattern of long trunks and short legs was not present during growth; moreover, they found that Alaskan Inuit children were similar to Europeans in height, weight, sitting height, shoulder width and hip width.

In an early study, Hrdliˇcka(1941) analyzed measurements of stature and weight from Yupik children attending a government-run school in Bethel, Alaska. The sample consisted of 49 boys and 48 girls, ranging in age from 6 to 15 years. The children were found to be both shorter and lighter than a comparative American sample of Dutch ancestry. Hrdlika did note that the results could be due to a poor nutritional environment.

Heller et al.(1967) studied the growth of Alaskan Inuit and Yupik children to determine when in development they deviated in size from American growth standards. The sample consisted of 643 infants and 561 children aged 3 to 16 years. Height and weight at birth were found to correspond to growth standards, but a lag in length became evident Chapter 2. Background 16 after 3 months of age. Weight per unit of stature was also found to be heavier in comparison to the American standard, suggesting that short stature could not necessarily be blamed on nutritional deficiency. The authors concluded that the Inuit and Yupik tendency to exhibit short height per unit weight was present from early infancy. Rode and Shephard(1973) studied height and body weight in Inuit children from Igloolik, Nunavut. The sample of 58 boys and 52 girls, aged 9 to 19, was found to be substantially shorter than modern reference samples from Canada, the United States and the United Kingdom. Additionally, both sexes weighed absolutely less than the comparison samples, but they did not differ in weight relative to stature. Johnston et al.(1982) reported on the growth and body proportions of a sample of Alaskan Yupik children from St. Lawrence Island. The sample included 57 males and 56 females, aged 1 to 20 years. Both males and females were found to be significantly shorter than Euro-American and African-American standards, with shortness occurring in the legs and not in sitting height. No significant differences were found in weight, confirming a physique characterized by large body mass relative to height, with shortened legs. Becker-Christensen(2003) studied the heights and weights of a sample of west coast Greenlandic Inuit children. He found that the Greenlanders’ height for age was similar to that of Europeans, until around 12-14 years of age; after which time, the Greenlanders’ height-for-age was smaller. Weight for height was greater, however.

More recently, Hadley and Hruschka(2014) examined weight-for-height ratios in a large sample of children from around the world with the aim of assessing the extent to which it is patterned by ecogeographic variables. The authors studied body mass index and weight-for-height Z-scores in 60772 children from 46 countries. In order to remove possible eﬀects of excess body fat (which would skew measures of body shape), the study focused on children living in rural areas of low-income countries, born to young mothers of low or no educational status, and living in poor households. Children living in warmer climates were found to be lighter for their height than those living in cooler Chapter 2. Background 17 climates. Coldest monthly temperature was not found to be a predictive factor. Weight- for-height among infants and children matched very closely with adult values, suggesting that population-level diﬀerences in adult body shape (i.e. slenderness) may be rooted in infancy and early childhood.

Juvenile body proportions in past populations

In a continuation of previous work on the Jomon, Temple et al.(2011) sought to iden- tify ontogenetic patterning in Jomon intralimb indices, and interpret the results within developmental and ecogeographic contexts. The authors hypothesized that, if intralimb indices are more conserved in response to ambient temperature, over the course of ontogeny the Jomon sample would maintain ecogeographically appropriate similarities and diﬀerences with comparative samples. This was based on the idea that brachial index may shift at an earlier temporal point than crural index following exposure to a new climate. In other words, across age groups, Jomon brachial index and crural index should remain similar to warm-adapted and cold-adapted samples, respectively. The authors also hypothesized that Jomon crural index would express a greater degree of developmental constraint, while brachial index would express a greater degree of evolvability (i.e. signiﬁcantly lower correlations between radial and humeral lengths compared to tibial and femoral lengths).

Data were collected from subadult and adult skeletal remains dated to the Late/Final Jomon period (4000-2300 BP), derived from eighteen archaeological sites on Honshu and Hokkaido Islands, Japan. Comparative samples were composed of Tigara period individuals from Point Hope, Alaska, and medieval Nubians from Kulubnarti, Sudan. Subadults were divided into three age groups: 0-2 years, 2.1-10.9 years, and 11 years to epiphyseal fusion. Both intralimb indices exhibited higher values in infancy, a decline during childhood, and an increase in adolescence. Moreover, the Jomon sample maintained similar positions relative to the comparative samples in each age group. This meant that rela- Chapter 2. Background 18 tive ecogeographic relationships observed in adults appeared at an early point in life, and were maintained throughout development. The authors attributed the subtle changes in intralimb indices during ontogeny to normal growth patterns of proximal/distal limb segment growth. A greater level of correlation was observed in the elements of the lower limb compared with the upper limb in Jomon subadults and adult males; adult females fell just below significance. The differences in correlation may relate to the functional requirements of bipedal locomotion. Shifts in brachial index would not impact locomotion as would similar shifts in crural index. This, the authors argue, may account for the more modified Jomon brachial index relative to that of their putative cold-adapted ancestors (Temple et al., 2011).

Cowgill et al.(2012) used both anthropometric data from living children and skeletal data from archaeological samples to investigate changes in ecogeographic body proportions in immature individuals. The authors aimed to understand how ecogeographic variation in immature individuals interacts with allometric changes in body shape, and whether ecogeographic body proportions are maintained throughout ontogeny. They tested whether the body form of immature individuals relates to latitude and climatic variables following the same pattern seen in adults, and whether this relationship is as strong in immature individuals as it is in adults. Anthropometric data were taken from Eveleth and Tanner(1976) and supplemented with other published data, with a focus on Old World groups only. The data set included 153 male and 158 female juvenile sets of group means, and 46 male and 46 female adult means. The juvenile sets were each composed of means from eighteen age categories (ages 1 to 18 years). Skeletal data were derived from eight archaeological assemblages, from both Old and New World contexts, and included a total of 560 juvenile and 419 adult individuals. The anthropometric data were used to investigate variation in basic body proportions relative to latitude and climatic variables; the skeletal data were used to examine general patterns of brachial and crural indices across ontogeny. Chapter 2. Background 19

Moderate to strong correlations were found between climatic data and immature stature, weight, body mass index, and bi-iliac breadth; moreover, these relationships were as strong in immature individuals as they were in adults. Brachial and crural indices were found to remain constant over the course of growth, and maintained moderate correlations with latitude across ontogeny. The authors also found that some features of body form were strongly dictated by ecogeographic principles (e.g. bi-iliac breadth and intra-limb indices), while others appeared to be inﬂuenced by factors such as nutrition and basic growth constraints (e.g. relative trunk height, lower leg length). They concluded that immature individuals display ecogeographic patterns similar to those of adults, but cautioned against assuming juvenile patterns to be identical to those seen in adults (Cowgill et al., 2012).

Bleuze et al.(2014) examined changes in ecogeographic morphology during development in a large skeletal sample from the Kellis 2 cemetery in the Dakhleh Oasis, Egypt. They hypothesized that due to the extreme climatic conditions in the Dakhleh Oasis, adult patterns in brachial and crural indices would be present early in ontogeny and maintained throughout development. The study used a sample of 301 individuals ranging in age from fetal/perinate to adult. The juvenile sample was divided into age groups: fetus/perinate, infant, early childhood, middle/late childhood. Both brachial and crural indices were found to be significantly different among age groups. Brachial index was greatest during the fetal/perinate period, declined during early childhood, and increased again in middle/late childhood. The adult brachial pattern was first present during infancy, but not maintained during childhood. Crural index was also greatest in the fetus/perinate group, decreased during infancy and early childhood, and increased slightly during middle/late childhood. The adult crural pattern was first present during early childhood, and was maintained from that point onward. Bleuze et al.(2014) argued that the results were consistent with the view that intralimb proportions are genetically conserved traits – although adult intralimb proportions were not found in utero, they did Chapter 2. Background 20 appear early in ontogeny.

2.1.3 Emerging perspectives on human ecogeographic pattern-

ing

Several recent approaches to human ecogeographic body patterning have incorporated the perspective that multiple factors can inﬂuence human body size and shape. These factors include concepts of tissue economy, childhood growth trade-oﬀs, allometry, and population structure and history.

Tissue economy

Stock(2006) tested for a complimentary relationship between mobility and climate, which could influence diaphyseal strength in human limbs. The author theorized that distal limb segment morphology should face greater selective pressure for structural optimization, while proximal limb segment morphology could afford to maintain a higher level of variation. This was based on evidence for greater selective pressure on tissue economy in distal limb segments, relating to the energetic trade-off between bone strength and weight. The study sample included mature adult skeletons from four hunter-gatherer skeletal collections, representing varying degrees of intensities of terrestrial and marine mobility: the Yahgan, protohistoric foragers of Tierra del Fuego; Late Archaic people of the Great Lakes region; Later Stone Age foragers of South Africa; and Andaman Islanders of Southeast Asia. Climatic differences were represented by effective temperature1, while long bone diaphyseal robusticity was quantified using cross-sectional geometry.

Variation in diaphyseal strength was found to correlate with both eﬀective temperature and mobility pattern; eﬀective temperature negatively correlated with strength. The relative correspondence between robusticity and climate/mobility factors varied through-

1A measure of the amount of solar energy available at any given location; calculated based on mean temperatures for warmest and coldest month, plus three empirically determined constants (Binford, 2001) Chapter 2. Background 21 out the body. In the lower limb, correlations of strength with effective temperature exhibited a gradient decreasing from proximal to distal, while the relationship of strength with mobility increased in significance from proximal to distal. In the upper limb, the correlation between effective temperature and strength increased in magnitude from proximal to distal – opposite to the trend observed in the lower limb. Stock(2006) argued that this could indicate different adaptive constraints on upper limb morphology, with a greater selection for mechanical optimization in the distal end of the lower limb, and a greater selection for thermoregulatory optimization in the distal segment of the upper limb. Moreover, the differences in coefficients of variation between upper and lower limb robusticity suggested greater adaptive constraints on lower limb skeletal morphology. A higher level of variation was found to be maintained in the upper limb, perhaps more related to thermoregulation and energetic selection. Distal elements were found to have lower levels of inherent variability, suggesting a greater optimization of the relationship between safety factors and tissue economy (Stock, 2006).

Childhood growth trade-oﬀs

Pomeroy and colleagues (2012; 2013; 2014) conducted a series of studies looking at patterns of environmental sensitivity in limb segments, as these patterns could have implications for understanding the nature of adaptive growth trade-offs under stress conditions. Pomeroy et al.(2012) investigated heterogeneity in the sensitivity of different body regions to childhood conditions. By comparing two populations with contrasting ecological and environmental stress burdens, the study could look at the patterning of environmental sensitivity in different limb segments. The authors explored three hypotheses relevant to explaining heterogeneity in growth plasticity: the thrifty phenotype hypothesis, the distal blood flow hypothesis, and the cold adaptation hypothesis.

The study consisted of a cross-sectional sample of highland and lowland Peruvian children, aged 6 months to 14 years. A suite of anthropometric measurements were Chapter 2. Background 22

taken, including total upper and lower limb lengths, distal element lengths, head-trunk height, and head circumference. The two communities offered contrasting environments: highland life was associated with hypoxia, poorer nutrition and healthcare access, poverty, and cold exposure; highland children experienced higher rates of childhood stunting and wasting. In comparison, lowland children experienced markedly reduced stress. After controlling for head circumference, significant differences in the measurements were found between groups, but were most marked with regard to limb and limb segment lengths. In both limbs, differences in distal element length were slightly greater than in total limb length. Differences between groups were present from 6 months of age onwards, but the nature of the sample restricted investigation from birth. Pomeroy et al.(2012) argued that the results support the phenomenon of brain-sparing growth under conditions of stress (i.e. the thrifty phenotype hypothesis). Importantly, the authors suggested that body proportion data could be used more extensively to assess growth disturbance. Tibia length showed the greatest differences between groups, supporting its use as a particularly sensitive environmental marker.

As another way to look at the mechanisms underlying altered body proportions under stress conditions, Pomeroy et al.(2013) investigated the relationship between peripheral arterial oxygen saturation and measures of body size in a sample of Peruvian infants and children living at high altitude (>3000 m). The authors hypothesized that peripheral arterial oxygen saturation would mirror the eﬀects of environmental stress on the anthropometric measures (i.e. decreasing from distal element > total limb > hand/foot > trunk). The sample was derived from small rural communities engaged in subsistence agriculture and herding, and was divided into three altitude groups. Overall, it was found that any correlations between oxygen saturation and anthropometric measures were low, indicating a weak relationship. Anthropometric z-scores correlated similarly with altitude and oxygen saturation, and after adjusting for altitude the anthropometry-oxygen saturation associations vanished. The low correlations between oxygen saturation and Chapter 2. Background 23 anthropometry indicated that factors other than oxygen saturation exerted a substantial inﬂuence on anthropometry. The authors stated that this is in line with evidence that nutrition and other factors, rather than hypoxia, likely explain the shorter height found in highland populations (Pomeroy et al., 2013).

In further work on the Peruvian sample, Pomeroy et al.(2014) investigated the relationship between birth month and anthropometry. In a sample of highland and lowland Peruvian children, aged 6 months to 8 years, the authors tested for associations between birth month and height, limb length, limb segment length, trunk length, and head circumference. While the lowland sample exhibited no association between birth month and anthropometry, the highland sample exhibited a significant association between birth month and, in order of decreasing strength, tibia length, relative tibia length, total lower limb length, stature, and relative upper limb length. These associations only had low R- squared values, indicating that birth month accounted for a small proportion of variance. Environmental factors associated with birth season may be strong enough in early life in highland Peru to influence body size and proportion development, even with maternal buffering provided during gestation and breastfeeding. Pomeroy et al.(2014) suggested that seasonal variation in diet or morbidity may be the most plausible explanation for the results, especially since disease prevalence (both parasitic and infectious) shows seasonal variation.

Allometry

Allometry, the relationship between body size and shape, is an important component of human body proportionality. This idea was explored by Sylvester et al.(2008) and Auerbach and Sylvester(2011). In a large, world-wide sample of data, Sylvester et al. (2008) found that the allometric coefficient for the distal limb segment was more positive than for the proximal segment. The general effect of this pattern is that the distal element should make up a larger proportion of the total length of a limb in larger individuals. The Chapter 2. Background 24 fact that males are generally larger means this pattern should manifest as males having relatively high brachial and crural indices compared to females. (Sylvester et al., 2008). The authors found, however, that in their sample females could exhibit slightly longer tibia and shorter femurs than males (after controlling for size and size-correlated shape). These two effects — males having more positive tibial allometry, and females having longer tibiae and shorter femora — may cancel each other out, and result in relatively equal crural indices (Sylvester et al., 2008).

Auerbach and Sylvester(2011) discussed the fact that both limb lengths and limb indices exhibit relationships with climate and latitude, but do not show a strong relationship with each other. The authors aimed to investigate and resolve this apparent ‘paradox’ – namely, that differential allometry within limbs does not result in intralimb index allometry. They noted that previous studies had calculated allometries using the geometric mean of the four limb lengths under consideration. The choice of the geometric mean as a scaling factor has an important effect on the pattern of allometry, however: the coefficients that are calculated using the geometric mean as the size variable must all average to one when using ordinary least-squares regression. The mathematics underlying the geometric mean partially explains the paradox. Stature is presented as an alternative scaling factor, as it is at least partially independent of limb segment lengths. It is a better indicator of size as it is a biologically meaningful measurement against which linear dimensions may be scaled (Auerbach and Sylvester, 2011).

The allometry paradox was explored in a sample of 1007 adult indigenous Ameri- can skeletons dating prior to European colonization. Measurements included maximum lengths of the humerus, radius, and tibia, as well as femoral bicondylar length. Inter- estingly, the use of stature as a scaling factor in calculating allometries continued to demonstrate more positive allometry in distal elements compared to proximal elements. In addition to higher allometric coefficients, distal elements also exhibited higher variance. The results also suggested that the relationships between intralimb proportions and Chapter 2. Background 25 relative limb length (e.g. relative to sitting height or total height) with climatic factors are not necessarily equivalent. High correlations between intralimb indices and climate, or between relative limb length and climate, do not necessitate high correlations between intralimb indices and relative limb length. The study reinforces the finding that in some especially tall human groups (e.g. the Arikara), taller individuals will have proportionately longer distal elements and in turn higher limb proportion indices. This is the result of allometric scaling, and not thermoregulatory effects (Auerbach and Sylvester, 2011).

Population structure and history

Genetic population structure has been recognized as a possible contributor to observed human ecogeographic patterns, and recent papers by Roseman and Auerbach(2015) and Hruschka et al.(2015) have addressed this.

Roseman and Auerbach(2015) situated their analysis within the context of understanding the human fossil record. They argued that species identification, and the establishment of relationships among species, rely on human variation as a benchmark for what constitutes an acceptable level of variation. Recent human variation is often used as a model for what constitutes evidence of natural selection, gene flow, or random genetic drift in the fossil record. The study of ecogeographic variation figures prominently in this literature, but Roseman and Auerbach(2015) argue that not enough attention has been paid to the way genetic population structure may be influencing ecogeographic patterns. Most studies of ecogeographic variation have assumed that each group is no more genetically similar to any other group, and that they evolve by natural selection. Groups that are genetically similar, however, will resemble one another in ways shaped by the history of population fissioning, admixture, gene flow, accumulation of neutral mutations, and fluctuations in population size. Moreover, the factors that are thought to influence natural selection on human body form are often structured geographically; and, this may be similar to the patterns of genetic differences among groups (Roseman Chapter 2. Background 26 and Auerbach, 2015).

To address this, Roseman and Auerbach(2015) examined different scenarios for the evolution of human body form via two techniques. Computer simulations were used to demonstrate how population relatedness could influence phenotype distribution, and lead to the acceptance of ecogeographic hypotheses more often than warranted. Generalized linear mixed models were used to compare the distribution of population phenotypes by including terms that reflected both natural selection and selectively neutral resemblances arising from population structure. The study focused on limb element lengths, femoral head diameter, bi-iliac breadth, and brachial and crural indices. The study sample was comprised of 2187 male skeletons from 121 groups spanning climates from the Equator to the Arctic. A sample of human microsatellite variations from 2610 individuals from 59 world-wide groups was matched to the morphological data. Three models were tested: one with a term reflecting clinal selection along latitude; the second with a term reflecting random genetic drift and gene flow; and the last with a combination of terms from both models.

Roseman and Auerbach(2015) found that population structure accounted for a substantial portion of among-group variance in all considered traits. The models that did not include a term representing population structure found low support. This suggested that population structure is an important part of the global distribution of human body size and shape – meaning, ecogeographic patterning of human body form is not entirely attributable to clinally distributed natural selection. Population structure, shaped by random genetic drift, mutation, and gene ﬂow over human population history, plays a role in structuring among-group morphological diﬀerences.

The authors emphasized, however, that these results do not overturn the concept that human body form is influenced by climate-motivated natural selection. Rather, the results strengthen the notion by showing that human body form is not entirely the product of random genetic drift and gene flow. Of note, distal limb elements were found Chapter 2. Background 27 to be influenced by both natural selection and population structure. Conversely, proximal limb elements did not show strong relationships with latitude in any model. Additionally, bi-iliac breadth was found to be influenced by natural selection, but estimates of the effect of latitude were much smaller than previously found by Ruff(1994)(Roseman and Auerbach, 2015).

Hruschka et al.(2015) investigated the degree to which the contemporary relationship between human body form and climate could be attributed to plastic responses to current conditions, independent of the genetic affinity among populations. The authors reasoned that if most differences in body form were due to genetic differences, then groups with similar genotypes would show little or no effect of climate on body form, and groups with similar genotypes could have similar body forms. Conversely, if plastic responses were responsible for some of the observed relationship between body form and climate, two groups with close genetic affinity would be expected to exhibit different body forms if living in different climatic conditions.

The study combined anthropometric, genetic, and climatic data from 80 ethnolin- guistic populations worldwide. The anthropometric data were comprised of basal body mass index (bBMI) in adults and basal weight-for-height (bWH) in children. The measure of genetic aﬃnity between populations was estimated from 14 genetic clusters, using nuclear microsatellite and insertion/deletion markers. Temperature stress was measured using mean, minimum and maximum local temperatures from ecological databases. One immediate limitation of the study was the fact that the anthropometric data limited the sample to tropical and subtropical-dwelling populations. As such, the authors emphasize that their ﬁndings were limited to theories based on heat stress only.

The authors found that nearly all heat-related variation in bBMI/bWH was accounted for by genetic affinity, and the addition of temperature to the genetic affinity model did not improve model fit. This suggested that a large part of the ecogeographic associations in the sample could be attributed to genetic population structure. In turn, this could be Chapter 2. Background 28 reflecting longer-term genetic adaptations to climatic stressors, or gene flow and genetic drift that are correlated with geography and climate. The findings place some limits on the degree to which plastic responses to current environments alone are responsible for heat-related variation (Hruschka et al., 2015).

2.1.4 Methodological considerations

The study of ecogeographic body proportions in past populations lacks one single standardized methodology, and researchers instead pull techniques together from a variety of accepted methods. A consensus exists regarding the various indices used to explore body proportions (e.g. brachial, crural), but the measurements used to calculate these indices are more variable. Generally, the variation relates to tibial measures in adults, and side selection in both juveniles and adults.

Adult tibial length is measured as either maximum length or ‘Fully’ physiological length (as described in Raxter et al.(2006)). Auerbach(2007) notes that the tibial ‘Fully’ measure is a closer approximation of living leg length than tibial maximum length, and would be more appropriate in crural index calculations. Convention has dictated, however, that tibial maximum length had been used in the calculation of crural index. Tibial maximum length is used by Auerbach (Auerbach, 2007, 2012; Auerbach and Sylvester, 2011), Bleuze and colleagues (Bleuze et al., 2014), Cowgill et al.(2012), and Holliday and Hilton(2010). Conversely, tibial ‘Fully’ length has been employed by Temple and colleagues (Temple et al., 2008, 2011; Temple and Matsumura, 2011).

A second point of methodological diﬀerence lies in the selection of right versus left measures. Two options exist in the literature: selecting the left side, with replacement by the right in cases of missing or damaged elements; or, measuring both left and right sides and taking the average. Bleuze and colleagues (Bleuze et al., 2014) employ left- with-replacement, while Auerbach(2007, 2012) employs an average of both sides. This speciﬁc point of methodology is not stated by Cowgill et al.(2012), Holliday and Hilton Chapter 2. Background 29

(2010), and Temple et al.(2008, 2011).

2.2 Linear growth

The following sections will explore linear human growth: its general pattern and variability, as well as ways to study it. Subsequent sections will move on to human growth in past populations, and a technique used in its study (the Humphrey method). Lastly, two broad areas will be considered: concepts to consider within the study of growth, including intergenerational eﬀects and developmental plasticity; and, the eﬀects of growth on body proportion development.

2.2.1 General pattern and normal variability of linear growth

The study of human growth has been a major area of emphasis in biological anthropology, as this is the process by which human variation is produced (Johnston, 1969; Hoppa and Fitzgerald, 1999). Growth variation has been studied with regard to evolutionary selection and environmental adaptation, both among individuals and populations (Bogin, 1999). Human growth studies are based on the premise that the health and nutritional status of a nation’s citizens are accurately reﬂected by its children’s average heights and weights (Eveleth and Tanner, 1976; Lampl and Mummert, 2014).

The growth pattern of all normal/healthy children follows a similar course; growth during infancy and young childhood is very predictable, both within individuals and between populations (Johnston, 1986). Human linear growth is characterized by four distinct phases. The infant period involves rapid, high-velocity growth — although this velocity is actually decelerating from a peak occurring between approximately 20 and 30 gestational weeks. Childhood, from approximately 5 to 10 years of age, involves steady growth and does not depart signiﬁcantly from a straight line. Adolescence, from approximately 10 to 18 years of age, brings a return to rapid growth and a sigmoid-shaped Chapter 2. Background 30 curve. Lastly, very slow growth brings an individual to the asymptote of adulthood (Cameron, 2012). Growth curves that express a measurement (e.g. height) attained at a given age are called distance curves, while those that express increments in a given period are called velocity curves (Lejarraga, 2012). The period where maximum growth occurs is called peak growth velocity (Hauspie and Roelants, 2012).

Variation, both normal and abnormal, has been a persistent focus of human growth studies. Variations in size, rates of growth, and timing of maturational stages have been studied within individuals, across individuals within populations, across groups within populations, within populations over time, and across populations (Himes, 2004). Deviations from the expected growth pattern of children can be used as the basis for detecting health disorders on the individual and population levels (Bogin, 1999).

Growth is subject to both genetic and environmental influences. Environmental factors that can affect growth include nutrition level, prevalence of childhood infection, socioeconomic status, physical environment, and psychological well-being (Eveleth and Tanner, 1990; Bogin, 1999). In the absence of environmental constraint, the course and pattern of growth in normal/healthy children would follow a genetically predetermined trajectory (Himes, 2004; Vercellotti and Piperata, 2012). This is an example of canalization — developmental reactions that are ‘adjusted so as to bring about one definite end-result regardless of minor variations in conditions during the course of the reac- tion’ (Waddington, 1942, pg. 563). Smith et al.(1976) described genetic influences on growth as self-stabilizing and target-seeking (after Tanner(1963)), and canalized (after Waddington(1942)). All children, in the absence of environmental constraint, will exhibit a pattern of growth that is parallel to a growth chart centile-line or an imaginary ‘growth channel’ (Cameron, 2012). Infant size at birth is strongly related to intrauterine living conditions and maternal size, rather than average parental size. Consequently, a healthy infant will often shift percentiles on a growth chart until the second year of life, when he or she reaches a genetically determined percentile location (Lejarraga, 2012). More than Chapter 2. Background 31

50% of infants exhibit this ‘catch-up’ or ‘catch-down’ growth (Cameron, 2012).

Genotype and environment interact to produce an outcome that reﬂects an individual’s unique life history (Vercellotti and Piperata, 2012). Insults during the growing period can result in the slowing of growth, in an adaptive response to support critical biological activities (e.g. brain function, circulation, respiration) (Himes, 2004). These adaptations may result in a slower tempo of growth, and/or a smaller body size. After a slow-down in growth, the body will follow with catch-up growth – a period of rapid growth which restores the child to his or her original ‘growth channel’ (Eveleth and Tan- ner, 1990; Hauspie and Roelants, 2012). Catch-up growth will only occur, however, if a child is receiving adequate nutrition. Catch-up growth requires a much greater energy intake than that needed to sustain normal growth. Therefore if nutrition if not adequate, the energy required for catch-up growth is not achieved, and stunting will occur (Eveleth and Tanner, 1990).

Stein et al.(2010) assessed the relative impact of early- and mid-childhood growth retardation on final height, with the aim of identifying periods of growth that might be critical in determining final height. They looked at data from five birth cohort studies in low- and middle-income countries: Brazil, Guatemala, India, Philippines, and South Africa. Variables included length at birth and 12 months, and height at 24 months, mid-childhood (variable between cohorts), and adulthood. Lengths and heights were converted to z-scores. By 24 months of age, all five cohorts had experienced some degree of growth retardation. Mean height-for-age z-scores at this point ranged from -0.61 in Brazil to -3.28 in Guatemala. All cohorts exhibited growth retardation from birth through 2 years of age, with modest recovery by mid-childhood. Differences across adult height tertiles in height-for-age z-scores were apparent at birth, and become stable by 12 months — despite a wide range of intercohort mean height-for-age z-scores. Overall, the data suggested that across the five cohorts, growth failure occurred during gestation and in the first two years of life, with little loss of z-score after that time. This pattern was consistent Chapter 2. Background 32 even with settings that differed widely: Guatemala was rural, Philippines mixed rural- urban, the other three urban; per capita incomes at the time cohort inception, as well as today; and differences in nutrition and infection rates, with mortality rates varying from 27 (South Africa) to 75 (Guatemala) per thousand.

Longitudinal and cross-sectional growth studies

Growth and development can be assessed via longitudinal and cross-sectional studies. The most common form of growth study is the cross-sectional study, where data are collected from children over a range of ages, and each child contributes a measurement at a single moment in time (Cole, 2012). Cross-sectional studies cannot provide information on individual rates of growth, only the average growth rate of a population (as inferred from trends in average size over time). Moreover, they cannot express the variability around that average (Eveleth and Tanner, 1990; Frongillo, 2004). In contrast, longitudinal studies measure individuals periodically over many years. This allows for the direct estimation of growth rate and its variability, as well as the calculation of growth velocity and acceleration (Baxter-Jones and Mirwald, 2004; Frongillo, 2004). Longitudinal study is required to obtain a picture of the overall growth process, and a correct view of the relationship between diﬀerent growth phases (Molinari and Gasser, 2004). Lon- gitudinal and cross-sectional studies are complimentary in nature, and both should be used for a full understanding of the growth process (Eveleth and Tanner, 1990). When trying to understand growth and development in skeletal populations, one is limited to cross-sectional data only. This is due to the nature of a skeletal sample. It represents individuals who have died, and are thus captured at only one stage of their lives – that which immediately preceded their deaths. Chapter 2. Background 33

2.2.2 Eﬀects of growth on body proportion development

In the human body, different body segments grow at different rates during different phases of development. This leads to changes in bodily proportions (Sinclair, 1987; Tanner, 1989; Humphrey, 1998; Vercellotti and Piperata, 2012). In general, the growing human body exhibits a maturity gradient: at all ages during growth, the dimensions of the head are advanced over those of the trunk, the trunk advanced over the limbs, and the more distal elements of the limbs are advanced over the more proximal elements (Sinclair, 1987; Tanner, 1989).

In a cross-sectional study of growth based on 119 fetuses, Moss et al.(1955) found that distal limb segment bones grew relatively faster than their respective proximal limb segments; additionally, each lower limb segment grew relatively faster than its corresponding upper limb segments. These patterns remained constant over the developmental period covered by the study (approximately 60 to 140 gestational days). Cameron et al.(1982) employed longitudinal data from the Royal Hospital School Longitudinal Growth Study to look at relative limb segment growth. A total of 52 boys (aged 11-18 years) and 44 girls (aged 8.8-16.5 years) were included in the sample. The authors found a distal to proximal general trend for limb maturity gradients in peak growth velocity, but they also emphasized that a considerable amount of individual variation was apparent. Smith and Buschang(2004) examined interlimb and intralimb growth patterns in a subsample of data from the Denver Growth Study. They looked at long bone diaphyseal lengths for 31 boys and 36 girls, aged from 3 to 10 years. Higher growth velocities were found in proximal limb segments versus their corresponding distal segments, which supports a proximal-distal growth gradient within each limb. The authors also found that the lower limb bones were more variable than the upper limb bones, for both absolute size and growth, and the tibia showed the highest degree of variation. Smith and Buschang (2004) also highlighted the fact that relationships among variation in limb segments are complex, since they are related to size, allometry, and responses to environmental factors. Chapter 2. Background 34

Using data from Schultz(1956), Aiello and Dean(1990) explore how proportion indices change during human ontogeny. They report that brachial index decreases with age (from 80 to 76), while crural index increases slightly with age (from a value of 79 to 83). These descriptions generally accord with the patterns exhibited by children in the Denver Growth Study. Brachial and crural index changes with age are plotted in Figure 2.1, based on data from Maresh(1970). In Figure 2.1a, brachial index values are high in early life, decline rapidly during the first two years of life, and remain relatively stable thereafter. In Figure 2.1b, crural index values show a general increase with age, peaking at approximately 12 years and then falling again. The above mentioned studies, with the exception of Moss et al.(1955), focused on healthy children — and showed that a range of variation can be present in inter- and intra-limb growth. One further confounding factor in the study of body proportion development is the sufficiency of growth experienced within a population. Malnutrition and poor health can lead to reduced growth and stunting, reflected by reduced stature or long bone length-for-age (Saunders and Hoppa, 1993; Binns, 1998; Johnston, 1998); this, in turn, can lead to lower limb to trunk proportions and foreshortened limb segments (Tanner et al., 1982; Jantz and Jantz, 1999). Body shape appears to be more resistant to nutritional deficiency or disease than body size/stature (Eveleth and Tanner, 1976). Children of migrants tend to retain ancestral body proportionality, rather than develop- ing the proportions characteristic of their new homeland — despite often exhibiting an increase in stature (Holliday, 1997a). Body breadth has been found to be very conser- vative; studies of secular change in body size have indicated very little effect on bi-iliac breadths (Ruff, 1991). Chapter 2. Background 35

● Sexes Pooled ● Male ● Female 84

●

● ● 82

●●

● 80 ●●

● ● 78 Brachial Index Brachial ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

76 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

74 ●

0 5 10 15

Ages (yrs)

(a) Brachial Index 84

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 82 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● 80 Crural Index 78 76

● Sexes Pooled 74 ● Male ● Female

0 5 10 15

Ages (yrs)

(b) Crural Index

Figure 2.1: Body proportion indices from Denver Growth Study data (Maresh, 1970) Chapter 2. Background 36

2.2.3 Growth studies in past populations

Growth studies of past populations contain the basic assumption that the growth of a child is the single best indicator of his or her health and development (Johnston, 1969). Skeletal growth studies based on archaeological samples often use linear growth as a proxy for health, with the growth of a child (or group of children) being used to make interpretations about the health and well-being of a population. The cross-sectional analysis of long bone growth is used as a non-specific indicator of nutritional status within a juvenile sample, and differences in growth between samples are considered evidence for differential growth between populations. Studies based on growth-related measurements (e.g. diaphyseal lengths) cannot speak to the cause of a detected problem, only that there is a problem within a population. This is because growth-related measurements are non-specific indicators of health (Hoppa and Fitzgerald, 1999). The inception of skeletal growth studies can be traced back to the early work of Stewart(1954) and Johnston(1962). Stewart(1954) constructed a generalized postnatal growth curve for Alaskan Inuit femora, while Johnston(1962) explored growth in the Indian Knoll skeletal collection. Both studies provided information on prehistoric and non-European populations, which was important because previous human growth studies had largely focused on modern children of European ancestry (Johnston, 1962). Many skeletal growth studies followed in the 1970’s-1990’s, including Y’Edynak’s (1976) study of Inuit and Aleut; work on the Arikara by Merchant and Ubelaker(1977), Jantz and Owsley(1984) and Owsley and Jantz(1985); Hummert and van Gerven’s (1983) work on medieval Nubians; Mensforth’s (1985) study of the Libben population; Saunders and Melbye’s (1990) work on historic period Iroquoians; and Steyn and Henneberg’s (1996) work on an Iron Age South African sample. Chapter 2. Background 37

Humphrey methodology and the Denver Growth Study

An individual’s tempo of growth can be measured by evaluating physical development in terms of percentage of mature state attained at given ages (Eveleth and Tanner, 1990). Evaluating growth tempo is feasible in longitudinal studies of populations, but problematic when working with cross-sectional studies, such as those conducted on any past population (Humphrey, 2003). Humphrey(2000, 2003) described a method for evaluating tempo of growth in past populations, by assessing percentage of adult size attained and then comparing this to a standard reference sample. The ﬁnal growth attainment of any immature individual is hypothetical and unknown, but if the mean value for mature individuals in the population can be ascertained, this can be used as an estimator of ﬁnal growth attainment (Humphrey, 2003).

There are several advantages to evaluating growth in terms of percentage of adult size, rather than in measures of absolute size attainment – especially when comparing populations. Firstly, the method compensates for differences in adult size; secondly, it places the emphasis on rate of progress towards adult size, rather than actual size attained; and thirdly, provided that studies are internally consistent, it negates any differences in measurement techniques between studies (Humphrey, 2003). Humphrey’s technique is a useful way to visualize fluctuations in growth tempo, as compared to a reference sample.

The Denver Growth Study data presented by Maresh(1943, 1955, 1970) are the data used most often as a modern reference sample in studies of growth in past populations (Humphrey, 2003). The Denver data represent a mix-longitudinal radiographic growth study on healthy children conducted by the Child Research Council at the University of Colorado School of Medicine. The study ran from October 1927 to January 1967, and its subjects were mainly upper-class Americans of northwest European descent (Maresh, 1970). Radiographic studies on the long bones of the left arm and leg were started in 1935, although standardization of radiograph scheduling occurred in 1941. The earlier- Chapter 2. Background 38 born subjects had initial radiographs taken between 6 months and 1.5 years of life; from 1941 onwards, radiographs were taken at 2, 4, and 6 months of age. Thereafter, radiographs were taken at birthday and half-year examinations, until long bone growth was complete. The mean number of radiographs per subject was 24.5, with a range of 15 to 40 radiographs for two-thirds of the sample. A total of 5980 sets of radiographic measurements were available for 123 male and 121 female subjects (Maresh, 1970). Two sets of data are presented: from two months to 12 years of age, diaphyseal length only; and, from 10 to 18 years, length including epiphyses. Maresh (1970) presents mean, standard deviation, 10th, 50th, and 90th percentile values for males and females for each age interval. Humphrey(2003) suggested that the Denver growth data (Maresh, 1955, 1970) can be used to represent the growth process of a healthy, well-nourished population. This was confirmed by Schillaci et al.(2012) in a comparison of the Denver stature data to the World Health Organization Multicenter Growth Reference Study (WHO MGRS). The WHO MGRS standard was created to depict normal human growth under optimal conditions, for use to assess children worldwide. Schillaci et al.(2012) found that while the Denver dataset is inappropriate for identifying stunting and estimating stunting prevalence, it does generally conform to the WHO MGRS standard and does reflect a normal human growth pattern. It therefore can serve as a suitable reference for comparative growth studies. Significant deviations from the Denver standard can be interpreted as reflecting health and environmental differences, rather than ethnic or population differences (Schillaci et al., 2012). Chapter 2. Background 39

2.2.4 Considerations for the study of growth in Arctic contexts:

growth in early life, intergenerational eﬀects, develop-

mental plasticity, and adolescent pregnancy

The first 1000 days of life, from conception to two years of age, is widely recognized as a critical period for human growth and development (Victora et al., 2008; Martorell and Zongrone, 2012; Ramakrishnan et al., 2014). Importantly, it is the only consistently demonstrated period of growth failure in populations in poor countries; after two years of age, there is some evidence of modest catch-up growth, but adult heights generally reflect this period of early growth faltering (Stein et al., 2010; Martorell and Zongrone, 2012). Based on evidence from around the world, growth failure has been found to begin in utero, to be pronounced during the first year of life, and to continue with lesser force until around two years of age (Victora et al., 2010; Martorell and Zongrone, 2012). Fetuses and newborns may be small because of a poor nutrient supply and/or because they have limited room to grow. The practice of ‘eating down’ — eating less during pregnancy to reduce the size of the baby at birth and avoid obstructed labour — has been cited as a reason for reduced growth (Martorell and Zongrone, 2012). There is no evidence of this practice occurring in Arctic groups, however.

Human life-history traits, such as growth and maturation, have generally been considered highly heritable — but evidence has accumulated to suggest that they demonstrate significant plasticity (Wells and Stock, 2011). The relative contributions of genetics and plasticity to human life history is discussed by Wells and Stock(2011), with human stature offered as an example. Human statural growth is complex, being distributed across several developmental periods, and involving an array of genes. Adult stature also reflects several life-history events: size at birth, infant growth rate, and timing and duration of puberty. Human foraging populations exhibit considerable variation in age of maturation and final size; it is unclear how much of this variation represents either Chapter 2. Background 40 longer-term genetic adaptation to selective pressures, or shorter-term optimization of growth in the face of more random local ecological conditions (Wells and Stock, 2011).

The trans-generational transmission of phenotype (intergenerational effect) is funda- mental to life-history traits such as birth size, rate of childhood growth and maturation, and adult size. All these traits are sensitive to nutritional influences (Wells and Stock, 2011). The authors suggest that the maternal life-history profile could ‘drive’ the life- history trajectory in the next generation through mechanisms that allow a phenotype to change across several generations, depending on ecological cues. In this way, trans- generational phenotypic plasticity is advantageous in species occupying uncertain environments. Genetic adaptation would mean a reduced flexibility to address short-term environmental change, thus risking ‘over-commitment’ (Wells and Stock, 2011). Impor- tantly, however, plasticity and heritability act in a complimentary fashion. Plastic traits can absorb ecological pressures and buffer traits that are canalised (less flexible), and canalised traits can offer developmental scaffolding around which plastic traits can vary (Wells and Stock, 2011).

The intergenerational influences hypothesis suggests that the health, growth, and development of one generation can be affected by the conditions, exposures, and environments experienced by the previous one (Emanuel, 1986). Further, the nutritional history of matrilineal ancestors, including nutrition during the prenatal stage and first years of postnatal life, influences the growth trajectory of descendent children (Kuzawa, 2005). Intergenerational effects on growth operate via complex mechanisms, ranging from biological to socio-cultural, and are directly responsible for perpetuating an ‘intergenerational cycle of growth failure’ (Ramakrishnan et al., 1999; Martorell and Zongrone, 2012). This cycle involes poor nutrition in utero and during early childhood in girls, who grow poorly, perhaps facing continued poor nutrition, and become stunted women; their capacity to support health fetal and infant growth is constrained, and they are more likely to have small babies, who, if female, will continue the cycle (Martorell and Zongrone, Chapter 2. Background 41

2012; Mason et al., 2012).

Azcorra et al.(2015) investigated intergenerational effects with a focus on postnatal growth, since most evidence of the intergenerational influences on growth are based on the prenatal stage. They focused on the Maya from Yucatan State in Mexico, who have historically lived under very unfavourable socioeconomic conditions, and continue to do so. The Maya have demonstrated high rates of undernutrition during growth, resulting mainly in short stature. Additionally, there has been no evidence of any positive secular change in stature. The study employed a biocultural model to test the intergenerational influences hypothesis, and to ascertain the influence of the biosocial background of urban Maya grandmothers (F1 generation) and mothers (F2) on the linear growth and nutritional status of their children (F3). The study sample was comprised of 109 triads of urban Maya children of 6-8 years of age, their mothers, and their maternal grandmothers. Height and sitting height were measured, and leg length was calculated; height and leg length were transformed to z-scores. The biosocial background of mothers and grandmothers was analyzed through anthropometric measures and socioeconomic indicators experienced during childhood. As expected, the maternal z-scores for height and leg length were positively related to children’s measures of growth. Grandmaternal house type, specifically the type and quality of construction materials, was significantly associated with children’s height and leg length. These results suggest that adverse early environments experienced by grandmothers and mothers are negatively associated with their own growth, and are partially related to their descendants’ growth trajectories.

Vercellotti and Piperata(2012) explored issues of developmental plasticity and environmental stress on growth, with a focus on adolescent pregnancy. The authors noted that, while the impact of early pregnancy on fetal growth has been explored, fewer studies have focused on the impact that early pregnancy may have on maternal growth. The study sample was comprised of 172 Brazilian adults (88 female), aged 18-77 years, from rural Amazonian communities. Overall, the study communities were subject to chronic Chapter 2. Background 42 stress associated with nutritional stress during growth and development, and poor access to healthcare. The authors measured height, sitting height, and skinfold thickness; they calculated total leg length and percentage body fat. Individual height-for-age z- scores were calculated and individuals were assigned to different growth outcome groups (stunted and non-stunted). Reproductive histories were conducted for the female par- ticipants, and these included information on age at menarche, age at birth of first child, and date of birth of each child. This was supplemented with dietary data collected on 23 lactating women and 52 children. Comparisons were made between males and females, stunted and non-stunted individuals, and sex-specific stunted and non-stunted groups.

Vercellotti and Piperata(2012) found that stunting was not accompanied by significant changes in sitting height to height proportions, and argued that this could indicate that stress leading to stunting affected the trunk and lower limbs equally. Males and females, however, exhibited significantly different body proportions. Females exhibited a significantly higher rate of stunting, and males tended to have relatively shorter sitting heights than females.

Age-at-first-birth was positively correlated with terminal height and leg length, suggesting that early reproduction had a negative impact on the mother’s own skeletal growth, and perhaps even inducing cessation of growth in female adolescents in under- privileged settings. Fieldwork in the region indicated that adolescents became sexually active in their early teens, and teen pregnancy was common due to limited access to contraception. Analysis of the maternal dietary intake data indicated that it was insufficient to meet the additional energy demands of reproduction, especially lactation, and would force women to rely on their own energy reserves. The authors stated that it is plausible that women reproducing when their own growth is not complete will experience greater nutritional stress than their adolescent male counterparts. Comparisons of catch-up growth between male and females in the population found no difference in rates among children, but marked differences between adolescents: 90% of males versus 8% Chapter 2. Background 43 of females exhibited signs of catch-up growth. This difference could also be attributed to greater potential access to resources (i.e. food) for male adolescents (Vercellotti and Piperata, 2012).

The authors found that leg length demonstrated growth retardation associated with early reproduction, but sitting height did not. Since trunk growth velocity increases at the onset of puberty, it might be expected that adolescent pregnancy and lactation would have a greater impact on axial rather than appendicular skeletal growth. The authors reason that this can be explained in terms of developmental plasticity and growth canalization. Sitting height may be more constrained in size due to its relation to the vital organs of the chest and abdomen. Given the increased spatial demand in the torso associated with pregnancy, it would be reasonable to assume that leg length may be preferentially sacriﬁced (Vercellotti and Piperata, 2012).

2.3 Main sample: the Sadlermiut Inuit

The following sections will consider the Sadlermiut Inuit within the contexts of available ethnographic information and Arctic population history.

2.3.1 Sadlermiut introduction and ethnographic contact

The Sadlermiut were an Inuit group living on Southampton, Coats, and Walrus Islands in north-western Hudson Bay, Nunavut. Their residence on Southampton Island (at the Native Point site, KkHh-1) likely extended at least 500 years into the past (Merbs, 1983), but it abruptly came to an end in the winter of 1902-1903 when all individuals succumbed to an introduced disease. This disease, possibly dysentery, was introduced after a Scottish whaling ship visited a nearby whaling station. It is thought that the population at Native Point at this time numbered approximately 60 individuals (Ross, 1977). Chapter 2. Background 44

The Sadlermiut are not well known ethnographically because they had little contact with neighbouring Inuit groups and Europeans during the historic era. Ryan and Young (2013) provide a thorough overview of the history of contact with the Sadlermiut. To briefly summarize, Parry(1824) and Back(1838) were the first to report signs of the Sadlermiut on Southampton Island. The first recorded instance of contact occurred on Coats Island in 1824 (Lyon, 1825), when a Sadlermiut man approached the vessel of George Francis Lyon; Lyon then visited the man’s camp. Subsequently, Sadlermiut individuals were occasionally observed distantly on Southampton Island, and may have travelled to the mainland in about 1830 (Boas, 1907). Commercial whalers visited a Sadlermiut camp at Manico Point in 1865 (Boas, 1888) and regularly met Sadlermiut on the west coast of Southampton Island in 1878 and 1879 (Ferguson, 1938). In 1900, Sadlermiut individuals visited a newly established whaling station at Cape Low in Bay of God’s Mercy (Clark, 1986), and subsequently visited several times (Mathiassen, 1927).

2.3.2 Sadlermiut within Arctic population history

Within the history of the Eastern Canadian Arctic, the Sadlermiut belong to the second of two phases of occupation. An earlier Paleo-Eskimo group moved into the Eastern Canadian Arctic from the Bering Strait approximately 4500 BP, and developed into the Dorset culture by approximately 2500 BP. A second, later, Neo-Eskimo group arrived from Alaska after approximately 800 BP; also known as the Thule, they were the ancestors of modern Inuit (McGhee, 1996, 2000; Friesen, 2000; Friesen and Arnold, 2008). While the exact length of time the Sadlermiut were settled on Southampton Island is not known, their ancestors would have been exposed to similarly cold environments for a considerable period of time. Humans colonized the North American Arctic via the Bering Land Bridge, which was inundated by rising sea levels by approximately 10000 BP (Bever, 2001). The time involved in the expansion from Siberia to the shores of Hudson Bay is thought to allow a suﬃcient number of human generations for evolutionary processes to operate, in Chapter 2. Background 45 conjunction with stresses such as climate, disease, and nutrition (Laughlin, 1967, 1970).

The Sadlermiut have consistently been singled out for the ambiguity of their relationship with the Dorset and Thule. They have been considered an aberrant population in Eastern Arctic prehistory, mainly because of their disappearance prior to extensive European contact, their ‘primitive’ material culture, and the disrespect with which they were treated by other Inuit groups (Rowley, 1994). The Sadlermiut have been variously described as a remnant Dorset population exhibiting Neo-Eskimo influence (e.g. Collins, 1955, 1956a), a Neo-Eskimo population with Dorset influence (e.g. Clark, 1980), or an isolated Neo-Eskimo population well-adapted to their specific environment (Merbs, 1983; Rowley, 1994). The most recent archaeological assessment of the Sadlermiut is Rowley’s (1994) extensive review of Sadlermiut origin theories. In this study, Rowley compared Sadlermiut material culture with neighbouring Inuit groups and other archaeological cultures, and found no evidence for a strictly Dorset origin. The differences between the Sadlermiut and other Inuit groups were no more pronounced than the expected differences between Inuit groups due to regional stylistic expression and adaptation to different environmental conditions (Rowley, 1994).

Until recently, analysis of Sadlermiut mitochondrial DNA (mtDNA) from archaeolog- ically derived tissues had yielded ambiguous results. The five Native North American mtDNA haplogroups are defined by the presence or absence of specific restriction sites and a deletion marker in the mtDNA molecule hypervariable I (HVS1) region. These haplogroups are referred to as A, B, C, D, and X (O’Rourke et al., 2000). Most modern (Inuit) and ancient (Thule) Neo-Eskimo populations belong to haplogroups A2a and A2b, with a low representation (approximately 5%) of D3 (Gilbert et al., 2008). Hayes et al.(2005) analyzed mtDNA from Dorset, Thule and Sadlermiut samples. The Dorset sample was considered to be fixed for haplogroup D, although this was based only on one individual. The Thule sample was found to be consistent for haplogroup A, while the Sadlermiut sample was split almost evenly between haplogroups A and D. Without Chapter 2. Background 46 further refinement of these results, however, it was not clear if the Sadlermiut exhibit admixture between Dorset and Thule populations, or if they simply display both haplogroups that can be found in Thule and Inuit populations.

In a broad genetic study of New World Arctic population history and relationships, Raghavan et al.(2014) revealed more detailed information about the origins of the Sadler- miut. The study typed ten Sadlermiut individuals, to mtDNA haplogroups A2b and D3a2a. These haplogroups are characteristic of the Thule/Inuit, and thus suggests that the Sadlermiut were indeed a Thule Inuit group.

2.3.3 Sadlermiut as a single population

It is reasonable to treat the Sadlermiut skeletal collection as being representative of a single population, based on radiocarbon dating and stable isotope analysis of the remains. Coltrain et al.(2004) and Coltrain(2009) reconstructed the diets of Eastern Arctic Dorset, Thule, and proto-historic skeletal remains through carbon and nitrogen stable isotope analysis of bone collagen. With the exception of three outliers, the Sadlermiut sample of 48 individuals exhibited positive stable isotope ratios, indicating that they were uniformly reliant on high trophic level marine foods such as ringed seal and seabird. No diﬀerences were found between males and females, nor between individuals recovered with grave goods versus those without. With the same three exceptions, the Sadlermiut burials dated to a calibrated two sigma range of AD 1308-1896.

The three Sadlermiut outliers (XIV-C:299-1, 302, and 304-1) were found to have stable isotope readings well outside of the Sadlermiut range (‘European’ in diet), and were dated as modern or near modern in age (calibrated two sigma range of AD 1531-1948). Coltrain et al.(2004) contended that these three historic individuals were unlikely to have been indigenous to Native Point or the adjacent mainland. The authors stated that two individuals (XIV-C:299-1 and 304-1) exhibited diets relatively low in trophic level, and may have consumed diets largely comprised of European foods; the third individual Chapter 2. Background 47

(XIV-C:302) was clearly ‘European in origin, with a diet virtually devoid of marine foods, low in animal protein and high in cereal grains and/or beans’ (Coltrain et al., 2004, pg. 53). The latter sample was subsequently discovered to have been taken from a caribou rib (Coltrain, 2011; Ryan, 2011). The other two outlier samples derive from individuals with problematic catalogue numbers; at least seven individuals were randomly spread between ten catalogue numbers. This discovery was made by this author (N. Symchych) in the course of repatriation activities, and makes it diﬃcult to tell exactly which individual was sampled. The sample could very well have included subadult remains. Taken as a whole, the Sadlermiut sample covers a consistent temporal range and exhibits a very uniform and narrow isotopic range. Moreover, they frequented the Native Point site and interred their dead around the village for hundreds of years. It is therefore reasonable to treat the Sadlermiut as a single population for this study.

2.4 Arctic contextual information

This section will present information pertaining to weaning, diet, childbirth, growth and infant mortality in Inuit populations.

2.4.1 Ethnographic information

The study of skeletal growth and development in Arctic populations can benefit greatly by the incorporation of contextual ethnographic information. Although the Sadlermiut Inuit are largely unknown from an ethnographic standpoint, the broader body of Arctic ethnographic literature can be drawn upon to inform analysis. Captains’ ship logs, such as those of Parry(1824) and Lyon(1825), were the first substantive descriptions of central Arctic people and cultures (Damas, 1998). The next major contribution to central Arctic ethnography would be Boas’ (1888) extensive manuscript on the Baffinland, Iglulik, and Netsilik Inuit groups. The period extending roughly from 1910-1924 was the Chapter 2. Background 48 next productive period of Arctic ethnographic study. This was due to the Canadian Arctic Expedition (1913-1916), and the Fifth Thule Expedition (1921-1924) (Damas, 1998).

Traditional Arctic ethnographies tended to focus on technology, social organization, resource procurement, seasonality, language and religion. Generally speaking, the lives of children were not as well-documented as those of adults. Information can be gleaned, however, on topics that are of relevance to growth and development. This information relates, broadly, to food consumption, including infant nutrition and weaning, and maternal diet in the postnatal period. It is important to explore how concepts of food, diet, and access could have impacted the health of women and children, and the growth status of children.

Infant weaning practices are touched on by several writers. Boas(1888) noted that children were weaned at around two years of age, but often continued to suckle until they were three or four years old; during this time they were frequently fed from their mothers’ mouths. Rasmussen(1927) observed that weaning among the Netsilik took at least three years. Also observing the Netsilik, Balikci(1970) commented that infants were generally breastfed, although mothers used a mouth-to-mouth feeding technique when necessary. No elaboration was made on when this technique might be necessary, however. The author does not address weaning age specifically, but makes an oblique reference to a two-to-three year breastfeeding period when addressing a woman’s chance of having male offspring. Balikci(1970) also emphasized that infants were breastfed on demand — that is, feeding was not necessarily prolonged, but it was quite frequent and provoked by the infant. Lastly, Jenness(1922) suggested that since the diet of the Copper Inuit was confined to meat and fish, a mother must breastfeed her infant up to three or four years, and occasionally up to five.

These descriptions do not indicate when the period of exclusive breastfeeding termi- nated (and thus when complementary foods are ﬁrst introduced). Full weaning (i.e. no Chapter 2. Background 49 longer receiving breastmilk (Humphrey, 2014)) seems to have occurred around three to four years of age.

A child’s access to food is another important consideration, since the central Eastern Arctic does not abound in food sources that can be easily manipulated by a child on his or her own. During times of resource availability, it seems that children were afforded unrestricted access to food. According to Jenness(1922), food belonged equally to all members of a family; while a woman or mother would take charge of it, the rest of the family could help themselves at any time. Balikci(1970) noted that among the Netsilik, food was rigidly controlled and that children did not help themselves directly. When they asked for food, however, they were always gratified. Moreover, Rasmussen(1927) described how in times of dearth, Netsilik parents would, as a matter of course, feed children first — even if there might not be enough food for all.

Descriptions of general diet and food abound in the ethnographic literature. Diet was not homogeneous across Eastern Arctic groups, although it would be comprised of some combination of marine mammals, ﬁsh, birds, musk ox, and caribou — depending on location and season. Jenness(1922) and Boas(1888) refer to seal and walrus meat as being the staple, and sometimes only, winter food. Caribou and salmon formed part of the summer diet (Boas, 1888). Food preparation method varied depending on the food and time of year. Marine mammal meat was often boiled, although it could be served not thoroughly cooked: ‘more smoked than boiled’ according to Jenness(1922, pg. 105). During the summer months a scarcity of fuel could mean food was eaten raw, or sundried (Jenness, 1922). Liver and kidneys were usually eaten raw and unfrozen (Boas, 1888; Jenness, 1922). Jenness(1922) reported that among the Copper Inuit, bearded seal intestines were a delicacy, while raw caribou intestines were enjoyed in the summer and autumn when they were coated with a thick lining of fat. For consumption of the caribou intestines, Jenness(1922) noted that they were eaten raw and intact, with the excrement usually, but not always, removed. Boas(1888) stated, however, that among Chapter 2. Background 50 the Central Inuit groups he studied, intestines were only consumed when there was no meat available. Boas(1888) also noted that ﬁsh were eaten in raw as well as cooked states, and birds were often consumed raw.

While it is not evident if pregnant women observed restrictions on food consumption, many ethnographic reports detail the taboos placed on women after delivery. The most commonly reported is the avoidance of raw meat for one year after birth; further observances include only consuming food caught by the husband or by a boy on his ﬁrst hunting expedition for a certain period of time, avoiding meat of any animal save those killed in certain ways, or limiting eating to early morning and late at night (Boas, 1888; Rasmussen, 1927; Balikci, 1970).

Infant adoption was reported by Balikci(1970) to be a common practice among the Netsilik Inuit, and was mentioned by Jenness(1922) as occurring among the Copper Inuit — although in the latter case, Jenness stated that adoption of older children was more common, since new born infants required their mothers’ milk. This is an interesting point, since adopted infants would have to be fed somehow. Balikci(1970) noted that adopted infants were usually fed by mouth-to-mouth technique, with seal or caribou soup. The lack of access to breastmilk, the lack of immune protection it confers, and the very early introduction of ‘supplementary’ foods would possibly have detrimental eﬀects on adopted infant health and growth.

One last subject to be touched on is the age at which girls would be married, and the age at which sexual activity would begin. Several ethnographic accounts include this information. Jenness(1922) observed that girls were often married before reaching puberty, though they would not bear children until three or four years later. Balikci (1970) stated that girls were married at an early age (14 to 15 years), and it was rare to see single girls past this age. Rasmussen(1931, pg. 197) observed that girls and boys would ‘lie together at a very early age, sometimes at ten or twelve’. A lack of contraception, plus potentially early sexual activity, could have resulted in adolescent Chapter 2. Background 51 pregnancy — with potential implications for maternal and fetal growth.

2.4.2 Inuit diet and the health ‘paradox’

Temple et al.(2013) highlighted the so-called ‘Inuit Paradox’, in which Arctic foragers maintain low levels of nutritional stress by exploiting foods that are calorie-dense and micronutrient rich, despite the limited food base (Draper, 1977; So, 1980; Cordain et al., 2000). Draper(1977) evaluated the traditional Inuit diet from a modern nutritional perspective, and found that it provided all the essential nutritional elements if prepared and consumed according to traditional methods. Vitamins A and D are found in abundance in the oils of fish and marine mammals, vitamin K and E are present in animal meat. Vitamin C is obtained by consuming meat in a raw or lightly cooked state, and provides the small amount required to prevent scurvy. With regard to minerals, Draper(1977) was less specific, stating that the diet appears to contain adequate amounts of essential minerals, with the possible exception of calcium. Calcium may have been obtained from the consumption of fish bones, or the spongy bone of mammals. Overall, Draper(1977) suggested that any nutritional crises experienced by Inuit groups would have been brought about by periodic shortages of food, rather than deficiencies in nutritional quality of the food consumed.

Draper’s work focused on Alaskan Inuit communities, however. So(1980) noted that Inuit dietary intake was not uniformly adequate: for example, a study by Ellestad-Sayed et al.(1976) found that the Iglulingmiut of Igloolik, Nunavut, showed low consumption of folic acid, calcium, and magnesium. The importance of not treating all Arctic groups as a homogeneous group, particularly with regard to stress experiences, is emphasized by Temple et al.(2013). Although reliance on high calorie foods was common among high latitude foragers, the authors argue that stress experience would have varied. As an example, they refer to Lieverse and colleagues’ (2007) study of Kitoi foragers from Cis-Baikal, Siberia, who were found to exhibit high levels of linear enamel hypoplasia Chapter 2. Background 52 despite reliance on marine mammals and ﬁsh.

A last consideration with regard to the Inuit health paradox, and Inuit diet in general, is the potential exposure to zoonotic diseases and parasites. A variety of parasitic infections can occur in Canadian Inuit communities, due to eating uncooked or under- cooked meat from mammals, fish and birds (Goyette et al., 2014). Goyette et al.(2014) surveyed 36 Inuit communities across the Inuvialuit Settlement Region (in the Northwest Territories), Nunavut, and Nunatsiavut (Labrador). The overall prevalence of infection for Toxoplasma gondii and Trichinella sp. was found to be 27.2% and 18.5%, respectively. Nunavut had the highest levels of prevalence, with a 32.5% infection rate for T. gondii and 23.6% for Trichinella. The authors found that risk of infection was associated with consumption of marine mammal foods. A study by Simon et al.(2011) found a 10% prevalence of T. gondii in Arctic seals (ringed, bearded, and harbour) harvested by Inuit communities in seven communities in Nunavut and the Northwest Territories. Pu- fall et al.(2012) investigated the prevalence of anisakid nematodes in harvested fish and marine mammals from Nunavik, Nunavut, and Nunatsiavuit. They found anisakids in seven out of eight fish species tested, with prevalence rates ranging from 87% in longhorn sculpin, to 7.7% in Atlantic whitefish. Beluga whales were found to have an infection prevalence of 79%, while bearded and ringed seals exhibited infection rates of 75% and 18%, respectively. Zoonotic disease transmission risk is not limited to marine animals, as Arctic ungulates (e.g. caribou, musk ox) carry a variety of parasitic organisms (Kutz et al., 2012).

Interestingly, some small evidence for supplementary feeding is provided by the presence of Trichinella larvae in the muscle tissue of a mummiﬁed child (approximately 18 months of age) from Pissisarﬁk, Nuuk fjord, Greenland (Lynnerup, 2015). Since the parasite cannot be transmitted through breastmilk, and the time from ingestion to en- cystment is approximately one month, the child would have been given raw meat around 18 months of age — suggesting that weaning using raw foods had commenced (Lynnerup, Chapter 2. Background 53

2015).

2.4.3 Infant mortality data in recent historical Inuit popula-

tions

Infant mortality data from recent historical Inuit populations can provide important contextual information, against which the results of the present study can be interpreted. Infant mortality rate (IMR) is defined as the number of infants who die during their first year after birth for every thousand live births within a certain population (Smylie et al., 2010). Table 2.1 lists historical Inuit IMR’s for three geographical regions: Northwest Territories (which includes present-day Nunavut), Alaska, and Greenland. The use of historical IMR’s presents a challenge, due to the quality of the data upon which they are based. There is a dearth of demographic and medical records for Canadian indigenous populations prior to the 1950’s (Moffat and Herring, 1999), and several authors have highlighted deficiencies in the reporting of IMR for Canadian indigenous populations, particularly the Inuit (Douglas, 2006; Smylie et al., 2010; Ellsworth and O’Keeffe, 2013; Elias, 2014).

Given the limitations inherent in the numbers presented in Table 2.1, they should be used cautiously; in the earlier time periods, they are likely under-estimations. For example, the IMR for the Northwest Territories in 1955-1959 represents a signiﬁcant increase from the 1935-1939 period. Legare(1989) suggests that this is likely due to under-registration of events in earlier periods, rather than an actual increase in IMR over time. Additionally, the IMR’s presented here are aggregates from large geographic areas, and variation within regions would certainly have existed. Hobart(1975) calculated community-speciﬁc Inuit IMR’s for fourteen northern communities, based on birth and death information supplied by the Northern Region Medical Services section of the Department of National Health and Welfare. The calculated IMR’s were consistently elevated above the IMR for the whole of Canada, but ranged widely from a low of 44.1 Chapter 2. Background 54

Table 2.1: Historical Inuit infant mortality rates (IMR)

Region Year/Period IMR Reference (/1000) 1930-1934 133.2 Legare(1989) 1935-1939 127.1 Legare(1989) 1951-1954 164.5 Hobart(1975) 1955-1958 231.5 Hobart(1975) Northwest Territories 1955-1959 228.2 Legare(1989) 1959 129.3 Albrecht(1965) 1960 144.4 Albrecht(1965) 1962-1971 98 Milan(1980) 1943 180.3 Milan(1980) Alaska 1950 95.3 Albrecht(1965) 1960-1962 102.6 Lum et al.(1986) 1861-1900 195 Legare(1989) Greenland 1901-1930 152 Legare(1989)

per 1000 in the Coppermine and Holman Island region, to a high of 124.4 per 1000 in Frobisher Bay. In general, if appears that historical IMR’s for Inuit communities were very high. It must be noted, however, that IMR’s for Canada as a whole would have been similarly elevated. The IMR for all of Canada in 1926 was 101.9 per 1000; provincial rates ranged from 58.6 in British Columbia, to 142 in Quebec (Statistics Canada, 2009). Postneonatal infant mortality (infant mortality which occurs from 28 days to one year after birth) has generally been attributed to infection, congenital conditions, and sudden infant death syndrome (Smylie et al., 2010). Milan(1980) reported that the greatest hazards to life for Inuit children up to four years of age were inﬂuenza and pneumonia, fever of unknown cause, and diarrhea; after this age, environmental hazards such as freezing and drowning became more predominant. These observations were made during the late 1960’s and early 1970’s in Wainwright, Alaska, and Igloolik, Nunavut as part of the International Biological Program (IBP) study. Additionally, Milan(1980) stated that approximately 25% of all children born alive in the Alaskan, Canadian, and West Greenlandic communities under study in the IBP had died by 15 years of age. Chapter 3

Materials and Methods

This chapter will outline the studied archaeological skeletal samples and the methods employed in this thesis. Firstly, the four study samples (Sadlermiut, Point Hope, Si- lumiut/Kamarvik, Greenland) will be described, with information on location and age, history of archaeological investigation, and current curation status. Secondly, the methods employed in the analysis are outlined. Two phases of analysis are involved: the investigation of growth, and the investigation of body proportion development.

3.1 Materials

In addition to the main Sadlermiut sample, the study includes three comparative samples. These comparative groups serve to illustrate the range of variation present in Arctic past populations, in terms of growth and ecogeographic body proportionality. Sites from which skeletal materials are derived are presented in Figure 3.1.

3.1.1 Sadlermiut

The Sadlermiut skeletal collection is curated by the Canadian Museum of History (CMH, formerly the Canadian Museum of Civilization) in Gatineau, Quebec. It was collected

55 Chapter 3. Materials and Methods 56

Figure 3.1: Sample locations (Diamond, Sadlermiut; Star, Point Hope; Circle, Kamarvik; Square, Silumiut; Map pin, Greenland)

from the Native Point site (KkHh-1, see Figure 3.2), through a combination of excava- tion of semi-subterranean houses, opening of stone burial cairns, and surface collection. This collection occurred between 1954 and 1959 as part of three diﬀerent archaeological investigations. In 1954 and 1955, Taylor (of the National Museum of Canada, later the CMH) and Collins (of the Smithsonian Institution) reported that the site contained over 85 sod and stone houses, and 150 burials; they removed 32 burials from the site (Collins, 1955, 1956a,b). In 1956, Taylor returned to Native Point to study Sadlermiut house architecture. While excavating two houses, he recovered the remains of at least seven individuals (Taylor, 1956, 1960). In 1959, Merbs (of the University of Wisconsin- Madison) and Laughlin (of the University of Connecticut) excavated 145 burials from the site (Merbs and Wilson, 1962; Merbs, 1983). A sample of 48 Sadlermiut individuals has been radiocarbon dated to a calibrated two sigma range of AD 1308-1896 (Coltrain et al., 2004; Coltrain, 2009). Chapter 3. Materials and Methods 57

Figure 3.2: Sadlermiut site locations

3.1.2 Point Hope

The Point Hope skeletal material used in this study is curated by the American Mu- seum of Natural History in New York, New York. This sample represents two culturally distinct groups who lived at the Point Hope site, located at the far end of Lisburne Penin- sula on the northwest coast of Alaska (Hilton et al., 2014). The site was excavated by the Rainey-Larsen Point Hope Expedition between 1939 and 1941 (Rainey, 1941, 1947; Larsen and Rainey, 1948). Over 500 permanent dwellings were excavated, and the skeletal remains of almost 500 individuals recovered. Point Hope shows evidence of repeated occupations over the span of 2000 years, and appears to have experienced longer periods of occupation than other prehistoric communities in northwest coastal Alaska (Hilton et al., 2014). Two main cultural periods are represented at Point Hope: the Ipiutak period, dating c. 1600 to 1100 years BP, and the Tigara period, dating c. 800 to 300 years BP (Hilton et al., 2014). Archaeological evidence strongly suggests the existence Chapter 3. Materials and Methods 58 of substantial cultural differences between the two occupations, particularly with regard to subsistence economy: caribou figured significantly in the Ipiutak culture, while the Tigara fit within the Thule whale-hunting tradition (Tattersall and Thomas, 2014). The two groups are distinguished by cranial morphologies, dental microwear, and postcranial lesions, all evidence supporting cultural differences in prey choice and subsistence patterns; the two groups are not distinct in other morphological dimensions (i.e. body size and shape), except for significant differences between Tigara and Ipiutak female crural indices (Holliday and Hilton, 2010; Auerbach, 2014).

The health status of the residents of Point Hope was investigated by Dabbs(2011), using the Mark I Health Index (MIHI) introduced by Steckel and Rose(2002). The sample was comprised of 76 Ipiutak and 298 Tigara individuals, both juveniles and adults. Dabbs employed six skeletal indicators of health: anemia and linear enamel hyploplasia (LEH, of the permanent canine), which reflect health during childhood; dental health and degenerative joint disease, which reflect adult well-being; and trauma and infection, which can affect health at any age. The overall computed health scores for the Point Hope groups were functionally indistinguishable, but the pattern by which each group achieved its score reflected a dichotomy of chronic versus acute stress. The Ipiutak exhibited a higher prevalence of chronic stressors, such as non-specific infection and mechanical loading. Conversely, the Tigara exhibited a higher prevalence of acute stressors, such as LEH, and traumatic injury. The higher prevalence of LEH of the permanent canine in the Tigara suggested that children experienced increased stress while 3 to 6.5 years of age. Anemia, LEH and infection had the greatest effect on the difference in health scores between the two groups. Since two of these three indicators reflect childhood health, Dabbs argued that the difference in health status between the Ipiutak and Tigara could be due to the way adult activities affected the childhood experience. The Tigara were heavily reliant on the bow head whale, which migrated past Point Hope for only approximately three months a year. This concentrated most economic production to a Chapter 3. Materials and Methods 59 short period of time. A set-back in whale-hunting could have major consequences for the rest of the year. Since children have high metabolisms, low nutritional reserves, and increased susceptibility to nutritional deficiencies, they may have borne the brunt of any shortages (Dabbs, 2011).

3.1.3 Kamarvik and Silumiut (Northwest Hudson Bay)

The Kamarvik and Silumiut skeletal material is curated by the Canadian Museum of History. The Kamarvik and Silumiut samples represent Thule Inuit populations who lived on the north western coast of Hudson Bay, and the remains were recovered by the Northwest Hudson Bay Thule Project (NHBTP) from 1967-1969. Sponsored by the National Museum of Canada and the National Geographic Society, the NHBTP was responsible for the archaeological and biological investigation of the Thule culture Inuit who once occupied that portion of Hudson Bay. The primary objective of the project was to establish a biological ‘baseline’ for pre-contact Canadian Inuit, with the broader aim of investigating human variability and adaptation in Canadian Inuit populations (Merbs, 1969).

Kamarvik (LeHv-1) is a coastal site located south of Wager Bay. Merbs’ (1969; 1997) and McCartney’s (1977) survey of the site yielded sixteen house ruins, with two partially excavated, and 127 burial cairns from which 127 human skeletons were collected. Silumiut (KkJg-1) is a small island which is attached to the mainland at low tide, located north of Chesterﬁeld Inlet. Survey yielded 28 house ruins, with seven excavated, four storage pits, and 185 burial cairns from which 190 skeletons were collected (McCartney, 1977). Merbs(1997) noted that the two sites were similar in general appearance, both having ruins of stone and sod winter houses and stone graves, and both were associated with Thule culture.

Coltrain et al. (2004; 2009) have dated a sample of 45 Kamarvik individuals to a two-sigma range of AD 1158-1678, and 66 Silumiut individuals to a two-sigma range of Chapter 3. Materials and Methods 60

AD 1047-1700. In the same studies, Coltrain and colleagues found the Kamarvik and Silumiut to have been more reliant on terrestrial and lower trophic level marine taxa than the Sadlermiut, and they exhibited no differences between males and females, or between individuals with grave goods versus those without. Interestingly, those individuals from Silumiut with confidence interval median dates after AD 1400 were significantly more reliant on lower trophic level marine taxa than those before, and than the Kamarvik in either time period (Coltrain et al., 2004; Coltrain, 2009). Lastly, both sites appear to have been abandoned by the mid-17th century, although the reason for this is not clear. Coltrain(2009) suggests that is could be related to a reduction in caribou and/or bowhead whale encounter rates, or an introduced epidemic from European explorers.

3.1.4 Greenland Thule and Inuit

The Greenlandic material is curated by the Unit of Forensic Anthropology in the Depart- ment of Forensic Medicine, University of Copenhagen. The sample is not homogeneous in terms of locations or dating, but instead represents 24 sites from throughout Greenland. Table A.1 in Appendix A lists each site, along with its general location in Greenland (following Nelson et al.(2012) and Gulløv(2012)) and associated references. Figure A.1 shows the location of these sites within Greenland.

3.2 Sample selection

A census of the Sadlermiut collection was undertaken in order to have an up-to-date assessment of the collection’s sex/age breakdown. All Sadlermiut individuals are therefore included in the study sample. For the comparative samples (Point Hope, NW Hudson Bay, Greenland), priority was given to including all present juveniles, as long as they had at least one measurable long bone, and regardless of the presence/absence of dentition. For adults, priority was given to collecting data from individuals with complete or Chapter 3. Materials and Methods 61 almost complete vertebral columns (thoracic/lumbar/sacral sections), and with at least one measurable long bone present. An eﬀort was made to create comparative samples with roughly equal sex ratios. Individuals with obvious pathological changes to their skeletons which would interfere with measurements or aﬀect body proportions (e.g. long bone shortening due to healed fracture, severe osteoarthritis of the femoral head) were excluded, with the exception of changes to the vertebrae. Individuals with pathological changes to vertebrae (e.g. degenerative changes, spondylolysis) were included, and observations on the nature and extent of changes were recorded.

3.3 Sex determination and age estimation

Sex determination in adult individuals was based on the pelvis and supplemented with the skull, using standard techniques (Buikstra and Ubelaker, 1994). Sex determination was not attempted for juvenile individuals, except for those oldest individuals with mature pelvic morphology. Adult age estimation was based on the auricular surface (Buckberry and Chamberlain, 2002) and pubic symphysis (Brooks and Suchey, 1990).

Juvenile age estimation was based on dental formation and eruption, using the Lon- don Atlas (AlQahtani et al., 2010). This method was developed as a comprehensive, evidence-based atlas, designed for age prediction; it has been shown to provide more acurate age estimations than either the Schour and Massler or Ubelaker dental age estimation techniques (AlQahtani et al., 2014). Age at death estimation was conducted for every juvenile that had at least one observable tooth present. Each tooth was assigned a crown/root development score. This score was then converted to an age based on the tooth formation stage tables in AlQahtani et al.(2010). These tables present the minimum, median, and maximum stages of development for each tooth at each age level. Since a particular tooth may fall into several age levels, an average age for a tooth was calculated when necessary. For example, a permanent maxillary ﬁrst incisor scored as Chapter 3. Materials and Methods 62

‘Crc’ (crown completed with deﬁned pulp roof) can fall into the 3.5, 4.5, 5.5, and 6.5 year levels. Thus an averaged age of 5.0 years would be assigned. All individual tooth ages were then averaged to create one overall dental age per individual. See Table B.1 and Table B.2 in Appendix B for examples of these calculations. Note that dental ages are presented to at least one decimal place, often two or more. This is not meant as an indicator of precision of accuracy; AlQahtani et al.(2010) does not provide error estimates or probability ranges for age categories. Rather, it represents the calculation of ages based on fetal weeks and postnatal months (see Appendix B).

3.3.1 Dental age intra-observer error

To investigate the reliability of the dental age estimates, repeat observations were made on a sub-sample of Sadlermiut juveniles. Observations were made on nineteen randomly selected individuals, in the course of one day, approximately two years after initial data collection occurred. A comparison of the two estimates is presented in Table B.3 in Appendix B. Repeat estimates generally fall close to the original, and in some cases are identical. The largest diﬀerences between estimates are no greater than half a year of age. There is no trend towards bias in the repeat estimates: seven scored younger than the original estimate, ten scored older, and two were the same. Additionally, the range of diﬀerence was roughly similar between those which scored younger and older: from -0.445 to -0.011 in the younger group, and from 0.011 to 0.417 in the older group. Overall, the repeat dental age estimates showed good correspondence with the original estimates.

3.3.2 Regression-based age estimation

Dental age estimates could not be obtained for all the Sadlermiut juvenile sample. This was due to either loss of dentition postmortem, or all present teeth being in a fully erupted state (i.e. no tooth in the process of crown development or eruption). Using the Chapter 3. Materials and Methods 63

Sadlermiut juveniles with dental age estimates, an ordinary least squares regression of dental age on maximum iliac breadth was performed (see Appendix B for equations and plots illustrating the regression). In this way, the juveniles without dental ages could be assigned an age by being seriated within the dental-aged juveniles. Maximum iliac breadth was chosen for the regression analysis because it is not used in the investigation of growth, and provides a limited contribution to the investigation of body proportions. The intention was that when iliac breadth was employed in an investigation, the individuals with regression-estimated ages would be omitted for the analysis, but in fact maximum iliac breadth was not included. Forty-two Sadlermiut juveniles were without dental age estimates. Of these, 25 individuals had maximum iliac breadth measures, while seventeen individuals did not. A plot of maximum iliac breadth versus dental age (Figure B.1 in Appendix B) revealed two distinct groups within the sample: those with maximum iliac breadths under 60 mm, and those with maximum iliac breadths over 80 mm. No individuals fell within 60-80 mm, reflecting the paucity of individuals in the Sadlermiut sample from approximately 2.5 to 5 years of age. Examination of the group without dental age estimates but with maximum iliac breadths revealed a similar trend: no individuals with maximum iliac breadths between 50 mm and 80 mm. A cubic polynomial regression visually fit the pattern of the data well (see Figure B.1), but it violated the assumptions of normally distributed errors and homoscedasticity (see Figure B.2)(Gelman and Hill, 2007). The natural gap in the data justified breaking it into two linear regressions (Figure B.3), and these models did not violate the above mentioned assumptions (Figure B.4 to Figure B.5). Table B.5 lists the regression-based age estimates and maximum iliac breadths for the 25 Sadlermiut individuals. Chapter 3. Materials and Methods 64

3.4 Osteometric variables

The osteometric variables employed in this study are listed in Table 3.1. Lengths were measured on four long bones: the humerus, radius, femur, and tibia. Diaphyseal lengths were taken on all four bones, and maximum lengths were taken on the humerus, radius, and femur. Further measurements include maximum mediolateral breadth of the distal femoral metaphyseal surface, femoral midshaft circumference, maximum iliac breadth and iliac height.

An attempt was made to estimate diaphyseal length if one epiphysis was fused. This estimation only occurred, however, if the epiphyseal line was still clear and some visual- ization of the diaphysis end could be made. Generally, estimation was used for epiphyses that have clear horizontal surfaces, such as the distal composite epiphysis of the humerus, the proximal epiphysis of the radius, or the distal epiphysis of the tibia. Estimation for a fused proximal humerus or femur epiphysis was avoided. A note was included with each estimation, allowing for identiﬁcation of all individuals with estimated diaphysis measurements. A total of thirteen individuals had estimated diaphyseal lengths. The most common element subject to estimation was the humerus (eight of thirteen individuals), all due to fusion of the distal composite epiphysis; second most common was the radius (four individuals), all due to fusion of the proximal epiphysis.

Maximum length of the adult tibia was substituted with the tibial ‘Fully’ physiological length (Raxter et al., 2006), a closer approximation of living leg length than tibial maximum length (Auerbach, 2007; Temple et al., 2008); refer to Section 2.1.4 for discussion. Additionally, femoral bicondylar length was measured in adult individuals so as to be consistent with previous studies on ecogeographic proportions in humans (e.g. Temple et al., 2008; Holliday and Hilton, 2010; Auerbach and Sylvester, 2011; Temple and Matsumura, 2011; Auerbach, 2012).

Vertebral and sacral measurements were taken in order to calculate skeletal trunk height. Skeletal trunk height is deﬁned as the summed dorsal body heights of vertebrae Chapter 3. Materials and Methods 65

Table 3.1: Osteometric variables

Measurement Juvenile / Reference Adult? Humeral max length Adult #40a Radial max length Adult #45a Femoral max length Adult #60a Femoral bicondylar length Adult #61a Distal femoral M-L max breadth Both #62a Femoral midshaft circumference Both #68a Tibial ‘Fully method’ length Adult Medial malleolus to lateral condyleb Humeral diaphyseal length Juvenile #14aa Radial diaphyseal length Juvenile #16aa Femoral diaphyseal length Juvenile #17aa Tibial diaphyseal length Juvenile #18aa Maximum iliac breadth Both #11a, 57a Iliac height Both #11b, 56a Thoracic/lumbar vertebral dorsal Both height Sacral ventral height Adult/older #53a juvenile Skeletal trunk height Both Summed dorsal heights of thoracic and lumbar vertebrae, plus sacral ventral lengthc Skeletal trunk height (no sacrum) Both Summed dorsal heights of thoracic and lumbar vertebrae aBuikstra and Ubelaker(1994) bRaxter et al.(2006) cFranciscus and Holliday(1992)

T1-L5, plus sacral ventral length (Franciscus and Holliday, 1992). Numerical variation in the spinal column is common in the populations under study (Merbs, 1974), leading to the calculation of skeletal trunk heights that included a T13 or L6. Chapter 3. Materials and Methods 66

Vertebrae that were missing postmortem, or could not be measured due to postmortem damage, were estimated using methods presented by Auerbach(2011). The general protocol for estimation was as follows: all vertebrae, except for the special cases listed below, could be estimated as the mean of their surrounding vertebrae (e.g. T4 could be estimated as the mean of T3 and T5); T2, T11, L1, and L5 were estimated either by regression equation, or as a proportion of a superior or inferior vertebra (one being more accurate than the other, diﬀering based on the vertebra). Auerbach(2011) found estimation by regression equation to be the more accurate of the two options, but its implementation is constrained by the requirement of several other vertebrae being present. Estimation of these exceptional vertebrae was accomplished via regression where possible; where not possible, estimation was based on the more accurate proportional technique, or as a last resort, on the less accurate proportional technique. Runs of up to three missing vertebrae could be accommodated by this technique. Although originally developed on adult vertebral columns, Auerbach’s estimation technique was also applied to the juvenile sample in order to increase sample size. Sacral vertebrae were not included in this estimation technique. All measurements were recorded to the nearest 0.5 mm, with the exception of data input directly to computer from Mitutoyo electronic calipers. All long bone length measurements were made using an osteometric board, and circumference measurements were taken using a ﬂexible measuring tape. All other measurements were taken with Mitu- toyo electronic calipers, with the exception of a small number of vertebral dorsal heights that were measured with spreading calipers due to shape constraints. Wherever possible, both right and left sides were measured for the long bones and ilia. For the dataset to be analyzed, left sides were selected; where the left was not available, the right side was substituted. All analyses and plotting were conducted using R version 3.1.1 (R Core Team, 2014). Chapter 3. Materials and Methods 67

3.5 Investigation of linear growth

Growth assessment followed the method developed by Humphrey(2000, 2003), whereby a skeletal sample’s tempo of growth is compared to the tempo derived from normative values from the Denver Growth Study (Maresh, 1943, 1955, 1970). Following Humphrey’s method (Humphrey, 2000, 2003; Harrington and Pfeiffer, 2008; Forrest, 2010; Pfeiffer and Harrington, 2010), for each individual length-for-age was considered as a percentage of adult length-for-age attained in the four long bones. Sample-specific mean adult size, sexes pooled, was calculated for each variable and served as the adult ‘endpoint’ (see Table 3.2).

Table 3.2: Adult long bone end points (mm) [# female/male/indeterminate]

Element Sadlermiut Point Hope (all) NW Hudson Bay Greenland Humerus 291.8 [51/39/8] 291.0 [39/43/1) 287.7 [18/18/0] 296.8 [11/10/1] Radius 208.8 [50/33/4] 217.7 [36/41/1] 209.1 [12/15/0] 217.0 [10/9/0] Femur 421.0 [47/39/7] 410.6 [38/42/1] 415.8 [20/20/0] 415.7 [14/12/1] Tibia 330.3 [48/38/5] 329.7 [35/41/1] 331.9 [18/19/0] 330.2 [12/12/1]

Since the Denver reference data are based on radiographs, a correction for image parallax was made following procedures outlined by Ruﬀ(2007). All Denver values referred to hereafter are adjusted measures. Denver adult ‘endpoints’ were calculated by averaging the means for 18 year-old males and 17 year-old females. The percentage of adult length attained for each juvenile was calculated, and plotted as a residual relative to the Denver values. Two regression equations were calculated to predict Denver size at a given age: one from 0.167 years (the youngest age group in the Denver sample) to 2 years, and one from 2 to 12 years (see Table 3.3). Since diaphyseal growth is not necessarily ﬁnished by the ages represented by the Denver adult ‘end-points’, percentages of attained adult size calculated for the samples represent maximum estimates. Chapter 3. Materials and Methods 68

Table 3.3: Denver regression equations

Age Element Regression Equation Category <2 years Humerus 3.3855x3 − 18.7037x2 + 55.9026x + 61.595 Radius 2.6475x3 − 13.9472x2 + 38.9456x + 51.6009 Femur −7.8010x4 + 42.30353 − 87.3092x2 + 117.9508x + 67.0302 Tibia −4.9905x4 + 27.6896x3 − 59.0633x2 + 87.2413x + 55.7705 >2 years Humerus 0.02532x3 − 0.82511x2 + 20.23577 + 88.51329 Radius 0.028558x3 − 0.770943x2 + 15.711649x + 66.354692 Femur 0.044767x3 − 1.375223x2 + 32.002919x + 109.030153 Tibia −0.002305x4 + 0.106256x3 − 1.807184x2 + 28.821697x + 84.851395

3.6 Investigation of body proportion development

Ecogeographic body proportion development was estimated by calculating a suite of body proportion indices: brachial index, crural index, and limb length relative to skeletal trunk height (see Table 3.4). Brachial and crural indices assess the length of the distal limb relative to its proximal counterpart within each limb; they have been shown to exhibit a signiﬁcant relationship with climate variables, with groups from colder climates displaying lower ratios (Coon, 1962; Trinkaus, 1981; Holliday and Hilton, 2010). Limb length segments divided by skeletal trunk height give a skeletal approximation of relative sitting height (for the lower limb) or relative span (for the upper limb) (Holliday and Hilton, 2010).

Since most juvenile individuals are without a sacral ventral height measure (due to age-related sacral fusion), skeletal trunk height (STH) was calculated both with and without sacral ventral height for both adults and juveniles. STH with the sacrum allows for comparisons with other studies, while STH without the sacrum (STHnoS) allows for inclusion of a larger sample size of juvenile individuals.

Boxplots will be used to explore patterning in adults, while plots of indices versus dental age will be used to explore age-related patterning in juveniles. Boxplots compute Chapter 3. Materials and Methods 69

Table 3.4: Body proportion indices

Index Description Reference

radius max length Brachial humerus max length × 100 Trinkaus(1981); Ruﬀ(1994)

tibial fully length Crural femoral bicondylar length × 100 Davenport(1933); Holliday (1999)

long bone length Relative Limb Length skeletal trunk height × 100 Holliday(1997a,b)

the median, ﬁrst, and third quartiles of the data, as well as outliers. The lines that extend from the box (whiskers) are constructed using the 1.5 rule: they extend to the most extreme data point that is no more than 1.5 times the length of the box (i.e. interquartile range) away from the box. Results will be compared to index data from the literature – speciﬁcally, Cowgill et al.(2012), Temple et al.(2011), and Bleuze et al. (2014) (see summaries in Table 3.5). Hypotheses will be tested by visual inspection of the data. Chapter 3. Materials and Methods 70

Table 3.5: Summary of comparative studies

Study Component Description

Sample 8 worldwide samples N=509 juveniles, N=420 adults Juvenile Absent dentition: age estimated by within-sample regression Cowgill et al. on long bones (2012) Age groups 0-2.9, 3-9.9, 10-17.9 yrs Adult Tibial max length

Sample Late/Final Jomon, Japan N=48 juveniles, N=88 adults Temple et al. Juvenile Only individuals with associated dentition (2011) Age groups 0-2.0, 2.1-10.9, 11-epiphyseal union Adult Tibial ‘Fully’ length

Sample Kellis 2, Dakhleh Oasis, Egypt N=145 juveniles, N=156 adults Juvenile Absent dentition: age estimation based on epiphyseal development/fusion, pars basilaris morphology, fusion of cranial Bleuze et al. elements, long bone diaphyseal lengths (fetal/perinate only) (2014) Age groups fetal/perinate, 0-0.9, 1.0-4.9, 5-14.9 yrs Adult Tibial max length

Juvenile Brachial and crural indices using max diaphyseal lengths All studies Adult Max lengths converted to diaphyseal lengths Chapter 4

Results

This chapter will present the results the analyses described in Chapter 3. The ﬁrst section describes the age and (for adults) sex composition of each of the four study samples. The second section will detail the analysis of long bone growth, by looking at actual size attained, percentage of adult size attained, and residuals from the Denver mean in percentage of adult size attained. The third section will detail the analysis of body proportion development, by comparing body proportion indices with age. All plots, unless otherwise noted in the text, include the Sadlermiut individuals who were aged via regression (named Sadlermiut Age Reg in plot legends).

4.1 Sample composition

Table 4.1 lists the number of juveniles per sample, and the subsets of juveniles with a dental age or a regression-based age estimate. Figure 4.1 illustrates the dental age distribution of the total juvenile sample, while Figure 4.2 illustrates sample-speciﬁc dental age distributions (Sadlermiut individuals aged via regression are included; see Figure C.1 and Figure C.2 in Appendix C for plots with these individuals removed). Table 4.2 shows the number of adult female, male, and indeterminate individuals in each sample.

A striking diﬀerence is apparent between the age-at-death distributions of the Sadler-

71 Chapter 4. Results 72

Table 4.1: Number of juveniles per sample

Sample Number of Subset with Subset with Juveniles Dental Age Regression Age (AlQahtani et al., Estimate 2010) Sadlermiut 111 69 25 NW Hudson Bay 18 17 — Point Hope (all) 64 58 — Point Hope (Ipiutak) 11 10 — Point Hope (Tigara) 48 43 — Point Hope (no time period) 5 3 — Greenland 14 11 — Total 207 155 25 60 50 40 30 Number of Individuals 20 10 0

0 5 10 15 20 25

Dental Age (yrs)

Figure 4.1: Juvenile sample distribution by dental age

miut and Point Hope groups (Figure 4.2). The number of Sadlermiut juveniles under 2.5 years of age is greater than the number of individuals above that age. Conversely, the Point Hope sample has only three individuals under 2.5 years of age. The high percentage of Sadlermiut infants is perhaps not unexpected, given the high infant mortality rates Chapter 4. Results 73

Sadlermiut Point Hope 60 15 50 40 10 30 5 20 Number of Individuals Number of Individuals 10 0 0

0 5 10 15 20 25 0 5 10 15 20 25

Dental Age (yrs) Dental Age (yrs)

NW Hudson Bay Greenland 5 4 4 3 3 2 2 1 1 Number of Individuals Number of Individuals 0 0

0 5 10 15 20 25 0 5 10 15 20 25

Dental Age (yrs) Dental Age (yrs)

Figure 4.2: Juvenile sample distribution by dental age and population

reported for historical Inuit groups (see Table 2.1).

Proportion of infants aside, the Sadlermiut and Point Hope groups have similar distributions during mid-childhood, with peaks around 10 years of age. This similarity is conﬁrmed by employing a Kolmogorov-Smirnov test: when individuals under 2.5 years of age were excluded from both the Sadlermiut and Point Hope samples, the test generated a p-value of 0.7622, indicating similar distributions. The NW Hudson Bay and Greenland samples are much smaller, but have opposite distributions: NW Hudson Bay exhibits a larger proportion of individuals from 15 to 20 years of age, while Greenland has peaks at Chapter 4. Results 74 early- and late-childhood (2.5-5 years and 10-15 years, respectively). It should be noted, however, that these histograms include only individuals who could be assigned dental ages. The Sadlermiut distribution includes a subset of individuals who were aged via regression, but this is not the case for the other three samples. Moreover, sample selection for the Point Hope, NW Hudson Bay, and Greenland samples prioritized individuals having dentition present. As such, these samples contain very few individuals who had measurements available but no dentition — unlike the Sadlermiut sample. In other words, the comparative collections may have contained infants who lacked dentition and at least one measurable variable, or who were in poor condition. As such, they would not be included in the study. Since these three are not fully ‘censused’ samples, it would be inappropriate to draw conclusions based on their age-at-death distribution. Overall, sample sizes are suﬃciently large enough to show the range of variation present in growth and body proportion development across Arctic past populations.

Table 4.2: Number of adults per sample, by sex

Sample Female/ Male/ Indeterminate Total Probable Probable Female Male Sadlermiut 62 52 46 160 Point Hope (all) 42 43 1 86 Point Hope (Ipiutak) 16 17 0 33 Point Hope (Tigara) 24 26 1 51 Point Hope (no time period) 2 0 0 2 NW Hudson Bay 20 20 0 40 Greenland 14 12 1 27 Chapter 4. Results 75

4.2 Investigation of linear growth

Maximum diaphyseal lengths for the humerus, radius, femur and tibia were plotted against dental age, with the Denver mean and plus/minus one and two standard deviations (Figure 4.3-Figure 4.6). Most of the sample falls more than two standard deviations below the Denver mean. This ﬁnding is consistent with the literature on Inuit body morphology, which is characterized by relatively short limbs and a long torso (i.e. high sitting height ratio) (Galloway et al., 2011; Boas, 1888; Hrdliˇcka, 1930, 1941; Heller et al., 1967; Auger et al., 1980). 350 300

● ●

250 ● ● ● ● ● ●

● ● ● ●

200 ● ● ●● ● ● ● ● ● ● ● Sadlermiut 150 Sadlermiut Age_Reg ●

Humerus Diaphyseal Length (mm) Humerus Diaphyseal Ipiutak ● Tigara ● ● NW Hudson Bay ●●●●

100 ● ● ●●● Greenland ●●● ● ●● ●● Denver Mean ●●●● ● ●● Denver +/− 1SD ● ● ● Denver +/− 2SD 50

0 5 10 15 20

Dental Age (yrs)

Figure 4.3: Humerus length by dental age, all samples

Percentage of attained adult size is plotted against age for the four diaphyseal lengths (Figure 4.7a-Figure 4.10a). In contrast with the plots of absolute size versus age, these plots show that a large portion of individuals over 2 years of age fall within plus or minus two standard deviations of the Denver mean. The group of individuals under 2 years of age is predominately composed of Sadlermiut infants, and this group falls consistently Chapter 4. Results 76 250

200 ● ● ● ●

● ● ● ●

● ● 150 ● ● ● ● ● ● ●

● ● ● ● ● Sadlermiut 100 ● Sadlermiut Age_Reg Radius Diaphyseal Length (mm) Radius Diaphyseal Ipiutak ●● ● ●● Tigara ●●●● ●● ● ●●● NW Hudson Bay ●● ●● ● ●● Greenland ● 50 Denver Mean Denver +/− 1SD Denver +/− 2SD

0 5 10 15 20

Dental Age (yrs)

Figure 4.4: Radius length by dental age, all samples 500

● 400 ● ●

● ● ● ● ● ● ● ● ●

300 ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● 200 Sadlermiut ● Sadlermiut Age_Reg Femur Diaphyseal Length (mm) Diaphyseal Femur Ipiutak

● Tigara ● ● NW Hudson Bay ● ● ●●●● ●●● ●●● ● Greenland ● ●● 100 ●●● Denver Mean ●● ● ●● ● Denver +/− 1SD Denver +/− 2SD

0 5 10 15 20

Dental Age (yrs)

Figure 4.5: Femur length by dental age, all samples Chapter 4. Results 77 400

●

● ● ● 300

● ● ● ● ● ●

● ● ● ● ● ● 200 ● ● ● ● ● ● Sadlermiut ● ● Tibia Diaphyseal Length (mm) Tibia Diaphyseal Sadlermiut Age_Reg ● Ipiutak ● Tigara ● ● NW Hudson Bay ● ● Greenland ●●● ● 100 ●● ● ●● ●●● Denver Mean ●● ●●● ●●● Denver +/− 1SD ● ●● ● Denver +/− 2SD

0 5 10 15 20

Dental Age (yrs)

Figure 4.6: Tibia length by dental age, all samples

below the Denver mean, with varying proportions of individuals falling at least one or two standard deviations below the mean. This is best illustrated in the plots of residuals from the Denver mean in percentage of adult size attained (Figure 4.7b-Figure 4.10b). These plots show percentage of adult size attained relative to the Denver normative standard of percentage of adult size attained. Points falling at the midline (0%) indicate the percentage of adult size attained is the same as that attained by the Denver children; points falling above or below the line suggest an acceleration or lag in growth relative to the Denver children. The pattern across samples is variable, with Point Hope and Greenland juveniles dispersed around the Denver mean (0% midline), while NW Hudson Bay individuals fall at or below the mean. The following sections will look more closely at each group’s growth analysis. Chapter 4. Results 78

● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara 80 ● NW Hudson Bay Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD ● ● ●

● ●● 60 ● ● ● ● ● ●

●

40 ●

● Humerus Attained % Adult Size ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●

●● ●●●● ● ●● ● 20

0 2 4 6 8 10 12

Dental Age (yrs)

(a) % Adult Size Attained

15 ● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara ● NW Hudson Bay 10 Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 5

●●

0 ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ●

●● −5 ● ● ● ●

● ● ● ●

−10 ●

●

● Residual from Denver Mean in % Adult Humerus Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Residuals from Denver Values

Figure 4.7: Growth of humerus, all samples Chapter 4. Results 79

● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara 80 ● NW Hudson Bay Greenland Denver Mean Denver +/− 1SD ● ● Denver +/− 2SD ●

● ●

● ● 60

●

● ● ●

● 40

●

Radius % Adult Size Attained Radius % Adult Size ● ● ● ● ● ● ●●● ● ●●● ● ● ●●● ● ● ● ● ● 20

0 2 4 6 8 10 12

Dental Age (yrs)

(a) % Adult Size Attained

● Sadlermiut 15 Sadlermiut Age_Reg Ipiutak Tigara ● NW Hudson Bay Greenland 10 Denver Mean Denver +/− 1SD Denver +/− 2SD 5

●

●● ● ● ● ● 0 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −5 ●

●

● ● ● ● −10 Residual from Denver Mean in % Adult Radius Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Residuals from Denver Values

Figure 4.8: Growth of radius, all samples Chapter 4. Results 80

● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara 80 ● NW Hudson Bay ● Greenland

Denver Mean ● Denver +/− 1SD

Denver +/− 2SD ● ●

● ● ● 60 ● ●

● ● ●

● 40 Femur % Adult Size Attained % Adult Size Femur

●

● ● ● ● ●●● ●●● ● ● ●●● ● ● ●● ● ●● ● ● 20 ● ●

0 2 4 6 8 10 12

Dental Age (yrs)

(a) % Adult Size Attained

15 ● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara ● NW Hudson Bay 10 Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 5

●

● ● 0 ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● −5 ● ● ●

● ● ●

● ● −10

● ● ●

● Residual from Denver Mean in % Adult Mean Femur Length Mean in % Adult Femur Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Residuals from Denver Values

Figure 4.9: Growth of femur, all samples Chapter 4. Results 81

● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara 80 ● NW Hudson Bay Greenland Denver Mean ● Denver +/− 1SD

Denver +/− 2SD ● ●

●

● 60

● ● ● ● ●

● ● ●

● 40 Tibia % Adult Size Attained Tibia % Adult Size

●

● ● ● ● ●●●●● ● ● ●● ●●● ●● ●●●● ● ● ● ● 20

0 2 4 6 8 10 12

Dental Age (yrs)

(a) % Adult Size Attained

20 ● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara ● NW Hudson Bay Greenland Denver Mean

10 Denver +/− 1SD Denver +/− 2SD

●

● ● ● ● ●● 0 ● ●● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● −10

● ● Residual from Denver Mean in % Adult Tibia Length Residual from Denver −20

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Residuals from Denver Values

Figure 4.10: Growth of tibia, all samples Chapter 4. Results 82

4.2.1 Sadlermiut linear growth

Figure 4.11 and Figure 4.12 show Sadlermiut percentage of adult size attained as a residual from the Denver standard, including all juveniles under 12 years of age; for simpliﬁed viewing, Figure 4.13 and Figure 4.14 show only individuals under 2 years of age. The pattern of Sadlermiut individuals faltering in growth with respect to the Denver tempo of growth is not consistent across the long bones. The femur shows the greatest proportion of individuals faltering, while the radius shows the smallest proportion, and the humerus and tibia are both intermediate in nature (i.e. radius < tibia < humerus < femur). Considering intra-limb relationships, the proximal limb elements (humerus and femur) show greater deviation from the Denver pattern than the distal elements (radius and tibia).

Humerus growth

With three exceptions, Sadlermiut juveniles exhibit humerus growth that falls below the Denver mean (0% midline), with most individuals falling more than one standard deviation below the mean (Figure 4.11a, Figure 4.13a). Of 47 individuals below the age of 2.5 years, 21 fall below two standard deviations below the Denver mean. Five individuals hover slightly below or above the mean; four are around 1 year of age, while one is slightly over 2 years. All twelve individuals older than 2.5 years fall at or below the minus one standard deviation line.

Radius growth

Most Sadlermiut juveniles exhibit radius growth that falls within plus/minus two standard deviations of the Denver mean. Eleven individuals fall under two standard deviations below the mean (Figure 4.11b, Figure 4.13b), and are spread evenly between the under-2.5 and over-2.5 years groups. One infant exhibits growth that is over one standard deviation above the mean; two infants fall between the mean and one standard deviation Chapter 4. Results 83 above. Eight infants hover around the Denver mean, as do one older juvenile (approx. 10 years). The bulk of the sample, however, still falls around or below one standard deviation below the Denver mean.

Femur growth

With four exceptions, Sadlermiut juveniles exhibit femur percentage length for age values at least one standard deviation below the Denver mean, with most individuals falling below two standard deviations (Figure 4.12a, Figure 4.14a). The exceptions to this pattern include two infants falling just above the minus one standard deviation line, and one individual approximately 10 years of age, falling almost one standard deviation above the mean. The older juveniles who died were also predominantly faltering in femur length for age, as compared to the Denver mean: eleven out of the twelve older individuals fall at least one standard deviation below the Denver mean, with seven falling below two standard deviations.

Tibia growth

Looking at percentage of tibia length for age (Figure 4.12b, Figure 4.14b), a large portion of Sadlermiut individuals who died prior to approximately two years of age scatter either side of the Denver mean, within one standard deviation. A number of individuals fall below two standard deviations from the mean, but substantially fewer than as seen in femur growth. Again, older juveniles scatter predominantly below the Denver mean: eleven out of twelve fall below the mean, with seven falling more than two standard deviations under the mean. Chapter 4. Results 84

15 ● Sadlermiut Sadlermiut Age_Reg Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5

●●

0 ●● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●

●● −5 ● ● ● ●

● ● ●

−10 ●

●

● Residual from Denver Mean in % Adult Humerus Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Humerus

● Sadlermiut 15 Sadlermiut Age_Reg Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5

●

●● ● ● ● ● 0 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −5 ●

●

● ● ● ● −10 Residual from Denver Mean in % Adult Radius Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Radius

Figure 4.11: Sadlermiut upper limb growth, % length attained residuals from Denver mean Chapter 4. Results 85

15 ● Sadlermiut Sadlermiut Age_Reg Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5

● 0 ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● −5 ● ● ●

● ●

● ● −10

● ● ●

● Residual from Denver Mean in % Adult Mean Femur Length Mean in % Adult Femur Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Femur

20 ● Sadlermiut Sadlermiut Age_Reg Denver Mean Denver +/− 1SD Denver +/− 2SD 10

●

● ● ● ● ●● 0 ● ●● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●

● ● ● ● −10

● ● Residual from Denver Mean in % Adult Tibia Length Residual from Denver −20

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Tibia

Figure 4.12: Sadlermiut lower limb growth, % length attained residuals from Denver mean Chapter 4. Results 86 5

● ●

0 ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● −5 ● ● −10 ●

● Sadlermiut Sadlermiut Age_Reg Denver Mean Denver +/− 1SD Denver +/− 2SD Residual from Denver Mean in % Adult Humerus Length Residual from Denver −15

0.5 1.0 1.5 2.0

Dental Age (yrs)

(a) Humerus 4

● 2

● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● −2 ●

● ● ● ● ● −4

●

−6 ● Sadlermiut Sadlermiut Age_Reg Denver Mean Denver +/− 1SD

Residual from Denver Mean in % Adult Radius Length Residual from Denver Denver +/− 2SD −8

0.5 1.0 1.5 2.0

Dental Age (yrs)

(b) Radius

Figure 4.13: Sadlermiut upper limb growth, % length attained residuals from Denver mean, individuals under two years Chapter 4. Results 87 5 0 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●

−5 ● ● ● ●

● −10

● Sadlermiut Sadlermiut Age_Reg ● Denver Mean Denver +/− 1SD Denver +/− 2SD Residual from Denver Mean in % Adult Mean Femur Length Mean in % Adult Femur Residual from Denver −15

0.5 1.0 1.5 2.0

Dental Age (yrs)

(a) Femur 4 2

● ● ● ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ●

−2 ● ● ● ● ● ● ● ● −4 ● ● ●

● Sadlermiut −6 Sadlermiut Age_Reg Denver Mean Denver +/− 1SD

Residual from Denver Mean in % Adult Tibia Length Residual from Denver Denver +/− 2SD −8

0.5 1.0 1.5 2.0

Dental Age (yrs)

(b) Tibia

Figure 4.14: Sadlermiut lower limb growth, % length attained residuals from Denver mean, individuals under two years Chapter 4. Results 88

Sadlermiut linear growth summary

The Sadlermiut individuals who died as juveniles show a range of variation in their attainment of long bone growth. While a few individuals exhibit a percentage of adult size in their long bones that is comparable with the Denver tempo of growth, the predominant trend is faltering of growth as compared to that sample. To varying degrees, depending on the long bone, this pattern suggests that most Sadlermiut juveniles who died either began life lagging in proportional growth, or fell behind very quickly after birth. Additionally, most juveniles who died later in childhood continued to falter in growth, indicating a lack of ‘catch-up’ growth.

4.2.2 Point Hope linear growth

Figure 4.15 and Figure 4.16 show Point Hope percentage of adult size attained as a residual from the Denver standard. The Point Hope sample is diﬀerentiated into Ipiutak and Tigara subsamples. Point Hope provides an interesting contrast with the Sadlermiut: while the Sadlermiut sample has a large number of individuals under two years of age, and the Point Hope sample has very few, the rest of the samples’ age distributions are broadly similar in shape (see Figure 4.2).

Looking at percentage of adult size attained in femur growth (Figure 4.16a), Point Hope juveniles under 4 years of age fall either close to the mean, or up to two standard deviations above the mean. This is in contrast to the Sadlermiut, who all fall below the Denver mean in percentage of adult femur length attained. Point Hope juveniles over 4 years of age exhibit a broad range of residual values, ranging from slightly above two standard deviations above the mean, to well below two standard deviations below the mean. A similar pattern is repeated in humerus, radius, and tibia growth (Figure 4.15a, Figure 4.15b, and Figure 4.16b). A small group of individuals fall under two standard deviations below the mean; the rest of the sample ranges within two standard deviations of the mean, with a few individuals over two standard deviations above the mean. Chapter 4. Results 89

15 Ipiutak Tigara Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5 0 −5 −10 Residual from Denver Mean in % Adult Humerus Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Humerus

Ipiutak 15 Tigara Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5 0 −5 −10 Residual from Denver Mean in % Adult Radius Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Radius

Figure 4.15: Point Hope upper limb growth, % length attained residuals from Denver mean Chapter 4. Results 90

15 Ipiutak Tigara Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5 0 −5 −10 Residual from Denver Mean in % Adult Mean Femur Length Mean in % Adult Femur Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Femur

20 Ipiutak Tigara Denver Mean Denver +/− 1SD Denver +/− 2SD 10 0 −10 Residual from Denver Mean in % Adult Tibia Length Residual from Denver −20

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Tibia

Figure 4.16: Point Hope lower limb growth, % length attained residuals from Denver mean Chapter 4. Results 91

Worth noting, the results here show some agreement with those of Dabbs(2011), who studied the relative health statuses of Ipiutak and Tigara individuals. The Tigara showed a higher prevalence of LEH of the permanent canine, suggesting an increase in stress experienced while 3 to 6.5 years old. In the present study, Tigara juveniles from approximately 4 to 7 years all fall below the Denver mean: two individuals fall under the minus two standard deviation line for humerus and radius; six individuals fall under the minus one standard deviation line for the femur (with ﬁve of the six falling under the minus two standard deviation line); and six individuals falling under the mean for the tibia.

Overall, the Point Hope sample exhibits a continuum of growth outcomes, ranging between signiﬁcantly accelerated and lagging as compared to the Denver standard. There are no immediate diﬀerences in growth outcomes between the Ipiutak and Tigara, although the Ipiutak subsample is represented by a small number of individuals (three to six individuals, versus 17-32 Tigara individuals). The Point Hope individuals represented here who died as infants show no evidence of growth faltering. The greater variation in attainment of growth over four years of age indicates that while some individuals experienced growth faltering, others experienced accelerated growth as compared to the Denver sample.

4.2.3 NW Hudson Bay and Greenland linear growth

Figure 4.17 and Figure 4.18 illustrate percentage of attained adult size as a residual from the Denver mean, for the upper and lower long bones of the NW Hudson Bay and Green- land samples. Samples sizes for both are small, with NW Hudson Bay represented by one to ﬁve individuals, and Greenland by one to six individuals. The plot for percentage of adult femur length attained (Figure 4.18a) has the largest number of individuals from both samples. It shows ﬁve Greenland individuals between the Denver mean and plus/minus one standard deviation, with one individual between one and two standard Chapter 4. Results 92 deviations below the mean. Northwest Hudson Bay individuals range from slightly above the mean, to slightly below two standard deviations below the mean. Since the sample size for the two groups is so small, particularly for the humerus, radius, and tibia, it is not possible to elucidate any particular patterns in growth. Overall, the NW Hudson Bay sample tends to fall at or below the Denver mean, similar to the Sadlermiut sample, while the Greenland sample ranges above and below the mean.

4.2.4 Summary of growth analysis

The comparisons of diaphyseal lengths in absolute terms against the Denver normative standard are consistent with the expectations that Arctic populations would exhibit relatively short limbs. The comparisons of lone bone growth in terms of percentage of adult size attained to the Denver standard of percentage adult size attained illustrate the range of variation in growth tempo across groups. While the Sadlermiut consistently exhibited faltering of growth as compared to the Denver tempo, as well as a lack of catch-up growth for those who died in later childhood, the Point Hope groups showed a wide range of growth outcomes. Chapter 4. Results 93

15 ● NW Hudson Bay Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5 0

● ● −5

● −10 Residual from Denver Mean in % Adult Humerus Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Humerus

● NW Hudson Bay 15 Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5 0

● −5 −10 Residual from Denver Mean in % Adult Radius Length Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Radius

Figure 4.17: NW Hudson Bay and Greenland upper limb growth, % length attained residuals from Denver mean Chapter 4. Results 94

15 ● NW Hudson Bay Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 10 5

● ● 0

● ● −5

● −10 Residual from Denver Mean in % Adult Mean Femur Length Mean in % Adult Femur Residual from Denver −15

0 2 4 6 8 10 12

Dental Age (yrs)

(a) Femur

20 ● NW Hudson Bay Greenland Denver Mean Denver +/− 1SD Denver +/− 2SD 10 0

●

● −10 Residual from Denver Mean in % Adult Tibia Length Residual from Denver −20

0 2 4 6 8 10 12

Dental Age (yrs)

(b) Tibia

Figure 4.18: NW Hudson Bay and Greenland lower limb growth, % length attained residuals from Denver mean Chapter 4. Results 95

4.3 Investigation of body proportion development

The body proportion indices listed in Table 3.4 in Chapter 3 were calculated for the adult samples, to provide an adult ‘endpoint’ reference (Figure 4.19a to Figure 4.25a; see Table 4.3 for values). The same indices were then calculated for the juvenile samples (Figure 4.19b to Figure 4.25b). Juvenile indices were plotted against dental age, with boxplots of sex-specific adult values included in the sample-specific plots. The following sections look at the adult results, and then sample-specific juvenile results.

4.3.1 Adult body proportions

Additional boxplots are available in Appendix E, showing adult limb proportion and relative limb length indices in their various states of calculation (i.e. diaphyseal/max length, with/without sacrum). Histograms (Figure E.1 to Figure E.4) show the range of adult values found in the sample groups, sexes combined. Table 4.3 lists the values calculated for adult brachial and crural indices, presented both as standard values as well as values based on estimated diaphyseal lengths. Indices calculated with estimated diaphyseal lengths are the values used in comparisons with the juvenile samples.

Table 4.3: Adult brachial and crural index values — mean, [SD], (# of individuals)

Sex Sample Brachial Brachial Crural Crural (diaphyseal) (diaphyseal) Male Sadlermiut 72.8 [2.6] (33) 73.2 [2.7] (33) 79.0 [1.8] (36) 77.1 [1.8] (36) Ipiutak 75.5 [2.1] (16) 76.0 [2.2] (16) 80.0 [2.6] (16) 78.1 [2.5] (16) Tigara 75.8 [2.4] (25) 76.3 [2.5] (25) 81.5 [1.6] (25) 79.4 [1.5] (25) NW Hudson Bay 72.7 [2.6] (14) 73.1 [2.7] (14) 79.4 [1.8] (19) 77.4 [1.7] (19) Greenland 73.2 [2.6] (9) 73.7 [2.6] (9) 81.0 [3.7] (11) 79.0 [3.6] (11) Female Sadlermiut 71.2 [2.0] (49) 71.6 [2.0] (49) 79.3 [1.8] (47) 77.4 [1.8] (47) Ipiutak 72.6 [1.7] (14) 73.1 [1.7] (14) 79.3 [1.5] (14) 77.4 [1.4] (14) Tigara 73.9 [2.6] (22) 74.3 [2.6] (21) 80.8 [2.2] (22) 78.8 [2.1] (21) NW Hudson Bay 71.6 [3.0] (12) 72.0 [3.0] (12) 81.2 [1.9] (16) 79.2 [1.9] (16) Greenland 73.2 [2.7] (9) 73.7 [2.7] (9) 79.8 [2.4] (12) 77.8 [2.4] (12) Chapter 4. Results 96 85

● 80 75 70 Brachial Index (diaphyseal RL:HL) (diaphyseal Index Brachial ● 65

F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Adult Diaphyseal

85 ● Sadlermiut Sadlermiut Age_Reg

● Ipiutak ● Tigara ●● ● ● NW Hudson Bay ● ● Greenland

80 ● ●

● ● ● ● ● ● ● ● ● ● ●

75 ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● 70 ● ● Brachial Index (RL:HL) Index Brachial ● ●

● 65

●

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.19: Brachial Index in adults and juveniles, all samples Chapter 4. Results 97 90 85

● 80 75 ● Crural Index (diaphyseal TL:FBL) (diaphyseal Crural Index 70

F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Adult Diaphyseal

90 ● Sadlermiut Sadlermiut Age_Reg Ipiutak

● Tigara ● ● NW Hudson Bay ● ● ● Greenland ● ● 85 ● ●

● ●

● ● ●●● ● ● ●

● ●●● ●● ● ● 80 ● ● ● ● ● ● ● ● ● ● ● ● ●

Crural Index (TL:FBL) Crural Index ● ● ● ● ● ●

●

75 ● ● ●

● 70

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.20: Crural Index in adults and juveniles, all samples Chapter 4. Results 98

The groups fall within a small range: a span of 3 points for brachial index, 2.5 points for crural index. The Point Hope groups exhibit slightly higher brachial values than the Sadlermiut, NW Hudson Bay, and Greenland groups; this diﬀerence is smaller in crural index. Values for the Sadlermiut, Ipiutak and Tigara are comparable to those previously found by researchers (e.g. Auerbach, 2007; Holliday and Hilton, 2010). Moreover, all the groups exhibit brachial and crural indices indicating relatively short distal limb segments — proportionality that is consistent with living in an environment with low ambient temperatures. Males exhibit larger brachial and crural indices than females across all groups, with a few exceptions: Sadlermiut males and females exhibit similar crural indices, and NW Hudson Bay females have crural values slightly elevated over males. Figure 4.21 plots body proportion indices against latitude for a selection of northern hemisphere populations, and Table 4.4 lists the comparative samples, their latitudes and the references from which the information was derived. The samples range over 70 degrees of latitude, from the equator to the northern tip of Alaska. Although index values span a relatively small range, both brachial and crural index show a general decrease with increasing latitude. The Alaskan and Canadian Arctic samples fall towards the lower right quadrant of the plot; the African samples fall towards the upper left quadrant. The intermediate-latitude samples (Mistihalj, Indian Knoll, and Jomon) diﬀer by index: Indian Knoll exhibits an intermediate brachial index but a lower crural index; Jomon exhibits an elevated brachial index, and low crural index; and Mistihalj exhibits a low brachial index as well as a low crural index. The Sadlermiut exhibit the lowest values for both, although they are at a lower latitude than Point Barrow or Point Hope. Chapter 4. Results 99 82

● 14 ● 16 80

● 18 ● 17 78 ● 15

● ● 11 13 ● 8 ● 6 ● 10 76 ● 9 ● 7

Brachial Index Brachial ● 2 ● 1 ● 12

74 ● 5

● 3 ● 4 72 70

0 10 20 30 40 50 60 70

Latitude (degrees North)

(a) Brachial Index 88

● 18

86 ● 15

● 17 84

● 16

● ● 8 1

●

82 10 ● ● 5 Crural Index ● 13 6 ● 12 ● 9

● 7 ● 2 ● 14 ● 3 80

● 4 78

0 10 20 30 40 50 60 70

Latitude (degrees North)

(b) Crural Index

Figure 4.21: Body proportion indices by latitude (numbers correspond to samples listed in Table 4.4) Chapter 4. Results 100

Table 4.4: Comparative northern hemisphere body proportion samples, with latitude, brachial and crural index values

# Sample Latitude Brachial Crural Reference (◦N) 1 Point Barrow, Alaska 71.4 74.5 82.5 Auerbach(2007) 2 Point Hope, Alaska 68.0 74.5 80.5 This study 3 NW Hudson Bay, Nunavut 64.2 72.2 80.3 This study 4 Sadlermiut, Nunavut 64.1 72.0 79.2 This study 5 Ikogmiut, Alaska 62.0 73.8 81.6 Auerbach(2007) 6 Norse, Orkney Islands 59.0 76.1 81.5 Holliday(1997b) 7 Koniag, Alaska 58.0 75.4 80.5 Holliday(1997b) 8 Prince Rupert Harbour, BC 54.3 76.6 82.4 Auerbach(2007) 9 Neo-Aleut, Alaska 53.0 75.5 81.2 Auerbach(2007) 10 Pre-Aleut, Alaska 52.9 75.9 81.9 Auerbach(2007) 11 Lake Baikal, Siberia 52.5 76.8 81.9 Stock et al.(2011) 12 Mistihalj, Montenegro 43.0 74.3 81.2 Cowgill et al.(2012) 13 Indian Knoll, Kentucky 37.0 76.7 81.4 Cowgill et al.(2012) 14 Jomon, Japan 35.7 80.5 80.3 Cowgill et al.(2012) 15 Kellis 2, Egypt 25.3 77.6 85.8 Bleuze et al.(2014) 16 Kulubnarti, Upper Nubia 21.0 80.0 83.1 Cowgill et al.(2012) 17 Sudan 19.7 78.5 85.2 Holliday(1997b) 18 East Africa (Uganda, Kenya) 2.0 78.9 86.2 Holliday(1997b)

Table 4.5 and Table 4.6 list the values for relative limb length indices, again with both standard index values and those calculated with estimated diaphyseal lengths. These indices represent the length of a limb bone as a proportion of torso length (skeletal trunk height, no sacrum). Overall, the calculated values are consistent with those found by Holliday and Hilton(2010), with allowances made for exclusion of the sacrum in this study. The exclusion of the sacrum from skeletal trunk height results in slightly elevated values. Males exhibit relative limb indices almost uniformly higher than females. The sole exception is NW Hudson Bay females, who exhibit a higher relative tibia length than males. This indicates that in general, males have longer limb segments than females, Chapter 4. Results 101 relative to torso length.

Table 4.5: Adult relative upper limb length (no sacrum) index values — mean, [SD], (# of individuals)

Sex Sample Humerus Humerus Radius Radius (diaphyseal) (diaphyseal) Male Sadlermiut 82.0 [4.7] (34) 76.0 [4.4] (34) 59.5 [3.8] (29) 55.5 [3.5] (29) Ipiutak 83.1 [4.0] (16) 77.0 [3.7] (16) 62.4 [2.4] (15) 58.2 [2.2] (15) Tigara 82.7 [4.1] (24) 76.6 [3.8] (24) 62.7 [2.9] (23) 58.5 [2.7] (23) NW HB 86.1 [5.5] (11) 79.8 [5.1] (11) 62.3 [3.7] (10) 58.1 [3.5] (10) Greenland 86.6 [3.0] (3) 80.2 [2.8] (3) 62.1 [0.7] (3) 57.9 [0.7] (3) Female Sadlermiut 80.0 [4.5] (39) 74.2 [4.1] (39) 56.9 [3.1] (39) 53.0 [2.9] (39) Ipiutak 81.0 [3.3] (16) 75.0 [3.0] (16) 59.0 [2.1] (15) 55.0 [1.9] (15) Tigara 79.7 [4.9] (23) 73.9 [4.5] (23) 58.9 [3.1] (22) 54.9 [2.9] (22) NW HB 83.5 [2.0] (5) 77.4 [1.8] (5) 59.6 [1.7] (3) 55.6 [1.6] (3) Greenland 81.9 [2.3] (4) 75.9 [2.1] (4) 60.6 [2.2] (4) 56.5 [2.1] (4)

Table 4.6: Adult relative lower limb length (no sacrum) index values — mean, [SD], (# of individuals)

Sex Sample Femur Femur Tibia Tibia (diaphyseal) (diaphyseal) Male Sadlermiut 118.0 [6.6] (33) 107.6 [6.0] (33) 92.7 [5.9] (33) 82.4 [5.3] (33) Ipiutak 113.6 [5.3] (16) 103.5 [4.8] (16) 91.1 [2.9] (15) 81.0 [2.6] (15) Tigara 117.2 [5.4] (23) 106.8 [4.9] (23) 95.5 [5.0] (24) 84.9 [4.4] (24) NW HB 121.7 [6.0] (11) 111.0 [5.4] (11) 95.6 [4.4] (11) 85.0 [3.9] (11) Greenland 121.0 [3.7] (3) 110.3 [3.4] (3) 97.6 [2.6] (3) 86.7 [2.3] (3) Female Sadlermiut 114.4 [5.7] (38) 104.2 [5.2] (38) 90.7 [4.8] (38) 80.6 [4.2] (38) Ipiutak 110.0 [3.8] (16) 100.3 [3.5] (16) 87.4 [3.4] (16) 77.7 [3.0] (16) Tigara 114.3 [6.1] (22) 104.2 [5.5] (22) 92.1 [5.6] (21) 81.8 [5.0] (21) NW HB 119.6 [1.6] (5) 109.1 [1.5] (5) 97.7 [3.1] (5) 86.8 [2.7] (5) Greenland 116.1 [3.9] (4) 105.8 [3.6] (4) 93.0 [5.5] (4) 82.6 [4.9] (4) Chapter 4. Results 102 95

90 ● ●

● 85 80

75 ● 70 Humerus diaphyseal:STHnoS 65

● 60

F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Adult 110

● 100

● ●

● ● 90

● ●

● ● ●

● 80 ● Humerus:STHnoS

●●

● ● ●

●

70 Sadlermiut ● ● ● ● Sadlermiut Age_Reg Ipiutak ● Tigara ● NW Hudson Bay Greenland 60

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.22: Relative humeral diaphyseal length (no sacrum), all samples Chapter 4. Results 103 70

65 ●

●

● 60 55 50 Radius diaphyseal:STHnoS

● ● 45 40

F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Adult

80 ● Sadlermiut Sadlermiut Age_Reg Ipiutak Tigara ● 75 NW Hudson Bay Greenland

● ● 70

●

● 65

● ●

60 ● ● ● ●

Radius:STHnoS ● ● ●

● ● ● ● 55

● ● ●

50 ● ● 45

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.23: Relative radial diaphyseal length (no sacrum), all samples Chapter 4. Results 104 130

120 ●

● 110 100 FBCL diaphyseal:STHnoS 90 ●

F M F M F M F M F M

Sadlermiut Ipiutak Tigara CMC Cop

(a) Adult

● ●

● 140

● 130 ●

● ● ● 120 ● ● ●

● 110 Femur:STHnoS 100 ● ●

● ●●

90 ● Sadlermiut

● ● Sadlermiut Age_Reg Ipiutak

● ● Tigara

80 ● NW Hudson Bay Greenland

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.24: Relative femoral diaphyseal length (no sacrum), all samples Chapter 4. Results 105 100

● 95 90

● 85 80 Tibia diaphyseal:STHnoS 75 70 65

F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Adult 120

● ● 110 ●

100 ●

● ●

● ● 90 ● ● ● ● Tibia:STHnoS 80 ●●

● ● ● ● ● Sadlermiut

70 ● Sadlermiut Age_Reg

● Ipiutak Tigara ● NW Hudson Bay Greenland 60

0 5 10 15 20

Dental Age (yrs)

(b) Juvenile

Figure 4.25: Relative tibial diaphyseal length (no sacrum), all samples Chapter 4. Results 106

4.3.2 Sadlermiut body proportions

Figure 4.26 to Figure 4.29 illustrate body proportion indices by dental age in the Sadler- miut juvenile sample, with boxplots of sex-speciﬁc adult diaphyseal values. Brachial and crural indices share a similar pattern of development: the youngest individuals (i.e. under two years) span a wide range of index values, extending from slightly above median adult values to slightly elevated above the adult range (brachial index) or highly elevated above the adult range (crural index). The older juvenile group scatters within the range of adult values, indicating establishment of adult index values by around 6 years of age. From Figure 4.26 it is not immediately obvious in brachial and crural indices if the juveniles under 2 years of age are scattered in a vertical column, or if they exhibit a negative trend with age. Figure 4.27 focuses on this younger cohort, and shows that there is indeed a clear negative trend with age, although there is considerable variation present. The correlation coeﬃcients for brachial and crural indices are -0.6 and -0.7, respectively, indicating a moderately-strong negative linear relationship with age. Juveniles under approximately 6 months of age exhibit uniformly high crural index values (above approximately 84), but a wider range of brachial index values (approximately 76-83). Two juveniles fall into the perinatal age category based on their dental ages estimates: XIV-C:281 (-0.003 years) and XIV-C:284 (-0.015 years). Both individuals have brachial and crural index values elevated well above adult values. Chapter 4. Results 107 85

● ● ●● ● ● ●

80 ● ●

● ●● ● ● ● ● ● ● ● ●

75 ● ● ● ● ● ● ● ● ● ● ●

● ● ● Brachial Index Brachial ● ● ● ● ● 70 ● ●

● 65

● Sadlermiut ● Sadlermiut Age_Reg

0 5 10 15 20 Male

Dental Age (yrs) Female

(a) Brachial Index 90

● ● ● ● ● ● ● 85 ● ● ●

● ● ●●● ● ● ●

● ●●● ●● ● ● 80 ● ● ● ● ● ● Crural Index ● ● ● ● ● ● ● ● ●

● ● 75

●

● Sadlermiut ● Sadlermiut Age_Reg 70

0 5 10 15 20 Male

Dental Age (yrs) Female

(b) Crural Index

Figure 4.26: Sadlermiut juvenile indices with adult reference values Chapter 4. Results 108

84 ● Sadlermiut Sadlermiut Age_Reg

● r = −0.60

● 82 ● ● ●

● ● 80 ●

●

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Figure 4.27: Sadlermiut juvenile indices, under two years of age Chapter 4. Results 109 110

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Figure 4.28: Sadlermiut juvenile relative upper limb length indices with adult reference values Chapter 4. Results 110

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Figure 4.29: Sadlermiut juvenile relative lower limb length indices with adult reference values Chapter 4. Results 111

Figure 4.28 and Figure 4.29 illustrate relative limb length indices in the Sadlermiut juvenile sample. A general pattern is consistent across these four indices: the younger group (under two years) clusters at or below adult values, while the older juvenile group ranges from above the adult medians to well above adult values. It should be noted, however, that a greater amount of uncertainty is built into the relative limb length index when calculated on young juveniles. This includes the assumption that the relative contribution of intervertebral disc space remains the same over the growing period. There is also some uncertainty added when measuring infant vertebral centra, since the vertebrae have not achieved their adult form. Additionally, individuals in mid-childhood will have vertebrae in adult form, but with partially or completely unfused annular rings. These caveats aside, a general pattern emerges from the data: long bones are relatively shorter during infancy, become relatively longer during mid-childhood, before becoming relatively shorter again in adult form.

4.3.3 Point Hope body proportions

Figure 4.30 to Figure 4.32 illustrate body proportion indices by dental age in the Tigara sample, with box-plots of sex-specific adult diaphyseal values. The youngest Tigara individual represented in the brachial index plot has a dental age of 1.75 years; the next oldest have dental ages of 3 years. In the crural index plot, the youngest individual represented has a dental age of 3 years. Thus, the Tigara sample does not cover the range of variation in body proportionality that could be present from birth to 3 years of age. The Ipiutak sample suffers from a small number of individuals: five for brachial index, seven for crural index, two to four for the relative limb length indices (see Figure E.10 to Figure E.12 in Appendix E). The youngest Ipiutak individual represented in the brachial and crural index plots has a dental age of 0.65 years; the next oldest in brachial index is 6.75 years, and in crural index is 2.64 years.

Tigara juveniles exhibit brachial and cural index values (Figure 4.30) from 3 years of Chapter 4. Results 112 85 80 75 70 Brachial Index (RL:HL) Index Brachial 65

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(a) Brachial Index 90 85 80 Crural Index (TL:FBL) Crural Index 75 70

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Figure 4.30: Tigara juvenile indices with adult reference values Chapter 4. Results 113 110 100

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Figure 4.31: Tigara juvenile relative upper limb length indices with adult reference values Chapter 4. Results 114 140

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(a) Relative femoral length 120 110 100 90 80 Tibia diaphyseal:STHnoS 70 60

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(b) Relative tibial length

Figure 4.32: Tigara juvenile relative lower limb length indices with adult reference values Chapter 4. Results 115 age onward that are consistent with the range of adult values, with no clear pattern of change over the growing period. The one Ipiutak infant (99.1/75) present in the sample exhibits a brachial index elevated above adult Ipiutak values (Figure E.10a), consistent with the pattern observed in the Sadlermiut juvenile sample and falling at the upper end of that sample’s range. This individual’s crural index (Figure E.10b), however, falls just slightly above the range of adult Ipiutak values, and falls in the middle of the Sadlermiut infant group’s range of values. Tigara juveniles exhibit relative limb length index values (Figure 4.31, Figure 4.32) consistent in pattern with those observed in the Sadlermiut. Although they lack the infant component, juveniles from mid-childhood onward show values at or above adult Tigara values. The Ipiutak juvenile sample conforms to this pattern as well, but has very few individuals represented (Figure E.11, Figure E.12).

4.3.4 NW Hudson Bay and Greenland body proportions

The NW Hudson Bay and Greenland samples were too small to provide pattern information on body proportion development. Brachial index is represented by two NW Hudson Bay juveniles (Figure E.13a), and one Greenland juvenile (Figure E.14a). All three individuals fall within the range of their respective adult values. Crural index provides slightly more information: six NW Hudson Bay juveniles (Figure E.13b) and seven Greenland juveniles (Figure E.14b) are represented. Both samples exhibit juveniles with crural index values falling within their respective adult ranges. Two NW Hudson Bay juveniles fall slightly outside the range of adult variation, one slightly above and one slightly below. Relative limb length indices were not plotted for these two samples: at most one or two individuals were represented, if any. Chapter 4. Results 116

4.3.5 Body proportion development summary and comparison

Brachial and crural index values in the Sadlermiut share a similar pattern of development: the youngest individuals span a wide range of values, from around the mid-range of adult values to elevated above the adult range; older juveniles scatter within the range of adult values. These results can be compared with those from three other studies, listed in Table 3.5, as well as with the Denver data plotted in Figure 2.1.

Cowgill et al.(2012) found that brachial and crural indices remained relatively constant over the course of growth. The sample combined all Point Hope cultural periods, and was divided into four age categories: 0-2.9, 3-9.9, 10-17.9 years, and adults. Mean brachial index was found to be slightly higher in the youngest juvenile group and adults, while being slightly lower in the middle age groups. Mean crural index was slightly elevated and consistent across juvenile age groups, and slightly lower during adulthood. The use of age categories in the study masks any potential variation present in these categories, with regard to changing index values during growth — particularly during the ﬁrst age category of birth to 3 years. This category showed the greatest range of brachial and crural index values in the Sadlermiut juvenile sample. The age distribution for the youngest Point Hope age category is not provided by Cowgill et al.(2012), so it is not clear if it contains a full range of ages from birth to 3 years.

In their study of Jomon foragers, Temple et al.(2011) found that both brachial and crural indices were higher in infancy, declined during childhood, and increased in adolescence. Since the authors present their Jomon data in a scatterplot of index versus age, it is possible to make a more direct comparison to the present study. The sample had a few individuals who were under 1 year of age: three in brachial index, four in crural index. In both indices, these youngest individuals cover a wide range of values — in other words, they are not uniformly elevated above adult values, nor uniformly placing within adult values. This is consistent with the Sadlermiut results from the present study. The rise described in Jomon adolescence is represented by only two individuals in brachial Chapter 4. Results 117 index, and 3 individuals in crural index. Indeed, the rise in crural index in this later period is attributable to one individual who exhibits a relatively high value; the other two individuals maintain values similar to those seen in the childhood age range. In general, adult index values are attained by mid-childhood in the Jomon sample.

In their Egyptian sample, Bleuze et al.(2014) found that brachial and crural indices were greatest in fetal/perinate individuals, decreased during infancy and early childhood, and increased slightly during middle/late childhood. The ﬁnding of elevated index values early in life is consistent with the Sadlermiut results; the authors also showed that both the fetal/perinate and infant groups exhibit a range of values, again consistent with the present study. Moreover, brachial index was found to approach adult values by mid- childhood (over 5 years of age), while crural index reached adult values during the early childhood period (1-4.9 years). This pattern of attaining adult proportionality during childhood is consistent with the present results. Dividing the youngest individuals into separate groups for fetal/perinate and infant ages is extremely useful, as the range of values exhibited by each of the two Egyptian groups is strikingly diﬀerent. This is a major advantage of the Bleuze et al.(2014) study.

Figure 4.33 illustrates Sadlermiut brachial and crural indices by age, plotted against those of the Denver children. The patterns of brachial and crural index development exhibited by the Denver children are broadly similar to those exhibited by the Sadler- miut, but with some areas of disagreement. In brachial index (Figure 4.33a), the Denver children show elevated values in the earliest years, followed by a sharp decline and establishment of adult values by early childhood. This is consistent with the pattern seen in the Sadlermiut. The pattern of crural index development (Figure 4.33b is more heteroge- neous, however, and shows sex-speciﬁc diﬀerences. Denver males exhibit an initial drop from elevated values, and then a steady climb in values, with peaks at approximately 6 and 15 years of age. Denver females do exhibit elevated values at the earliest age, but only in relation to subsequent age points; they are not elevated as compared to male Chapter 4. Results 118 85

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Figure 4.33: Sadlermiut and Denver index values by age Chapter 4. Results 119 values. Subsequently, Denver females exhibit a minor drop in values in infancy, and then a steady increase in values until approximately 12 years of age. After this time, values drop sharply and approach those of early childhood. The general increase in crural values seen in the Denver children is not observed in the Sadlermiut sample. Overall, the Sadlermiut and Point Hope body proportionality data conﬁrm that adult- like proportionality is established relatively early in life. While this proportionality is not present in the earliest few years of life, it appears to be established by early to mid-childhood — i.e. 5 or 6 years of age. By examining individual data, rather than aggregating it into age groups, the full extent of variation in body proportion development can be visualized. The observed pattern of body proportion development is consistent with the normal maturity gradient of growth. In early infancy, distal limb elements are generally more advanced than proximal elements. As growth progresses, proximal limb elements experience an increase in growth relative to distal segments. This results in a downward shift of index values. Chapter 5

Discussion and Conclusions

In this chapter the results of the analyses are discussed within the context of the background information covered in Chapter 2. First, the hypotheses proposed in Chapter 1 will be assessed in the context of the study results. Second, Sadlermiut long bone growth will be discussed with reference to knowledge of diet, cultural practices, and disease in the Arctic, as well as in context of the comparative samples. Third, body proportionality will be discussed, drawing on relevant literature from Chapter 2. Lastly, limitations of the study and the non-use of certain data will be discussed.

5.1 Hypotheses

Hypothesis: Sadlermiut adult skeletons will show proportionality consistent with adaptation to low ambient temperatures

Sadlermiut adults exhibit mean brachial and crural index values that are consistent with those expected by adaptation to low ambient temperatures. Both males and females exhibit relatively shorter distal limb segments.

120 Chapter 5. Discussion and Conclusions 121

Hypothesis: Sadlermiut juveniles will exhibit characteristic skeletal proportions from early life onwards

Sadlermiut juveniles did exhibit characteristic skeletal proportions from early life onwards. While brachial and crural index values exhibited by infants were generally elevated above adult values, adult proportionality was established by early- to mid-childhood (i.e. approximately 5 or 6 years of age).

5.2 Linear growth

The comparison of absolute long bone lengths to the Denver means is consistent with expectations of short stature and limbs in Arctic populations. The results of the exploration of growth tempo, using the Humphrey method, show a range of variation in growth toward adult size, and the four groups studied here highlight the variation in growth outcomes found across the North American Arctic. The four long bones showed a general gradient of compromised growth in percentage attainment of adult size, with the radius and femur being least and most aﬀected, respectively. The humerus and tibia were intermediate to these. This result makes sense, since the bones with the largest ‘distance’ to grow, and thus highest growth rates, would be most compromised. By exhibiting the greatest growth deﬁcit, the femur stands in contrast to the fact that the tibia is generally singled out as a sensitive environmental marker (e.g. Pomeroy et al., 2012).

Sadlermiut individuals who died as juveniles exhibit a range of variation in their attainment of linear growth, but the predominant pattern is a faltering of growth as compared to the Denver tempo of growth. While the femur exhibited the greatest growth faltering, measures of percentage of adult size attained for all four long bones showed most individuals falling below the Denver mean. This suggests that most Sadlermiut juveniles who died in infancy either began life lagging in proportional growth, or fell behind very quickly after birth. Most Sadlermiut juveniles who died later in childhood had continued Chapter 5. Discussion and Conclusions 122 to falter in growth, indicating either insufficient, or a lack of, catch-up growth. The first 1000 days of life are a critical period for human growth and development, and growth faltering during this period has been found to have detrimental effects on adult size and health (Victora et al., 2008; Stein et al., 2010; Martorell and Zongrone, 2012; Ramakrishnan et al., 2014). This is also the only consistently demonstrated period of growth failure in populations in poor countries; after two years of age, there is some evidence of modest catch-up growth, but adult heights generally reflect this period of early growth faltering (Stein et al., 2010; Martorell and Zongrone, 2012). Worldwide evidence suggests that growth failure begins in utero, is pronounced in the first year of life, and continues with lesser force until around two years of age (Victora et al., 2010; Martorell and Zongrone, 2012). It is not clear if the Sadlermiut experienced an inter-generational cycle of growth failure — that is, girls who experienced poor nutrition in utero and childhood, who grew into stunted women and perhaps continued to face nutritional challenges, and who had a constrained capacity to support healthy fetal and infant growth (Ramakrishnan et al., 1999; Martorell and Zongrone, 2012; Mason et al., 2012).

What is apparent is that the Sadlermiut individuals who died as infants likely experienced growth faltering in utero, possibly as a maternal response to environmental constraints, such as nutritional challenges or infectious disease. Growth faltering in utero suggests the imposition of energy constraints, such that basic maternal requirements are prioritized over fetal growth. While this response could be viewed as a survival strategy, it can also be detrimental in the long term — on both the individual and the population level: compared to people who grow well, individuals who experience compromised growth are more susceptible to infections and experience greater risk of mortality (Victora et al., 2008).

It is also not immediately clear if the Sadlermiut individuals who died as juveniles represent the growth experiences of the general Sadlermiut population. Is growth faltering Chapter 5. Discussion and Conclusions 123 in infancy representative of the broader Sadlermiut life experience, or do these juveniles represent ‘special’ cases in the 500-plus year history of the population? Additional information is required to attempt to address this question. Scott(2009) assessed Harris line formation in a sample Sadlermiut adults, and found some evidence for fluctuations in growth. The validity of associating Harris lines with periods of childhood stress or ill health has been questioned, however, as they may reflect normal fluctuations in growth (Scott and Hoppa, 2015).

The growth outcomes exhibited by the Sadlermiut juveniles are not consistent across the Arctic comparative groups. Indeed, the youngest Point Hope juveniles in this sample exhibited no evidence of growth faltering as compared to the Denver tempo of growth. Those older than 4 years of age exhibited a wide range of growth outcomes, from significant lagging to acceleration compared to the Denver mean. By looking at LEH, Dabbs (2011) found that the Tigara experienced stress episodes from approximately 3 to 6.5 years of age, and this could be seen as consistent with the linear growth results. In all four long bones, the Tigara individuals with dental ages of approximately 4 to 7 years exhibit growth at least one standard deviation below the Denver mean. Beyond that age, they show a range of variation in their growth. The Ipiutak and Tigara samples both have juveniles who died but either did not experience any growth faltering; or, for the older juveniles, experienced suﬃcient catch-up growth to negate any earlier growth faltering. While the pattern is most apparent in the femur and tibia, it could also be an artifact of the small number of individuals under 6 years of age.

The small number of Point Hope infants included in this study complicates comparisons between the Sadlermiut and Point Hope: while the number of Sadlermiut infants is fully documented here, observations were not recorded on Point Hope infants who were excluded due to poor preservation or lack of dentition. While it cannot be stated with certainty that relatively more Point Hope individuals survived to mid-childhood and later, from overall observation Point Hope did not match the Sadlermiut in terms Chapter 5. Discussion and Conclusions 124 of proportion of infants in the collection. It must also be remembered that all samples included in this study span time periods of approximately 500 years or more. The pattern of adult size attainment exhibited by these Point Hope samples could therefore be consistent with a population that experienced periods of both resource abundance and scarcity. Samples sizes for the NW Hudson Bay and Greenland groups were small, but generally individuals in the former group fell at or below the Denver mean (similar to the Sadlermiut), while those in the latter ranged above and below the mean (similar to Point Hope).

While the exact nature of the stress experienced by the Sadlermiut juveniles cannot be elucidated in this study, some aspect of life was contributing to growth faltering. A variety of factors could have operated on the Sadlermiut to bring about these growth outcomes. The seasonality of their diet could have contributed to maternal and fetal nutritional stress. Coltrain and colleages (Coltrain et al., 2004; Coltrain, 2009) reported that the Sadlermiut were heavily reliant on high trophic level marine taxa, and that their diet showed the least within-group variance as compared to the Kamarvik and Silumiut (NW Hudson Bay). This extremely narrow resource base would have had implications for nutritional status. The Sadlermiut exhibited increased reliance on these marine foods over the timespan represented in the study, and a signiﬁcant increase of δ13C values over time. This later component may indicate that periods of extreme resource stress became less frequent over the time period covered by the study. This timing accords with the onset of the Little Ice Age, which resulted in increased sea ice cover, and probable increases in the encounter rate of ringed seals due to the heavy ice cover (Coltrain, 2009).

The added burden of exposure to zoonotic infection/disease could have inﬂicted additional stress. Both acute and chronic episodes of infection can impair growth, especially in the cases of diarrheal pathogens and gut helminth infection (Stephensen, 1999). Para- sitic infections are not uncommon in Canadian Arctic communities (Goyette et al., 2014), and parasitic infections such as trichinellosis and anisakis can cause diarrhea, vomiting, Chapter 5. Discussion and Conclusions 125 and malnutrition (La Rosa et al., 2006; Gottstein et al., 2009).

Further analyses are required to understand these growth outcomes: stable isotope analysis could provide information on juvenile diet, which is not covered by Coltrain and colleagues’ (2004; 2009) analyses; and closer examination of LEH could provide a picture of stress episode prevalence and duration, along the lines of Temple and colleagues’ (2013) work on Jomon period Hokkaido foragers.

5.3 Body proportion development

All four samples in this study exhibited adult body proportion indices that are consistent with those expected by adaptation to low ambient temperatures. Sadlermiut infants exhibit brachial and crural indices scoring above adult values, although crural index values are relatively more elevated than those of brachial index. Both indices also show a moderately-strong decline in index value with age during infancy. This elevation of index values at the youngest ages, and subsequent decline with age, is consistent with the expected maturity gradient of growth. Elevated brachial and crural index values indicate relatively longer distal limb segments, and this is consistent with the distal limb elements being generally more advanced than proximal elements (Sinclair, 1987; Tanner, 1989).

The ﬁnding of elevated brachial and crural indices in early life, and subsequent decline to the adult range of values during childhood, is consistent with the results of both Temple et al.(2011) and Bleuze et al.(2014), but not those of Cowgill et al.(2012), where indices were found to remain stable throughout childhood. The latter study combined individuals into age categories, however, and the youngest category (0-3 years) would be too broad to capture the full range of variation in this period. While Temple et al.(2011) had only a few individual under 1 year of age, Bleuze et al.(2014) showed that fetal/perinate and infant individuals exhibited a range of index values. Both studies also demonstrated that Chapter 5. Discussion and Conclusions 126 brachial and crural indices reached adult values by mid-childhood, consistent with the results of this study.

The relative limb length indices are also consistent with normal growth patterns, in which trunk length is one of the last dimensions to accelerate during the adolescent growth spurt (Sinclair, 1987). Relative limb length indices exhibit low values during infancy, elevated values during childhood, and a return to lower values in adolescence (representing ﬁrst an increase in long bone length, followed by an increase in trunk length). These particular indices, however, assume that the relative contribution of intervertebral discs to skeletal trunk height remains the same throughout growth. It is not clear that this assumption is warranted, and these indices should be interpreted cautiously.

5.4 Broader Implications

The results of the present study, together with those of Temple et al.(2011) and Bleuze et al.(2014), suggest a consistent, underlying pattern of growth with regard to the timing of body proportion development — regardless of latitude, climate, or ultimate adult proportionality. The establishment of adult proportionality by mid-childhood in Arctic, temperate, and warm climate samples suggests a shared genetic basis, rather than an in situ adaptation by each group. Moreover, this suggests establishment of the pattern early in the history of modern human dispersal around the world. The relationship between climate and hominin morphological variation and evolution has been the subject of considerable interest, although the focus has largely been on adult body form. That Neanderthal body morphology was shaped by natural selection, acting to optimize thermoregulation in glacial Pleistocene Europe, has been argued by Trinkaus (1981), Ruff(1991, 2010), and Holliday(1997b). Thermoregulatory optizimation has also been used by several authors, such as Ruff(1991) and Weaver(2003) to explain body Chapter 5. Discussion and Conclusions 127 shape in central African hominins (e.g. Homo ergaster)(Roseman and Auerbach, 2015). An adaptationist perspective has been used to explain body proportion patterning in human fossils, but the exact evolutionary processes that contribute to this patterning have not been identified with certainty (Roseman and Auerbach, 2015).

Also of note in the present study is the finding that adult proportionality had been attained by most of the older juveniles in the Sadlermiut sample, despite a lag in linear growth. This finding is consistent with the concept that body shape is much more resistant to environmental disturbance than body size (e.g. Eveleth and Tanner, 1976). Ruff (1993) suggests that it is sensible, from an evolutionary perspective, for certain physical characteristics to be relatively immune to nutritional deficiencies since they are likely to be short-term relative to climate change. For example, as long as body breadth remains constant, the ratio of body surface area to mass is unaffected by changes in stature (Ruff, 1993). Moreover, body breadth and sitting height may both be constrained in size due to the vital organs of the chest and abdomen, thus causing limb growth to be preferentially sacrificed in the face of nutritional constraint (Vercellotti and Piperata, 2012). The fact that most older Sadlermiut juveniles exhibit adult proportionality, despite experiencing faltering growth, suggests a coordinated effort within the limbs to maintain appropriate proportionality. In turn, this suggests that proportionality plays an important part in survival. This line of reasoning assumes, however, that climate is a primary factor is determining body proportionality.

Returning to the ‘emerging perspectives’ on ecogeographic proportioning, it is clear that body proportionality is a more complex phenomenon than simply a response to environmental pressure. Indeed, the findings of Roseman and Auerbach(2015) call into question the relative importance of clinally distributed natural selection. Population structure, shaped by random genetic drift, mutation, and gene flow over human population history, plays a role in structuring among-group morphological differences. Roseman and Auerbach(2015) emphasize, however, that climate-motivated natural selection does Chapter 5. Discussion and Conclusions 128 play a role in shaping human body form, since they found that body form is not solely the product of random genetic drift and gene flow. The authors suggest that selection may be appreciably strong only at the extremes of latitude and/or climate, and that other evolutionary forces may predominate outside of those extremes. Certainly the Sadler- miut, as well as the comparative samples in this study, lived at the extremes of both latitude and climate.

Despite the uncertainty regarding the underlying evolutionary or environmental mechanisms, there are clear empirical ecogeographic patterns in human body shape (Ruﬀ, 1994, 2002; Holliday, 1997b; Roseman and Auerbach, 2015). The Sadlermiut exhibit a pattern of relatively short distal limb elements, although variation does exist within the adult sample. That this pattern of relatively short distal limbs is maintained, even in the face of growth impairment, strongly underscores its importance. Moreover, the time span of the samples in this study (approximately 800 years) suggests a consistency of patterning well into the past. It cannot be stated conclusively that climate is the primary or sole driver of proportionality is these Arctic samples, but it also cannot be ruled out as a major inﬂuence.

The question of whether Arctic populations exhibit cold-adapted morphology is not limited to discussions of past population history and adaptation. Inuit populations are experiencing a significant transition in diet and health, with high prevalence of obesity and associated metabolic disease risk (Galloway et al., 2010; Munch-Andersen et al., 2013; Hopkins et al., 2015). The metrics used to calculate obesity cut-offs are, potentially, influenced by relative differences in body proportionality. As such, body proportionality in Arctic populations must be considered. Galloway et al.(2011) found that there was no linear relationship between sitting height ratio and BMI, and suggested that factors other than a relatively longer torso may confound the interpretation of BMI in Inuit populations. Of note, the study found that many underweight women were misclassified when using an uncorrected BMI. This could have serious, negative consequences for Chapter 5. Discussion and Conclusions 129 women of childbearing age, in terms of both maternal health and fetal development (Galloway et al., 2011).

5.5 Notes on limitations and unexplored data

Attention must be paid to various limitations within the study. One limitation involves the use of a modern reference sample to explore growth in past populations. The Denver growth data (Maresh, 1970) has been shown to conform to the WHO Multicenter Growth Reference Study standard, and therefore to reﬂect a normal pattern of human growth (Schillaci et al., 2012); the present study assumes that this general pattern of human growth can be extended to past populations. A conclusion of faltering growth relative to the reference, or an apparent lack of catch-up growth, could also be related to a growth pattern with delayed attainment of adult stature — i.e. a slower tempo of growth.

A second area for consideration is the lack of Sadlermiut individuals who died aged approximately 2 to 5 years. This gap in the age-at-death distribution of the Sadlermiut juveniles perhaps reﬂects a period in which individuals have survived infancy and weaning, and continue to beneﬁt from close parental attention and a well-developed immune system. They have not yet transitioned to ‘adult’ activities, nor experienced the increased risk-taking associated with later childhood and adolescence. From this perspective, it is not a limitation of the data. It does, however, require a more cautious interpretation of the body proportion development data. While juvenile Sadlermiut individuals generally attained adult proportionality by the end of this gap, it is not clear when, within the 2 to 5 year age range, the pattern is established.

A minor issue to highlight is the use of the tibial ‘Fully’ measurement. Due to an oversight by this author, this was the only tibial measure taken on adults. Maximum tibial length (i.e. including the tibial spine) was not taken. The Denver data uses maximum tibial length, and therefore includes the tibial spine. Percentage of adult size attained Chapter 5. Discussion and Conclusions 130 would theoretically be slightly advanced in the study samples, since their adult measurements do not include the tibial spine. In reality, this leads to an approximately 5 mm diﬀerence in mean adult tibial length. For example, Auerbach(2007) lists Sadlermiut mean male and female left maximum tibial lengths as 349.28 mm and 324.15 mm, respectively. The average of these two values is 336.72 mm, while the average Sadlermiut tibial Fully length in the present study is 330.3 mm. The tibial spine therefore comprises approximately 2% of total length, and its incorporation results in a decrease in residual values of about 0.4% in infants, to about 1% in adolescents. Since a slight downward shift is applied across all individuals, this does not eﬀect the overall pattern of growth tempo. It only means that the results presented in the tibial growth plots (Figure 4.10, Figure 4.12b, Figure 4.14b, Figure 4.18b, Figure 4.16b) slightly overestimate growth. In addition to maximum iliac breadth, which was only used for dental age regression, several measurements were taken in the course of data collection but not used in the present analysis. Distal femoral medio-lateral maximum breadth and femoral midshaft circumference were collected as a way to incorporate body mass data, but this was not explored further. Iliac height was also collected, but not used. These data are available for future use. Chapter 5. Discussion and Conclusions 131

5.6 Conclusions

This research set out to explore patterns of growth and body proportion development in the Sadlermiut Inuit, and other past North American Arctic groups. The analysis of growth showed that the Sadlermiut who died as juveniles were generally faltering in growth as compared to a modern North American sample, even taking into account their short adult stature. This growth faltering was apparent from the earliest ages, and extended throughout childhood and adolescence. In contrast, Point Hope juveniles experienced a range of growth outcomes, exhibiting growth that was in line with that of the reference sample, as well as both faltering and acceleration of growth compared to the reference. While it is not possible to determine the underlying cause or causes of the growth faltering experienced by the Sadlermiut juveniles, it is clear that several factors could have contributed to this outcome, including a very narrow resource base, and exposure to zoonotic infection or disease. These factors would also be present to varying degrees in the comparative samples, however. The Tigara of Point Hope are known to have engaged in bowhead whale hunting, thus broadening their resrouce base, while the Sadlermiut do not appear to have done so. Further study is required to understand early life conditions for the the Sadlermiut, and indeed for groups who lived throughout the Arctic, and hopefully this thesis will serve as a jumping-oﬀ point for future work.

The analysis of body proportion development showed that population-speciﬁc body proportions develop by mid-childhood. While infants show a range of body proportion index values, generally elevated above adult values, juveniles from approximately 5 years of age exhibit brachial and crural index values consistent with those of adults. This reﬂects the normal maturity gradient of growth, in which distal limb elements are generally more advanced than proximal elements in infancy. As growth progresses, proportions shift along expected lines: proximal limb segments experience an increase in growth velocity relative to distal segments, and subsequently shift index values downward.

Adult proportionality was found to be established in most older Sadlermiut juveniles, Chapter 5. Discussion and Conclusions 132 despite a lag in linear growth. Consistent with the growth literature (e.g. Eveleth and Tanner(1976)), this illustrates that linear growth is more environmentally labile than body proportionality. While Sadlermiut linear growth reflects the immediate environment experienced by those juveniles, their body proportions reflect a more deeply entrenched growth pattern. The establishment of adult proportionality in childhood has previously been found in two past population samples: a warm-climate Egyptian sample (Bleuze et al., 2014) and a temperate/cool-climate Japanese sample (Temple et al., 2011). Taken together with the results of the present study, the data suggest a consistent pattern of growth with regard to proportionality, regardless of latitude, climate, and ultimate adult proportionality. Further, the consistency of timing among the disparate groups suggests an underlying genetic basis to body proportion development, rather than an in situ, plastic response by each group. This has important implications for the evolution of growth patterns in modern humans. The relative contribution of climate to the shaping of human body proportions, however, is unclear. While it certainly has some effect, climate is no longer considered the sole factor influencing human body proportionality. Bibliography

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Greenland Sample Information

Table A.1: List of Greenland sites and relevant references (where available)

Map Site Name General Region in # in Reference(s) # Greenland Sample

1 Godthaab/ Erssa 1X/7 Low Arctic 1 Southwest 2 Uunartoq/ Uunartoq Sub Arctic South 1 Mathiassen(1936), Fjord omr. Mathiassen and Holtved(1936) 3 Tuttutuup Isua/ Sub Arctic South 1 Mathiassen(1936), Tuttutooq omr. Mathiassen and Holtved(1936) 4 Uummannalik omr./ Low Arctic 2 Mathiassen(1936), Illutalik III Northwest Mathiassen and Holtved(1936) Inukasimiit/ Inukasigmiut ? 1 5 Quegerterssaq (sp? Low Arctic 2 Mathiassen(1934)? modern Qeqertarsuaq) Northwest 6 Ruinnaesset/ Akorninap Low Arctic 9 Mathiassen(1936) Kangerlua Southeast 7 Suukkersit/ Sermilik omr. Low Arctic 3 Mathiassen(1936)? Southeast 8 Dødemandsbugten/ High Arctic 3 Larsen(1934) Clavering Island Northeast 9 Scoresby Land/ High Arctic 1 Kepel(1986), Kangertertivarmiit/ Northeast Mobjerg(1988) Sydkap

161 Appendix A. Greenland Sample Information 162

Map Site Name General Region in # in Reference(s) # Greenland Sample

Vestgrnland West Greenland 2 10 Upernivik Low Arctic 1 Mathiassen(1933) Northwest 11 Umernersuk/ Low Arctic 1 Kangaamiut omr. Southwest 12 Ameralla/ Eqaluit Ilorliit Low Arctic 2 Southwest 13 Avaqqat Kangerluat/ Low Arctic 1 Mathiassen(1936) Avaqqat’s south fjord Southeast 14 Eqalunnguit/ Tasiusaq Sub Arctic South 1 15 Dove Bugt omr./ Snenaes High Arctic 1 Thostrup(1911), Northeast Grønnow and Fog Jensen(2003) 16 Nuussuaq omr./ Nuugaaq Low Arctic 2 Mathiassen(1934) Northwest 17 Ilimanaq/ Claushavn Low Arctic 1 Northwest 18 Ikerasaarsuk/ Illut Low Arctic 1 Northwest 19 Godthabsfjorden Low Arctic 1 Berglund(2003) Southwest 20 Asummiut Low Arctic 1 Grummesgaard- Southwest Nielsen (1997) 6 Ruinnaesset/ Skjoldungen Low Arctic 1 Mathiassen(1936) omr. Southeast 21 Ammassalik Fjord/ Low Arctic 1 Holm and Garde Kangaartik Southeast (1889a,b), Mathiassen(1933) Appendix A. Greenland Sample Information 163

Figure A.1: Greenland site locations (numbers correspond to those in Table A.1 Appendix B

Dental Age Estimation

B.1 Dental age estimation examples

Table B.1 and Table B.2 present examples of the age estimation technique using AlQah- tani et al.(2010). Age categories are in fetal weeks (wks), months (mos) and years (yrs). Fetal weeks were converted to negative values, with birth equal to zero: 30 fetal weeks = -10 wks = -2.5 mos = -0.20833 yrs 34 fetal weeks = - 6 wks = -1.5 mos = -0.125 yrs 38 fetal weeks = -2 wks = -0.5 mos = -0.04167 yrs Birth = 0

B.2 Intra-observer error

Table B.3 presents the original and repeat dental age estimates for nineteen Sadlermiut juveniles.

164 Appendix B. Dental Age Estimation 165

Table B.1: Example 1: XIV-C:120 (infant)

Tooth Development Age Categories Average Average Stage Age (mos) Age (yrs) 3 MaxRm2 Cr 4 4.5, 7.5, 10.5 mos 7.5 0.625 MandRi2 Ri 1.5, 4.5, 7.5, 10.5 mos 6.0 0.5 MandLi2 Ri 1.5, 4.5, 7.5, 10.5 mos 6.0 0.5 MandRc Ri 1.5 yrs 18 1.5 MandLc Ri 1.5 yrs 18 1.5 MandRm1 Ri 7.5, 10.5 mos, 1.5 yrs 12 1.0 MandLm1 Ri 7.5, 10.5 mos, 1.5 yrs 12 1.0 3 MandRm2 Cr 4 4.5, 7.5, 10.5 mos, 1.5 yrs 10.125 0.84375 3 MandLm2 Cr 4 4.5, 7.5, 10.5 mos, 1.5 yrs 10.125 0.84375 Average 11.08333 0.92361

Table B.2: Example 2: XIV-C:281 (perinate)

Tooth Development Age Categories Average Average Stage Age (mos) Age (yrs) 3 MaxRi1 Cr 4 30, 34, 38 wks, birth, 1.5 mo -0.60 -0.05 3 MaxLi1 Cr 4 30, 34, 38 wks, birth, 1.5 mo -0.60 -0.05 3 MaxRi2 Cr 4 30, 34, 38 wks, birth, 1.5 mo -0.60 -0.05 3 MaxLi2 Cr 4 30, 34, 38 wks, birth, 1.5 mo -0.60 -0.05 1 MandRm1 Cr 2 38 wks, birth, 1.5, 4.5 mos 1.375 0.11458 1 MandLm1 Cr 2 38 wks, birth, 1.5, 4.5 mos 1.375 0.11458 MandLm2 Cco 30, 34, 38 wks, birth, 1.5 mos -0.60 -0.05

Average -0.03571 -0.000298

B.3 Dental age regression

Figure B.1 to Figure B.5 show testing of regression models. Table B.4 lists the ordinary least squares regression equations for all iliac breadths, iliac breadth under 60 mm, and iliac breadth over 80 mm. Appendix B. Dental Age Estimation 166

Table B.3: Repeated Sadlermiut dental age estimates (yrs)

Individual Estimate 1 Estimate 2 Diﬀerence XIV-C:077 1.406 1.175 -0.231 XIV-C:078 5.571 5.714 0.143 XIV-C:085 0.542 0.458 -0.084 XIV-C:118 5.600 5.667 0.067 XIV-C:121 0.395 0.406 0.011 XIV-C:123 0.484 0.569 0.085 XIV-C:162-1 0.453 0.438 -0.016 XIV-C:188 9.083 9.500 0.417 XIV-C:203 1.317 1.269 -0.048 XIV-C:206 0.622 0.717 0.095 XIV-C:223 1.113 1.113 0.000 XIV-C:224 1.091 1.080 -0.011 XIV-C:231 9.333 9.600 0.267 XIV-C:232 8.278 7.833 -0.445 XIV-C:279 1.209 1.257 0.048 XIV-C:281 -0.003 0.033 0.036 XIV-C:740 0.611 0.611 0.000 XIV-C:768 11.333 11.167 -0.166 XIV-C:798-1 2.833 3.000 0.167

Table B.4: Ordinary least squares regression equations

Iliac Breadth # Individuals R2 adj. Equation Group All 49 0.97 0.000002454x3 + 0.001896695x2 0.075077822x + 0.699808332 <60 mm 29 0.81 0.054864x − 1.542549 >80 mm 20 0.83 0.23595x − 13.54829 Appendix B. Dental Age Estimation 167

● 20

● ●

●

15 ●

●

● ●

●

10 ● ● ● ●

● ● Dental Age (yrs)

● ●● 5

● ● ● ●●● ●● ● ●● ● ● ● ● ●●● ● ●● ● ●● ● ● 0

20 40 60 80 100 120 140

Iliac Breadth (mm)

Figure B.1: Cubic polynomial regression equation (all iliac breadths)

● ●

2 ●

2 ● ● ●

● ●

● ● ● ● 1 1

● ●

● ● ● ●●● ●●● ● ●●● ●● ● ●●● ●● ● ●●●●● 0 ● ●● 0 ●● ● ●●●●● ● ●●●●●●●● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ●●●● ● ● ● ● Standardized Residuals Standardized Residuals Standardized −1 −1 ● ●

● ● ● ● −2 −2

● ● ● ●

−1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.5 −2 −1 0 1 2

Standardized Predicted Values Normal Scores

(a) Standardized Residuals Plot (b) Normal Q-Q Plot

Figure B.2: Cubic model evaluation of ﬁt Appendix B. Dental Age Estimation 168 2.0

● 20 ●

1.5 ● ● ●

●

● ● ● ● ● ● ● ● ● ●

● 15 ●

1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● Dental Age (yrs) Dental Age (yrs)

● 10 ● ● ● ● ●

● ●

● ● ● 0.0 ●● 5 −0.5

25 30 35 40 45 50 55 80 90 100 110 120 130 140

Iliac Breadth (mm) Iliac Breadth (mm)

(a) <60 mm (b) >80 mm

Figure B.3: OLS regression of dental age on iliac breadth

● ●

● ● ● ●

● ● 1 1

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 0 0 ● ● ● ●

● ● ● ●

● ● ●

Standardized Residuals Standardized ● Residuals Standardized ● ● −1 −1 ● ● ● ●

● ●

● ● −2 ● ● −2

−2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 −2 −1 0 1 2

Standardized Predicted Values Normal Scores

(a) Standardized Residuals Plot (b) Normal Q-Q Plot

Figure B.4: <60 mm equation evaluation of ﬁt Appendix B. Dental Age Estimation 169

● ●

● 1.5 ● 1.5

● ●

1.0 ● ● ● ● 1.0 ● ●

● ● ● ● 0.5 0.5

● ●

● ● ● ● 0.0 0.0 ● ● ● ● ● ●

● ● −0.5 −0.5 Standardized Residuals Standardized Residuals Standardized

● ●

● −1.0 −1.0 ●

● ● −1.5 ● −1.5 ● ● ●

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 −2 −1 0 1 2

Standardized Predicted Values Normal Scores

(a) Standardized Residuals Plot (b) Normal Q-Q Plot

Figure B.5: >80 mm equation evaluation of ﬁt Appendix B. Dental Age Estimation 170

Table B.5: Sadlermiut individuals with regression-based age estimations

Individual Iliac Breadth Regression Age (mm) Estimation (yrs) XIV-C:083 35.50 0.405 XIV-C:106 36.04 0.435 XIV-C:122 36.50 0.460 XIV-C:158 140.87 19.690 XIV-C:161 34.00 0.323 XIV-C:162-2 36.00 0.433 XIV-C:198 133.00 17.833 XIV-C:201 49.00 1.146 XIV-C:204 34.50 0.350 XIV-C:205 36.00 0.433 XIV-C:209-1 34.50 0.350 XIV-C:210-2 33.00 0.268 XIV-C:211 32.00 0.213 XIV-C:212 39.00 0.597 XIV-C:225 38.00 0.542 XIV-C:226 41.50 0.734 XIV-C:228 31.50 0.186 XIV-C:236 30.00 0.103 XIV-C:250-3 102.50 10.637 XIV-C:250-5 85.00 6.507 XIV-C:280 38.50 0.570 XIV-C:285 33.00 0.268 XIV-C:286-1 37.50 0.515 XIV-C:760-2 26.50 -0.089 XIV-C:798-3 114.00 13.350 Appendix C

Additional Plots — Sample Distributions 40 30 20 Number of Individuals 10 0

0 5 10 15 20 25

Dental Age

Figure C.1: Juvenile sample distribution by dental age (regression-aged individuals removed)

171 Appendix C. Additional Plots — Sample Distributions 172

Sadlermiut Point Hope 40 15 30 10 20 5 10 Number of Individuals Number of Individuals 0 0

0 5 10 15 20 25 0 5 10 15 20 25

Dental Age Dental Age

NW Hudson Bay Greenland 5 4 4 3 3 2 2 1 1 Number of Individuals Number of Individuals 0 0

0 5 10 15 20 25 0 5 10 15 20 25

Dental Age Dental Age

Figure C.2: Juvenile sample distribution by dental age and population (regression-aged individuals removed) Appendix C. Additional Plots — Sample Distributions 173 4 14 12 3 10 8 2 6 Number of Individuals Number of Individuals 4 1 2 0 0

0 5 10 15 20 25 0 5 10 15 20 25

Dental Age (yrs) Dental Age (yrs)

(a) Ipiutak (b) Tigara

Figure C.3: Point Hope sub-distributions by dental age Appendix D

Additional Plots — Investigation of Growth 350 250 300

●

● 200 ● ● ● ● 250 ● ● ● ● ● ● ● ● ● ●

●

● 150 ● ● ● ● ● ● ● 200 ● ● ● ● ●● ● ●

● ● ● ● ● ● ● ● ● ● 150 100

Radius Diaphyseal Length (mm) Radius Diaphyseal ● Humerus Diaphyseal Length (mm) Humerus Diaphyseal ●

● ● ● Sadlermiut ● ● Sadlermiut ● ●● Point Hope ●●●● Point Hope ●● ● ●●● ● ●●● ● NW Hudson Bay ●● ● NW Hudson Bay 100 ● ● ●● ●● ●●●● ●● ● ● ● Greenland ● Greenland ●● ● ●● 50 ●●●● Denver Mean Denver Mean ● ●●● ● Denver +/− 1SD Denver +/− 1SD ● Denver +/− 2SD Denver +/− 2SD 50

0 5 10 15 20 0 5 10 15 20

Dental Age (yrs) Dental Age (yrs)

(a) Humerus (b) Radius

Figure D.1: Upper limb bone lengths by dental age, all samples, Sadlermiut regression- aged individuals excluded

174 Appendix D. Additional Plots — Investigation of Growth 175 500 400

400 ● ● ● ●

● ● ● ● ● ● ● ● 300 ● ● ●

● ● ● ● ● 300 ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ●

● 200 ● ● ● ● ● ● ● 200 ●

● Length (mm) Tibia Diaphyseal Femur Diaphyseal Length (mm) Diaphyseal Femur ● ● ●

● Sadlermiut ● ● Sadlermiut ● Point Hope Point Hope ● ● ● ● ● NW Hudson Bay ● NW Hudson Bay ●●●● ●●● ● ● ● ●● ● ●● ● Greenland 100 ●●● Greenland ● ● ● ●● 100 ●●●● Denver Mean ●●● Denver Mean ●● ●● ● ●●●● ● ● Denver +/− 1SD ●● Denver +/− 1SD ● ● ●● Denver +/− 2SD ● Denver +/− 2SD

0 5 10 15 20 0 5 10 15 20

Dental Age (yrs) Dental Age (yrs)

(a) Femur (b) Tibia

Figure D.2: Lower limb bone lengths by dental age, all samples, Sadlermiut regression- aged individuals excluded

● Sadlermiut 15 ● Sadlermiut Point Hope Point Hope ● NW Hudson Bay ● NW Hudson Bay Greenland Greenland

80 Denver Mean Denver Mean

Denver +/− 1SD 10 Denver +/− 1SD Denver +/− 2SD Denver +/− 2SD

● ● ● 5 ●

●● 60 ● ● ● ●●

● ● 0 ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●

●● 40 ● −5 ● ● ● ● Humerus Attained % Adult Size ● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●● ●

●● −10 ● ●●●● ● ● ● ● ● Residual from Denver in % Adult Mean Humerus Length Residual from Denver

20 ● −15

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Dental Age (yrs) Dental Age (yrs)

(a) % Adult Size Attained (b) Residuals from Denver Values

Figure D.3: Growth of humerus, all samples, Sadlermiut regression-aged individuals excluded Appendix D. Additional Plots — Investigation of Growth 176

● Sadlermiut 15 Point Hope ● NW Hudson Bay Greenland

80 Denver Mean

Denver +/− 1SD 10 Denver +/− 2SD

● ● ● 5

● ●

● ● 60 ● ● ●● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40 −5 ● Radius % Adult Size Attained Radius % Adult Size ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● Sadlermiut ● ●●● ● ● ● Point Hope ● ● ● −10 ● ● NW Hudson Bay Greenland Residual from Denver in % Adult Mean Radius Length Residual from Denver Denver Mean

20 Denver +/− 1SD Denver +/− 2SD −15

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Dental Age (yrs) Dental Age (yrs)

(a) % Adult Size Attained (b) Residuals from Denver Values

Figure D.4: Growth of radius, all samples, Sadlermiut regression-aged individuals excluded

● Sadlermiut 15 ● Sadlermiut Point Hope Point Hope ● NW Hudson Bay ● NW Hudson Bay Greenland Greenland

80 Denver Mean ● Denver Mean

Denver +/− 1SD 10 Denver +/− 1SD Denver +/− 2SD Denver +/− 2SD ●

● ● 5

●● ●

60 ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 40 −5 ● ● ● Femur % Adult Size Attained % Adult Size Femur ● ● ● ● ● ● ● ● ● ● ●●● ●●● −10 ● ● ●●● ● ● ● ● Residual from Denver in % Adult Mean Femur Length in % Adult Mean Femur Residual from Denver ●● ● ● ●● ●

20 ● ● ● ● −15

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Dental Age (yrs) Age (yrs)

(a) % Adult Size Attained (b) Residuals from Denver Values

Figure D.5: Growth of femur, all samples, Sadlermiut regression-aged individuals excluded Appendix D. Additional Plots — Investigation of Growth 177

● Sadlermiut 20 ● Sadlermiut Point Hope Point Hope ● NW Hudson Bay ● NW Hudson Bay Greenland Greenland

80 Denver Mean Denver Mean Denver +/− 1SD Denver +/− 1SD

Denver +/− 2SD ● Denver +/− 2SD 10

● ●

60 ●

● ● ● ● ● ● ● ●● 0 ● ●● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●

40 ●

Tibia % Adult Size Attained Tibia % Adult Size ● ●

● ● −10 ●

● ● ● ● ● ●●●●● ●● ● ● ● ●●●

●● in % Adult Mean Tibia Length Residual from Denver ●●●● ● ● ● ● 20 −20

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Dental Age (yrs) Dental Age (yrs)

(a) % Adult Size Attained (b) Residuals from Denver Values

Figure D.6: Growth of tibia, all samples, Sadlermiut regression-aged individuals excluded Appendix E

Additional Plots — Investigation of Body Proportion Development 20 20 15 15 10 10 Number of Individuals Number of Individuals 5 5 0 0

66 68 70 72 74 76 78 74 76 78 80 82 84

Brachial Index Crural Index

(a) Brachial (b) Crural

Figure E.1: Sadlermiut adult limb proportion indices, using maximum lengths

178 Appendix E. Additional Plots — Investigation of Body Proportion Development179 15 20 15 10 10 Number of Individuals Number of Individuals 5 5 0 0

68 70 72 74 76 78 80 82 74 76 78 80 82 84 86

Brachial Index Crural Index

(a) Brachial (b) Crural

Figure E.2: Point Hope adult limb proportion indices, using maximum lengths 5 5 4 4 3 3 2 2 Number of Individuals Number of Individuals 1 1 0 0

66 68 70 72 74 76 78 74 76 78 80 82 84 86

Brachial Index Crural Index

(a) Brachial (b) Crural

Figure E.3: NW Hudson Bay adult limb proportion indices, using maximum lengths Appendix E. Additional Plots — Investigation of Body Proportion Development180 3.0 2.0 2.5 1.5 2.0 1.5 1.0 Number of Individuals Number of Individuals 1.0 0.5 0.5 0.0 0.0

68 70 72 74 76 78 76 78 80 82 84 86

Brachial Index Crural Index

(a) Brachial (b) Crural

Figure E.4: Greenland adult limb proportion indices, using maximum lengths 85 90

80 ● 85 ● 75 80 Crural Index (TL:FBL) Crural Index

Brachial Index (RL:HL) Index Brachial ● 70 75

● 65 70

F M F M F M F M F M F M F M F M F M F M

Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland

(a) Brachial (b) Crural

Figure E.5: Adult limb proportion indices, using maximum lengths Appendix E. Additional Plots — Investigation of Body Proportion Development181 75 80 100

70 ● ● ● ● ● 95 75 ● ● ● 65 90 ● 70 60 85 65

● 80 55 Humerus:STH Humerus diaphyseal:STH Humerus:STHnoS 60 75

50 ● 55 70 ●

45 ● 50 65 F M F M F M F M F M F M F M F M F M F M F M F M F M F M F M Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland (b) Diaphyseal length with (a) Max length with sacrum (c) Max length no sacrum sacrum

Figure E.6: Adult relative humeral lengths, all samples 55 60 75

●

● 70 ●

● 50 55

● ●

65 ● ● 50 ● 45 60 Radius:STH 45 Radius diaphyseal:STH Radius:STHnoS 55 40 40

50 ● ● 35 35 45 F M F M F M F M F M F M F M F M F M F M F M F M F M F M F M Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland (b) Diaphyseal length with (a) Max length with sacrum (c) Max length no sacrum sacrum

Figure E.7: Adult relative radial lengths, all samples Appendix E. Additional Plots — Investigation of Body Proportion Development182 100 110 140 ●

● 95

105 ●

● ● 130 90

100 ● 95 85 120

● FBCL:STH 90 ● FBCL diaphyseal:STH 80 FBCL:STHnoS 110 85 75 80 100

70 ● 75 F M F M F M F M F M F M F M F M F M F M F M F M F M F M F M Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland (b) Diaphyseal length with (a) Max length with sacrum (c) Max length no sacrum sacrum

Figure E.8: Adult relative femoral lengths, all samples 80 85 110

● ● ● 75 ● ● 105 80

● ● 70

100 ● 75 95 65 Tibia:STH 90 Tibia diaphyseal:STH Tibia:STHnoS 70 60 85 55 65 80 50 60 75 F M F M F M F M F M F M F M F M F M F M F M F M F M F M F M Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland Sadlermiut Ipiutak Tigara NWHB Greenland (b) Diaphyseal length with (a) Max length with sacrum (c) Max length no sacrum sacrum

Figure E.9: Adult relative tibial lengths, all samples Appendix E. Additional Plots — Investigation of Body Proportion Development183 85 90 80 85 75 80 70 Crural Index (TL:FBL) Crural Index Brachial Index (RL:HL) Index Brachial 75 65 70

0 5 10 15 20 0 5 10 15 20 Male Male

Dental Age (yrs) Female Denal Age (yrs) Female

(a) Brachial Index (b) Crural Index

Figure E.10: Ipiutak limb index values 75 110 70 100 65 90

● 60 80 55 Radius diaphyseal:STHnoS Humerus diaphyseal:STHnoS 70 50 60 45

0 5 10 15 20 0 5 10 15 20 Male Male

Dental Age (yrs) Female Dental Age (yrs) Female

(a) Relative humeral length (b) Relative radial length

Figure E.11: Ipiutak juvenile relative upper limb length indices with adult reference values Appendix E. Additional Plots — Investigation of Body Proportion Development184 120 140 110 100 120 ● 90 ● 100 80 Tibia diaphyseal:STHnoS Femur diaphyseal:STHnoS Femur 70 80 60

0 5 10 15 20 0 5 10 15 20 Male Male

Dental Age (yrs) Female Dental Age (yrs) Female

(a) Relative femoral length (b) Relative tibial length

Figure E.12: Ipiutak juvenile relative lower limb length indices with adult reference values 85 90

● 80

85 ●

● 75 80 ● ●

● ●

70 ● Crural Index (TL:FBL) Crural Index Brachial Index (RL:HL) Index Brachial ●

● 75 ● ● 65 70

0 5 10 15 20 0 5 10 15 20 Male Male

Dental Age (yrs) Female Dental Age (yrs) Female

(a) Brachial Index (b) Crural Index

Figure E.13: NW Hudson Bay limb index values Appendix E. Additional Plots — Investigation of Body Proportion Development185 85 90 80 85 75 80 70 Crural Index (TL:FBL) Crural Index Brachial Index (RL:HL) Index Brachial 75 65 70

0 5 10 15 20 0 5 10 15 20 Male Male

Dental Age (yrs) Female Dental Age (yrs) Female

(a) Brachial Index (b) Crural Index

Figure E.14: Greenland limb index values Appendix F

Dataset

Below are tables with the juvenile and adult data used in this study. Not included is the vertebral data; if you would like access to this data, or full bilateral data, please contact natalie [dot] symchych [at] gmail [dot] com.

Sad Sadlermiut IBL Maximum iliac breadth NWHB Northwest Hudson Bay IHL Maximum iliac height Green Greenland FMXL Femur maximum length Ind Indeterminate FBCL Femur bicondylar length HDL Humerus diaphyseal length HLL Humerus maximum length RDL Radius diaphyseal length RLL Radius maximum length FDL Femur diaphyseal length TFL Tibia Fully length TDL Tibia diaphyseal length

Table F.1: Juvenile data

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

XIV-C:075 Sad 9.500 146.00 304.00 243.00 110.00 98.54 XIV-C:076 Sad 8.167 184.50 131.00 259.50 205.00 102.50 89.20 XIV-C:077 Sad 1.406 94.00 69.50 122.00 97.50 52.00 50.50 XIV-C:078 Sad 5.571 151.50 106.50 204.50 155.00 82.00 73.77 XIV-C:079 Sad 1.179 86.50 67.00 110.00 90.50 49.00 44.50 XIV-C:080 Sad 57.50 90.00 73.00

186 Appendix F. Dataset 187

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

XIV-C:083 Sad 0.405 68.00 53.00 81.00 35.50 30.00 XIV-C:084 Sad 2.000 113.00 133.00 105.50 XIV-C:085 Sad 0.542 71.50 59.00 82.50 72.00 38.00 33.50 XIV-C:087 Sad XIV-C:106 Sad 0.435 80.00 36.04 33.52 XIV-C:107 Sad 0.500 60.00 95.50 76.50 XIV-C:108 Sad 0.844 86.00 86.00 XIV-C:109 Sad 0.500 95.00 40.00 34.00 XIV-C:118 Sad 5.600 146.00 103.00 195.50 146.00 82.50 70.75 XIV-C:119 Sad 0.892 84.00 65.50 106.00 87.00 47.50 41.50 XIV-C:120 Sad 0.924 90.00 67.00 111.00 89.00 51.50 47.21 XIV-C:121 Sad 0.395 70.50 57.50 85.00 71.50 40.00 34.50 XIV-C:122 Sad 0.460 63.50 50.00 73.00 63.00 36.50 31.50 XIV-C:123 Sad 0.484 74.00 56.00 88.00 72.00 39.50 34.00 XIV-C:124 Sad 9.571 168.00 124.50 235.50 180.00 96.50 79.92 XIV-C:133 Sad XIV-C:134 Sad XIV-C:135 Sad 1.032 85.00 65.50 106.00 90.00 45.00 41.50 XIV-C:137 Sad 0.828 87.50 62.00 105.00 45.00 XIV-C:138 Sad 67.00 80.50 68.00 XIV-C:146 Sad 20.250 191.00 139.56 110.84 XIV-C:150 Sad 9.250 179.00 125.00 240.00 179.50 93.00 80.40 XIV-C:151 Sad 11.167 194.50 141.50 282.50 214.50 108.50 93.18 XIV-C:158 Sad 19.690 187.50 301.50 140.87 119.18 XIV-C:159 Sad 0.500 73.50 56.00 37.50 34.00 XIV-C:160 Sad XIV-C:161 Sad 0.323 67.00 51.00 75.50 64.00 34.00 31.50 XIV-C:162-1 Sad 0.453 65.00 53.00 79.50 68.00 34.50 30.00 XIV-C:162-2 Sad 0.433 69.00 54.00 67.00 36.00 30.50 XIV-C:163 Sad 0.688 72.00 86.00 69.00 35.50 30.00 XIV-C:171 Sad 5.500 XIV-C:172 Sad 12.500 200.00 137.50 279.50 199.00 104.00 83.41 XIV-C:186 Sad 1.072 86.00 66.00 107.50 89.00 47.50 24.50 XIV-C:187 Sad 0.358 63.50 51.50 75.00 64.50 32.50 30.50 XIV-C:188 Sad 9.083 Appendix F. Dataset 188

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

XIV-C:193 Sad 22.500 155.39 128.09 XIV-C:194 Sad 13.167 233.00 173.50 338.50 261.50 121.00 103.31 XIV-C:198 Sad 17.833 245.00 185.00 372.00 289.00 133.00 113.15 XIV-C:200 Sad 1.125 97.00 XIV-C:201 Sad 1.146 85.50 66.50 104.50 87.00 49.00 43.00 XIV-C:202 Sad 1.116 86.00 110.00 83.00 40.00 XIV-C:203 Sad 1.317 91.00 66.00 110.50 86.50 50.00 42.00 XIV-C:204 Sad 0.350 62.50 49.50 72.50 61.50 34.50 29.50 XIV-C:205 Sad 0.433 66.00 52.00 80.00 65.00 36.00 31.00 XIV-C:206 Sad 0.622 70.50 53.50 85.00 71.00 37.50 35.50 XIV-C:207 Sad 0.625 64.00 73.50 63.50 XIV-C:208 Sad 0.438 67.00 35.50 30.50 XIV-C:209-1 Sad 0.350 61.00 71.00 63.00 34.50 29.00 XIV-C:209-2 Sad 1.344 95.00 72.00 XIV-C:210-1 Sad 67.00 55.00 67.00 XIV-C:210-2 Sad 0.268 58.50 47.00 70.00 61.00 33.00 26.50 XIV-C:210-3 Sad XIV-C:211 Sad 0.213 48.50 70.00 60.50 32.00 27.00 XIV-C:212 Sad 0.597 77.00 90.50 76.50 39.00 36.00 XIV-C:213 Sad 56.00 58.50 XIV-C:214 Sad XIV-C:215 Sad XIV-C:220 Sad 14.417 202.50 142.50 276.00 217.00 111.00 91.07 XIV-C:222 Sad 1.217 81.00 61.00 101.50 81.50 44.50 39.50 XIV-C:223 Sad 1.113 87.50 69.00 110.50 89.50 52.00 44.00 XIV-C:224 Sad 1.091 87.50 69.50 109.00 91.00 45.50 42.00 XIV-C:225 Sad 0.542 72.00 55.50 84.50 72.50 38.00 34.00 XIV-C:226 Sad 0.734 76.00 60.00 93.00 76.00 41.50 36.50 XIV-C:227 Sad 8.667 XIV-C:228 Sad 0.186 46.00 66.00 57.00 31.50 27.00 XIV-C:231 Sad 9.333 178.00 130.00 253.00 194.50 97.00 82.95 XIV-C:232 Sad 8.278 158.00 116.00 224.50 174.50 82.50 76.29 XIV-C:236 Sad 0.103 57.50 58.50 30.00 25.50 XIV-C:238 Sad 6.133 163.00 110.50 221.00 167.00 83.50 74.93 XIV-C:239 Sad 18.000 219.00 158.00 332.00 253.50 122.00 105.73 Appendix F. Dataset 189

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

XIV-C:245 Sad 18.000 278.00 176.00 127.50 100.22 XIV-C:249 Sad 16.333 237.00 164.50 336.50 247.00 120.00 99.65 XIV-C:250-1 Sad 10.750 XIV-C:250-3 Sad 10.637 207.50 141.50 283.00 211.00 102.50 XIV-C:250-5 Sad 6.507 162.50 85.00 73.36 XIV-C:278 Sad 1.110 80.50 62.50 100.00 81.50 45.00 42.00 XIV-C:279 Sad 1.209 82.50 63.00 103.50 85.00 48.50 44.00 XIV-C:280 Sad 0.570 71.50 59.00 84.50 72.50 38.50 33.00 XIV-C:281 Sad -0.003 61.00 49.00 71.50 62.00 34.50 29.50 XIV-C:282-1 Sad 2.063 83.50 100.00 XIV-C:282-2 Sad XIV-C:284 Sad -0.015 58.00 47.50 67.50 58.50 29.50 XIV-C:285 Sad 0.268 59.50 48.50 71.50 61.50 33.00 28.00 XIV-C:286-1 Sad 0.515 70.50 54.50 84.50 72.00 37.50 34.50 XIV-C:296 Sad 12.500 229.00 163.00 325.50 258.50 116.50 101.84 XIV-C:300-1 Sad 10.400 197.00 143.50 282.50 221.00 104.00 91.47 XIV-C:300-2 Sad XIV-C:301 Sad 13.625 225.00 158.00 318.00 254.00 116.50 101.87 XIV-C:724-2 Sad 97.50 XIV-C:724-3 Sad XIV-C:725-2 Sad XIV-C:734 Sad 1.053 95.00 73.00 114.50 94.00 51.00 47.50 XIV-C:735 Sad 1.594 94.00 70.00 114.00 91.00 52.50 45.50 XIV-C:738 Sad 1.016 63.00 100.00 82.00 45.50 42.00 XIV-C:740 Sad 0.611 74.50 60.00 88.00 75.50 39.50 35.50 XIV-C:760-2 Sad -0.089 51.50 60.50 51.00 26.50 21.00 XIV-C:761-2 Sad 31.50 32.50 28.00 XIV-C:765 Sad 12.389 XIV-C:768 Sad 11.333 XIV-C:785 Sad 7.500 XIV-C:795 Sad 15.000 XIV-C:796 Sad 3.300 XIV-C:798-1 Sad 2.833 XIV-C:798-3 Sad 13.350 217.00 146.00 229.50 114.00 95.83 XIV-C:800 Sad 7.500 Appendix F. Dataset 190

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

99.1/075 Ipiutak 0.653 83.50 69.00 101.00 83.00 45.81 38.37 99.1/082 Ipiutak 10.142 280.50 218.50 99.86 99.1/083 Ipiutak 6.750 186.00 136.00 99.1/092 Ipiutak 13.500 255.00 184.50 349.00 285.00 123.01 112.57 99.1/095 Ipiutak 16.500 353.00 288.00 99.1/162 Ipiutak 9.273 179.00 130.00 233.00 177.50 97.59 99.1/167 Ipiutak 2.643 173.00 132.00 71.87 65.06 99.1/176-1 Ipiutak 15.500 223.00 169.50 120.38 99.1/176-2 Ipiutak 180.00 139.50 75.71 99.1/193 Ipiutak 8.333 204.00 136.50 278.50 105.44 90.08 99.1/670 Ipiutak 13.500 292.50 103.88 93.54 99.1/223 Tigara 11.053 203.50 149.00 301.50 233.00 103.15 96.04 99.1/244 Tigara 3.143 138.00 101.00 185.00 149.00 73.01 67.17 99.1/258 Tigara 7.484 227.00 79.49 66.39 99.1/264 Tigara 6.536 189.50 71.29 66.95 99.1/269 Tigara 8.967 183.00 258.00 211.00 95.40 82.99 99.1/279 Tigara 133.00 101.00 182.00 99.1/285 Tigara 15.000 333.50 99.1/286 Tigara 16.500 240.00 175.50 346.00 273.00 116.05 95.71 99.1/290 Tigara 12.600 289.00 239.00 99.1/291 Tigara 8.500 176.00 127.00 249.00 199.00 88.60 99.1/293 Tigara 9.364 197.00 273.50 103.75 87.37 99.1/299 Tigara 9.294 191.50 138.50 272.00 213.00 95.52 99.1/300 Tigara 13.500 213.50 157.00 307.00 247.00 102.32 87.88 99.1/304 Tigara 16.500 250.50 181.00 360.50 277.00 133.19 111.19 99.1/307 Tigara 6.708 185.50 99.1/308 Tigara 16.000 110.96 95.86 99.1/309 Tigara 7.556 226.50 182.50 99.1/313 Tigara 1.750 115.00 88.50 142.50 99.1/321 Tigara 266.00 377.50 309.00 122.15 107.12 99.1/323 Tigara 16.750 317.50 264.50 99.1/341 Tigara 11.167 183.50 147.00 271.50 221.00 100.15 86.86 99.1/342 Tigara 9.313 207.00 153.50 290.00 235.50 104.46 91.74 99.1/343 Tigara 205.00 153.00 293.50 234.00 108.43 95.39 99.1/344 Tigara 8.429 208.50 162.00 304.50 247.50 110.02 91.74 Appendix F. Dataset 191

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

99.1/351 Tigara 3.000 127.50 169.50 127.50 65.42 65.66 99.1/354 Tigara 10.625 225.00 166.00 316.00 254.50 112.50 104.04 99.1/360 Tigara 6.441 225.00 175.00 78.79 72.43 99.1/384 Tigara 9.857 220.00 157.00 307.50 245.00 105.01 92.51 99.1/385 Tigara 7.000 251.50 200.00 93.99 82.95 99.1/389 Tigara 17.000 268.00 194.00 369.50 298.50 125.36 109.41 99.1/391 Tigara 10.833 207.00 298.50 242.00 106.88 98.38 99.1/399 Tigara 7.684 282.00 232.00 99.1/405 Tigara 13.000 240.00 340.00 276.50 119.94 103.64 99.1/406 Tigara 254.00 183.50 341.00 270.00 118.83 105.99 99.1/411 Tigara 8.500 183.50 134.50 264.50 221.50 101.27 93.57 99.1/413 Tigara 11.375 223.00 162.50 308.00 247.50 99.1/418 Tigara 3.000 139.00 108.50 186.50 152.00 74.17 68.25 99.1/419 Tigara 11.500 201.50 148.50 288.00 233.50 99.99 96.67 99.1/427 Tigara 5.750 203.00 159.50 99.1/437 Tigara 3.056 173.50 141.20 99.1/448 Tigara 5.571 136.50 100.00 179.50 141.00 99.1/454 Tigara 21.000 365.50 301.00 137.23 114.56 99.1/457 Tigara 78.50 61.50 95.50 78.00 44.63 37.18 99.1/467 Tigara 6.091 150.00 105.00 206.00 164.00 79.59 68.56 99.1/484 Tigara 7.667 274.00 100.59 90.09 99.1/501 Tigara 8.708 186.50 275.50 216.50 99.36 90.88 99.1/518 Tigara 4.571 180.00 148.00 69.46 66.87 99.1/534 Tigara 9.938 177.50 127.00 245.00 192.00 93.91 83.93 99.1/088 255.00 185.00 370.50 292.00 130.19 116.79 99.1/185 335.00 255.00 142.92 109.60 99.1/188 11.500 304.00 233.50 99.1/221 2.474 113.00 81.50 146.00 115.50 57.25 99.1/361 13.500 262.00 185.00 378.00 305.00 136.98 105.58 XIV-C:365 NWHB 8.143 253.00 XIV-C:400 NWHB 18.000 XIV-C:406 NWHB 15.000 146.83 117.65 XIV-C:408 NWHB 313.00 XIV-C:409 NWHB 17.750 389.00 328.00 137.16 117.06 XIV-C:414 NWHB 18.500 140.11 116.72 Appendix F. Dataset 192

Table F.1: Juvenile data continued

Individual ID Sample Dental Age HDL RDL FDL TDL IBL IHL

XIV-C:417 NWHB 3.250 126.50 88.00 178.00 131.50 72.26 67.91 XIV-C:483 NWHB 5.167 148.00 198.00 153.50 XIV-C:584 NWHB 17.250 XIV-C:599 NWHB 17.500 240.00 335.50 XIV-C:616 NWHB 16.000 380.00 301.00 XIV-C:629 NWHB 6.737 216.00 167.50 71.21 67.35 XIV-C:632 NWHB 18.000 103.28 XIV-C:635 NWHB 11.450 325.50 XIV-C:636-1 NWHB 21.500 265.00 182.00 371.00 294.00 119.31 103.06 XIV-C:638 NWHB 15.500 333.00 XIV-C:640 NWHB 18.000 303.00 XIV-C:722 NWHB 10.455 190.00 KAL-0025X01 Green 139.00 105.00 185.00 KAL-0147X01 Green 296.00 240.00 KAL-0158X01 Green 16.000 339.00 273.00 KAL-0159X01 Green 11.000 228.00 323.00 254.00 KAL-0882X01 Green 3.400 172.00 KAL-0898X01 Green 13.500 363.00 291.50 KAL-1279X01 Green 4.333 200.00 KAL-1418X01 Green 59.00 74.50 60.00 31.00 28.00 KAL-1908X01 Green 3.500 112.00 KAL-1968X01 Green 6.333 235.00 190.00 KAL-2019X01 Green 4.778 195.00 155.00 KAL-2024X01 Green 12.500 220.00 167.00 304.40 253.00 KAL-4035X01 Green 12.500 349.00 282.50 KAL-4092X01 Green 9.846 295.00

Table F.2: Adult data

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:073 Female Sad 261.50 185.00 405.00 400.00 312.00 XIV-C:074 Male Sad 300.50 222.50 429.00 423.00 349.00 XIV-C:088 Ind Sad Appendix F. Dataset 193

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:096 Female Sad 281.00 202.00 420.00 416.00 327.50 XIV-C:097 Female Sad 301.00 213.00 427.00 423.00 326.00 XIV-C:098 Female Sad 275.00 202.00 410.00 407.50 315.50 XIV-C:099 Male Sad 293.50 218.00 448.50 443.00 348.50 XIV-C:100 Female Sad 295.50 209.00 421.00 417.00 332.50 XIV-C:101 Female Sad 285.00 210.00 407.50 401.50 318.50 XIV-C:102 Male Sad 294.50 208.50 415.50 408.50 316.50 XIV-C:103 Female Sad 277.00 210.00 404.00 400.00 327.00 XIV-C:104 Female Sad 286.50 197.50 400.00 395.00 312.00 XIV-C:105 Female Sad 275.00 203.00 388.00 385.50 308.00 XIV-C:111 Male Sad 311.00 225.50 443.50 438.00 357.00 XIV-C:112 Female Sad 317.50 233.00 450.50 445.50 365.50 XIV-C:117 Male Sad 303.50 205.50 427.50 425.00 342.00 XIV-C:125 Ind Sad 432.00 XIV-C:126 Male Sad 292.50 216.00 433.50 430.00 340.00 XIV-C:127 Ind Sad XIV-C:128 Ind Sad XIV-C:129 Ind Sad XIV-C:130-2 Ind Sad XIV-C:136 Ind Sad XIV-C:141 Ind Sad 293.50 432.00 427.50 XIV-C:142-1 Male Sad XIV-C:142-2 Ind Sad XIV-C:143 Ind Sad 272.50 XIV-C:144 Ind Sad 295.00 XIV-C:145 Female Sad 306.50 215.00 441.50 437.50 349.00 XIV-C:147 Female Sad 281.50 205.50 411.50 409.00 322.50 XIV-C:148 Female Sad 249.00 176.00 369.50 361.00 282.50 XIV-C:149 Female Sad 278.00 198.00 409.00 403.50 332.00 XIV-C:152 Male Sad 283.00 214.50 415.50 411.50 330.00 XIV-C:153 Female Sad 279.50 198.50 397.00 395.50 308.00 XIV-C:154 Ind Sad XIV-C:155 Female Sad 270.00 191.50 399.00 393.00 310.00 XIV-C:156 Male Sad 314.00 207.50 450.50 446.00 340.50 XIV-C:157 Male Sad 303.50 215.00 436.50 432.50 332.00 Appendix F. Dataset 194

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:164 Male Sad 297.50 217.00 442.50 436.00 348.00 XIV-C:165 Male Sad 284.50 199.50 397.50 394.50 311.00 XIV-C:166 Male Sad 282.50 403.50 403.00 311.00 XIV-C:167 Male Sad 322.00 227.00 357.50 XIV-C:168 Male Sad 316.00 220.00 437.50 432.00 333.50 XIV-C:169 Female Sad 286.50 200.50 400.00 397.50 309.50 XIV-C:170 Male Sad 310.50 229.50 472.50 470.00 379.50 XIV-C:173 Female Sad 285.00 200.00 418.00 415.50 325.00 XIV-C:174 Male Sad 322.50 447.50 444.00 364.50 XIV-C:175 Female Sad 240.50 176.00 357.00 352.50 287.00 XIV-C:178 Female Sad 271.00 196.50 378.50 376.00 294.00 XIV-C:179 Male Sad 286.50 434.00 424.00 328.00 XIV-C:180 Female Sad 287.50 195.50 412.50 405.00 318.50 XIV-C:181 Male Sad 293.50 215.00 453.00 448.50 336.00 XIV-C:182 Male Sad 291.50 216.00 420.50 411.50 332.00 XIV-C:183 Female Sad 280.00 192.50 409.50 407.50 322.00 XIV-C:184 Ind Sad XIV-C:185 Ind Sad 304.50 208.50 XIV-C:189 Ind Sad 277.50 196.50 325.00 XIV-C:190 Male Sad 285.50 216.50 412.00 408.50 328.00 XIV-C:191 Male Sad 289.50 432.50 428.00 339.00 XIV-C:192 Female Sad 282.50 207.50 412.00 407.00 325.00 XIV-C:195 Female Sad 305.00 212.50 434.00 427.50 343.50 XIV-C:197 Male Sad 306.00 230.50 449.00 445.50 359.00 XIV-C:199 Male Sad 299.00 424.00 418.50 324.00 XIV-C:216 Male Sad 305.00 216.00 439.50 437.00 342.00 XIV-C:217 Male Sad 312.50 221.50 441.50 437.50 333.00 XIV-C:219 Female Sad 291.50 199.50 412.50 408.50 314.00 XIV-C:221 Female Sad 286.50 205.00 428.00 425.00 325.00 XIV-C:229 Female Sad 283.00 203.50 414.00 412.00 335.50 XIV-C:230 Male Sad 324.00 230.00 470.00 464.00 363.50 XIV-C:233 Female Sad 308.50 220.00 427.50 422.00 350.00 XIV-C:234 Female Sad 292.00 197.00 409.00 406.50 321.00 XIV-C:235 Female Sad 261.50 186.00 389.00 381.00 291.00 XIV-C:237 Female Sad 281.00 199.00 414.50 412.00 336.00 Appendix F. Dataset 195

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:240 Female Sad 286.00 199.00 317.50 XIV-C:241 Female Sad 261.00 376.00 371.50 302.50 XIV-C:242 Female Sad 273.50 194.00 389.50 388.00 299.00 XIV-C:243 Male Sad 307.50 226.00 453.00 446.00 346.00 XIV-C:244 Female Sad 285.00 201.50 414.00 409.00 322.00 XIV-C:246 Male Sad 295.00 212.00 430.00 425.50 329.50 XIV-C:247 Female Sad 270.00 198.00 XIV-C:248 Female Sad 319.00 229.50 472.00 467.50 383.00 XIV-C:250-2 Female Sad 184.00 XIV-C:250-4 Ind Sad XIV-C:251 Ind Sad 400.00 393.50 XIV-C:252 Ind Sad XIV-C:253 Ind Sad 417.50 XIV-C:254 Ind Sad XIV-C:255 Ind Sad 337.50 XIV-C:257 Ind Sad 409.50 404.00 XIV-C:259 Female Sad 279.50 199.50 407.00 402.50 318.50 XIV-C:267 Ind Sad 302.50 432.50 XIV-C:268-1 Male Sad XIV-C:268-2 Ind Sad XIV-C:283 Female Sad XIV-C:286-2 Female Sad XIV-C:287 Male Sad XIV-C:289 Female Sad XIV-C:290 Ind Sad XIV-C:291 Ind Sad XIV-C:292 Male Sad XIV-C:293 Female Sad XIV-C:298 Female Sad 287.50 208.00 400.50 399.00 326.00 XIV-C:302 Female Sad 274.00 186.50 370.50 368.50 302.00 XIV-C:305 Male Sad 407.50 403.50 XIV-C:724-1 Ind Sad XIV-C:725-1 Ind Sad XIV-C:726 Ind Sad XIV-C:727 Ind Sad Appendix F. Dataset 196

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:733 Ind Sad XIV-C:736 Male Sad 328.00 238.00 483.50 479.50 376.50 XIV-C:737 Male Sad 334.50 240.00 470.50 464.00 378.00 XIV-C:739 Female Sad 292.00 216.00 421.00 417.50 340.50 XIV-C:741 Male Sad 274.50 210.50 408.00 402.00 316.50 XIV-C:742 Male Sad 286.00 219.50 423.50 420.00 337.50 XIV-C:743 Female Sad 280.00 203.50 398.00 396.00 298.00 XIV-C:744 Female Sad 320.50 222.50 472.50 464.50 364.00 XIV-C:745 Male Sad 282.50 215.50 419.00 410.00 324.00 XIV-C:746 Female Sad 281.50 193.00 391.00 388.00 307.50 XIV-C:747 Female Sad 399.00 393.50 305.00 XIV-C:748 Ind Sad 308.50 217.50 429.50 424.50 343.00 XIV-C:749 Female Sad 289.00 202.50 XIV-C:750 Female Sad 316.00 220.00 452.00 447.50 350.50 XIV-C:751 Male Sad 303.00 219.50 425.00 423.00 343.00 XIV-C:752 Female Sad 283.50 217.00 413.00 405.50 330.00 XIV-C:753 Male Sad 290.50 225.50 417.50 415.00 337.00 XIV-C:754 Male Sad 296.00 415.50 414.00 XIV-C:755 Male Sad 313.50 226.00 458.50 450.00 358.00 XIV-C:756 Male Sad 287.00 214.50 424.00 413.00 XIV-C:757 Male Sad 322.50 227.00 469.00 465.00 364.50 XIV-C:758 Female Sad 268.50 185.50 375.00 372.00 299.50 XIV-C:759 Male Sad 366.00 XIV-C:760-1 Female Sad 294.50 XIV-C:761 Female Sad 266.00 182.00 388.00 385.00 301.00 XIV-C:762 Ind Sad 321.00 XIV-C:763 Ind Sad 284.50 XIV-C:764 Female Sad 297.50 213.50 XIV-C:767 Female Sad XIV-C:770 Ind Sad XIV-C:771 Female Sad XIV-C:773 Male Sad XIV-C:776 Male Sad XIV-C:778 Ind Sad XIV-C:780 Ind Sad Appendix F. Dataset 197

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:781 Male Sad XIV-C:782 Male Sad XIV-C:786 Male Sad XIV-C:787 Ind Sad XIV-C:789 Ind Sad XIV-C:790 Ind Sad XIV-C:791 Ind Sad XIV-C:792 Female Sad XIV-C:793 Male Sad XIV-C:794 Female Sad XIV-C:797 Male Sad XIV-C:798-2 Ind Sad XIV-C:799 Ind Sad 205.50 321.50 XIV-C:801 Ind Sad XIV-C:802 Ind Sad XIV-C:809 Ind Sad XIV-C:SAD-007 Female Sad 99.1/077 Male Ipiutak 300.00 228.50 410.00 409.50 343.00 99.1/080 Male Ipiutak 289.00 223.50 397.50 395.00 319.00 99.1/086A Female Ipiutak 261.50 182.50 354.00 349.50 279.00 99.1/086B Female Ipiutak 271.00 196.00 374.50 371.50 297.50 99.1/087 Male Ipiutak 315.00 236.00 430.50 429.00 347.00 99.1/089 Male Ipiutak 316.50 233.00 434.00 431.00 358.00 99.1/089A Male Ipiutak 305.00 233.00 402.50 400.50 336.00 99.1/090 Male Ipiutak 297.00 218.50 395.00 393.50 99.1/094 Male Ipiutak 280.50 214.50 403.50 400.00 315.00 99.1/096A Male Ipiutak 282.50 221.50 403.00 401.50 325.50 99.1/097 Male Ipiutak 278.00 218.50 398.00 396.00 315.00 99.1/099 Female Ipiutak 269.50 197.50 376.50 374.00 286.50 99.1/102 Female Ipiutak 270.00 198.50 369.00 365.50 296.00 99.1/103 Male Ipiutak 305.00 222.50 393.00 391.50 315.00 99.1/104 Female Ipiutak 292.50 207.50 406.50 401.00 313.00 99.1/105 Male Ipiutak 305.50 228.50 421.50 419.50 333.00 99.1/109 Male Ipiutak 316.00 233.50 449.00 446.50 345.00 99.1/111 Female Ipiutak 280.00 203.50 402.50 397.50 309.00 Appendix F. Dataset 198

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

99.1/160 Female Ipiutak 273.00 380.00 379.50 295.50 99.1/161 Male Ipiutak 325.50 254.00 439.50 438.50 348.00 99.1/163 Male Ipiutak 327.50 432.50 430.50 355.50 99.1/166 Male Ipiutak 352.50 259.50 513.00 506.00 376.00 99.1/168 Female Ipiutak 285.00 212.50 388.00 386.50 305.00 99.1/169 Female Ipiutak 277.00 209.00 386.00 382.00 99.1/191 Female Ipiutak 292.50 214.50 392.50 387.50 99.1/196 Male Ipiutak 295.50 228.50 403.00 401.50 315.50 99.1/197 Female Ipiutak 269.50 192.50 371.00 369.50 297.00 99.1/198 Female Ipiutak 286.50 212.50 391.00 390.00 310.00 99.1/199 Female Ipiutak 293.00 216.00 396.00 393.50 319.00 99.1/200 Female Ipiutak 281.00 200.50 374.00 371.00 292.00 99.1/201 Female Ipiutak 278.50 387.50 384.50 304.00 99.1/204 Female Ipiutak 291.50 204.50 386.50 383.00 313.50 99.1/660 Male Ipiutak 315.50 226.00 421.00 417.50 322.00 99.1/069 Male Tigara 309.50 229.50 443.50 442.50 360.00 99.1/222 Female Tigara 287.50 217.00 420.50 415.50 332.00 99.1/224 Female Tigara 273.00 196.00 390.50 386.50 295.00 99.1/225 Ind Tigara 274.50 199.50 399.00 398.50 329.00 99.1/227 Male Tigara 309.50 222.00 443.00 436.50 362.00 99.1/228 Male Tigara 299.00 231.50 434.50 432.00 346.50 99.1/232 Female Tigara 270.00 197.00 370.00 369.50 296.00 99.1/234 Female Tigara 273.00 204.50 392.50 391.00 335.50 99.1/235 Male Tigara 325.00 248.00 450.50 446.00 370.00 99.1/241 Male Tigara 316.00 239.50 444.50 441.50 354.50 99.1/251 Female Tigara 250.00 194.00 365.00 363.00 296.00 99.1/252 Male Tigara 289.50 99.1/255 Male Tigara 292.50 215.00 423.50 414.50 332.00 99.1/263 Female Tigara 286.50 445.00 442.00 99.1/268 Female Tigara 266.50 202.50 376.00 374.00 301.00 99.1/276 Female Tigara 275.50 196.50 385.50 380.00 302.00 99.1/277 Male Tigara 292.50 218.00 415.00 412.50 346.00 99.1/284 Male Tigara 303.00 234.00 428.50 427.50 345.50 99.1/287 Female Tigara 261.50 198.50 396.50 395.00 319.00 99.1/294 Female Tigara 292.50 221.50 418.50 414.50 333.00 Appendix F. Dataset 199

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

99.1/298 Male Tigara 291.00 213.50 409.50 408.00 331.00 99.1/316 Female Tigara 278.50 205.50 398.00 395.00 312.50 99.1/327 Female Tigara 277.00 198.50 396.50 394.50 312.00 99.1/330A Male Tigara 279.00 211.00 401.50 398.50 324.00 99.1/334 Female Tigara 282.00 215.00 402.50 400.50 328.50 99.1/336 Male Tigara 306.00 236.50 439.00 437.50 356.50 99.1/339 Male Tigara 311.50 239.50 446.00 445.00 361.00 99.1/340 Male Tigara 341.50 245.00 463.50 463.00 372.00 99.1/346 Male Tigara 267.50 215.50 382.50 380.50 300.50 99.1/348 Female Tigara 267.00 189.50 380.50 379.00 294.00 99.1/349 Male Tigara 293.00 220.50 415.00 414.50 345.00 99.1/352 Female Tigara 289.00 223.50 431.50 436.50 353.00 99.1/353 Male Tigara 297.00 223.50 423.50 421.50 343.00 99.1/357 Male Tigara 319.00 231.50 447.50 442.50 375.00 99.1/362 Female Tigara 262.50 199.50 99.1/373 Male Tigara 284.50 230.50 444.00 443.00 356.00 99.1/377 Female Tigara 287.50 222.50 426.00 423.00 336.00 99.1/381 Male Tigara 304.00 236.00 442.50 441.50 366.00 99.1/386 Male Tigara 310.00 231.00 426.50 425.00 333.00 99.1/392 Male Tigara 331.50 246.50 475.50 473.00 380.00 99.1/397 Male Tigara 305.50 237.50 442.50 439.50 352.50 99.1/407 Male Tigara 306.50 242.50 422.50 421.50 350.00 99.1/453 Male Tigara 315.50 242.50 445.50 444.00 371.00 99.1/485 Female Tigara 286.00 197.50 403.50 402.00 336.00 99.1/487 Female Tigara 280.00 204.50 410.00 408.50 338.00 99.1/489-1 Female Tigara 277.00 193.50 388.00 386.50 320.50 99.1/496 Female Tigara 268.50 196.50 377.00 374.50 313.00 99.1/497 Male Tigara 294.50 219.50 416.50 411.50 336.00 99.1/514 Female Tigara 99.1/666 Female Tigara 274.50 203.00 396.50 392.00 315.00 99.1/667 Female Tigara 290.50 205.00 384.50 382.00 309.00 KAL-0005X01 Male Green 418.00 323.00 KAL-0035X01 Female Green 256.00 196.00 354.00 351.00 287.00 KAL-0036X01 Male Green 431.00 426.00 337.00 KAL-0037X01 Ind Green 294.00 429.00 422.50 339.00 Appendix F. Dataset 200

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

KAL-0049X01 Male Green 317.00 230.50 454.00 450.00 378.50 KAL-0054X01-A Male Green 330.50 230.00 465.00 460.00 353.00 KAL-0054X01-B Female Green 275.00 387.00 385.00 309.00 KAL-0055X01 Male Green 329.00 253.00 471.50 470.00 402.00 KAL-0056X01 Female Green 291.00 210.00 415.50 414.00 339.00 KAL-0121X01 Female Green 423.00 421.00 333.00 KAL-0152X01 Female Green 289.00 216.50 412.50 409.90 339.00 KAL-0886X01 Male Green 304.00 214.00 413.00 410.00 319.50 KAL-1243X01 Male Green 310.00 423.00 412.00 358.50 KAL-1256X01 Female Green 282.50 207.00 401.50 396.00 313.00 KAL-1419X01 Female Green 292.00 219.00 407.00 394.00 317.00 KAL-1420X01 Female Green 194.50 399.50 396.00 KAL-1421X01 Male Green 313.00 228.50 442.00 433.00 351.50 KAL-1423X01 Female Green 269.00 203.50 387.50 384.00 294.00 KAL-1426X01 Male Green 295.50 223.00 418.00 412.00 326.50 KAL-1427X01 Male Green 310.00 232.50 424.00 422.00 339.00 KAL-1428X01 Female Green 384.00 379.00 312.00 KAL-1430X01 Male Green 303.00 214.00 429.00 426.00 321.50 KAL-1990x01 Female Green 309.00 220.00 430.00 428.00 KAL-4051X01 Female Green 297.00 213.00 419.00 415.00 334.50 KAL-4056X01 Female Green 281.00 389.00 383.00 301.00 KAL-4061X01 Female Green 270.00 184.00 373.00 370.00 276.00 KAL-4063X01 Male Green 312.50 234.00 423.00 420.50 350.50 XIV-C:342 Female NWHB 275.50 197.50 404.50 402.50 320.00 XIV-C:345 Male NWHB 306.00 208.00 430.00 426.00 337.00 XIV-C:352 Male NWHB 312.50 224.50 449.50 448.00 350.00 XIV-C:353 Male NWHB 272.00 201.50 385.00 381.00 302.50 XIV-C:364 Male NWHB 303.50 429.00 419.50 350.00 XIV-C:371 Male NWHB 283.00 205.00 413.50 411.50 324.50 XIV-C:373 Female NWHB 258.00 205.00 393.50 388.00 314.50 XIV-C:374 Female NWHB 275.50 397.50 393.00 314.00 XIV-C:375 Male NWHB 296.00 213.50 420.00 416.50 319.50 XIV-C:383 Female NWHB 283.50 192.00 409.50 404.50 327.00 XIV-C:384 Female NWHB 262.50 188.50 379.00 302.00 XIV-C:390-2 Female NWHB 276.00 202.00 420.50 414.50 326.00 Appendix F. Dataset 201

Table F.2: Adult data continued

Individual ID Sex Sample HLL RLL FMXL FBCL TFL

XIV-C:416 Male NWHB 312.00 238.50 455.50 453.00 361.50 XIV-C:420 Female NWHB 293.50 204.00 431.00 422.50 342.50 XIV-C:466 Female NWHB 256.50 379.50 378.00 314.50 XIV-C:490 Female NWHB 263.50 187.00 379.50 378.00 320.00 XIV-C:499 Male NWHB 437.00 435.00 XIV-C:513 Male NWHB 226.50 456.00 453.00 363.00 XIV-C:518 Male NWHB 308.00 233.00 442.00 438.50 350.00 XIV-C:531 Female NWHB 279.00 410.50 406.50 329.00 XIV-C:532 Female NWHB 379.00 378.50 312.00 XIV-C:533 Male NWHB 304.00 432.50 429.00 349.00 XIV-C:535 Female NWHB 268.50 388.50 312.00 XIV-C:539 Male NWHB 322.50 456.50 448.00 366.00 XIV-C:587 Female NWHB 247.00 371.50 364.00 XIV-C:589 Female NWHB 261.50 189.00 396.50 395.00 312.50 XIV-C:607 Male NWHB 302.00 218.00 436.50 432.00 341.00 XIV-C:610 Male NWHB 292.00 212.00 419.00 417.50 332.00 XIV-C:611 Male NWHB 287.00 215.00 420.00 414.50 332.00 XIV-C:617 Female NWHB 417.00 332.00 XIV-C:619 Male NWHB 301.00 202.00 401.50 400.00 302.00 XIV-C:622 Female NWHB 298.50 203.00 428.50 424.50 340.00 XIV-C:623 Female NWHB 280.00 202.00 402.50 398.00 329.00 XIV-C:624 Male NWHB 283.00 212.00 420.00 416.00 321.50 XIV-C:626 Female NWHB 270.50 189.50 384.00 380.50 309.00 XIV-C:627 Male NWHB 317.50 436.50 431.00 342.50 XIV-C:634 Female NWHB 292.50 211.50 416.00 414.50 351.50 XIV-C:637 Female NWHB 271.00 381.00 382.00 301.00 XIV-C:643 Male NWHB 324.50 233.00 468.00 466.00 376.00 XIV-C:646 Male NWHB 316.50 231.50 454.00 452.00 352.00