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Does Allen's rule rule? : a reanalysis of ecogeographic variation in modern human limb proportions Dembo, Mana 2007

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DOES A L L E N ' S R U L E R U L E ? A R E A N A L Y S I S OF E C O G E O G R A P H I C V A R I A T I O N IN M O D E R N H U M A N L I M B PROPORTIONS by Mana Dembo H o n . B . S c , The University of Toronto, 2005 A THESIS S U B M I T T E D I N P A R T I A L F U L F I L L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F M A S T E R OF A R T S in T H E F A C U L T Y OF G R A D U A T E S T U D I E S (Anthropology) T H E U N I V E R S I T Y OF B R I T I S H C O L U M B I A August 2007 © Mana Dembo, 2007 Abstract In the late 1970s Derek Roberts presented the first systematic analyses of the impact of climate on human morphological variability. One of his key findings was that humans follow Allen 's Rule, which holds that there is a positive correlation between peripheral body part size and temperature in homeothermic species. Roberts' conclusions regarding the applicability of Al len ' s Rule to humans have been widely accepted. However, three features of his analyses are potentially problematic. First, he used a sample that is strongly biased towards warm climates. Second, he maximized sample size of each limb segment at the expense of among-segment comparability. Third, he ignored the confounding effects of phylogeny. In this study the reliability of Roberts' conclusions were evaluated in relation to the aforementioned problems. In the first set of analyses, Roberts' analyses were replicated to ensure the dataset is comparable to his. In the second, the impact of sampling was investigated by examining the range of variation present especially among the warm climate populations. In the third, the impact of over-representation of warm temperature populations was evaluated by stratifying random samples by temperature. In the fourth, stratified analyses were conducted using populations with data for all limb segments. In the last set o f analyses, the influence of phylogeny was investigated through phylogenetically-controlled correlation analyses. i i The first set of analyses was consistent with Roberts' conclusions indicating that the dataset is appropriate. In the second, the slope of the regression line was variable such that both positive and negative correlations were obtained for many segments, which emphasizes the impact of sample selection. The third set supported the idea that over-representation of warm climates is problematic as only two segments yielded statistically significant correlations. In the fourth, the pattern of limb proportion variation did not support previous analyses, suggesting that Roberts' decision to maximize sample size at the expense o f among-segment comparability is problematic. The fifth set o f analyses indicated that phylogeny has a significant impact on the results of the correlation analyses. Together, the results of the analyses reported here cast doubt on the claim that humans follow Allen 's Rule. i i i Table of Contents Abstract i i Table of Contents iv List of Tables v List of Figures v i Acknowledgements v i i Chapter 1 Introduction 1.1 Ecogeographical rules 1 1.2 Bergmann's and Al len 's Rules in Anthropology 5 1.3 Problems with Roberts' analyses of modern human ecogeographic variation 7 1.4 Aims of this study 9 Chapter 2 Materials and Methods 3.1 Data 11 2.2 Analyses 13 Chapter 3 Results 3.1 Replicating Roberts' study 22 3.2 Analyses of minimum and maximum correlation coefficient samples 22 3.3 Segment specific stratified analyses 23 3.4 Multi-segment stratified analyses 24 3.5 Phylogenetically-controlled analyses 25 Chapter 4 Discussion 4.1 Summary of the results 27 4.2 Reliability of the study 28 4.3 Implication of the results 31 4.3.1 Explanations for the observed morphological variation 31 4.3.2 Other ways of adapting to the environment 34 Chapter 5 Conclusion 39 References 42 Appendix I Anthropometric data 49 Appendix II Sources of data 59 iv List of Tables Table 1 Anthropometric variables used in this study 12 Table 2 Results of the first set of analyses 22 Table 3 Results of the second set of analyses 23 Table 4 Results of the third set of analyses 24 Table 5 Results of the fourth set of analyses 25 Table 6 Results of the fifth set of analyses 26 v List of Figures Figure 1 Phylogenetic tree of lower arm random sample 1 17 Figure 2 Phylogenetic tree of lower arm random sample 2 17 Figure 3 Phylogenetic tree of lower arm random sample 3 18 Figure 4 Phylogenetic tree of lower arm random sample 4 18 Figure 5 Phylogenetic tree of lower arm random sample 5 19 Figure 6 Phylogenetic tree of foot length random sample 1 19 Figure 7 Phylogenetic tree of foot length random sample 2 20 Figure 8 Phylogenetic tree of foot length random sample 3 20 Figure 9 Phylogenetic tree of foot length random sample 4 21 Figure 10 Phylogenetic tree of foot length random sample 5 21 vi Acknowledgements This research would not have materialized without invaluable contributions of many people whom I would like to thank. First, I would like to thank my supervisor, Dr. Mark Collard for his guidance, support, and advice from the very beginning. I am very fortunate to have a supervisor who often goes above and beyond his call o f duty for his students. I also wish to thank my committee advisor, Dr. David Potokylo for juggling his unbelievably hectic schedule to meet with me throughout the M A program. I am truly grateful to him for introducing me to the wonderful world of SPSS. I am lucky to be surrounded by great minds and wonderful people. Many thanks are due to the past and present members of the Laboratory of Biological Anthropology for their advice and help in keeping my sanity: Marina Elliott, Briggs Buchanan, Hartley Odwak, Harald Yurk, Michael Kemery, and Kimberly Casey. I would especially like to thank Alan Cross for his advice and for making this project possible. In addition, I need to send a million emails to thank Pauline Shou and Kevin Quan for their expertise in computer programming that made the climate data less of a nightmare. Lastly, I would like to thank my family who has given me endless encouragement and support from half way across the country. Also many thanks are due to Anna Oh, Tom Porawski, Bobo L u i , Denise Edwards, Eric Burling, Shaun Deonarine, and Mariam Nargolwalla whose support and friendship made the life of a graduate student worthwhile. vi i 1. Introduction 1. Introduction The study reported here focused on what has become a widely accepted idea within the discipline of anthropology, namely that the limb proportions of modern humans follow Allen 's Rule. This rule states that, in homeothermic species, the size of peripheral body parts correlates with temperature such that populations l iving in colder climates have smaller peripheral body parts than populations living in warmer climates. The aim of the study was to evaluate the impact of several analytical shortcomings of the work that underpins the consensus that modern human limb proportions follow Al len ' s Rule—the second edition of Derek Roberts' 'Climate and Human Variabil i ty ' , published in 1978. The remainder of this chapter provides some background to Al len ' s Rule and the other main ecogeographical rule, Bergmann's Rule, and anthropological work dealing with the impact of climate on human morphological variation. The second chapter describes the data used and the analyses conducted in this study. The results of the analyses are presented in the third chapter. The fourth chapter discusses the reliability of results and their implications. The conclusions of the study are presented in chapter five. 1.1 Ecogeographical rules Ecogeographical rules are empirical generalizations that describe the correlation between aspects of organismal morphology and features of the physical environment. Bergmann's Rule and Al len 's Rule are perhaps the best known ecogeographical rules. Bergmann's Rule states that body size of homeothermic organisms increases as mean annual ambient temperature decreases (Bergmann 1847). Al len 's Rule is an extension of Bergmann's Rule. A s noted above, it states that in homeothermic species the size of peripheral body parts such as limbs, ears and tails correlates positively with temperature (Allen 1877). 1 1. Introduction Many studies have assessed the applicability o f Bergmann's Rule in a variety of vertebrate and invertebrate species since the appearance of Bergmann's (1847) study (e.g. Ray 1960; Lindsey 1966; James 1970; McNab 1971; Zink & Remsen 1986; Geist 1987; Paterson 1990; Blackburn & Gaston 1996; Ashton et al. 2000; Ashton 2002; Yom-Tov et al. 2002; Mei r i & Dayan 2003; Freckleton et al. 2003; Blackburn & Hawkins 2004; Y o m -Tov & Yom-Tov 2005; Rodriguez et al. 2006). Collectively, these studies suggest that Bergmann's Rules holds for the majority of mammalian and avian species. For example, Rensch (1938) noted that upwards o f 70% o f North American bird species conformed to the predictions of Bergmann's Rule. More recently, a meta-analysis conducted by Mei r i and Dayan (2003) found that 68 of 94 avian species and 97 of 149 mammalian species follow Bergmann's Rule. Fewer studies have assessed the applicability of Al len ' s Rule in natural populations (e.g. Niles 1973; Stevenson 1986; Yom-Tov & N i x 1986; Lindsay 1987; Rasmussen 1994; Fooden & Albrecht 1999; Laiolo & Rolando 2001; Cartar & Morrison 2005). These studies provide evidence that variation in the size of peripheral body parts of some mammalian and avian species conform to the predictions of Al len ' s Rule. For example, the lengths of ear, foot and tail in five mammalian species from Australia, beak length of Imperial Shags from South America, bi l l and tarsus lengths of Chough species, and the tail length in Macaques followed the trends expected by Al len ' s Rule (Yom-Tov & N i x 1986; Rasmussen 1994; Laiolo & Rolando 2001; Fooden & Albrecht 1999). However, other species do not follow the rule. For example, Stevenson (1986) found that the ear and tail lengths of hares and the hind limb lengths of hares and rabbits of North America do not vary in the manner predicted by the rule. 2 1. Introduction Thermoregulation is usually cited as the mechanism producing the patterns described by Bergmann's and Al len 's Rules (e.g. Weaver & Ingram 1969; Riesenfeld 1973; Baker 1988; Frisancho 1993). In homeothermic species, the maintenance of body heat is vital for proper organ function (Baker 1988; Frisancho 1993; Parsons 2003). The maintenance of body heat equilibrium involves basal metabolic rate, mechanical work done by the body and heat transferred into and out of the body via conduction, convection, radiation and evaporation through the skin surface (Frisancho 1993; Parsons 2003). Since body heat is generated through the process of metabolic energy conversion needed to support the mechanical work done by the muscles of the body, heat production is proportional to the amount of muscle and fat within the body (Baker 1988; Frisancho 1993; Parsons 2003). Therefore, heat production is largely a function of body mass (Baker 1988; Frisancho 1993; Parsons 2003). In contrast, heat loss is largely dependent on the surface area of the skin (Baker 1988; Frisancho 1993; Parsons 2003). The relationship between surface area and volume is such that increasing body size and reducing limb length results in a reduction in relative surface area, while decreasing body size and increasing limb length has the opposite effect. Hence, individuals who live in cold environments are expected to have been selected to have larger bodies and shorter limbs than conspecifics l iving in warmer environments. Not all researchers accept the idea that thermoregulation drives Bergmann's and Al len 's Rules. It has been argued that there are other ways to maintain body heat that are more efficient than altering body sizes and proportions, including increasing body insulation and altering vascular controls (Scholander 1955, 1956; Irving 1957; McNab 1971; Geist 1987). Skeptics of the thermoregulation hypothesis have also pointed out that 3 1. Introduction many ectothermic species such as insects, fish, and lizards, also exhibit patterns of body size variation consistent with Bergmann's Rule (Ray 1960; Lindsey 1966; Cushman et al. 1993; Van Voorhies 1996; Mousseau 1997). These researchers contend that thermoregulation cannot explain the pattern observed in ectotherms since the maintenance of internal body heat equilibrium is not as vital in cold-blooded organisms as it is in warm-blooded species. Therefore, the pattern observed in homeotherms is unlikely to be due to heat conservation and dissipation. Several alternative mechanisms have been proposed to explain Bergmann's Rule. Rosenzweig (1968) and Kolb (1978) have suggested that body size correlates better with food availability in the environment than temperature. Fewer species are capable of surviving in the harsher environmental conditions of higher latitudes. Therefore, there are greater opportunities for niche exploitation in such environments. Although the amount of primary productivity may be lower in higher latitudes, there are fewer species to exploit it. Thus, those that are able to persist in the environment have access to more resources, which may lead to the attainment of larger body sizes as observed. There may be another advantage to large body size in harsher environments, namely larger body mass increases starvation resistance (Calder 1984; Lindstedt & Boyce 1985). This would favour selection for larger body sizes in higher latitudes where resources are seasonally scarce and unpredictable year to year. A further possibility is that the pattern of body size variation may reflect the dispersal ability of organisms. Large body mass is associated with higher dispersal ability (Blackburn et al. 1999). Therefore, small bodied species are under-represented in the higher latitudes because they cannot disperse to exploit those niches as frequently as larger bodied species. A fourth possibility is that higher latitude 4 1. Introduction populations may have larger body masses because their ancestors happened to be larger by chance (Blackburn et al. 1999). These ancestral populations could have subsequently given rise to generations of organisms that are larger simply by descent. Lastly, it is possible that Bergmann's Rule could be due to linkage disequilibrium. That is, the pattern could be due to natural selection acting not on body size but on a trait that is linked to it (Blackburn et al. 1999). It has been argued that there need not be a single mechanism that applies to all taxa (Blackburn et al. 1999). There could be one mechanism for ectotherms and another for homeotherms. Moreover, various mechanisms could possibly interact to produce the pattern predicted. A certain mechanism, such as altering body sizes and proportions, may not necessarily be the most efficient in terms of thermoregulation. However, as long as it confers a slight advantage, natural selection could potentially favour that unique trait. Therefore, even i f body insulation and altered vascular controls are more efficient for thermoregulation, an organism with thick fur, decreased peripheral circulation, and larger body size with relatively shorter limbs could have higher fitness than the one with only thick fur, and decreased peripheral circulation (Mayr 1956: emphasis added). The cause of the patterns described by Bergmann's and Al len 's Rules could potentially be a complex interplay of various factors of phylogeny, physiology and ecology. 1.2 Bergmann's and Allen's Rules in Anthropology The first person to propose that Bergmann's and Al len 's Rules might apply to modern humans appears to have been Wi l l i am Ridgeway. In his presidential address at the 1908 meeting o f British Association for the Advancement of Science, Ridgeway argued that Homo sapiens can be expected to follow ecogeographical rules like the rest of the animal 5 1. Introduction kingdom. Given that humans are the most widely distributed species in the animal kingdom, H. sapiens has been subjected to greater extremes of temperature than any other mammalian species. Subsequent to Ridgeway's lecture, many studies have been conducted in which the morphology of humans and their fossil relatives has been analyzed with respect to climate. Several researchers have focused on body weight and its correlation with ambient temperature to demonstrate Bergmann's Rule in human populations (Schreider 1950, 1957, 1964; Roberts 1953; Katzmarzyk & Leonard 1998). Others have studied limb proportions and climate to provide evidence for Al len 's Rule in humans and other hominins (e.g. Newman 1953; Hiernaux & Froment 1976; Roberts 1978; Trinkaus 1981; Macho & Freedman 1987; Ruff 1991, 1993, 1994, 2002; Holliday 1997a, 1997b, 2006; Weinstein 2005). Today, these ecogeographical rules are widely considered to be supported by robust empirical evidence. This consensus is so wide spread among anthropologists that they are routinely taught in introductory biological anthropology classes as examples of climatic adaptation in human evolution (Boaz & Almquist 1999; Jurmain et al. 2003; Jurmain et al. 2004; Stanford et al. 2006). These rules are often demonstrated in a photograph of an Inuit alongside an equatorial African to show the marked differences in human body morphology (Jurmain et al. 2003; Jurmain et al. 2004; Stanford et al. 2006). O f the numerous studies dealing with the impact of climate on human morphology, by far the most influential are Derek Roberts' 1953 article 'Body weight, race, and climate' and his 1978 book 'Climate and Human Variabil i ty ' . In the former, Roberts reported analyses that he interpreted as demonstrating that modern humans follow Bergmann's Rule. Colder climate populations had relatively larger body weights 6 1. Introduction than warmer climate populations that generally had relatively lower body weights. In his 1978 volume, Roberts analyzed numerous anthropometric variables from globally distributed populations and studied bivariate correlations with mean annual temperature. Roberts found strong positive correlations with the limb segment lengths and mean annual temperature. Populations from colder climates had relatively shorter limb segments and conversely, populations from warmer climates had relatively longer limbs. Roberts concluded from these findings that modern humans follow Al len ' s Rule as well as Bergmann's Rule. Together, Roberts' (1953, 1978) works established the consensus that human morphology has been selected with respect to climate. In the last 30 years, numerous authors have referenced Roberts' works in the discussion of Bergmann's and Allen 's Rules to modern humans and fossil hominins (e.g. Trinkaus 1981; Austin & Lansing 1986; Macho & Freedman 1987; Hanna et al. 1989; Stinson 1990; Ruff & Walker 1993; Ruff 1991, 1993, 1994, 2002; Shea & Bailey 1996; Holliday 1997a, 1997b, 2006; Katzmarzyk & Leonard 1998; Bogin & Rios 2003; Jurmain et al. 2003; Stanford et al. 2006). More recently, Katzmarzyk and Leonard (1998)'s study has confirmed Roberts' conclusions and the validity of these rules. 1.3 Problems with Roberts' analyses of modern human ecogeographic variation Notwithstanding the fact that Roberts' (1978) conclusions have been widely accepted within anthropology, there are several reasons to be skeptical about his conclusions. First, the samples he used are dominated by warm-climate populations. It is possible that the skewed distribution in favour of one climatic region over others may have biased the 7 1. Introduction results of his correlation analyses. Although the issue of over-representation in such correlation analyses has been raised by some (e.g. Hiernaux & Froment 1976), no study to date has corrected for this problem. Second, the range of variation within Roberts' warm-climate populations is almost as great as the range of variation in the entire sample. A s it was reported in Roberts' (1978), some populations between 70-80°F fall well below the regression line. In fact, there are warm-climate populations with shorter relative span than some cold-climate populations. The wide range of variation in warm climates in and of itself indicates that human limb proportion variation cannot be explained by temperature differences alone. A third problem with Robert's (1978) study is that he maximized sample size of each limb segment by using different subsets of populations for each segment. Roberts (1978) recognized that this sampling practice is problematic. He warned that only the pattern of correlation, not the actual values of the correlations, is significant since the correlation coefficients for the various segments cannot be compared. However, the notion that he used different samples for different segments, comparing the patterns of correlation is no more appropriate than comparing the correlation coefficients. More problematical still, it is possible that the significant correlations are in fact artifacts of the sampling procedure. If populations adapted differently to the same climatic regime (e.g. one population may have evolved relatively long lower limb segments while another has evolved relatively long upper limb segments), the use of different subsamples may produce spurious correlations. The current study does not address this possibility. However, it would be an interesting topic to investigate in the future. 8 1. Introduction A fourth problem with Roberts' (1978) study is that he did not address the potentially confounding effects of phylogeny in his comparative analyses. Comparative studies of limb length using simple correlation analysis assume that data points are independent of one another (Felsenstein 1985; Harvey & Pagel 1991). However, because all organisms are related to one another, this is a potentially major flaw. Phylogenetic relationships lead to non-independence of data points. When data points of closely related populations are treated as independent data points, it may lead to a pseudoreplication of data. Pseudoreplication is more likely to produce statistically significant results because the degrees of freedom w i l l inevitably be larger than when the phylogenetic relationship between data points is considered (Felsenstein 1985). Thus, population history must be considered when investigating morphological variations with respect to climate. The underlying premise is that closely related populations tend to be more similar than distantly related populations due to the more recently shared ancestry. Therefore, this may give rise to the morphological pattern observed, which could be driven by genetic propinquity, not by climatic adaptation. 1.4 Aims of this study With the foregoing in mind, this study reinvestigates the validity of Roberts' (1978) conclusion that Al len 's Rule is applicable to modern human populations. The impact of sample choice w i l l be assessed by employing various sampling strategies. The impact of Roberts' failure to correct for phylogeny wi l l be investigated by carrying out correlation analyses in which the phylogenetic relationships among populations are taken into 9 1. Introduction account. If humans do indeed follow Allen 's Rule, then all of the analyses should return correlations between temperature and limb segment size that are positive and significant. 10 2. Materials and Methods 2. Materials and Methods 2.1 Data The morphological data were compiled from published anthropometric studies of populations in Africa, Eurasia, Oceania and the Americas. Populations were limited to the groups discussed in Outline of World Cultures (Murdock 1976), History and Geography of Human Genes (Cavalli-Sforza et al. 1994) American Indian Languages (Campbell 1997) and Ethnologue (Gordon 2005). To be included in the sample, groups had to be distinguished by dialectical differences. Comparisons o f first and second generation migrants indicate that environment can have a major impact on morphology via phenotypic plasticity as well as by means of natural selection (Boas 1912; Shapiro & Hulse 1939; Harrison 1988). Thus, in order to avoid introducing another source of variability, only populations that have existed in their current location since 1492 were used. Individuals reported in the original studies to be o f mixed ancestry were excluded from the dataset. Since Roberts (1978) used males in his study investigating limb proportions, only adult males were considered here. A n individual was considered to be adult i f they were 17 or older at the time of the data collection. Data were used i f the sample size of the population exceeded 10 individuals, and the sex of the individuals was stated. Variables for which values were collected include: stature, upper arm length, lower arm length, hand length and width, upper leg length, lower leg length, and foot length and width. Definitions of these variables are presented in Table 1. Descriptions of the landmarks and methods of measurement can be found in standard anthropometric texts (e.g. Hrdlicka 1947; Weiner & Lourie 1969). In order to counter the confounding 11 2. Materials and Methods effects of body size differences, relative segment measurements were used in all o f the statistical analyses. These were obtained by dividing each of the segment values by the stature of the individuals in question. Variable Definition Stature the superior point of the head to the ground Upper arm length the humeral head to the olecranon process of the ulna Lower arm length the olecranon process to the distal ulnar stylus Hand length the midline connecting the proximal limits of the hypothenar and thenar eminences of the palm to the tip of the third digit Hand width across the distal ends of the metacarpals Upper leg length the greater trochanter of the femur to the tibial plateau Lower leg length the tibial plateau to the distal malleolus of the tibia Foot length the back of the heel to the longest toe Foot width the maximum horizontal distance perpendicular to its longest axis wherever it is found across the foot Table 1. Anthropometric variables used in this study Geospatial coordinates were obtained from the original sources i f provided by the investigators. In most cases, however, only the location of the study site was reported. In such cases, any information given on the location (e.g. topographic description of the site of study, village names or maps) was used to obtain latitude and longitude from Geographic and Geospatial Information (Borseth 2006). If the data were compiled from populations sampled from multiple locations, the arithmetic means of the latitude and longitude of all o f the sites were calculated and taken as the geospatial coordinates. In a few cases where the national mean measurements were used, such as for the Italians or 12 2. Materials and Methods Czechs, no information on specific sites o f investigation was given. In these cases, the geospatial coordinates for the capital city of that country were used. Mean annual temperature data was obtained from the Climate Research Unit 's Global Climate Database (New et al. 1999). This database contains the mean monthly temperature from the period of 1961 to 1990 for 12,092 locations worldwide. The closest meteorological station to the site of investigation was determined based on the geospatial information. Mean annual temperature was calculated from the mean monthly data given for the meteorological station. Given that the mean monthly temperature data from the meteorological station is the average of a thirty year period, the climatic data and the geospatial coordinates should not be potential sources of error. The anthropometric and temperature data are provided in Appendix I. A list of relevant sources is presented in Appendix II. 2.2 Analyses Five sets of analyses were conducted. The aim of the first set was to confirm that the sample is suitable to evaluate the potential shortcomings of Roberts' (1978) study. In this analysis, only groups with sample sizes greater than 20 individuals were included. The sampled groups did not necessarily have all o f the l imb segment data. Bivariate correlations were calculated for all o f the limb segments and mean annual temperature. The analyses were conducted in SPSS version 12.0 (SPSS Inc, Chicago I L L ) . The goal of the second set of analyses was to examine the range of variation in limb segment lengths and widths among the sampled populations. The minimum and maximum correlation coefficients for each segment were generated by selecting a maximum of three populations from every 2.5°C increment in mean annual temperature 13 2. Materials and Methods to either minimize or maximize the slope of the regression line. For the minimum coefficient, the slope of the regression line was minimized by selecting populations with the longest relative segment lengths from the colder climates, and the populations with the shortest relative segment lengths from the warmer climates. Conversely, the maximum coefficient was calculated by selecting populations with the shortest relative segment lengths from the colder climates and populations with the longest relative segment lengths from the warmer climates. The colder climate represents areas with mean annual temperature below 15 °C and the warmer climate represents areas with mean annual temperature equal to and above 15 °C. Again, the analyses were conducted in SPSS. The third set o f analyses investigated the impact of the over-representation of warm-climate populations. This was accomplished by stratifying samples on the basis of mean annual temperature. Each anthropometric variable was analyzed separately to maximize the size of the samples. Sample sizes ranged from 28 populations (upper leg length) to 45 populations (hand length). Mean annual temperature was divided into 2.5°C increments and a maximum of three populations were randomly selected from each temperature category. Five random stratified samples were drawn for each limb segment. In order to maximize sample size, the minimum number of individuals per sample was reduced to 10 individuals. These analyses were also conducted in SPSS. The fourth set of analyses assessed the impact of Roberts' decision to maximize sample size at the expense of among-segment comparability. Only populations with all of the anthropometric variables were used. To ensure equal representation from all o f the temperature ranges, five stratified samples were randomly generated according to mean 14 2. Materials and Methods annual temperature. In order to maximize sample size, the mean annual temperature was divided into 5°C categories. From each mean annual temperature category, a maximum of three groups were randomly sampled. The stratified samples consisted of 14 populations each. Using the mean annual temperature and the relative segment lengths and widths, correlation coefficients were calculated in SPSS. The fifth set of analyses investigated the potential confounding effects of phylogeny. The populations were stratified based on mean annual temperature for every 2.5 °C and a maximum of three populations were selected from each temperature category. Five random samples were generated and then analyzed using the program Comparative Analysis by Independent Contrast (CAIC) version 2.6.9 (Purvis & Rambaut 1995). C A I C was used to calculate 'independent contrasts' at every node of the phylogeny. Independent contrasts measure the change in limb segmental lengths and widths along each branch o f the phylogenetic tree. The contrasts are calculated as the differences in trait X between two sister taxa divided by the variances in the trait X of these taxa and the time elapsed since the divergence of these two sister taxa (Felsenstein 1985). Each contrast is divided by its standard deviation. B y performing the above calculations, these contrasts are drawn independently from a bivariate normal distribution (Felsenstein 1985). Thus, even when the taxa analyzed are part of a nested hierarchy, these independent contrasts can be calculated from a known phylogeny and used in correlation and regression analyses. C A I C requires a fully resolved cladogram. The primary phylogenetic tree used in these analyses is the average linkage tree published in Cavalli-Sforza et al. (1994:78). 15 2. Materials and Methods This is a phylogenetic tree based on genetic distances (F S T ) calculated from 120 allele frequencies of 42 populations worldwide. Not all groups used in the analyses were included in Cavalli-Sforza et al.'s (1994) study. The missing groups were included by using subsets o f regionally specific genetic analyses as reported by Cavalli-Sforza et al. (1994), and the linguistic associations reported in Campbell (1997) and Gordon (2005) to produce fully resolved trees. The resulting trees are depicted in Figures 2-11, and were drawn using Mesquite 1.06 (Maddison & Maddison 2005). C M C offers two models of data analysis: ' C R U N C H ' and ' B R U N C H ' . These models differ on the assumptions made in the evolutionary model used to estimate evolutionary change. C R U N C H uses each value entered for a taxon to calculate an independent contrast between two sister taxa and in turn, uses those values to estimate the values for higher nodes. In contrast, B R U N C H uses the values only once, and thus calculates only half as many contrasts as the number of taxa (Purvis & Rambaut 1995). The B R U N C H algorithm is designed for use in studies in which a continuous variable is regressed against a discrete variable (Purvis & Webster 1999). The authors of C A I C recommend that C R U N C H should be used for continuous data (Purvis & Rambaut 1995). Therefore, since this study involves only continuous variables, the C R U N C H model was used. Furthermore, it was assumed that all branches were o f equal lengths. The results of the second set of analyses, segment-specific stratified samples indicated that lower arm length and relative foot length correlate significantly with mean annual temperature in all o f their respective five random samples. Thus, the influence of phylogeny was investigated using these samples. Regression analyses were performed and the R, R and P values were obtained using C A I C . 16 2. Materials and Methods Figure 1. Phylogenetic tree of random sample 1 used in the analysis of lower arm segment T Figure 2. Phylogenetic tree of random sample 2 used in the analysis of lower arm segment 17 2. Materials and Methods Figure 3. Phylogenetic tree of random sample 3 used in the analysis of lower arm segment m to ~ ra <a Figure 4. Phylogenetic tree of random sample 4 used in the analysis of lower arm segment 18 2. Materials and Methods Figure 5. Phylogenetic tree of random sample 5 used in the analysis of lower arm segment Figure 6. Phylogenetic tree of random sample 1 used in the analysis of foot length o a - s c S "5 2 o ffl « <3 ^  g1 3> * < s < Q a i < ? ; _ j u j O ^ : h - u - t o o > i < < J 2 ' s w x z o S 5 N S o c f l _ ) O X O Q . 2 f 2. 5 -g I -o _ a 3 to  o 5 o £ 8 *. I c n> <» 11 s I O 0 3 m £ .2 9> § | I <a to co o ce _LJJ UL] I^ JLJ jLLjJ a o> m 19 2. Materials and Methods Figure 7. Phylogenetic tree of random sample 2 used in the analysis of foot length -o IB a. 5 .._ w S a £ TO w o ^ a o ra .2 cu » a o co ~ S « a -2 § § X z 5 X 5 » co ^ til r to a. >* r £ J r -KJ < o o >-u u u u TO u S CQ < U l Figure 8. Phylogenetic tree of random sample 3 used in the analysis of foot length 20 2. Materials and Methods Figure 9. Phylogenetic tree of random sample 4 used in the analysis of foot length Figure 10. Phylogenetic tree of random sample 5 used in the analysis of foot length c o 1 § -a = c I 1 5 8 € 3 B - - a o 3 2 <u — I 3 Jd 2 O 8 !»* W ^ o £ oS ~ 3 § c _i §- -g 8 2 05 § 5 « » ° 5 & w * o « i z O < Q . X ( » o 5 z S x a 3 Z m m j UJUULJLJJ |LLJJUUJ UJ 3 1 < 5 e U 21 3. Results 3. Results 3.1 Replicating Roberts' analyses The results of the first set of analyses are summarized in Table 2. Most of the variables show a trend of increasing lengths in warmer climates, as predicted by Al len 's Rule. Relative upper arm, lower arm, upper leg, and foot lengths all correlate positively with mean annual temperature at a significance level of P=0.01. Relative foot width also shows a positive correlation with mean annual temperature, with the correlation significant at the P=0.05 level. However, two variables—hand length and width—do not show a trend of increasing lengths in warmer climates. Thus, the results of the first set of analyses are generally consistent with those obtained by Roberts (1978), although there are some differences like the hand length and width. Variable Sample size Pearson's r Upper arm length 130 .246 (**) Lower arm length 132 .435 (**) Hand length 204 -.097 Hand width 157 -.114 Upper leg length 69 .342 (**) Lower leg length 70 .157 Foot length 125 .458 (**) Foot width 111 .216 (*) Table 2. Results of the first set of analyses. Single asterix (*) indicates statistical significance at 0.05 level (two-tailed). Double asterix (**) indicates statistical significance at 0.01 level (two-tailed). 3.2 Analyses of minimum and maximum correlation coefficient samples The maximum correlation coefficients yielded strongly positive correlations with mean annual temperature for all segments (see Table 3). With the exception of hand width and upper leg length, all o f the variables achieved statistical significance at P=0.01. Upper leg 22 3. Results length achieved statistical significance at P=0.05. Hand width did not yield a statistically significant positive correlation in the maximum coefficient analysis. In the minimum coefficient analysis, a negative trend resulted in six of the eight segments. Hand length and hand width were negatively correlated at P=0.01 and upper arm length and foot width achieved negative correlations significant at P=0.05. Variable Sample size Min imum R Maximum R Upper arm length 36 -.355(*) .561 (**) Lower arm length 37 .104 .785(**) Hand length 45 -.604(**) .505 (**) Hand width 41 -.729 (**) .222 Upper leg length 28 -.157 .446(*) Lower leg length 33 -.201 .453(**) Foot length 41 .032 .627(**) Foot width 36 -.404 (*) .556(**) Table 3. Results of the second set of analyses. Single asterix (*) indicates statistical significance at 0.05 level (two-tailed). Double asterix (**) indicates statistical significance at 0.01 level (two-tailed). 3.3 Segment specific stratified analyses The third set of analyses investigated the potential problem of over-representation of warm-temperature populations, the results of which are presented in Table 4. When the samples were stratified, a significant correlation was observed only in the relative lower arm length and relative foot length in all five random samples at the P=0.01 level. A positive correlation between relative foot width with mean annual temperature was significant in four of the five random samples at the P=0.01 level. A positive trend following Al len ' s Rule was observed in relative upper arm, relative upper leg and relative lower leg lengths, but none of these correlations were statistically significant even at 23 3. Results P=0.05. The correlation coefficients of the relative lower leg lengths were only slightly positive in three analyses. Relative hand length and width were negatively correlated with mean annual temperature, and in one sample the correlation was statistically significant at the P=0.05 level. Variable Sample size Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Upper arm length 36 .210 .212 .160 .201 .052 Lower arm length 37 .638 (**) .466 (**) .518 (**) .572 (**) .588 (**) Hand length 45 -.142 -.218 -.336 (*) -.090 -.210 Hand width 41 -.271 -.208 -.337 (*) -.167 -.256 Upper leg length 28 .149 .223 .243 .293 .222 Lower leg length 33 .078 .098 .080 .247 .292 Foot length 41 .531 (**) .469 (**) .467 (**) .443 (**) .455 (**) Foot width 36 .435 (**) .402 (*) .351 (*) .215 .343 (*) Table 4. Results of the third set of analyses. Single asterix (*) indicates statistical significance at 0.05 level (two-tailed). Double asterix (**) indicates statistical significance at 0.01 level (two-tailed). 3.4 Multi-segment stratified analyses The results of the correlation analyses using the populations with data for all eight variables are presented in Table 5. In all five random samples, relative upper leg length and relative foot width followed the patterns predicted by Al len ' s Rule. However, these patterns were not all statistically significant. The correlation for relative foot length was significant at P=0.05 level in two of the five samples. Relative lower arm length, hand 24 3. Results width, lower leg length and foot length were even more sensitive to the composition of the random samples, as the correlations were both negative and positive in the five random samples. Relative upper arm length, hand length and hand width were always negatively correlated with mean annual temperature, which opposes the pattern predicted by Allen 's Rule. Hand length was statistically significant at P=0.05 in one of the five samples. Thus, the results of this set of analyses were not compatible with Roberts' (1978) findings. Variable N Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Upper arm length 13 -.144 -.246 -.083 -.006 -.166 Lower arm length 13 -.022 .089 .032 -.078 .193 Hand length 13 -.336 -.291 -.420 -.442 -.630(*) Hand width 13 -.317 -.002 -.269 -.070 -.364 Upper leg length 13 .384 .430 .456 .415 .518 Lower leg length 13 .007 -.092 -.098 -.049 .148 Foot length 13 -.159 .045 .112 .087 .081 Foot width 13 .254 .566 (*) .447 .673(*) .172 Table 5. Results of the fourth set of analyses. Single asterix (*) indicates statistical significance at 0.05 level (two-tailed). 3.5 Phylogenetically-controlled analyses Regression analyses on relative lower arm and relative foot lengths were conducted using independent contrasts with mean annual temperature using C A I C . To reiterate, these segments were selected because they were significantly correlated with mean annual temperature at P=0.01 in all random samples in the third set of analyses. The correlation coefficients, R 2 and P values from SPSS and C A I C for relative lower arm length and relative foot length are presented in Table 5. When the phylogenetic relationships were 25 3. Results taken into account, the correlations became insignificant in all five of the foot length samples and three of the five lower arm length samples. Standard Independent Contrasts R R 2 P R R P Lower arm length Sample 1 0.638 0.407 0.000 0.188 0.035 0.295 Sample 2 0.466 0.217 0.004 0.230 0.053 0.183 Sample 3 0.518 0.269 0.001 0.202 0.041 0.260 Sample 4 0.572 0.327 0.000 0.511 0.261 0.002 Sample 5 0.588 0.346 0.000 0.445 0.198 0.009 Foot length Sample 1 0.531 0.282 0.000 0.178 0.032 0.299 Sample 2 0.469 0.220 0.002 0.075 0.006 0.651 Sample 3 0.467 0.218 0.002 0.058 0.003 0.729 Sample 4 0.443 0.196 0.004 0.079 0.006 0.633 Sample 5 0.455 0.207 0.003 0.297 0.088 0.079 Table 6. Results of fifth set of analyses. Standard = correlation coefficients produced by regular analyses. Independent contrasts = correlation coefficients produced by independent contrasts. Single asterix (*) indicates statistical significance at 0.05 level (two-tailed). Double asterix (**) indicates statistical significance at 0.01 level (two-tailed). 26 4. Discussion 4. Discussion 4.1 Summary of results The results of the first set of analyses were generally consistent with those obtained by Roberts (1978). They indicate that the size of most limb segments is positively and significantly correlated with mean annual temperature. This suggests that the dataset is suitable to evaluate the impact of the shortcomings of Roberts' analyses. The results of the second set of analyses indicated that variation among the populations is so great that the strength of correlation, the pattern of correlation, and the statistical significance of the correlations are all dependent on which populations are chosen for analysis. This suggests that Roberts' finding o f a positive correlation between limb length and temperature is sample specific. The results of the third set of analyses indicate that Roberts' finding of a positive correlation between limb length and temperature is largely due to the over-representation of warm-climate populations in his sample. When the data for each segment were stratified by mean annual temperature, only two limb segments produced statistically significant positive correlations. The results o f the fourth set o f analyses also suggest that Roberts' finding of a positive correlation between limb length and temperature is due in large part to the over-representation of warm-climate populations in his sample. When only populations with data for all limb segments were analyzed and the warm-climate bias was taken into account, only two of the eight variables yielded positive correlations with temperature, and none consistently achieved statistical significance. The results of the fifth set of analyses suggest that Roberts' findings are not only sample specific, but also undermined by data point interdependence. When phylogenetic effects were removed, the correlation remained significant in only two of 27 4. Discussion the 10 random samples. Overall, the results of the study strongly suggest that Roberts' findings are invalid. This in turn casts doubt on the idea that modern humans follow Allen 's Rule. 4.2 Reliability of the study One of the potential shortcomings of this study concerns the size of the samples used in the third and fourth sets of analyses. In order to correct for the problem of over-representation of warm-climate populations, the samples were stratified on the basis of mean annual temperature. Due to a shortage of data for cold-climate populations, the stratified sampling reduced sample sizes to 28-45 in the third set of analyses. The sample size was further reduced to 13 populations in the fourth set of analyses. However, as the results of the third set of analyses indicated, the problem of over-representation of warm-climate populations is significant. Thus, stratified sampling of populations at the expense of reduced sample size was necessary. Furthermore, in a study reported by Trinkaus (1981), limb length variability was analyzed using only 13 populations. Given that Trinkaus (1981) is often cited as a reliable source regarding hominin morphological variation and climate (e.g. Ruff 1991, 1994; Holliday 1997a, 1997b), the small sample size used in the third and fourth sets of analyses should not undermine the results of this present study. A second potential shortcoming o f this study is the use o f anthropometric data only from male individuals. Froment and Hiernaux (1984) suggest that there may be a differential response in morphology to climatic variation between the sexes. These authors investigated morphological variation among 10 African populations living along 28 4. Discussion the Niger River in two ecological zones. Some populations were classified as Sahelian, and others as Sudanian. The Sahelian populations live in the dry environment of the Sahel Desert, while the Sudanian populations live in environments with greater moisture content south of the Niger River bend. Various anthropometric measurements such as limb segment lengths and facial dimensions were taken on male and female subjects. Although this study was limited in geographical scope, the authors reported that the Sahelian populations had significantly longer lower limbs, lower arms, larger hands and narrower faces (Froment & Hiernaux 1984). These were hypothesized to be adaptations to the drier environments characterized by higher peaks of heat than their Sudanian neighbours. However, this pattern was only observed in the female subjects. The limb proportion variation in males did not produce a significant correlation with climatic variables. Among the males, the only significant correlation was in the dimensions of the ear. Thus, it is possible that different results would have been obtained i f values for females had been included. However, given that all of the correlations related to limb segments reported by Roberts (1978) were based on males, the exclusion o f female individuals from this study should not undermine the results presented here. Another potential criticism also concerns the dataset used in this study. Since Roberts did not provide a list o f the sources of anthropometric data he employed, it was not possible to use exactly the same sample of populations. However, as the results of the first set of analyses indicate, most of the limb segments produced correlation coefficients and P-values comparable to those obtained by Roberts. This suggests that the dataset used here is comparable to his. However, unlike Roberts' study, relative hand length and width yielded negative correlations, albeit they were statistically insignificant. One likely 29 4. Discussion explanation for this difference is that this study incorporated more data on the dimensions of the hand than did Roberts. In addition, the bulk of the data were published after Roberts' study. A s Katzmarzyk and Leonard (1998) have noted, it is possible that more recent data could reflect the secular changes that have been documented in various populations. However, it seems unlikely that the impact of secular changes in the last 30 years would be great enough to mask the effects o f thousands o f years o f climatic adaptation. A s a case in point, consider Katzmarzyk and Leonard's (1998) study. Their analyses still supported Bergmann's Rule even though they found evidence of secular changes. Thus, it seems reasonable to conclude that the dataset is appropriate for the purposes of this study and that discrepancies between the two sets of results are mainly due to the problems with Roberts' study. The fourth potentially problematic issue is the phylogenetic trees used in the last set of analyses. The trees in question were primarily based on genetic distance tree published by Cavalli-Sforza et al. (1994). However, the resolution of the genetic tree was not good enough to accommodate all of the populations used in the analyses. Thus, modifications to the genetic distance tree were made by incorporating information on the linguistic affinities of various populations (Murdock 1976; Greenberg 1987; Campbell 1997; Gordon 2005). Unfortunately, there is considerable debate surrounding linguistic affiliations in some regions of the world. The use of molecular genetics has helped resolve some problematic cases (Renfrew 2000). For example, molecular data have supported the validity of the existence of various suggested linguistic phyla such as the existence o f Hokan and Penutian linguistic phyla in North America (Eshleman et al. 2004). However, in other cases there is still some uncertainty (e.g Ruhlen 2000; Torroni 30 4. Discussion 2000). With this in mind, I chose to incorporate interpretations that seem to be accepted by the majority of researchers, such as the recognition of the Hokan linguistic group as a valid and distinct group from the Penutian family in the Amerind linguistic phylum (e.g. Greenberg 1987; Campbell 1997; Eshleman et al. 2004). While it is possible that my combined genetic/linguistic phylogenetic trees may not be entirely accurate, it seems unlikely that the errors are sufficiently numerous to undercut the results. Thus, the results of the phylogenetically-controlled analyses should not be undermined by the uncertainties of the phylogenetic trees used. 4.3 Implications of the results The results of the present study suggest that, contrary to what Roberts (1978) concluded, variation in limb proportions in modern humans does not correspond to changes in mean annual temperature as predicted by Al len 's Rule. This raises two questions. First, i f variation in modern human limb proportions is not linked to thermoregulation, what drives it? Second, i f not through changing limb proportions, how do humans respond to various climates? 4.3.1 Explanations for the observed morphological variation The only climatic factor addressed in this study was mean annual temperature. This variable was chosen because it was used by Roberts (1978). However, it is possible that the human morphology has been selected in relation to other environmental factors. Froment and Hiernaux's (1984) study suggests that moisture content of the air may be important. It has been noted that the perspiration is the most effective method o f lowering body temperature in hot, dry environments (Baker 1988). Thus, it would be valuable to 31 4. Discussion further investigate the relationship between human limb proportion variation and other factors of the environment such as aridity, precipitation, and available net primary and secondary productivity to infer diet. These environmental factors may have stronger explanatory power for human limb proportion variation than temperature. The fifth set of analyses indicated that phylogeny, which has heretofore never been investigated in relation to limb proportion variation and climate, has a significant impact on human morphology. This suggests that modern human limb proportions may simply reflect the population history of H. sapiens. Some support for this hypothesis comes from analyses o f mitochondrial D N A . Europeans, Asians and the indigenous inhabitants of the Americas exhibit much less m t D N A variability than Africans (Cann et al. 1987). This pattern is thought to be the result of founder effect (Cann et al. 1987). A s a type of genetic drift, founder effect involves changes in allele frequencies occurring as a result of a non-selective process. In this case, the change in allele frequencies occurs due to a daughter population being a small non-representative sample o f its parent population (Mayr 1963). Recent studies by Manica et al. (2007) and Weaver et al. (2007) suggest that the founder effect has played a role in shaping human cranial diversity. With this in mind, it is worth considering the possibility that founder effect has shaped the variability in limb proportions among human populations. It is possible that as groups migrated out of Africa to inhabit various parts of the world, limb proportion variation became progressively more limited. The populations that migrated out of Africa and settled in Asia would represent the initial reduction in limb proportion diversity. A s groups moved from As i a into the Americas and the Pacific, the variation in l imb proportion would be further reduced due to founder effect. Thus, it is possible that the repeated impact of the 32 4. Discussion founder effect in various parts of the world such as Eurasia, Oceania, and the Americas may underlie the observed limb proportion variability in modern humans. Another possibility is that the limb proportion variation reflects the phenotypic plasticity of modern humans. Phenotypic plasticity refers to the ability of a genotype to produce various phenotypes in response to different environmental conditions (Futuyma 1998). The limb proportion variation could be the result of phenotypic plasticity as individuals respond to various environmental conditions. These responses may occur as a result of changes in developmental processes and growth rates, which may eventually lead to changes in morphology. However, these morphological changes are not heritable; they are adjustments made by the individuals to the environment (Agrawal 2001). One example of phenotypic plasticity in modern humans is the so-called secular changes documented in the last century. Such changes have often been attributed to improved nutrition, health care and environmental conditions (Tanner 1988). In Katzmarzyk and Leonard's (1998) study, the correlation coefficients between body weight and mean annual temperature and relative sitting height and mean annual temperature obtained were smaller than the reported values of Roberts' 1953 study. They attributed this to secular changes, especially among tropical populations. If secular changes play an important role in determining body shape and proportions of modern humans, this could potentially be reflected in the variations recorded in the present study. It has been observed that secular changes have been most significant in the length o f the lower limb (Shapiro & Hulse 1938; Bogin & Rios 2003). Thus, it is possible that the populations from more recent studies possess relatively longer lower l imb than ancestral populations in the same geographical locations in the late 19 t h century. If this is the case, then the 33 4. Discussion morphological pattern observed in this present study may reflect the improvement in r nutrition and health care, especially in developing parts o f the world where major changes have taken place in the last century. Thus, human limb proportion variation may be responding to these non-climatic factors. 4.3.2 Other ways of adapting to the environment A s mentioned in the Introduction, many scholars have questioned the thermoregulatory explanation for Bergmann's and Allen 's Rules (e.g. Scholander 1955, 1956; Irving 1957; McNab 1971; Geist 1987). Most notably for present purposes, Steegmann (2007:224) has recently suggested that there are so many unanswered questions regarding thermoregulatory explanations for human morphological variation that such explanations should be considered no more than "folklore". It has been argued that other ways of maintaining body heat equilibrium, such as increasing body insulation and altering vascular controls, are more efficient than altering body sizes and proportions (Scholander 1955; 1956). The corollary of this is that, relative to such physiological factors, limb proportion changes may only play a minor role in temperature tolerance in modern humans (Hanna et al. 1989). Physiological responses to climatic factors have been investigated by various researchers (see Hanna et al. 1989 for a review). Human physiology is able to cope with considerable differences in environmental conditions. With highly efficient sweat glands and sparsely haired surface, heat dissipation occurs well in hot, dry environments (Rowell 1977; Baker 1988). Evaporation of sweat requires heat to convert water from liquid into gaseous phase of the molecules (Parsons 2003). Thus, in theory, evaporation of one litre of sweat uses up 25,000 kilo-joules of heat (Baker 1988). A s such, the loss of body heat 34 4. Discussion occurs efficiently through evaporative cooling in environments where water evaporation can be induced readily. However, this relationship changes in a hot, humid environment where the air is so saturated that evaporation does not occur. It has been observed that the effectiveness of evaporative cooling depends on the percentage of active sweat glands (Baker 1988). The percentage of active sweat glands in turn depends on the exposure of high heat loads during an individual's childhood (Kuno 1956). Thus, it is possible that the key factor in warm, dry climates is the percentage of active sweat glands o f an individual, not the amount of skin surface area. Therefore, even i f the limb length of a warmer population is the equivalent to that of a colder population, i f the percentage of active glands is greater in the warmer population, the limb is more effective at dissipating body heat. If this is the case, then the limb proportional variation among modern humans need not produce a pattern predicted by Al len 's Rule. Vasoconstriction is a response to cold temperatures. It involves reducing skin blood flow by reducing the diameter of the blood vessels, thereby minimizing heat loss (Wade et al. 1979; Baker 1988). In a laboratory experiment conducted by Wade et al. (1979), the amount of blood flow to various body parts was measured after the subjects were immersed in 25°C water for 30 minutes. They reported that during the immersion experiment, average heat loss from different body segments varied. The upper torso lost 24 W, the head 17 W , the calf 17W, the hands 1.5 W each and the feet 1.5 W each (Wade et al. 1979). Compared to the torso, limb segments became so vasoconstricted that little heat was lost through the skin surface during immersion. If little heat is lost through the limbs because blood flow to these structures is reduced, then it may not be necessary for these structures to become shorter in colder environments. 35 4. Discussion In addition to vasoconstriction, heat loss can be reduced in colder environments by rerouting blood circulation in the extremities from the superficial to deep veins (Frisancho 1993). A s blood circulates through the arms and legs back into the heart, the blood in deep veins absorbs heat from nearby arteries that are pumping warm blood from the heart to the periphery in a process called countercurrent heat exchange (Frisancho 1993; Parsons 2003). When blood leaves the heart, it has a temperature of approximately 37°C. A s blood travels along the length of the arm and reaches the hand, the temperature drops to 20°C (Frisancho 1993). However, as it circulates back to the heart through the deep veins, the blood warms up to 28°C (Frisancho 1993). The countercurrent heat exchange maintains blood flow to the limbs while reducing heat conductance to the extremities (Frisancho 1993). It is possible that this mechanism may also override the need for l imb length variability as expected by Al len ' s Rule. Acclimatization may also be an important factor. Acclimatization to various heat conditions have been observed in experimental conditions (Hanna et al. 1989). Once the body acclimatizes to a higher heat load, the amount of sweat production increases within several days, the sodium content of sweat decreases, the body core temperature stabilizes at a lower point, and the heart rate lowers to reduce the cardiovascular strain in the new environment (Baker 1988). These physiological changes can be induced within a couple of days in order to optimize the body heat equilibrium in a new environment. Thus, even in the absence of the theoretically optimal body shape and size, the body responds to ambient temperature in order to maintain proper functioning o f the vital organs. It is possible that these physiological acclimatization processes allow modern humans to 36 4. Discussion exploit various thermal environments without necessitating changes in the morphological structures of the body. It has been noted, however, that the mechanisms of acclimatization to colder temperatures are limited in humans (Baker 1988). This suggests that the efficiency and need for morphological changes as an adaptive response may be different in colder and warmer environments. In warm climates, the body can respond through various acclimatization processes to optimize thermoregulation. However, since acclimation responses to colder temperatures are limited, morphological changes may be a more efficient means of regulating body temperature specifically in response to cold climates. This may explain the wider range of variation observed among warmer climate populations in comparison to the smaller range of variation among the colder climate populations, as was reported in Roberts (1978). In addition, behavioural responses may greatly enhance the heat and cold tolerance of modern humans in the absence of morphological changes. The use of fire, protective shelter and thermal clothing are obvious cultural adaptations to cold environments. Anthropologists have observed numerous accounts of such cultural adaptations by various populations like the construction of semi-circular windbreaks of grass and bough huts by the Bushmen of the Kalahari Desert and the caribou fur clothing of the Inuit in the Canadian arctic (Frisancho 1993; Stenson 1991). Cree and Ojibway fishermen rely on the use their mouths to get fish off the nets during the winter possibly to minimize the exposure of their hands to the cold (Steegmann 2007). Ethnographic reports of such behavioural adaptations to the environment are numerous, as are 37 4. Discussion anecdotal accounts of our own responses to the environmental conditions here and elsewhere. A s recently pointed out by Steegmann (2007), these physiological and biocultural aspects of human climate-related adaptability have not been adequately addressed and have been largely ignored by human biologists since the 1970s. Over the last 30 years, the focus of human biological research has shifted to molecular genetics and these issues have been left under explored (Steegmann 2007). However, the applicability of ecogeographical rules to morphological variations in modern humans has been widely accepted in biological anthropology despite these unanswered physiological and biocultural questions. In light o f the results of this study, perhaps the acceptance of the applicability of Al len ' s Rule to modern humans has indeed been insufficiently critical. 38 5. Conclusion 5. Conclusion The study reported here focused on the reliability of the conclusion Roberts reached in his 1978 book 'Climate and Human Variability ' that modern human limb proportion variation is consistent with Al len 's Rule. It did so because 'Climate and Human Variability' is primarily responsible for establishing the current consensus in anthropology that modern humans follow Allen 's Rule. The impact of a number of the shortcomings of Roberts' analyses was assessed. One problem that was examined is that the sample used in Roberts' analyses is strongly biased towards warm climates. Another is that Roberts employed different subsamples for each limb segment, thereby making it impossible to compare the impact of temperature on different l imb segments. A third problem that was examined is that the potentially confounding effects of population history were not addressed in his correlation analyses. Five sets of analyses were carried out. The first set of analyses replicated Roberts' methodologies to evaluate the appropriateness of the data used in this study. The results of these analyses generally supported the conclusions o f Roberts' (1978), which indicated that the sample is appropriate. The results of maximum/minimum correlation coefficient analyses suggested that the range of variation among populations is so great such that the results of the correlation analyses were largely dependent on the composition of the sampled populations. Segment-specific stratified analyses indicated that the over-representation o f warmer climate is problematic as only two limb segments yielded statistically significant correlations. In multi-segment stratified analyses, the pattern of limb proportion variation did not support the findings of previous three sets of analyses. This underscores the sensitivity of the correlations to sample selection with the 39 5. Conclusion implementation of a more rigorous selection criterion. Phylogenetically-controlled analyses returned statistically significant associations consistent with Al len ' s Rule in only two of 10 correlation analyses. This highlights the importance of Roberts' failure to control for phylogeny. Together, the results of the analyses support the idea that Roberts' work on the impact of temperature on modern human limb proportions is problematic, and call into question the notion that modern humans follow Al len ' s Rule. Several authors have highlighted species that do not follow Bergmann's and Allen 's Rules in body size and shape variations (Rensch 1938; McNab 1971; Riesenfeld 1980; Stevenson 1986; Mei r i & Dayan 2003). Therefore, it is not entirely surprising that modern human limb segment variability does not follow Al len ' s Rule. Given the numerous physiological and behavioural responses to heat and cold stresses that are available to humans, the traditional thermoregulation model for Al len ' s Rule may be limited to account for the morphological variability in modern humans. With regard to future research, one obvious course of action would be to revisit the applicability of Bergmann's Rule with the sampling strategies and analytical techniques used here. A s it has been pointed earlier in this study, Katzmarzyk and Leonard (1998) recently conducted analyses investigating body size variation in different climates. They found that human body mass follows Bergmann's Rule. However, their study also suffers from the same problems as Roberts with respect to the over-representation of warm-climate populations and the failure to control for phylogenetic relationships. It is possible that once the samples are stratified and the effects of phylogeny are removed, the correlation between body size and temperature may not 40 5. Conclusion conform to Bergmann's Rule and the findings o f Roberts (1953) and Katzmarzyk and Leonard (1998). Climatic factors have long been applied to explain morphological variation in modern humans. However, it may be necessary to seek explanations other than mean annual temperature to account for the observed variation. Exploring physiological and biocultural responses in various environmental conditions may lead to better insight regarding human climatic adaptation. There is also a need to study the entire scope of modern human variation. Many populations have been excluded in this study simply because the data were not available. The trend in anthropological research has moved away from anthropometric studies of the early 20 t h century. 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Current Ornithology 4, 1-69. 49 OS Burkina Faso I Burkina Faso Burkina Faso Burkina Faso Brazil Bolivia Bolivia Australia Australia Australia Australia Australia Argentina Angola Angola Angola Angola 1 Angola Angola Algeria Algeria Algeria Algeria Algeria Country Malebe 1 Gurmanche Bwaba Bella Wapisiana Quechuas Aymara Victoria River South North Melville & Bathurst Isles Central O 3 ss O o' Muila Luimbe Ginga Chokwe Bieno Tuareg Reguibat Mekhadma Chaamba Beni Group Ul SO o NJ U) U) NJ Ul NJ U) 00 Ul NJ 00 NJ o Ui NJ O © o o o o o o o o o © o OJ 00 UJ o u> Ul SO SO 4^  4^  N NJ bo bo 4* U) © • SO U) NJ • Ul NJ 1 i U) U) 1 OS i NJ NJ i Ul UJ bo 1 Os b • SO 1 to Ul 1 SO 4* • bo • to NJ so NJ Os <l u> 4^  <1 u> 4^  os U) NJ b Latitude (90°S=-90) t © Ui O • i O Ul Os o bo 1 OS OS i Os ^ ) NJ 129.7 131.6 133.6 130.7 131.9 • OS 1^ NJ os ;-J IK) OS 4^  NJ O SO b 1 © i OS b Ul Ul bo Ul U) Longitude (180°E=-180) 29.36 | 28.44 27.65 29.36 26.86 <l bo SO OS 27.11 17.60 26.47 27.29 22.34 Ul so oo 25.33 18.93 19.98 20.94 | 23.16 18.55 24.83 25.17 19.58 16.94 22.41 Mean annual temp (°C) 1687.5 1 1720.4 1706.6 1684.2 1594.0 1596.0 1589.5 1692.9 1626.8 1700.5 1668.3 1684.0 1738.4 1655.0 1668.0 1681.0 1622.0 | 1599.0 1666.0 1750.0 1659.5 1630.8 1668.5 1640.0 Stature (mm) 0.194 1 0.195 0.192 0.196 0.194 0.191 0.189 0.189 0.190 0.191 0.190 0.190 | 0.191 0.193 0.190 Rel up arm length 0.160 | 0.159 0.159 0.162 0.168 0.164 0.163 0.151 0.161 0.162 0.164 0.162 | 0.161 0.163 0.154 Rel low arm length o o Ul o o o o so o o SO o o o o SO o o o o oo o o o o o o NJ o © o OS o o os o NJ o o o Ul Rel hand length 0.049 | 0.049 0.050 0.048 0.051 0.048 0.047 0.047 0.049 0.046 0.049 0.050 0.051 0.050 0.050 \ 0.050 0.051 0.052 0.051 0.051 Rel hand width 0.306 1 0.305 0.301 0.302 0.275 0.263 0.264 Rel up leg length 0.250 0.245 0.242 0.245 0.238 Rel low leg length 0.156 0.149 0.149 0.149 0.149 0.148 0.152 0.154 0.160 0.153 0.153 Rel foot length 0.063 0.062 0.062 0.056 0.056 0.059 0.061 0.061 0.060 Rel foot width I xiptraddy IS China China China China China China China China China China China Chile Chad Chad Chad Canada Canada Canada 1 Canada Canada Cameroon Cambodia Burkina Faso Burkina Faso Burkina Faso Country Changtang Shui-his Miao Pai-Y Pa Miao Mongolians Lu-jen Jinuo Hani Setchuan Bulang Black Lolo Yahgans Toubous 00 (a *-t P Goranes Labrador Inuits Igloolik Inuits Cree Chipewyan Beaver Fang Khmers Rimaibe Mossi of Kokologo Mossi of Donse Group SO OS to o o UJ © to Ul to Ul UJ Ul to 4^ 00 © © Ul ^ 1 OS © Ul to Ul to 4^ 4^ to oo 4^ 4^ 4^ © Ul SO 4^ 4^ © UJ UJ to SO Ul © UJ Os Ul UJ oo to 4^ SO N UJ IO © UJ to Ul b UJ 4^ 4^ © to Ul © to Ul b to Ul b UJ © to Ul b UJ i Ul 4i. SO to Ul © Ul UJ Ul OS OS Os SO 4^ Ul 4^ Ul Ul so to Ul OS b bo UJ UJ -o to to 4=> Latitude (90°S=-90) SO o b © SO ui o to b © SO Ln to © © to © © to © © to b © 4^ © to © © SO Ul 1 Os oo © Ul ^ 1 SO to to b 1 OS "o i OO bo • so 4i--O 1 -109.5 -121.9 k) 103.7 i © Ul 1 so i to k) Longitude (180°E=- 180) • UJ o 14.73 14.89 14.73 UJ 4^. 14.89 14.89 14.89 16.19 14.89 14.73 4^ k) 4^. 21.94 27.44 24.18 Ul UJ Ul -14.23 i SO t^ • to bo UJ Ul © 23.71 27.49 29.36 28.14 27.97 Mean annual temp (°C) 1668.8 1561.0 1588.0 1558.8 1640.1 1601.0 1566.3 1594.8 1624.7 1555.5 1674.0 1571.3 1679.2 1739.2 1705.5 1584.8 1637.9 1715.0 1 1647.0 1683.0 1679.0 1610.5 1725.1 1689.3 1677.7 Stature (mm) 0.181 0.179 0.172 0.184 0.192 0.190 0.201 0.198 0.190 0.194 0.195 0.200 Rel up arm length 0.156 0.150 0.148 0.153 0.155 0.160 0.148 0.138 0.162 0.153 0.161 0.163 0.166 Rel low arm length o © o © © © © © © © © © © o o © © © o © © © o © © to UJ o 4^ © SO © OS to UJ © SO © oo to SO © Os © OS Ul © to Ul o SO Ul Ul Rel hand length 0.051 0.054 0.048 0.055 0.050 0.042 0.052 0.051 0.052 0.045 0.050 0.050 0.050 0.050 0.051 | 0.054 0.050 0.050 0.052 0.049 0.049 0.049 Rel hand width 0.250 0.251 0.248 0.253 0.249 0.291 0.294 0.305 0.303 0.303 Rel up leg length 0.227 0.216 0.220 0.221 0.223 0.237 0.228 0.238 0.224 Rel low leg length 0.152 0.152 0.148 0.147 0.148 0.142 0.155 0.153 0.152 0.143 0.154 0.157 Rel foot length 0.062 0.064 0.062 0.061 0.062 0.053 0.059 0.059 0.060 0.062 Rel foot width I xipuaddy D R Congo D R Congo 1 DR Congo D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo Czech Rep Cyprus Congo Congo Columbia | Columbia Columbia Columbia Columbia Columbia Columbia China China China Country Lese Hunde 1 Humu Havu Fulero Efe and Basua Beyru Bali Bira of savannah Bira of rain forest Aka Czech Cypriotes Luba Ituri Pygmy Totoro Quisgo Purace Paez Kokonuko Gaumbiano Ambalo C Tsang Kam Group Os o o © o o o o o U ) 00 OS U l t o t o t o o o oo U l to OS SO oo oo t o Os Os SO t o oo o - = SO OS t o t o U l 00 t o U l U ) N tO bo • o 4*. 1 t o 1 4^ bo t o t o OS o 4^ t o 4^ 50.1 34.7 -11.7 SO t o Os t o t o t o bo t o bo t o ON t o bo 32.0 32.0 32.0 Latitude (90°S=-90) 29.0 29.1 1 29.9 28.9 29.1 28.8 28.0 27.3 30.1 28.6 18.0 14.5 32.9 27.5 30.0 -76.3 1 -76.4J -76.4 -76.0 -76.0 -77.0 -76.3 90.0 90.0 90.0 Longitude (180°E=- 180) 23.55 18.18 | 17.68 16.91 21.98 23.06 23.15 24.16 22.59 22.88 24.87 8.53 17.60 20.22 22.67 14.95 ! 18.63 14.95 13.27 13.27 24.43 18.63 -3.20 -3.20 -3.20 Mean annual temp (°C) 1585.6 1639.8 | 1577.8 1623.5 1590.6 1438.1 1618.6 1608.9 1612.3 1580.3 1444.1 1726.0 1649.0 1656.1 1441.9 1571.0 | 1568.0 1598.0 1583.0 1548.0 1576.0 1555.0 1650.9 1648.9 1664.3 Stature (mm) o o o o o o o o o o o o o o o o o o o o © SO o oo SO oo U l oo ^4 oo so 4^ SO t o o SO 41. SO 4^ SO o 00 SO SO 4i. SO oo 4^ 00 o 00 4=. SO oo oo Rel up arm length o o o © © o © o o O o o O O © O o o o o © U l SO Os o OS t o U l oo Os o U l o 4*. U l U ) OS U l Os Os 4^ IO 4^ 4*. Os 41. 4*. SO U i t o U l Os U l t o U l U i U l U l U l U l Rel low arm length o o © © o o o o o O o O o O o o o o o o © © © © © o o t o o o oo o o U l o U l o OS o OS O o Os O OS o oo O SO o )^ 4^ t o - o © t o t o Rel hand length 0.046 0.046 0.048 0.048 0.050 0.049 0.051 0.051 0.051 0.051 Rel hand width 0.272 0.272 0.297 0.288 0.279 0.233 0.291 | 0.297 0.303 0.299 0.304 0.303 0.297 Rel up leg length 0.234 0.224 0.236 0.232 0.220 0.225 j 0.221 0.232 0.226 0.224 0.225 0.228 Rel low leg length © © p o o o p p U l U l U l 4^ U l U) U l OS U l U l U l u> U l Rel foot length | 0.062 | 0.063 0.060. 0.061 0.062 0.058 0.061 Rel foot width I xtpuaddv es India India Guyana Guinea Guinea Guinea Guatemala Greece France France France Finland Tl Egypt Egypt Egypt Egypt D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo D R Congo Country Car Nicobarese Andamanese Caribs Toma Kissi Guerze Mam Indians Greeks Normandie Lifu Islanders Basque Finns Lauans Siwah Sidi Barrany Salloum Marsa Matrouh Tembo Swaga 00 c 00 Rega Nyanga Ndaka Mbuba Group Ul oo OS to to o to to o OS 1,084 o o o oo U J OS SO U J 4^ Ul to SO U J 4^ o 4^ o o o © o o o oo o o o o Ul 4^ o o N SO k> to Ui -J b 00 Ul SO to <i bo Ul u> U J 00 b 4^ SO b • to b 4^ U J U J Os Ul bs 1 U J to SO to u> bs to Ul to U ) 4*. • to • OS Ul o to 1 to Ul • Os so • to bs 1 -o. Latitude (90°S=-90) SO to bo so tO bo • u i oo Ul • SO Ul 1 SO Ul • 00 bo • SO Ul to U J -o o b 167.0 1 to oo U J -179.0 to Ul "o to Ul SO U J bs to to to oo SO to 4^ bo to SO U J to 00 so to •o bs to 00 to oo Ul Ul Longitude (180°E=-180) 27.00 26.54 26.55 24.81 24.41 24.52 19.22 16.68 10.47 23.00 11.76 o b U J 25.73 21.33 20.33 23.60 19.23 16.91 24.70 20.19 17.77 25.11 21.79 23.55 24.63 Mean annual temp (°C) 1587.0 1489.9 1577.0 1697.0 1698.0 1681.0 1559.7 1705.1 1642.0 1691.0 1676.2 1729.1 1676.3 1645.9 1619.5 1670.2 1590.1 1616.5 1622.8 1638.8 1621.7 1601.8 1588.0 1580.8 Stature (mm) 0.185 0.212 0.167 0.188 0.188 0.186 0.198 0.207 0.186 0.195 0.192 0.190 0.193 0.185 0.186 0.189 0.188 0.182 0.181 0.195 0.179 Rel up arm length 0.154 0.163 0.171 0.163 0.159 0.161 0.149 0.163 0.155 0.152 0.149 0.149 0.146 0.156 0.163 0.164 0.162 0.159 0.159 0.151 0.161 Rel low arm length 0.113 0.081 0.108 0.115 0.112 0.113 0.109 0.110 0.114 0.123 0.099 0.114 0.114 0.114 0.107 0.103 0.104 0.102 0.106 0.103 0.105 0.103 Rel hand length 0.053 0.051 0.046 0.051 0.059 0.048 0.050 0.052 0.052 0.051 0.049 Rel hand width 0.276 0.273 0.277 0.275 0.257 0.298 0.309 0.293 0.285 Rel up leg length 0.210 0.267 0.236 0.228 0.232 0.224 0.253 0.242 0.223 0.222 0.242 0.230 0.231 Rel low leg length 0.152 0.158 0.155 0.155 0.154 0.156 0.160 0.152 0.151 0.156 0.156 0.153 0.156 Rel foot length 990 0 0.064 0.063 0.059 0.065 0.058 0.060 0.061 0.060 0.062 Rel foot width I xipusddv | Kenya Kenya | Kenya Kenya Kenya Japan (a T3 05 3 Japan Japan Japan •a 3 ET •<* India India India India India India India India India India India India Country | Abanyore Abalogoli | Abakisa Abagushi Ababukusu Yamaguchi Fukushima Fukuoka Hokkaido Hiroshima Ainu Italians Toda Terressan Southern Nicobarese Sikh Punjabis Muhammadan Punjabis Malavar Lahaul Kanets Kurumba Kulu Kanets Kota Garhwali Chowrite Group 42. OO ui (ji SO (J I Os —1 L O (Jl to OS to to 4^ Os oo to to 1,357 to (Jl OS C O OS OS oo to U l OS o to U l L O O to U l L O o N O to o as o to • o "-j o bs L O (Jl to L O (Jl L O L O OS 42-L O bo L O 42. 42. 42-L O bo 42. to © 42. 00 Lo 42. L O O b L O o b 00 L O to bs 42. L O to b 42. L O O 42. 00 (Jl Latitude (90°S=-90) L O 42. L O 42. bs L O 42. L O 42. 00 L O 42-bs 134.8 140.3 130.4 142.4 132.5 142.4 ps (Jl SO L O to SO L O 42. b 42. b )^ •o. b -J OS Ui ;-J L O 1^ O N U i 00 O b NO L O b Longitude (180°E=-180) 21.03 19.08 | 21.03 19.24 19.08 12.67 10.95 14.54 to SO L O 14.70 to SO L O 14.98 25.57 26.82 26.82 26.08 26.08 27.25 to L O 25.57 00 oo 25.57 Ul jit 26.82 Mean annual temp (°C) 1698.9 1693.2 1 1697.9 1725.1 1738.1 1583.3 1571.9 1610.0 1671.0 1581.8 1622.0 1706.0 1696.0 1591.0 1614.0 1721.0 1720.0 1679.0 1618.0 1540.3 1654.0 1590.2 1598.0 1567.0 Stature (mm) 0.186 0.184 0.190 0.184 0.209 0.188 0.191 0.203 0.204 0.204 0.189 Rel up arm length 0.145 0.148 0.144 0.146 0.163 0.157 0.160 0.153 0.153 0.169 0.148 0.159 Rel low arm length o 42. o to o 42. o L O o to o o 42. o o oo o o oo o o SO o L O o o o oo o OS o o SO o o o o o IO o o to o Rel hand length 0.051 0.050 0.052 0.051 0.050 0.052 0.048 0.052 0.052 0.050 0.050 0.049 0.052 0.052 0.051 0.054 Rel hand width 0.220 0.233 0.220 0.287 0.229 0.270 0.267 0.282 0.281 0.269 0.272 0.277 Rel up leg length 0.202 0.202 0.204 0.256 0.198 0.259 0.249 0.208 0.197 0.240 0.236 0.233 0.204 Rel low leg length 0.155 0.155 f 0.155 0.154 0.153 0.148 o (ji to o (ji (J I o (J I 42. o ui L O o U l L O o U l to o U l o U l o 42-SO o OS © U l o OS o o 42. SO o ui oo Rel foot length 0.061 0.059 1 0.061 1 0.061 0.059 0.060 0.065 0.063 0.056 0.056 0.052 0.052 0.056 0.058 0.067 Rel foot width I xipuaddv ss Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mali 2 Malaysia Malawi Magascar Kenya Kenya Kenya Kenya Kenya Kenya Kenya Kenya Kenya Kenya Kenya Country Yaqui Tarasco 1 Tarahumare Otomi Maya Cora Choi Chamula Indians Aztec Dogon Dogon Brunei Chewa Antandroy Wataita Sabaot Marakwet Keiyo Ja-Luo Iteso Akikuyu Abetakho Abesukha Abatsotso Abatirichi Group so U l o to U> U l © oo U l o © © © U l U l oo to to © © © © to - J OS © OS to U l SO to Os OS OS U J OS © U l © N IO SO U J SO to tO 00 U l to © U l so U J to to © u> Os bo 00 bo U i U J U l U l 1 U i © i to U J 4^ • U J 4^ © bo © © © to © bs i U J © p U J © U J © Latitude (90°S=-90) -110.7 -101.8 | -106.0 sb SO © 1 oo SO © -105.0 • SO to • so to i SO SO © 1 "o 1 U l U l 115.8 U J U l 4^ U J "o U J oo U J U J bo U J U l bs U J U l © U J 4^ bo U J 4^ bs U J OS U J 4^. SO U J 4^ bo U J 4^ bo U J U l Longitude (180°E=-180) 22.11 23.77 | 18.98 21.88 25.48 19.51 25.43 22.63 17.92 29.07 28.61 22.73 19.93 23.73 22.66 19.08 22.12 16.90 22.23 19.08 18.28 21.03 21.03 21.03 17.43 Mean annual temp (°C) 1748.0 1631.0 | 1642.0 1585.0 1551.1 1650.0 1585.5 1557.2 1608.5 1703.7 1685.0 1563.5 1660.6 1670.0 1646.3 1749.6 1700.5 1702.9 1751.5 1731.1 1678.1 1697.3 1706.2 1714.0 1712.9 Stature (mm) 0.189 0.191 0.160 0.202 Rel up arm length 0.153 0.160 0.146 0.162 Rel low arm length o U J o © o o © U> © to 4^ © o o © oo © © © © © o to © U l © to © © © © © © to © U J o U J © to © U J © U J © © Rel hand length 0.051 0.050 | 0.051 0.052 0.058 0.050 0.052 0.050 0.051 0.049 0.051 0.048 0.049 0.049 0.050 0.050 0.050 0.049 0.051 0.052 0.049 Rel hand width 0.203 0.300 0.207 Rel up leg length Rel low leg length o u i © U i to © SO © U l U l © U l SO © U l to © U l © U l © U l U J © U l © U l U J © U l oo © U l Os © U l to © U l U J © U l to © U l U J © U l 4* o U l 4^ © U l U l Rel foot length | 0.062 | 0.061 1 0.058 | 0.064 0.067 0.064 0.064 0.060 0.061 0.061 0.061 0.059 0.060 0.058 0.060 0.057 0.059 0.061 Rel foot width I xipuaddy 9£ Russia Russia 1 Peru -o CP s *o CD 2 ••0 CI 2 PNG PNG PNG PNG PNG PNG PNG Panama Panama Panama 1 Norway Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Namibia Namibia Country Samoyedes Ostiaks 1 Sipibo Quicha y. O Macheyenga Tairora Ontenu New Ireland Gadsup Butam Awa Auyana San Bias Cuna Choco r •8 •a Sumus Sumo Subtiava Ramas Rama Miskito Kavango !Kung Group U l L O t o i t U i t o L O SO L O O t o U l L O U l t o t o t o L O U l SO 0 t o OS 0 42! 10 0 t o 0 10 0 t o t o 0 t o U l t o U l t o ^1 L O O L O 42! N OS 42. ON U l — i Lo • U l bo 1 L O Lo l • L O "-J OS 42. • OS 42-i L O Lo • Os 42. 1 L O Lo • OS OS Os bs 00 U l 00 U l 00 U l OS b L O b L O b t o 42. t o t o t o t o L O so 1 SO 1 t o p Lo Latitude (90°S=-90) 42. © ui OS 00 U l • 42. Lo i OS I ^4 ! ° Lo I bo 42. U l bo 42. U i bo U i t o © 42. U l so U l t o b 42. U i 42. U l • U l 1 ^1 U l 1 •-4 ; - J U i OS SO • 00 L O bo 1 00 L O bo 1 00 OS SO 1 00 os Lo • 00 42. io 1 00 42. bs SO bo 00 b Longitude (180°E=- 180) © U i 42. o bo L O | 26.96 00 t o L O 23.62 oo bo L O 24.45 24.45 23.90 24.45 23.90 24.36 24.36 26.48 26.48 26.48 42. U l 25.48 25.48 27.56 26.10 24.34 21.83 21.90 21.00 Mean annual temp (°C) 1568.0 1565.0 | 1568.0 1585.0 1613.0 1610.0 1559.7 1574.8 1610.7 1582.8 1580.8 1508.5 1536.8 1499.0 1549.0 1564.0 1 1581.0 1581.6 1586.4 1632.7 1661.0 1632.6 1640.3 1706.1 1614.4 Stature (mm) © OS OS o OS SO o 42. 00 o 00 L O o o o L O © oo SO o oo SO o SO 42-0 00 OS 0 SO 0 0 SO 0 0 00 SO | 0.195 0.199 0.191 0.201 0.196 0.190 0.188 Rel up arm length o U l 00 o U i OS O - J U l O OS o OS SO O OS 42. o U l oo o OS o U l U l 0 U i SO 0 U l OS 0 U l 0 U l •0. | 0.152 0.160 0.153 0.166 0.166 0.164 0.158 Rel low arm length o o © O o O o o o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 t o 42. t o L O o O © O SO 00 ^1 42! 00 SO 0 SO ~ J 0 00 0 L O 42. 0 00 0 0 42. 42. 0 00 Rel hand length 1 0.055 0.054 | 0.053 0.052 0.053 0.063 0.061 0.050 0.062 0.052 0.062 0.063 0.054 0.053 1 0.052 0.048 0.048 0.050 0.047 Rel hand width 0.226 0.275 0.269 | 0.264 Rel up leg length 1 0.213 0.212 0.223 0.221 0.215 | 0.217 Rel low leg length | 0.152 0.152 | 0.156 0.154 0.154 0.158 0.159 0.162 0.153 0.156 0.151 | 0.155 0.153 0.152 0.157 0.150 Rel foot length | 0.063 | 0.062 0.066 0.064 0.061 0.066 0.066 0.063 0.067 0.068 0.057 0900 0.061 0.062 0.058 Rel foot width I xipusddv Tanzania Tanzania 1 Tanzania Tanzania Sudan Spain South Africa Solomon Isl Solomon Isl Solomon Isl Solomon Isl Solomon Isl Solomon Isl Solomon Isl Solomon Isl Senegal Senegal Senegal Senegal Rwanda Rwanda Rwanda Rwanda Rwanda Russia Country Wanyamwezi Hadza 1 Bazinja Baziba West Nile Nilotes Basque Venda Ulawa Ontong Java Nasioi Nagovisi r c Kwaio Baegu > & Peuls Ouolof Mandyago-Diola Bedik Twa pygmies Tutsi Hutu Batutsi Bahutu Vogouls Group U> o Ul to 4^ UJ UJ 4* ^ 1 o Ch 4*. ui SO © Ch Ch Ul -O to Ul 00 o to oo Ul to ^1 o SO SO to to 42. SO o © Ul ~-4 Ul N • UJ o^ 1 4^ b • U) UJ 1 to b oo 4^ 4*. UJ i to 4^ b 1 so bo i Ul UJ i s b i s b 1 SO b • SO b sb b 1 oo SO 4^ Ul to bs UJ b • SO • bs to i OS • to Ul UJ Latitude (90°S=-90) U> UJ U*l UJ to bs UJ bs UJ 'o. 1 to UJ UJ © UJ Ch to b Ul SO ui Ul b Ul Ul b Ch b Ch b OS b 4^ to so 1 OS 4^ 1 OS UJ i Ul SO • to UJ to SO bs to SO SO to SO bo to SO SO to SO bo OS oo Ul Longitude (180°E=-180) 20.98 18.45 1 22.29 21.27 27.70 11.22 18.49 25.76 27.01 22.18 22.18 25.40 25.40 25.40 26.95 26.22 25.69 26.28 28.51 16.65 16.65 19.18 16.65 19.18 p bo UJ Mean annual temp (°C) 1655.9 1609.5 1 1628.5 1648.8 1723.8 1699.5 1616.0 1642.0 1621.0 1596.0 1625.0 1603.0 1613.0 1604.0 1700.0 1732.0 1712.0 1672.5 1577.4 1765.2 1674.3 1720.0 1643.7 1567.0 Stature (mm) 0.195 0.187 | 0.204 0.196 0.195 0.194 0.213 0.216 0.217 0.216 0.220 0.211 0.213 0.216 p oo -o p oo ^ 1 o oo oo © SO Ul p so OS p oo -o p oo 00 o SO Os o SO oo p - J UJ Rel up arm length © OS o o ui J> o Os UJ o to © Ch Ui o 4^ SO o o UJ o - J to o o 4^ o <l -o o ^) o o UJ o o o OS UJ o OS UJ o OS Ul o OS o o Ul ^ 1 o Ul SO o OS 4^ o Ul SO © OS UJ o Ul OS Rel low arm length 0.113 0.106 | 0.117 0.117 0.111 0.100 0.114 0.113 0.117 0.117 0.116 0.115 0.116 0.118 0.112 0.112 0.113 0.113 0.116 0.099 0.103 0.108 0.112 0.123 Rel hand length 0.051 0.047 | 0.049 0.047 0.051 0.051 0.052 0.049 0.052 0.052 0.052 0.050 0.048 0.050 0.046 0.051 0.056 Rel hand width 0.274 1 0.273 0.270 0.275 0.305 0.279 0.284 0.284 0.299 0.278 0.272 Rel up leg length 0.235 | 0.241 0.240 0.246 0.220 0.234 0.233 0.236 0.230 0.243 0.241 0.213 Rel low leg length 0.158 0.156 0.157 0.159 0.160 0.158 0.159 0.156 0.156 0.157 0.153 Rel foot length 0.065 0.063 0.064 0.063 0.066 0.064 0.066 0.064 0.059 0.059 0.064 Rel foot width I xxpuaddv 85 US US US US us US US US us us Urundi Urundi Uganda Uganda Uganda Uganda Uganda Uganda Uganda Uganda Uganda Uganda Turkey Timor Tanzania Country Maricopa Laguna Hopi Hooper Bay Hano Eastern Aleut Comanche Barrow Apache Anaktuvuk Pass Tusti Hutu Teso North Uganda Bantu Lubgwara Batoro Banyoro Bakiga Bahiru Bahima Baganda Acholi Turks Timor Zanzibar Arabs North Uganda Bantu Group 42. © OS o LO SO to o SO Ul SO to to ^ i SO to OS to o - 42-^1 to oo 42. so Os 42. to 41. SO Ul LO ^ 1 42. 42. SO N 33.1 35.0 35.8 61.5 35.8 53.4 34.4 71.3 33.9 68.1 i LO 42. 1 LO 42. to b to to 42. © to bo © so 1 O © LO i O to 39.9 1 SO t OS to Latitude (90°S=-90) -112.0 -107.4 -110.4 -166.1 -110.4 -167.7 -98.0 -156.8 -105.7 -151.7 29.4 29.4 33.8 33.2 31.2 30.0 31.8 31.2 31.4 30.2 31.7 33.2 32.8 124.9 39.2 Longitude (180°E=- 180) SO 00 SO i SO to Os • to i IO to to to LO to LO to 42-to to to to |o to © to to to LO © to Ui to Ul KJ LO SO LO 42-O SO LO 42-o b 00 00 LO to ~ J OS OS 42. o SO oo SO oo to LO SO b os os 42. SO to © LO os 42. 42. © LO SO LO LO 42- bo so Mean annual temp (°C) ^1 41. SO OS LO LA OS 42-SO OS to Ul Os to © OS 42-OS oo to OS OS to Os so OS 42. so Ul OS OS ~ J OS LO Os SO LO OS oo © OS OS 42. OS —1 SO Os Ul 42. OS oo 00 OS Ul SO to OS SO to Ul OO Os Ul SO Stature (mm) b b b b b Ul SO b b b so 42- bo b SO SO © Ul OS LO OS SO •-' p p p p O © © © p © © p © © oo OS oo OS so Os SO OS SO OS SO SO Ul SO 42. SO Ul to © © SO 00 SO LO to © SO LO Rel up arm length o o o o o o © © © © © © © © OS o OS 42. OS Os 42. OS Ul OS © Ul —1 Os Ul SO OS © OS OS OS OS LO Ul LO Rel low arm length o o o o o o O o o o o o © o © O © © o o © © © © © o o SO o oo © oo LO O OS OS o o LO LO LO to i o to LO LO Rel hand length o o o o o O © o p p o o p © © © © © © © © © b LA b Ui to b Ul o b Ul 42. b Ul o b Ul LO b Ul b Ul to b Ui b Ul to b Ul o b 42. ~J b Ul © © 42. SO © 42. 00 © 42. SO © 42. SO © 42. Os © Ul © © 42. oo b Ul © Ul oo Rel hand width o o © © © © o © © © © © to ~J to to -o LO ^4 OO to © to LO i o © to to oo © to —1 42-to ~J Os to Ul 42. to to Rel up leg length o o © o © © © © © © © © to 42. to 42. to to 42-OS to 42. © to 42-© to 42. to 42. to to 42. SO to LO SO to 42-to Ul to to LO © Rel low leg length o o © O o o o o o o © Ln tO Ul 42. 00 42. - J 42. 42. 42. Ul 42- Ul LO Ul Ul to Ui Ul Rel foot length o o O O o O o © o o © b OS o b Ul SO b Ul b Ul oo b Ul b Ul Ul b OS o b OS to b Ul oo b Ul SO © OS © Rel foot width I xipuaddy 65 Vietnam Viet Nam Viet Nam Venezuela Venezuela C GO d oo C oo d oo C 00 C 00 d oo d oo d 00 d 00 d 00 d 00 d oo d oo Country Annamites Vietnamese Mois Yupa Warao Choctaw Zuni Yuma Western Aleuts Wainwright Inuits St.Lawrence Island Southern Ute Sioux Point Hope Pima Papago Nunivak Island Navaho Mohave Group u i o -tk SO to oo to U ) •o to 00 J> OS o to SO -p* OS U J Ul o to to Ul Ul U J Ui o SO so o -P. Ui N to to b to b Ul b o b SO to U J Ul Ul U J Ul U J to "o Ul to bs -o o bs OS U J bo U J 00 U J -p* OS Os OO •p* to so •p> U J Os to OS p U J Ul U J 4^ bo Latitude (90°S=-90) 105.0 105.9 108.0 • U J OS Ul • SO ~J Ul 1 o oo bo 1 i t bs • U J •o. i OS o b i 1 o 00 U J i o o bs OS ON 00 1 o •fc. SO -p. b 1 OS os to 1 o so Ul i bs Longitude (180°E=-180) 23.72 23.65 25.73 24.73 26.14 16.24 00 to 22.69 bs -11.30 • ;P* Ul OS © Ul Ul b U J •o SO o 19.54 14.62 1 o SO OS SO Ul to 16.03 Mean annual temp (°C) 1622.8 1573.0 1559.5 1537.8 1554.9 1708.0 1635.0 1731.0 1585.9 1662.8 1633.0 1669.0 1740.0 1638.0 1718.0 1709.0 1618.0 1699.0 1716.0 Stature (mm) 0.188 0.191 0.199 0.184 0.190 Rel up arm length 0.151 0.154 0.137 0.158 0.147 Rel low arm length o o o OS o u> o o Ul o U J o o oo o o o Ul o U J o o o o o o Ul o o o o o -P-o © o Rel hand length 0.048 0.051 0.049 0.052 0.053 0.051 0.051 0.056 0.052 0.053 0.052 0.054 0.052 0.050 0.050 0.054 0.052 0.052 Rel hand width 0.283 0.297 0.260 Rel up leg length 0.219 0.224 0.233 Rel low leg length o Ul U J o Ul to o Ul o Ul o Ui U J o oo © Ui o o -p-^ i o Ul U J o t^ o Ul o Ui -pk o Ui to o Ui o o Ul o Ul © o .pi. oo o Ul to Rel foot length | 0.061 1 0.062 0.062 0.062 0.060 0.058 0.061 0.059 0.061 0.059 0.058 0.057 0.060 0.059 0.058 0.061 0.055 0.062 Rel foot width I xipuaddy Appendix II Country Group Source Algeria Beni Gessa in&Lhote(1961) Algeria Chaamba Coblentz(1968) Algeria Mekhadma Coblentz(1968) Algeria Reguibat Coblentz(1968) Algeria Tuareg Verneau(1916) Angola Bieno Rosing (1977) Angola Chokwe Rosing (1977) Angola Ginga Rosing (1977) Angola Luimbe Rosing (1977) Angola M u i l a Rosing (1977) Angola Oio Rosing (1977) Argentina Ona Lehmann-Nitsche (1927) Australia Central Abbie(1975) Australia Melv i l le & Bathurst Isles Howells (1937) Australia North Macho & Freedman (1987) Australia South Macho & Freedman (1987) Australia Victoria River Howells (1937) Bol iv ia Aymara Chervin etal.(1907) Bol iv ia Quechuas Chervin et al. (1907) Brazil Wapisiana Farabee (1918) Burkina Faso Bel la Froment & Hiernaux (1984) Burkina Faso Bwaba Froment & Hiernaux (1984) Burkina Faso Gurmanche Froment & Hiernaux (1984) Burkina Faso Malebe Froment & Hiernaux (1984) Burkina Faso Moss i of Donse Froment & Hiernaux (1984) Burkina Faso Moss i of Kokologo Froment & Hiernaux (1984) Burkina Faso Rimaibe Froment & Hiernaux (1984) Cambodia Khmers Olivier & Moullec (1968) Cameroon Fang Lalouel(1957) Canada Beaver Grant (1936) Canada Chipewyan Grant (1936) Canada Cree Grant (1929) Canada Igloolik Inuits de Pena(1971) Canada Labrador Inuits Wei l (1971) Chad Goranes Coblentz(1968) Chad Sara Crognier (1972) Chad Toubous of Tibesti Coblentz(1968) Chile Yahgans Hyades and Deniker (1891) China Black Lolo Woo (1942) China Bulang Zhongguo ren lei xue xue hui (1982) China Setchuan Legendre(1910) China Hani Zhongguo ren lei xue xue hui (1982) China Jinuo Zhongguo ren lei xue xue hui (1982) China Lu-jen Woo (1942) 60 Appendix II China Mongolians Buxton (1926) China Pa Miao Woo (1942) China Pa i -Y Woo (1942) China Shui-hsi Miao Woo (1942) China (Tibet) Changtang Buch ie t a l . (1965) China (Tibet) K a m Buch ie t a l . (1965) China (Tibet) Tsang Buchi etal . (1965) China (Tibet) U Buchi et al. (1965) Columbia Ambalo Lehmann & Marquer (1960) Columbia Gaumbiano Lehmann & Marquer (1960) Columbia Kokonuko Lehmann & Marquer (1960) Columbia Paez Lehmann & Marquer (1960) Columbia Purace Lehmann & Marquer (1960) Columbia Quisgo Lehmann & Marquer (1960) Columbia Totoro Lehmann & Marquer (1960) Congo Ituri Pygmy Cavalli-Sforza(1986) Congo Luba Hiernaux (1972) Cyprus Cypriotes Angel (1972) Czech Republic Czech Prokopec(1977) Dem Rep Congo A k a Gusinde(1948) Dem Rep Congo Bira of rain forest Sporcq(1975) Dem Rep Congo Bira of savannah Sporcq (1975) Dem Rep Congo Ba l i Gusinde(1948) Dem Rep Congo Beyru Gusinde(1948) Dem Rep Congo Efe and Basua Gusinde(1948) Dem Rep Congo Fulero Hiernaux (1956) Dem Rep Congo Havu Hiernaux (1956) Dem Rep Congo Humu Hiernaux (1956) Dem Rep Congo Hunde Hiernaux (1956) Dem Rep Congo Lese Gusinde(1948) Dem Rep Congo Mbuba Hiernaux (1956) Dem Rep Congo Ndaka Gusinde (1948) Dem Rep Congo Nyanga Hiernaux (1956) Dem Rep Congo Rega Hiernaux (1956) Dem Rep Congo Shi Hiernaux (1956) Dem Rep Congo Shu Hiernaux (1956) Dem Rep Congo Swaga Hiernaux (1956) Dem Rep Congo Tembo Hiernaux (1956) Egypt Marsa Matrouh Godycki(1961) Egypt Salloum Godycki(1961) Egypt Sidi Barrany Godycki(1961) Egypt Siwah Godycki(1961) Fij i Lauans Lourie(1972) Finland Finns Kivalo(1957) France Basque Marquer (1963) 61 Appendix II France Li fu Islanders Ray (1917) France Normandie Garnier-Mouronval (1913) Greece Greeks Hertzberg et al. (1963) Guatemala M a m Indians Goff(1948) Guinea Guerze Vallois(1941) Guinea Ki s s i Vallois (1941) Guinea Toma Vallois(1941) Guyana Caribs Farabee (1924) India Andamanese M a n (1882) India Car Nicobarese Ganguly (1976) India Chowrite Ganguly (1976) India Garhwali Eickstedt(1926) India Kota Kumar (2000) India K u l u Kanets Holland (1902) India Kurumba Kumar (2000) India Lahaul Kanets Holland (1902) India Malavar Fawcett(1903) India Muhammadan Punjabis Eickstedt(1923) India Sikh Punjabis Eickstedt(1923) India Southern Nicobarese Ganguly (1976) India Terressan Ganguly (1976) India Toda Gates (1961) Italy Italians Hertzberg et al. (1963) Japan A i n u Picon-Reategui et al. (1979) Japan Hiroshima Shapiro & Hulse (1939) Japan Hokkaido Picon-Reategui et al. (1979) Japan Fukuoka Shapiro & Hulse (1939) Japan Fukushima Shapiro & Hulse (1939) Japan Yamaguchi Shapiro & Hulse (1939) Kenya Ababukusu Winkler (1984) Kenya Abagushi Winkler (1984) Kenya Abakisa Winkler (1984) Kenya Abalogoli Winkler (1984) Kenya Abanyore Winkler (1984) Kenya Abatirichi Winkler (1984) Kenya Abatsotso Winkler (1984) Kenya Abesukha Winkler (1984) Kenya Abetakho Winkler (1984) Kenya A k i k u y u Winkler (1984) Kenya Iteso Winkler (1984) Kenya Ja-Luo Winkler (1984) Kenya Keiyo Winkler (1984) Kenya Marakwet Winkler (1984) Kenya Sabaot Winkler (1984) Kenya Wataita Winkler (1984) 62 Appendix II Magascar Antandroy Rouquette (1914) Malawi Chewa Nurse (1972) Malaysia Brunei Knocker (1907) M a l i Dogon -Sanga Huizinga & Birnie-Tellier (1966) and Huizinga & de Vetten (1967) M a l i Dogon Froment & Hiernaux (1984) Mexico Aztec Hrdlicka(1935) Mexico Chamula Indians Leche(1936) Mexico Choi Gould (1946) Mexico Cora Hrdlicka(1935) Mexico Maya Steggerda(1932) Mexico Otomi Hrdlicka(1935) Mexico Tarahumare Hrdlicka(1935) Mexico Tarasco Hrdlicka(1935) Mexico Yaqui Hrdlicka(1935) Namibia !Kung Winkler & Christiansen (1991) Namibia Kavango Winkler & Christiansen (1991) Nicaragua Miski to De Stefano & Jenkins (1972) Nicaragua Rama De Stefano & Jenkins (1972) Nicaragua Ramas Schultz(1926) Nicaragua Subtiava De Stefano & Jenkins (1972) Nicaragua Sumo De Stefano & Jenkins (1972) Nicaragua Sumus Schultz(1926) Norway Lapp Bryn(1932) Panama Choco Hrdlicka(1926) Panama Cuna Hrdlicka(1926) Panama San Bias Harris (1926) P N G Auyana Littlewood(1972) P N G A w a Littlewood(1972) P N G Butam Schlaginhaufen (1964) P N G Gadsup Littlewood(1972) P N G N e w Ireland Schlaginhaufen (1964) P N G Ontenu Littlewood(1972) P N G Tairora Littlewood(1972) Peru Macheyenga Farabee(1922) Peru Piro Farabee(1922) Peru Quicha Ferris (1916) Peru Sipibo Farabee (1922) Russia Ostiaks Roudenko (1914) Russia Samoyedes Roudenko(1914) Russia Vogouls Roudenko (1914) Rwanda Bahutu Oschinsky (1954) Rwanda Batutsi Oschinsky (1954) Rwanda Hutu Hiernaux (1956) Rwanda Tutsi Hiernaux (1956) 63 Appendix II Rwanda Twa pygmies Desmarais (1977) Senegal Bedik Gomila(1971) Senegal Mandyago-Diola Vallois (1941) Senegal Ouolof Vallois (1941) Senegal Peuls Vallois (1941) Solomon Islands Ai t a Friedlaender (1987) Solomon Islands Baegu Friedlaender (1987) Solomon Islands Kwaio Friedlaender (1987) Solomon Islands Lau Friedlaender (1987) Solomon Islands Nagovisi Friedlaender (1987) Solomon Islands Nasioi Friedlaender (1987) Solomon Islands Ontong Java Friedlaender (1987) Solomon Islands Ulawa Friedlaender (1987) South Africa Venda de Vil l iers (1972) Spain Basque Marquer (1963) Sudan West Ni le Nilotes Oschinsky (1954) Tanzania Baziba Oschinsky (1954) Tanzania Bazinja Oschinsky (1954) Tanzania Hadza Hiernaux & Boedhi Hartono (1980) Tanzania Wanyamwezi Oschinsky (1954) Tanzania Zanzibar Arabs Oschinsky (1954) Timor Timor Lammers (1963) Turkey Turks Hertzberg et al. (1963) Uganda Achol i Oschinsky (1954) Uganda Baganda Oschinsky (1954) Uganda Bahima Oschinsky (1954) Uganda Bahiru Oschinsky (1954) Uganda Bakiga Oschinsky (1954) Uganda Banyoro Oschinsky (1954) Uganda Batoro Oschinsky (1954) Uganda Lubgwara Oschinsky (1954) Uganda North Uganda Bantu Oschinsky (1954) Uganda Teso Oschinsky (1954) Urundi Hutu Hiernaux (1956) Urundi Tusti Hiernaux (1956) U S Anaktuvuk Pass Jamison (1978) U S Apache Hrdlicka(1935) U S Barrow Jamison (1978) U S Comanche Goldstein (1934) U S Eastern Aleut Laughlin(1951) U S Hano Hrdlicka(1935) U S Hooper Bay Hrdlicka(1928) U S Hopi Hrdlicka(1935) U S Laguna Hrdlicka(1935) U S Maricopa Hrdlicka(1935) 64 Appendix II U S Mohave Hrdlicka(1935) u s Navaho Hrdlicka(1935) u s Nunivak Island Hrdlicka, 1928 u s Papago Hrdlicka(1935) u s Pima Hrdlicka (1935) u s Point Hope Jamison (1978) u s Sioux Hrdlicka (1931) u s Southern Ute Hrdlicka (1935) u s St. Lawrence Island Hrdlicka, 1928 u s Wainwright Inuits Jamison & Zegura (1970) u s Western Aleuts Laughlin(1951) u s Yuma Hrdlicka (1935) u s Zuni Hrdlicka (1935) u s Choctaw Collins (1928) Venezuela Warao Fleischman(1980) Venezuela Yupa Gusinde(1956) Viet Nam Mois Oliver (1968) Viet Nam Vietnamese Oliver (1968) Vietnam Annamites Roux (1905) Sources of Data Abbie, A . 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