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AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 136:108–113 (2008)

Human Cranial Variation Fits Iterative Founder Effect Model Brief Communication: Human Cranial Variation Fits With African Origin Iterative Founder Effect Model With African Origin Noreen von Cramon-Taubadel1 and Stephen J. Lycett2* 1

Leverhulme Centre for Human Evolutionary Studies, University of Cambridge, The Henry Wellcome Building, Fitzwilliam Street, Cambridge CB2 1QH, UK 2 British Academy Centenary Research Project, SACE, University of Liverpool, Hartley Building, Brownlow Street, Liverpool L69 3BX, UK KEY WORDS human origins

cranial diversity; within-group variance; iterative founder effect model; modern

ABSTRACT Recent studies comparing craniometric and neutral genetic afﬁnity matrices have concluded that, on average, human cranial variation ﬁts a model of neutral expectation. While human craniometric and genetic data ﬁt a model of isolation by geographic distance, it is not yet clear whether this is due to geographically mediated gene ﬂow or human dispersal events. Recently, human genetic data have been shown to ﬁt an iterative founder effect model of dispersal with an African origin, in line with the out-of-Africa replacement model for modern human origins, and Manica et al. (Nature 448 (2007) 346–349) have demonstrated that human craniometric data also ﬁt this model. However, in contrast with the neutral model of cranial evolution suggested by previous studies, Manica et al. (2007) made the a priori assumption that cranial form has been subject to climati-

cally driven natural selection and therefore correct for climate prior to conducting their analyses. Here we employ a modiﬁed theoretical and methodological approach to test whether human cranial variability ﬁts the iterative founder effect model. In contrast with Manica et al. (2007) we employ size-adjusted craniometric variables, since climatic factors such as temperature have been shown to correlate with aspects of cranial size. Despite these differences, we obtain similar results to those of Manica et al. (2007), with up to 26% of global within-population craniometric variation being explained by geographic distance from sub-Saharan Africa. Comparative analyses using non-African origins do not yield signiﬁcant results. The implications of these results are discussed in the light of the modern human origins debate. Am J Phys Anthropol 136:108–113, 2008. V 2007 Wiley-Liss, Inc.

Recently, a number of studies have assessed the extent to which modern human cranial diversity patterns ﬁt an evolutionary model of neutral expectation (e.g. Relethford, 2002; Gonzalez-Jose et al., 2004; Roseman, 2004; Harvati and Weaver, 2006a,b; Smith et al., 2007). If neutral forces are largely responsible for shaping human phenotypic diversity patterns, then population afﬁnity patterns can be employed to infer past population history. Roseman (2004) tested the neutral hypothesis by comparing human morphological afﬁnity patterns with microsatellite data and found that, on average, modern human cranial morphology varied among populations according to neutral expectation, although some aspects of cranial shape in high latitude populations may have been shaped by natural selection. Subsequent analyses employing 3D geometric morphometric data largely concur with these conclusions. Harvati and Weaver (2006a,b) show that neurocranial and temporal bone shape track neutral genetic distances, while facial shape reﬂects climate. Smith et al. (2007) conﬁrm the results of Harvati and Weaver (2006a,b) in ﬁnding that temporal bone morphology reﬂects neutral rather than selective patterns of evolution. It has been demonstrated previously that global human craniometric and genetic afﬁnity patterns ﬁt a model of isolation by distance (IBD) (Cavalli-Sforza et al., 1994; Eller, 1999; Relethford, 2004a,b), which predicts that as the geographic distance increases between populations, so too will their genetic and phenotypic dissimilarity (Wright, 1943). This is because in cases where selection has not had a dominant effect on structuring

variance between populations, geographic proximity mediates potential for migration and thus potential for gene ﬂow. Relethford (2004b) points out that a ﬁt to an IBD model does not make clear the underlying evolutionary process, and does not make possible the distinction between a model of long-distance gene ﬂow mediated by geographic distances or one of a global dispersal event. Manica et al. (2007) demonstrated recently that modern human cranial diversity patterns ﬁt a dispersal model of iterative founder effects (repeated bottlenecking) with an African origin. Their results are in accordance with similar results obtained using neutral autosomal microsatellite markers, which show that population heterozygosity ﬁts a stepping-stone model of dispersal from an African origin (Prugnolle et al., 2005;

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Grant sponsors: St. John’s College, University of Cambridge; Gates Trust Scholarship; British Academy Centenary Research Project: ‘‘Lucy to Language’’. *Correspondence to: Stephen J. Lycett, School of Archaeology, Classics and Egyptology, Hartley Building, Brownlow Street, University of Liverpool, Liverpool L69 3BX, UK. E-mail: [email protected] Received 9 August 2007; accepted 7 November 2007 DOI 10.1002/ajpa.20775 Published online 27 December 2007 in Wiley InterScience (www.interscience.wiley.com).

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HUMAN CRANIAL VARIATION TABLE 1. Twenty-eight human populations sampled, including samples sizes, within-group variances, and geographic co-ordinates Region Africa

Europe Asia

Oceania Americas

Polynesia

Population

Sample size

WGV

Latitude, longitude

Teita Egypt Zulu San Dogon Zalavar Berg Norse Andamanese Buriats Hainan Anyang Philippines Atayal South Japan North Japan Ainu Guam Australian Tolai Tasmania Santa Cruz Arikara Peruvian Inugsuk Mokapu Moriori Easter Island

33 58 55 41 47 53 56 55 35 55 45 42 50 29 50 55 48 30 52 56 45 51 42 55 53 51 57 49

0.85 0.64 0.77 1.01 0.83 0.64 0.74 0.66 0.81 0.76 0.68 0.75 0.70 0.70 0.73 0.80 0.63 0.58 0.56 0.60 0.70 0.59 0.59 0.62 0.77 0.61 0.65 0.60

3.3S, 38.6E 30.0N, 31.5E 28.5S, 30.2E 22.4S, 19.0E 14.5N, 0.5E 46.8N, 17.1E 46.8N, 13.2E 59.9N, 10.8E 12.4N, 92.8E 52.8N, 108.7E 19.4N, 110.2E 36.0N, 114.3E 14.6N, 121.0E 24.3N, 121.1E 33.0N, 131.0E 43.0N, 141.0E 43.5N, 142.0E 13.4N, 144.8E 35.0S, 139.8E 5.4S, 151.0E 41.7S, 146.1E 34.0N, 119.8W 44.7N, 100.1W 12.5S, 75.9W 61.7N, 48.7W 21.5N, 157.9W 43.9S, 176.5W 27.0S, 109.3W

Ramachandran et al., 2005; Liu et al., 2006). Moreover, Manica et al. (2007) test a model of multiregional origins for modern humans but fail to ﬁnd evidence of a second non-African origin capable of explaining any residual variation in the data. Here we employ Howells’ (1996) craniometric dataset to further investigate whether global human cranial diversity patterns can be explained by a dispersal event out of Africa, following an iterative founder effect model. This study differs from that of Manica et al. (2007) in both theoretical expectation and methodological approach. In direct contrast with the neutral model for human cranial form purported on the basis of previous analyses (e.g., Roseman, 2004; Harvati and Weaver, 2006b), Manica et al. (2007) make an a priori assumption that cranial dimensions are correlated with climate, implying that natural selection has played a signiﬁcant role in the evolution of the modern human cranium. Accordingly, Manica et al. (2007) correct for within-population climatic variability prior to conducting their analyses. Here we do not assume that climate has had a signiﬁcant impact on overall cranial shape, but acknowledge that climatic variables such as mean annual temperature have been shown to be correlated with aspects of cranial size (Harvati and Weaver, 2006a; Smith et al., 2007) indicating a conformation (at least in part) of the cranium to Bergmann’s rule. As in Manica et al. (2007), a signiﬁcant inverse relationship between geographic distance from sub-Saharan Africa (i.e. along a hypothesized dispersal route) and within-population variability is taken as support for the iterative founder effect model of dispersal. For comparative purposes, three non-African origins were also tested.

TABLE 2. Geographic co-ordinates for the three African and three non-African dispersal origin points and for the ﬁve way-points used to calculate the geographic distances along dispersal routes Start points

Waypoints

Addis Ababa Central Africa Southern Africa Tel Aviv Delhi Beijing Cairo Istanbul Phnom Penh Anadyr Panama

9.0N, 38.0E 0.0N, 25.0E 20.0S, 25.0E 32.0N, 34.8E 28.5N, 77.0E 40.0N, 116.4E 30.0N, 31.0E 41.0N, 28.0E 11.0N, 104.0E 64.0N, 177.0E 13.5N, 86.2W

MATERIALS AND METHODS Cranial measurement data were obtained from W.W. Howells’ extensive and freely-available database (Howells, 1973, 1996). These data comprise 57 cranial variables for 28 globally distributed human populations, including three Polynesian populations. Manica et al. (2007) removed oceanic populations from their study due to the uncertain settlement history of these islands. Here, to produce results comparable with those of Manica et al. (2007), all analyses were ﬁrst conducted with the Polynesian populations excluded and subsequently all analyses were repeated with Polynesians included. All of the populations sampled by Howells contain male specimens but not all contain data for females. Therefore, to avoid the potentially confounding effects of sexual dimorphism, only male data were used. Table 1 provides population names, sample sizes, within-population variance, and geographic co-ordinates for all craniometric data employed. To remove the potentially confounding inﬂuence of climate on cranial size (Harvati and Weaver, 2006a; Smith et al., 2007) all raw cranial measurements were sizeadjusted by dividing each measurement by the geometric mean of all measurements for that individual (Jungers et al., 1995). This method removes isometric scaling, thereby maintaining the overall shape of the object (Falsetti et al., 1993). The average within-group variance across all 57 cranial characters for each of the 28 populations was calculated in RMET 5.0 (Relethford and Blangero, 1990). Longitude and latitude co-ordinates were estimated for all populations (see Table 1), following the map and population descriptions provided by Howells (1989). Three dispersal origins were chosen in sub-Saharan Africa (Table 2). The ﬁrst of these, Addis Ababa (Ethiopia), was chosen following Prugnolle et al. (2005), as the location where some of the oldest anatomically modern human remains have been discovered (White et al., 2003). Ramachandran et al. (2005) sought the most likely origin of a human expansion from a lattice of over 4,000 possible origin points and were able to identify an area in central sub-Saharan Africa. This empirical observation was conﬁrmed by Manica et al. (2007) using a similar analysis of the same genetic data. They also identiﬁed a subSaharan region giving the strongest relationship between within-population phenotypic variance and geographic distance, which differed from the genetic estimate by including South Africa amongst the likely origins. Therefore two additional African origin points were chosen for this study—one from central equatorial American Journal of Physical Anthropology

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Fig. 1. Regression of average within-group variance of 57 size-adjusted craniometric variables on great circle distance (km) from African dispersal origins. A: Addis Ababa (Ethiopia). B: Central Africa (Democratic Republic of the Congo). C: Southern Africa (Botswana). Diamonds 5 African populations. Open squares 5 European populations. Triangles 5 Asian populations. Stars 5 Oceanic populations. Open circles 5 American populations.

African (within the Democratic Republic of the Congo) and another from southern Africa (Botswana) (see Table 2 for co-ordinates). For comparative purposes, three nonAfrican start points were also tested. These comprised Tel Aviv (Israel), Delhi (India) and Beijing (China) (Table 2). Following Ramachandran et al. (2005), hypothetical dispersal routes were estimated using way-points between continents (Table 2). These were comprised of: 1) Cairo, Egypt (entry/exit point to Africa); 2) Istanbul, Turkey (entry/exit point to Europe); 3) Phnom Penh, Cambodia (entry/exit point to Oceania including Polynesia); 4) Anadyr, Russia (entry/exit point to New World); 5) Panama (entry/exit point to South America). Geographic distances between origin points and all populations were calculated as great circle distances based on the haversine (Sinnott, 1984). Great circle distances take account of the earth’s curvature and are appropriate measures of distance when covering large geographic areas. Least-squares regressions were performed in SPSS 12.0.1 using the three African and three non-African dispersal origins. Analyses were repeated following removal of the Polynesians. In each case the independent variable of average within-group variance was regressed on the dependent variable of geographic distance from the point of dispersal.

RESULTS Figure 1 shows the regression results for the three African dispersal origins when the Polynesians were American Journal of Physical Anthropology

excluded. All origin points yield a signiﬁcant inverse linear relationship between within-population phenotypic variance and geographic distance, as predicted by the iterative founder effect model. Between 19 and 26% of the within-group variance was explained by distance from an African origin, with the southern African origin yielding the best ﬁt to the model. In contrast, Figure 2 shows the regression results taking a non-African dispersal origin, none of which yield a signiﬁcant result. Although not all possible alternative origins have been tested, these results concur with those of Manica et al. (2007), which found no origin outside Africa capable of explaining any residual variation in the data. Table 3 provides the comparable results when all 28 populations, including the Polynesians, were employed. Inclusion of the Polynesian populations strengthens the ﬁt of the data to an iterative founder effect model with between 24 and 31% of within-group variance being explained by distance from an African origin. Therefore exclusion of the Polynesian populations can be considered a conservative analytical approach to this analysis. Manica et al. (2007) make the assumption that climatically driven natural selection has shaped some aspects of cranial variability, and thus take a conservative approach to their data by correcting for climatic variation prior to analysis. Relethford (2004a) has shown that climatic variation only accounts for a small amount of the residual craniometric variation when compared with geographic distance, indicating that cli-

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Fig. 2. Regression of average within-group variance of 57 size-adjusted craniometric variables on great circle distance (km) from non-African dispersal origins. A: Tel Aviv (Israel). B: Delhi (India). C: Beijing (China). Diamonds 5 African populations. Open squares 5 European populations. Triangles 5 Asian populations. Stars 5 Oceanic populations. Open circles 5 American populations. TABLE 3. Regression of average within-group variance of 57 size-adjusted craniometric variables on great circle distance (km) from three African dispersal origins and three non-African dispersal origins, when the three Polynesian populations were included in the analysis Dispersal origins African Non-African

R-values (P-values) Addis Ababa Central Africa Southern Africa Tel Aviv Delhi Beijing

20.49 20.52 20.56 20.41 20.23 0.01

(0.008) (0.004) (0.002) (0.030) (0.230) (0.950)

mate does not obscure the overall pattern of IBD on a global scale. Although some studies (Relethford, 2004a; Roseman, 2004; Roseman and Weaver, 2004; Harvati and Weaver, 2006b) have shown that certain aspects of morphology do correlate with climatic variation, it has been demonstrated that this effect is strongest in high latitude populations, who are subject to the greatest extremes of climate (Roseman, 2004; Harvati and Weaver, 2006b). Indeed, Harvati and Weaver (2006b) found that when populations from high latitudes were removed from the analysis, no cranial features were found to correlate with climate. To evaluate the impact of the high latitude populations on our analysis, we removed the Inugsuk (Greenland Inuit) from the dataset and reran the analysis with the southern African startpoint, which had yielded the strongest R2-value previously (when Polynesians were excluded). When the Inuit

population was removed, the strength of the relationship between geographic distance from Africa and within-population variance increased signiﬁcantly (R2 5 0.42, P 5 0.001).

DISCUSSION The use of an additional dataset of global craniometric variation corroborates the results of Manica et al. (2007) in demonstrating that modern human craniometric variance patterns ﬁt a model of iterative founder effects along a dispersal route from an African origin. Despite the theoretical and methodological differences, our results are remarkably similar to those of Manica et al. (2007) who found that distance from Africa could account on average for 19–25% of the variation in craniometric traits. We ﬁnd an African origin dispersal model could explain at least 19–26% of within-population variation, when the Polynesian populations were excluded. Although we do not explicitly correct for climatic variation, by removing isometric scaling prior to analysis, the effects of environmentally driven selection on size (Beals et al., 1983; Roseman, 2004; Harvati and Weaver, 2006a; Smith et al., 2007) were mitigated. However, removing high latitude populations had a more dramatic effect on the results, greatly increasing the explanatory power of the model. This presumably reﬂects the environmentally induced adaptation to extremes of temperature experienced by such populations. A neutral model for the evolution of cranial variation in humans would predict that phenotypic afﬁnity patterns should match closely those of neutral genetics. American Journal of Physical Anthropology

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Thus the ﬁt of neutral genetic data with an African dispersal model (Prugnolle et al., 2005; Ramachandran et al., 2005; Liu et al., 2006) indicates that craniometric variation should also ﬁt a dispersal model. In the case of the genetic data, however, over 87% of population heterozygosity can be explained by the model, contrasted with 25% of within-population variance for craniometric traits. However, as pointed out by Manica et al. (2007), cranial variation is only partly determined by genetic variation, with average heritability values for craniometric traits commonly cited as h2 5 0.55 (Relethford, 1994; Relethford and Harpending, 1994). Therefore, once heritability and potential diversifying selection are accounted for, these lower estimates for phenotypic data are to be expected. In addition, the results of Harvati and Weaver (2006a,b) indicate that individual regions of the human cranium may be reﬂecting neutral evolutionary history more effectively than others. Therefore, future studies may beneﬁt from assessing whether individual cranial regions provide a better ﬁt with the iterative founder effect model than the cranium as a whole. Some have claimed that with multiple lines of genetic, phenotypic, and archeological evidence pointing toward an African origin for modern humans (e.g. Lahr, 1994; Stringer, 2002), one end of the polarized ‘‘multiregional’’ versus ‘‘out of Africa’’ debate may have been satisfactorily rejected in favor of the latter (Foley and Lahr, 2004). Others, however, contend that rumors of the demise of the multiregional model may have been greatly exaggerated (e.g. Relethford, 1998; Templeton, 2007). According to such workers, alternative hypotheses of modern human origins have not been falsiﬁed, resulting in a situation where genetic data are compatible with a number of working hypotheses (Templeton, 2007). As Relethford (2004b) points out, a ﬁt with an IBD model does not allow for the distinction to be made between a long-term pattern of geographically mediated gene ﬂow and a pattern of demographic migration. Therefore, a ﬁt with an IBD model cannot in itself inform the debate regarding modern human origins. All that can be concluded with some certainty is that human craniometric and neutral genetic data is hierarchically structured according to the geographic distance between populations. The results of this study and those of Manica et al. (2007) inform the modern human origins debate in so far as they are compatible with the hypothesis that Africa is the source of all modern human genetic diversity (Jorde et al., 1998, 2000; Yu et al., 2002), and that the initial dispersal of modern humans proceeded as a series of repeated bottlenecking events, as humans spread around the globe.

ACKNOWLEDGMENTS We are grateful to Christopher Ruff, the associate editor and two anonymous reviewers for their helpful and constructive comments on an earlier draft of this manuscript.

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