Craniometric variation and the settlement of the Americas Testing hypotheses by means of R-matrix and matrix correlation analyses.код для вставкиСкачать
AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 116:154 –165 (2001) Craniometric Variation and the Settlement of the Americas: Testing Hypotheses by Means of R-Matrix and Matrix Correlation Analyses Rolando González-José,1* Silvia L. Dahinten,2,3 Marı́a A. Luis,4 Miquel Hernández,1 and Hector M. Pucciarelli3,4 1 Unitat d’Antropologia, Facultat de Biologia, Universitat de Barcelona, 08027 Barcelona, Spain Centro Nacional Patagónico-Consejo Nacional de Investigaciones Cientı́ficas y Técnicas, 9120 Puerto Madryn, Argentina 3 Consejo Nacional de Investigaciones Cientı́ficas y Técnicas, Buenos Aires, Argentina 4 Departamento Cientı́fico de Antropologı́a del Museo de La Plata, Facultad de Ciencias Naturales y Museo, 1900 La Plata, Argentina 2 KEY WORDS Amerindians; craniometrics; R-matrix methods; matrix permutation ABSTRACT New archaeological findings and the incorporation of new South American skull samples have raised fundamental questions for the classical theories of the Americas’ settlement. The aim of this study was to estimate craniometric variability among several Asian and Native American populations in order to test goodness of fit of the data to different models of ancient population entries and dispersions into the New World. Our data set includes Howells’ variables recorded on East Asian, North American, and South American natives (except for NaDene speakers). Five Fuego-Patagonian samples and one Paleoamerican sample were also included. A multivariate extension of the R-matrix method for quantitative traits was used to obtain Fst values, which were considered estimations of intergroup variation. Three main models for the peopling of the New World were represented in hypothetical design matrices. Matrix permutation tests An inspection of the last 15 years’ literature in biological anthropology evidences a great amount of scientific activity referring to the original settlement of the Americas (Greenberg et al., 1986; Guidon and Delibrias, 1986; Neves and Pucciarelli, 1990, 1991, 1998; Szathmáry, 1993a,b; Torroni et al., 1993; Merriwether et al., 1995; Neves et al., 1999a,b; González-José et al., 2001). In fact, this subject has been the topic of numerous disciplines such as archaeology, genetics, linguistics, physical anthropology, and quaternary research. As expected, several hypotheses about the time and characteristics of human entry into the New World arose and at the moment compete in the explanation of that process. Archaeologists and quaternary researchers tend to focus the debate on the dates of first peopling (Borrero and McEwan, 1997; McCulloch et al., 1997). Radiocarbon dates and stratigraphy are of major importance because they allow us to calibrate hypotheses about the processes involved in the settlement. Despite the presence of archaeological sites © 2001 WILEY-LISS, INC. were performed to quantify the fit of the observed data with 1) geographical separation of the samples and 2) three ways of settlement, which were the Three Migration Model (TMM), the Single Wave Migration model (SWM), and the Two Components Settlement Model (TCS). R-matrix results showed high levels of heterogeneity among Native Americans. Matrix permutation analyses suggested that the model involving high Amerindian heterogeneity and two different morphological patterns or components (derived “Mongoloid” vs. generalized “non-Mongoloid”) explains better the variation observed, even when the effects of geographical separation are removed. Whether these patterns arose as a result of two separate migration events or by local evolution from Paleoamericans to Amerindians remains unresolved. Am J Phys Anthropol 116:154 –165, 2001. © 2001 Wiley-Liss, Inc. in Tierra del Fuego and other regions by at least 11,000 –10,500 years ago, and despite the discovery of several sites of pre-Clovis dates in South America, the “Clovis First” model is still accepted by some archaeologists. Nevertheless, solid evidence at the Monte Verde site in southern Chile and other localities now indicates that dates of entering the New World proposed by the Clovis model are incongruent with any possible human migration from Beringia to the southern tip of South America (Dillehay and Collins, 1988; Roosevelt et al., 1996; Borrero and Mc Ewan, 1997; Meltzer, 1997). Geneticists vary in their opinions about the meaning of molecular variability in Amerindians. In a *Correspondence to: Rolando González-José, Unitat d’Antropologia, Facultat de Biologia, Universitat de Barcelona, Avinguda Diagonal 645, 08028 Barcelona, Spain. E-mail: email@example.com Received 24 April 2000; accepted 26 June 2001. CRANIOMETRIC VARIATION AND AMERICAS’ SETTLEMENT classic work, Greenberg et al. (1986) reviewed gene and/or genotype frequency distributions, and haplotype frequencies. Most of the data were serological in nature, involving blood-group antigens, serum proteins, erythrocyte enzymes, immunoglobulins, and leukocyte antigens. The interpretation of this evidence led the authors to support the inference based on linguistic and dental data: the Three-Migration Hypothesis. They, however, stated that from a genetic perspective, the tripartite division of modern Native Americans is still without strong confirmation. Later studies examined variation of blood polymorphism (Salzano and Callegari-Jacques, 1988), Y-chromosome DNA (Bianchi et al., 1997, 1998), mtDNA (Schurr et al., 1990; Torroni et al., 1993; Bailliet et al., 1994; Merriwether et al., 1995; Lalueza et al., 1997), and nuclear DNA (Novick et al., 1998; Da Silva et al., 1999) in Native Americans. In general terms, these authors disagree about the existence of a severe bottleneck, about the coalescence times of purported ancestors, and about the amount of variation to have entered the continent (for reviews, see Szathmáry, 1993a,b; Lahr, 1995; Crawford, 1998). Overall, the genetic evidence for the origins of Amerindian variation does not yet provide a clear picture, although it indicates genetic heterogeneity within the “Amerindian” group of Greenberg et al. (1986; see also Szathmáry, 1993b; Lahr, 1995). Linguistic studies were a major focus of interest in the debate about the settlement of the Americas, and an important support for the Three-Migration Model. In fact, Greenberg et al. (1986) argued for a clear separation of American natives into three different linguistic families: the Na-Dene speakers, the Amerind speakers, and the Eskimo-Aleut speakers. Nevertheless, this point of view received considerable criticism on linguistic grounds (Morell, 1990). Morphological studies are not the exception in terms of controversies, since different models seem to be supported by the evidence. The dental and craniometric evidence strongly supports an Asian origin of Amerindians and Eskimos. The shovel shape of the incisors of Northeast Asia and New World populations occurs in 50 –100% of subjects, in contrast to European and other Asian groups where the frequency of this trait is under 50% (Harris, 1980; Turner, 1987). After a multivariate analysis performed on measurements of skull samples from different geographic sites, Howells (1989) noted that Native American crania are placed metrically among the East Asiatic groups. Simply put, it seems like a morphological continuity from North-Asian to Amerindian populations, with Eskimos and Aleuts in an intermediate position (Crawford, 1998). However, several authors have challenged such a scheme. Certainly, they state that Paleoamerican cranial morphology is very distinct from that of modern native Indians and from that of Northern Asians (Neves and Pucciarelli, 1990, 1991, 1998; Lahr, 155 1995; Neves et al., 1999a,b). Moreover, other authors were unable to attribute a derived East Asian/ Amerindian morphological pattern, to the FuegoPatagonian morphological series (Lahr, 1995; González-José et al., 2001). Despite the complexity and variety of interpretations, we can identify three clear and documented ideas or hypotheses about the way in which the continents were peopled. In the first case, we should consider the Three-Migration Model, proposed in a series of well-known papers by Turner (1987) and Greenberg et al. (1986). This model is based mainly on linguistic, dental, and genetic data studied across Asian and Amerindian populations. In this model, ancestral Amerind-speaking people would have undertaken the first (and oldest) migration into the continent, followed by a second migration that involved the Na-Dene speakers, who currently inhabit the interior of Alaska, the Yukon and Northwest Territories of Canada, and the North Pacific coast (with some large pockets of population further south). The last independent group of people to enter the continent would have been the Eskimo, who at the moment inhabit the Arctic. However, it is unclear whether the authors regard the Na-Dene speakers as the second or third migratory wave (Greenberg et al., 1986; Turner, 1987). Secondly, we may consider theories based on a single migration into the New World. To be accurate, this concept can be traced back to the sixteenth century, when the Spanish naturalist José de Acosta (Acosta, 1962 ) first enunciated the idea of a single origin for all Native Americans. Almost three centuries later, Hrdlička (1925) picked up this theory and developed the notion of a “Mongoloid” ancestry for the Americans. Evidence to support a “one-migration” model, including the possibility of a migration before the coalescence of the great North American ice shields, has been published by Szathmáry (1981, 1984, 1993a,b). More recently, and after the analysis of the distribution of four founding mitochondrial DNA lineages, Merriwether et al. (1995) concluded that a single migration and/or single source is the most parsimonious explanation for this distribution. Later studies on mtDNA (Bonatto and Salzano, 1997a,b) and Y-chromosome polymorphisms (Bianchi et al., 1997, 1998) also defended a one-migration and/or population source for Native Americans. Finally, we consider the model proposed by Neves and one of us (Neves and Pucciarelli, 1990, 1991, 1998; Neves et al., 1999a,b). This model was developed after several cranial comparative analyses of the first known South and North American crania, and rests on the fact that the morphology of the Paleoamerican crania is more consistent with a “non-Mongoloid” definition, and less “Sinodont-Mongoloid” than are recent Amerindians. According to the authors, the best way of interpreting the observed cranial variation involves an earlier generalized “non-Mongoloid” population occupying the 156 R. GONZÁLEZ-JOSÉ ET AL. TABLE 1. Populations considered in this analysis, codes, sample sizes, geographic origin, and sample description’s references Population Code Sample size Geographic origin Description in Hainan Island An-Yang Atayal Buriats North Japan South Japan Inugsuk Eskimo HAIN ANYA ATAY BURI NJAP SJAP ESKI 45 42 29 55 55 50 53 Howells, Howells, Howells, Howells, Howells, Howells, Howells, Early Arikara Santa Cruz Yauyos, Peru Paleoamerican Chilean Araucano Fueguian marine hunter-gatherers Fueguian terrestrial hunter-gatherers Fueguian marine hunter-gatherers Patagonian terrestrial hunter-gatherers Total ARIK SANT PERU LAGO ARAU KAWE 42 51 55 14 17 16 South China Honan Province, East China Taiwan Siberia, Baikal Region Hokkaido, Japan North Kyushu, Japan West and southeast Greenland (as far as Scoresby Sound) South Dakota, USA Santa Cruz Island, California, USA 50–100 km southeast of Lima, Peru Lagoa Santa sites, Minas Gerais, Brasil Central Chile Chilean Pacific Coast, Chile SELK 52 This work YAGH 39 Isla Grande, Tierra del Fuego, Chile, and Argentina Beagle Channel, Chile, and Argentina TEHU 41 Northeast Patagonia (Chubut and Rı́o Negro), Argentina This work 1989 1989 1989 1973 1989 1989 1973 Howells, 1973 Howells, 1989 Howells, 1989 This work This work This work This work 656 Americas. Native Americans can be, in consequence, divided into two “components:” one early Paleoamerican component (not necessarily Paleoindian), showing a generalized morphological pattern, and a later component of derived characteristics, related to modern Asiatic groups and including past and modern Amerindian populations. Even when several authors have discussed these and other hypotheses, the attempts made to test the models in numerical or statistical terms are almost nonexistent. In this context, R-matrix methods (Harpending and Jenkins, 1973) and matrix permutation test (Sokal et al., 1992; Waddle, 1994) could be of great importance as an approach to test the explanatory power of the several theories and mechanisms proposed. An R-matrix is the normalized covariance matrix of allele frequencies across populations. R-matrix analysis has several advantages over other methods of estimating genetic similarities and distances (Relethford and Harpending, 1994). Moreover, it can be applied to a wide range of data, including allele frequencies (Harpending and Jenkins, 1973), metric traits (Relethford and Blangero, 1990), and migration matrices (Rogers and Harpending, 1986). Further details of these methods are provided by Williams-Blangero and Blangero (1989), Relethford and Blangero (1990), Relethford and Harpending (1994), Relethford (1996), and Relethford et al. (1997). The matrix permutation techniques were suggested and preliminary developed to test human settlement and dispersion models for Europe by Sokal et al. (1992). Waddle (1994) published an outstanding work in which three main models for the origin of modern Homo were numerically represented and analyzed by means of matrix permutation tests. These techniques were then applied in other studies and/or populations (see Sokal et al., 1997; Waddle et al., 1998; González-José et al., 2001), and were extensively criticized, improved, and commented on by Konigsberg et al. (1994), Cole (1996), and Konigsberg (1997). Our purpose here is to test three of the current models of the Americas’ settlement using craniometric information either published or collected by us. Simply put, we will compare observed cranial dissimilarities among several samples of Asian and Native American populations, with hypothetical differences expected under the three competing models proposed for the settlement of the Americas, and with several representations of geographical separation. Analyses will focus on the validity of including all Amerindian morphological variability (with special emphasis on South American and Paleoamerican series) in a single group, rather than focusing on North American group relationships. Cranial dissimilarities (distances) will be obtained following a “model bound” approach in order to estimate specific parameters as average withingroup phenotypic variance, Wright’s Fst, minimum genetic distances, etc. (Relethford and Blangero, 1990; Relethford, 1994; Relethford and Harpending, 1994). Matrix permutation techniques will be used to test the fit of observed data with hypothetical models peopling of the Americas. We also attempt to improve and update the comparative framework by including samples from Fuego-Patagonia and Paleoamerican sites. Since these samples were not included in some earlier studies, we will show their importance as a source of variation when included in the Amerindian group. MATERIALS AND METHODS The sample Table 1 lists the populations considered in this study, their sample size, and the geographical origin of the samples. To avoid sex differences, only male CRANIOMETRIC VARIATION AND AMERICAS’ SETTLEMENT TABLE 2. Craniometric variables used as markers (for definitions see Howells, 1973) Measurement Code Glabello-occipital length Basion-nasion length Basion-bregma height Maximum cranial breadth Maximum frontal breadth Biauricular breadth Nasion-prosthion height Nasal height Nasal breadth Palate breadth, external Orbit height, left Bimaxillary breadth Bifrontal breadth Biorbital breadth Nasion-bregma chord (frontal chord) Bregma-lambda chord (parietal chord) Lambda-opisthion chord (occipital chord) GOL BNL BBH XCB XFB AUB NPH NLH NLB MAB OBH ZMB FMB EKB FRC PAC OCC 157 to the south by the Magellan Straits. The Selk’nam were confined to the steppe regions from Isla Grande (Tierra del Fuego) and were also terrestrial huntergatherers. Both Tehuelches and Selk’nam were adapted to the hunting of the guanaco (Lama guanicoe), one of the four South American camelids. Finally, we included two marine hunter-gatherer “canoeros:” the Kaweskar, or Alakaluf (KAWE), and the Yahgan, or Yámana (YAHG) groups. The Yahgan inhabited the coastal areas around the Beagle Channel, south to the Brecnock Peninsula, including Cape Horn. They are the only human group ever to have inhabited a region below 55°S (Hernández et al., 1997). The Kaweskar lived on islands and channels of the Chilean Pacific coast, from the Brecknock Peninsula to the Gulf of Penas. Biological distances individuals were considered. In Table 2, we show the craniometrical variables used in the present study. In order to eliminate size influence on the variables, a Q-mode standardization was performed, where each original measurement is divided by the object (individuals) arithmetic mean, calculated over all variables (Corruccini, 1973). This cancels out size differences by giving each individual the same average character state or magnitude over all measurements taken on it (Corruccini, 1973). Q-mode standardized measurements are analogous to the C-scores used by Howells (1989). Detailed information about the variables is available in Howells (1973, 1989), and original data were downloaded from the web site http://konig.la.utk. edu/howells.htm, and combined with data collected by us, in order to cover East Asian and Native American populations. The materials used in this work comprise 656 modern human crania divided into 16 cranial series: 6 from East Asia, and 10 from North and South America. Since our interest is focused on the validity of the “Amerind” or “Paleoindian” group (sensu Greenberg et al., 1986) we included no samples of crania from Na-Dene speakers. Nevertheless, we extend the data set of Howells (1973, 1989) to include six new populations. One of the new American series (LAGO) corresponds to a prehistoric sample of male individuals recovered from Paleoamerican horizons of Brazil (Lagoa Santa) with an estimated age dating of 12,000 – 8,000 years BP (Neves and Pucciarelli, 1991). The Araucanian (ARAU) sample corresponds to a historical population which came from central Chile and settled at the Argentinean Pampas from the 18th century. This ethnic group was mainly influenced by the Andean culture and by the South American version of the horse complex. Fuego-Patagonian samples include the Tehuelches (TEHU), Selk’nam (SELK), Kaweskar (KAWE), and Yahgan (YAHG) groups. The Tehuelches were terrestrial hunter-gatherers who occupied continental Patagonia. Their territory was limited to the north by the Colorado River and Biological distances were assessed using Mahalanobis generalized distances (D2), after the modifications of Williams-Blangero and Blangero (1989). These modifications assume an additive polygenic model for the traits in which the expectation of environmental deviations is zero. The phenotypic variance, composed of genetic and environmental components (p2 ⫽ g2 ⫹ e2), must be greater than or equal to the genetic variance (p2 ⱖ g2). Those authors demonstrated that “Dp2 represents a matrix containing the minimum genetic distances derived from the phenetic variation” (Williams-Blangero and Blangero, 1989, p. 5). The resulting equation can be written as: d ij 2 ⫽ r ii ⫹ r jj ⫺ 2r ij where rij are the elements of an R-matrix computed for each trait in populations i and j (Relethford et al., 1997). The diagonal elements rii also give the genetic distance of each population to the group centroid, and the average diagonal element of the R-matrix weighted by population size is equal to Wright’s Fst, a measure of average genetic differentiation relative to the contemporary gene pool (Relethford, 1996). The level of craniometric differentiation was assessed here from both Fst values, and a biological distance matrix. This matrix (BIO) represents the distances or dissimilarities (dij2) based on 17 Howells craniometric variables observed in 13 populations and obtained following the methodology described above. Equal relative population sizes were assumed for the hunter-gatherer groups (ESKI, LAGO, KAWE, SELK, YAHG, and TEHU), and twice for the nonhunter-gatherers. Analysis of data and computation of distances were performed using the software RMet for Windows, version 4.0 (Relethford, 1998), provided by Dr. J. Relethford at the World Wide Web (http://konig. la.utk.edu/relethsoft.html). 158 R. GONZÁLEZ-JOSÉ ET AL. Spatial separation In order to test geographical patterns of cranial variation, we constructed two matrices of spatial distance. When testing geographic variation for significant departures from randomness, investigators will want to know the effects of coding their data in various ways (Sokal, 1979). The choice of a given geographic distance model depends on the nature of the mechanisms involved in the origin of geographic variation. Thus, we focused our analyses on two different patterns of geographic separation. Firstly, we assumed a simple isolation-by-distance model, in which biological distance should increase with increased geographic distance (Wright, 1943). If one consider a process working on a continuous surface between all pairs of localities, an ordinary geographic distance matrix should be used (Sokal, 1979). In this matrix (GEO), the elements are equal to the linear distance in kilometers between populations i and j. Obviously, distances between Asian and American samples were computed considering a route across the Bering Strait rather than direct distances to avoid illusory (transoceanic) distances. Secondly, one may state that phenetic differences will be for short distances and that all the great distances have the same effect and can be grouped into a non-neighbor group. This is the case with our asymptotic distance connectivity matrix (ADM), in which distances between neighbor localities are equal to their actual separation in thousand of kilometers, and the distance between not-connected localities get a constant value slightly superior to the higher distance observed between any pair of connected localities (9,500 km in our sample). Following Sokal and Oden (1978), we consider that A is the nearest neighbor of B if no other locality lies on or within the circle whose diameter is the line AB. Thus, A and B are connected when d2AB ⬍ d2AC ⫹ d2BC, where d2AB is the squared distance between A and B, and C is any third locality. Models and design matrices In a typical matrix permutation study, dissimilarities between the samples are estimated after any character observable in the population, and are then represented in a distance matrix. Then, hypothetical dissimilarities expected under a particular model are set and described in connection schemes and design matrices (Waddle et al., 1998). A design matrix describes the relative distances among populations expected under a particular model (Waddle et al., 1998). An element of a biological distance matrix, as much as an element of a design one, describes the strength of the link between two populations. Construction and handling of design matrices was welldescribed in several papers by Sokal et al. (1992, 1997), Waddle (1994), and Waddle et al. (1998). Mantel tests (Mantel, 1967) are usually used to compare the degree of association between the observed and the design matrices. An appropriate design ma- Fig. 1. Connection scheme for one model of human settlement of the Americas: the Three-Migration Model (Greenberg et al., 1986). Numbers represent hypothesized distances. trix is critical to successful discrimination of an observed distance matrix (Sokal et al., 1997). We intend to evaluate here three main documented models of dispersion of humans into the Americas. Models are quite simple and, of course, can be improved in several ways. Nevertheless, simple models are preferable and easy to express upon design matrices. In Figure 1, we show the connectivity scheme corresponding to the Three-Migration Model (TMM). In this scheme, samples are regrouped in four boxes. The first corresponds to Atayal (ATAY) and Hainan (HAIN) samples, and represents the more generalized sundadont dental pattern, from which typical “Mongoloids” are derived (Turner, 1992; Lahr, 1995). Turner (1987) also recognizes a derived expression of this dental complex, typical of Eastern and Northeastern Asian populations and Amerindians, which he named sinodonty. Sinodont morphology is derived from the sundadont and is represented by the Anyang (ANYA), Buriats (BURI), North Japan (NJAP), and South Japan (SJAP) samples grouped in a single box and playing a role of “source” for the peopling of the Americas. In fact, from this group we derived the hypothetical first wave, represented in a third box containing all the Amerindians and, separately in a fourth box, the Eskimos (Greenberg et al., 1986). A distance of zero was assumed for samples included in the same box and a value of one for samples of different but connected boxes, except for the connection between the sinodonts and Amerindians, which is assumed to be equal to two in order to represent an older separation and divergence from the Asian stock in comparison with Eskimos. As a general rule for all models, the distance between any pair of samples of nonconnected boxes was obtained by adding together the values along the path (e.g., distance between HAIN and SANT is equal to 3). We also assembled a connectivity scheme for the Single Wave Migration Model (SWM) (Fig. 2). It represent the conclusions obtained by Merriwether CRANIOMETRIC VARIATION AND AMERICAS’ SETTLEMENT 159 Fig. 2. Connection scheme for one model of human settlement of the Americas: the Single Wave of Migration Model (Merriwether et al., 1995). Numbers represent hypothesized distances. et al. (1995) after examining distribution patterns of the four founding mtDNA lineages. These authors postulated that all Native Americans came from a single migratory wave as did Bonatto and Salzano (1997a,b). The model was symbolized here by simply joining the Eskimo sample within the Amerindian box into the anterior scheme. This scheme attempts to equate a molecular common ancestor with a morphological one. The elements on the design matrix were obtained from the SWM connection scheme in the same manner as for the preceding model. The Two-Components Settlement Model (TCM) was developed by Neves and Pucciarelli (1990, 1991, 1998) and Neves et al. (1999a,b), and was supported by other studies (Steele and Powell, 1992, 1993; Powell and Neves, 1999). It was observed that traits characterizing both typical East Asians and recent Amerinds are absent from Paleoamericans. These differences lie in the more generalized morphological pattern of the Paleoamericans, and indicate that early people were not part of the typical “Mongoloids” we currently associate with Asia. In previous papers, Neves et al. (1999a) and Powell and Neves (1999) discussed this theory under the title FourMigration Model, a modification of the “Three-Migration Model” by Greenberg et al. (1986). In the following, however, we will refer to this theory as Two-Component Settlement Model (TCM), since this label reflects better the validity of Amerindians as a heterogeneous group, without reference to any cause of heterogeneity (i.e., migration). In this context, we must note that discussion about the grade of genetic, biological, or linguistic closeness between Eskimos, Aleuts, and Indians of the northwestern part of North America is not of primary interest in this model, since TCM is based mainly on relationships between Paleoamerican remains and modern Amerindian variation. This may be more relevant to the TMM model, though, since blood group analyses showed that Eskimos and Indians of the Na-Dene language phylum (Haida, Tlingit, and Athapaskan) are more closely related to each other than to other Fig. 3. Connection scheme for one model of human settlement of Americas: the Two-Component Settlement Model (Neves and Pucciarelli, 1990, 1991, 1998; Neves et al., 1999a,b; Powell and Neves, 1999). Two modifications are included in this scheme: 1) the distance between Paleoamerican and Fuego-Patagonia is increased (model TCM2 and TCM3, distance value in parentheses); and 2) Eskimos are connected with Amerindians rather than with Asians (model TCM3, dotted line). Numbers represent hypothesized distances. Americans or Asians (Szathmáry, 1979). Furthermore, Ossenberg (1976) demonstrated the biological closeness between Na-Dene speakers (specifically Athapaskan speakers) and Aleuts (Eskimo-Aleut family) after the analysis of a battery of 24 discrete cranial traits (see also Szathmáry and Ossenberg, 1978; Szathmáry, 1993b; more recent molecular data confirm the relative closeness of Eskimo and Na-Dene speakers). The connectivity scheme representing the TCM is shown in Figure 3. Samples were divided into five boxes by geographical location and according to the divergences predicted by the TCM: Paleoamerican (LAGO), Asian (HAIN, ANYA, ATAY, BURI, NJAP, and SJAP), Eskimo (ESKI), Amerindian (ARIK, SANT, PERU, and ARAU), and Fuego-Patagonia (TEHU, SELK, YAHG, and KAWE). In this scheme, the Asian and Amerindian boxes are connected by a relatively small distance value (1) to indicate the predicted dispersion from a modern differentiated stock. The Paleoamerican sample is connected to Fuego-Patagonia by the same small distance value (1) regarding their belonging to the hypothetical early wave. In this context, Lahr (1995) concluded that Fueguian-Patagonian groups also show this mosaic of primitive, generalized pattern and stated that this phenomenon could represent a retention of traits from Paleoamerican populations. Next, the Paleoamerican box is connected to the Amerindian one by a distance of 2, representing the predicted 160 R. GONZÁLEZ-JOSÉ ET AL. TABLE 3. Distance matrices considered in this study Code Description BIO GEO Biological distance matrix obtained after an R-matrix Geographical separation expressed in thousands of kilometers Asymptotic distance matrix (spatial separation) “Three-Migration Model” “Single Wave of Migration Model” “Two-Component Model” TCM, but equal distance between Paleoamerican, Fuego-Patagonia, and Asia TCM2, but ESKI connected with Amerindian rather than Asian groups ADM TWM SWM TCM TCM2 TCM3 separation between a morphologically generalized modern Homo sapiens (early wave) and a specialized East Asian-Amerindian group. Finally, we derived the Eskimos from the Asian stock, joining both with the minimum distance (0.5) predicted in order to represent a recent divergence. Two additional design matrices departing from this initial scheme were also constructed. Basically, we changed the original scheme of TCM by deriving the Eskimos from the Amerindian box rather than the Asian one (TCM2), and finally we altered the triangle formed by Paleoamerican, Fueguian-Patagonians, and Amerindians by increasing the distance value between Paleoamerican and Fuego-Patagonia from one to two (TCM3). Modifications are shown in Figure 3, with dotted lines for the TCM3 model and in parentheses for TCM2. In Table 3, we give a brief description of the eight matrices (one biological, two geographic, and five design ones) involved in this study. Correlation among distance matrices Mantel statistic tests (Mantel, 1967) were used to analyze correlations among different types of distance matrices. The Mantel statistic tests associations between distance matrices, and gives us a way of testing the significance of these associations. Significance of the correlation was determined by a permutation test: the rows and columns of one matrix are permuted and the Mantel statistic is calculated 9,999 times, creating a distribution that is used to evaluate the significance of the observed correlation (Mantel, 1967; Smouse et al., 1986; Waddle, 1994; Sokal and Rohlf, 1995). Alternatively, the Smouse-Long-Sokal test (Smouse et al., 1986) was used to yield partial matrix correlations. The Smouse-Long-Sokal method extends Mantel’s statistic to three or more matrices and tests whether an association between matrix A and B is significant when one or more matrices C, D, . . . are held constant. The Smouse-Long-Sokal test was used to test partial correlations after removing the effects of geography. RESULTS Figure 4 shows the plot of the first two principal cooordinates obtained after the BIO matrix. The first two eigenvalues collectively account for 54.8 % of the variation. Clearly, LAGO appears to be distinct from the remaining samples. Except for BURI, Asian groups tend to form a separate cluster. Conversely, Native Americans constitute a highly heterogeneous and polymorphic group. Cranial breadth (XCB), frontal breadth (XFB), and auricular breadth (AUB) highly contributed to the first coordinate, whereas cranial length (GOL), basion-nasion length (BNL), basion-bregma height (BBH), and frontal, parietal, and occipital chords (FRC, PAC, and OCC) negatively contributed to the first coordinate. This pattern clearly separates between groups with short and wide crania (e.g., BURI) from those with a more long and narrow skull (e.g., LAGO). Variance along the second axis was mainly generated by cranial breadth (XCB) and frontal breadth (XFB) (positive values), and bimaxillary breadth (ZMB), bifrontal breadth (FMB), and biorbital breadth (EKB) (negative values). Minimum Fst values were computed for different subsets of data according to several comparison criteria. Results are shown in Table 4. Minimum Fsts are obtained assuming that all heritabilities are equal to 1, and that the additive genetic covariance matrix is proportional to the phenotypic covariance matrix (Williams-Blangero and Blangero, 1989). This is a conservative statistic because it implies that the minimum Fst should be less than an estimate of Fst computed from genetic markers (Relethford, 1994). In order to compare our results with further studies, minimum Fsts were estimated considering the following subsets: Asian, Native Americans, South Americans, South Americans except Paleoamericans (LAGO), and North Americans. The minimum Fst values range from 0.074 – 0.162. As expected, Fst values differ much from either array of samples. The smallest value is obtained when only Asiatic samples are considered (minimum Fst ⫽ 0.074). In contrast, South American groups give the highest Fst value (0.176). Geographic distribution of the samples is an important factor that could be responsible for this difference: the local samples in the Americas are widely separated, and the Asian ones are close together geographically. Because of isolation by distance, populations further apart geographically are expected to be more dissimilar from each other and from the regional centroid. Nevertheless, the results do not always match that rule, since Native Americans in general and South Americans in particular show higher Fst values (Fst ⫽ 0.161 and Fst ⫽ 0.162, respectively) than the entire sample, which covers the widest geographic area. Variability in South Americans is clearly generated by Paleoamericans: Fst value drops from 0.162 to 0.116 when this sample is removed. Pairwise correlations between the biological distance matrix (BIO), spatial separation matrices (GEO, ADM), and five design matrices (TMM, SWM, TCM, TCM2, and TCM3) are given in Table 5. As CRANIOMETRIC VARIATION AND AMERICAS’ SETTLEMENT Fig. 4. 161 Principal coordinate plot obtained from minimum genetic distances (BIO) between samples. TABLE 4. Minimum Fst values for craniometric traits calculated for several arrays of the total set of populations Samples included All samples Asian Native Americans South Americans South Americans except Paleoamerican (LAGO) North Americans Minimum Fst SE 0.150 0.074 0.161 0.162 0.116 0.005 0.005 0.006 0.009 0.008 0.111 0.008 expected, the two arrays of spatial separation are correlated (r ⫽ 0.440; P ⬍ 0.0001). The settlement models as much as the biological distances matrix were correlated with the separation in kilometers between samples (GEO) and with the array representing long-distance isolation (AMD). The Three-Migration Model and Single-Wave Model failed to correlate with biological distances expressed in BIO (Table 5). Conversely, biological distances gave significant, positive correlations with the three variants of the Two-Component Settlement Model. The correlation between BIO and TCM2 was the strongest of these comparisons (r ⫽ 0.386; P ⬍ 0.05). Because both biological variation and the models are closely related with geographic distances, results of correlations could be best interpreted as partial matrix correlations holding geography constant (Sokal et al., 1992; Waddle et al., 1998). In Table 6, we show the partial Mantel correlations among the BIO and five design matrices (TMM, SWM, TCM, TCM2, and TCM3) with spatial separation held constant. Neither the Three-Migration Model nor the Single-Wave Model correlated well with the biological distance matrix (rBIO.TMM ⫽ 0.033, P ⬎ 0.05 and rBIO.SWM ⫽ 0,013, P ⬎ 0.05, respectively). Conversely, the TCM models give positive, significant correlations when tested against the BIO matrix. The strongest correlation was observed between BIO and design TCM2 (r ⫽ 0.364; P ⬍ 0.02). This design describes equal distances between Fuego-Patagonians, Paleoamericans, and Amerindians. However, note that these results cannot clearly differentiate the effects of the three variant forms of TCM (TCM, TCM2, TCM3). In order to evaluate the explanatory power of TCM by alternative methods, we performed two additional tests. First, we applied the method developed by Dow and Cheverud (1985). This test assumes the null hypothesis of no difference between the correlations of an observed distance matrix C with two explanatory matrices A and B. Thus, the Dow-Cheverud test determines whether two correlations r(AC) and r(BC) differ significantly from each other. Table 7 shows the results of the DowCheverud test. Matrix correlations for TCM vs. TMM and SWM are given as the difference between design matrices (after standardization). Negative correlation indicates a better fit between BIO and 162 R. GONZÁLEZ-JOSÉ ET AL. TABLE 5. Complete matrix correlations among two spatial separation matrices (GEO and ADM), five design matrices (TMM, SWM, TCM, TCM2, and TCM3), and one biological distance matrix (BIO)1 ADM TMM SWM TCM TCM2 TCM3 BIO 1 GEO ADM TMM SWM TCM TCM2 TCM3 0.440 (0.000) 0.720 (0.000) 0.699 (0.000) 0.651 (0.002) 0.629 (0.002) 0.640 (0.001) 0.233 (0.009) 0.000 0.299 (0.008) 0.294 (0.016) 0.177 (0.043) 0.187 (0.037) 0.185 (0.043) 0.176 (0.006) 0.000 0.749 (0.052) 0.476 (0.009) 0.443 (0.008) 0.378 (0.010) 0.084 (0.275) 0.000 0.270 (0.022) 0.240 (0.024) 0.318 (0.019) 0.064 (0.286) 0.000 0.988 (0.000) 0.961 (0.001) 0.354 (0.009) 0.000 0.975 (0.001) 0.386 (0.010) 0.000 0.384 (0.016) Exact probabilities are in parentheses, under each correlation value. TABLE 6. Partial Mantel correlations among one measure of biological distance (BIO) and five design matrices (TMM, SWM, TCM, TCM2, and TCM3), with spatial separation held constant1 TABLE 8. Matrix correlation test between BIO and TCM and partial correlations involving these distances and geographic, TMM, and SWM distances1 BIO, TCM .GEO .GEO, TMM .GEO, TMM, SWM 0.354 (0.009) 0.333 (0.012) 0.356 (0.043) 0.358 (0.036) r BIO BIO BIO BIO BIO ⫻ ⫻ ⫻ ⫻ ⫻ TMM SWM TCM TCM2 TCM3 0.033 (0.373) 0.013 (0.390) 0.333 (0.012) 0.364 (0.016) 0.363 (0.023) 1 Column r gives matrix correlations. Exact probabilities are in parentheses, under each correlation value. TABLE 7. Matrix correlation tests for models of New World’s peopling using Dow-Cheverud approach (Dow and Cheverud, 1985)1 BIO TMM SWM TMM SWM TMM SWM ⫺ ⫺ ⫺ ⫺ ⫺ ⫺ TCM TCM TCM2 TCM2 TCM3 TCM3 ⫺0.264 (0.841) ⫺0.240 (0.864) ⫺0.286 (0.831) ⫺0.261 (0.860) ⫺0.268 (0.817) ⫺0.275 (0.849) 1 Exact probabilities are in parentheses, under each correlation value. TCM, whereas a positive correlation indicates a better fit of the TWM or SWM models. Although this test suggests that the hypothesis depicted in TCM better fits the data (negative values imply a stronger effect of TCM on BIO when compared against TMM and SWM), P values were not significant. However, since Oden and Sokal (1992) demonstrated that this test is highly vulnerable to spatially autocorrelated data, those results must be taken with caution. Another important issue regarding the application of this method is that the Dow-Cheverud approach detects only additive effects of a data matrix on a pair of predictor matrices. The generalized regression approach advocated in the Smouse-Long-Sokal test can easily incorporate both types of dependency, additive and nonadditive (Smouse and Long, 1992), and can be extended to multiple matrices. Since we need to control the effects of more than one matrix (e.g., GEO and SWM) to further corroborate the significance of the BIO-TCM correlation, we applied an alternative method described in Sokal et al. (1992). This approach consists of the computation of partial correlations of BIO on TCM holding 1 Partial correlations, which are all of BIO against TCM with various other distances held constant, are indicated by a period followed by the constant variables. Thus, .GEO, TMM stands for rBIO,TCM.GEO,TMM. Exact probabilities are in parentheses, under each correlation value. the remaining models constant until all matrices are considered. Thus, we tested whether any correlation remained between BIO and TCM, once the correlation between these two variables due to one or more regressor variables (spatial separation and competing models) was eliminated. In Table 8, we present the results of the Smouse-Long-Sokal test performed in this way. Note that partial correlation of BIO on TCM with added distance matrices held constant does not decrease further and continues to be significant. In general terms, results indicate that spatial separation and a model considering two different patterns of morphological variability in the Americas are the most dominant influence on the degree and pattern of craniometric differentiation. DISCUSSION Phenotypic data in the form of craniometrics were analyzed in order to describe the genetic characteristics of the Asian and American populations considered here. Inferences performed using this approach are based on the premise that phenotypic variation adequately reflects genetic variation (Williams-Blangero and Blangero, 1989). We used the Relethford-Blangero method, because it provides an analytical framework that explicitly delimits the genetic inferences that can be obtained from purely phenetic data. O’Rourke et al. (1992) give Fst values in a summary of statistics for Amerindian genetic data (see Table 1 in O’Rourke et al., 1992). Fst values for North America and South America were 0.090 and 0.091, respectively. Results presented in this report tend to show a greater level of differentiation between populations (0.111 and 0.162, respectively). Several explanations could account for those differences. First, the populations sampled in both studies are not the same (e.g., Fuego-Patagonian groups CRANIOMETRIC VARIATION AND AMERICAS’ SETTLEMENT were not considered by O’Rourke et al. (1992), and our study included no crania from Na-Dene-speaking populations). This raises once again the question of sampling in these kinds of analyses. As stated by Schanfield (1992), a broader approach requires that the origin of all Amerindians be considered not the limited picture that is formed by looking at the “usual” groups. Moreover, Lahr (1995) demonstrated how much the variation between modern populations is affected by varying the populations that are sampled. In this context, the lack of clustering between Fuego-Patagonian populations and the remaining Amerindians was also demonstrated in other studies (Lahr, 1995; Lalueza et al., 1997), and further research on those groups is necessary. Secondly, natural selection and developmental acclimatization could affect cranial variation. This issue was discussed by several authors. Recent analyses gave little basis to consider selection as a cause of such differences (see Relethford, 1994; Relethford and Harpending, 1994; Neves et al., 1999b). Finally, one may suspect that genetic drift generated high levels of variability within the Native American subset. For instance, O’Rourke et al. (1992) suggested that the levels of heterogeneity observed in modern populations of South Amerindians could have arisen in part because of the great density of population in Central America. This increase in population, according to Ward (O’Rourke et al.,1992), could have blocked further migrants who sought to enter South America via the north. Relethford (1996) demonstrated that when population sizes are different, as in the present case, differential genetic drift can obscure the underlying pattern of population history. The author developed a method in which the R-matrix is modified (scaled) after a migration matrix, and estimations population sizes control differential drift to some extent. A preliminary application of this method to the data set considered here showed that the general pattern of between-group dissimilarities remained unaltered. Nevertheless, estimations of relative population sizes based on scaled R-matrices are just broad approaches. Scaling methods can be improved by better estimated demographic parameters. Results presented here agree with early findings made by several researchers that levels of variability observed in South America cannot be accommodated in a single group (Neves and Pucciarelli, 1990, 1991, 1998; Neves et al., 1999a,b). Moreover, high variability is also found in several earlier studies with different genetic markers. Classic protein markers, as well as mitochondrial and nuclear DNA polymorphisms, revealed that Amerindian populations present a higher within-group genetic heterogeneity than any other major human ethnic group (Deka et al., 1995; Urbanek et al., 1996; Zago et al., 1996; Bortolini et al., 1997; Novick et al., 1998; Da Silva et al., 1999). As stated by Stone and Stoneking (1998), it does not appear that European contact, on the whole, significantly altered patterns of Amerind 163 mtDNA variation, despite the accompanying sudden and drastic decrease in population size, although some reduction in diversity may have occurred in many populations (Wallace and Torroni, 1992). To further explore underlying and/or hypothetical reasons for the arrangement of the samples, we performed matrix permutation tests analyzing several patterns of spatial separation and models for the Americas’ settlement. Cole (1996) and Konigsberg (1997) have extensively criticized the use of matrix permutation test in paleoanthropology, and in particular, the analysis of modern human origins by Waddle (1994). We intended to take into account the points reviewed by them about matrix permutation methods (e.g., sample sizes are stable and customary in such studies, Mahalanobis generalized distance was used, significance values were obtained by means of randomization tests, and models fulfilled the triangle inequality). Tables 5– 8 show connections between hypothesized matrices and a particular way to estimate genetic distances between samples of skulls. Thus, the “explanatory power” of the different models can be evaluated. Despite the possibility of misunderstanding the true magnitude of correlations because of the spatial separation effect, results show that TMM and SWM are untenable, and TCM emerges as more strongly associated with morphological variation. This association cannot be reduced after geography has been held constant, since the TCM model improved performance in comparison with the remaining design matrices. In fact, the association between biological distances and TCM remains positive and significant, even when the effects of spatial separation are removed. Keeping other models constant does not reduce this correlation (Table 8). The present study was unable to provide a clear separation between the three variants based on the Two-Components Settlement Model. This model implies that an early component corresponds to a generalized “non-Mongoloid” group, represented by Paleoamerican populations dated from 12,000 – 8,000 years BP. The remaining component corresponds to differentiated groups, which arose in South America around 9,000 – 8,000 years BP, giving rise to most of the historic Amerindian groups. A central issue is that this theory does not necessarily imply that early inhabitants made any contributions to the genetic pool of later Native American populations. Matrix permutation analyses were unable to discriminate clearly between models in which the relative position of Fuego-Patagonia was arbitrarily altered (see Tables 5 and 6). Lahr (1995) suggested that both Fuego-Patagonian and Paleoamerican populations might reflect the morphology of a more generalized ancestral group. In agreement with this idea, Lalueza et al. (1997) suggested that the distinctive distributions in the continent of mtDNA haplogroups A and B, which appear to have been absent from Fueguian-Patagonians, traced back to a population of more ancient ancestry, distinct from 164 R. GONZÁLEZ-JOSÉ ET AL. those Amerindian populations harboring all four primary lineages. According to Lalueza et al. (1997), such a process is possible despite the potential influence of genetic drift, reasonably presumed to have been rather small, under the demographic conditions depicted by missionaries’ census surveys and conservative estimates. However, this assertion was challenged by Moraga et al. (2000), and it is evident that more research is needed in this field. The results obtained here show that the Paleoamerican sample departs considerably from the morphological pattern of the remaining Native Americans, and also places Fueguians and Patagonians at one extreme of Amerindian morphological variability (Fig. 4). CONCLUSIONS The present study was conclusive about two points. First, models involving clustering of Native Americans in a single migration, or deriving from a single ancestor, have little support from craniometric variability. Second, from the three hypotheses examined, the one that represented departures from a typical East Asian morphology in Paleoamerican and Fuego-Patagonian series gives the best fit to the data tested. 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