13 Menendez Et Al. 2014 AJPA

Effect of Bite Force and Diet Composition onCraniofacial Diversification of Southern South AmericanHuman Populations

Lumila Menendez,1* Valeria Bernal,1 Paula Novellino,2 and S. Ivan Perez1

1CONICET, Division Antropologa, Museo de La Plata (UNLP), Paseo del Bosque S/N La Plata, 1900, BuenosAires, Argentina2CONICET, Laboratorio de Bioarqueologa, Museo de Ciencias Naturales y Antropologicas J.C.Moyano, Mendoza,Argentina

KEY WORDS morphological variation; 13C isotopes; caries; bite force; spatial regression;masticatory loading

ABSTRACT Ecological factors can be important toshape the patterns of morphological variation amonghuman populations. Particularly, diet plays a fundamen-tal role in craniofacial variation due to both the effect ofthe nutritional statusmostly dependent on the type andamount of nutrients consumedon skeletal growth andthe localized effects of masticatory forces. We examinethese two dimensions of diet and evaluate their influenceon morphological diversification of human populationsfrom southern South America during the late Holocene.Cranial morphology was measured as 3D coordinatesdefining the face, base and vault. Size, form, and shapevariables were obtained for 474 adult individuals comingfrom 12 samples. Diet composition was inferred from car-ious lesions and d13C data, whereas bite forces were esti-mated using traits of main jaw muscles. The spatial

structure of the morphological and ecological variableswas measured using correlograms. The influence of dietcomposition and bite force on morphometric variationwas estimated by a spatial regression model. Cranial var-iation and diet composition display a geographical struc-ture, while no geographical pattern was observed in biteforces. Cranial variation in size and form is significantlyassociated with diet composition, suggesting a strongeffect of systemic factors on cranial growth. Conversely,bite forces do not contribute significantly to the patternof morphological variation among the samples analyzed.Overall, these results show that an association betweendiet composition and hardness cannot be assumed, andhighlight the complex relationship between morphologicaldiversification and diet in human populations. Am J PhysAnthropol 155:114127, 2014. VC 2014 Wiley Periodicals, Inc.

Ecological factors can shape the patterns of morpho-logical variation among populations either because theyact as a selective pressure during the divergence of pop-ulations spatially localized in specific environmental con-texts or because organisms respond to environmentalinfluences during ontogeny (phenotypic plasticity or eco-phenotypic responses; Schluter, 2000; Roseman, 2004;Collard and Wood, 2007). In particular, morphologicalchange among populations with a recent divergence andoccupying heterogeneous environments can result fromdirectional selection and phenotypic plasticity ratherthan processes such as genetic drift and mutation (Car-roll et al., 2007; Perez and Monteiro, 2009).

Accordingly, patterns of variation in modern humansat a worldwide scale show thatalthough the overallcranial variation is mainly geographically structuredand probably related with random processessome cra-nial traits are associated with environmental variablessuch as temperature (Harvati and Weaver, 2006; Hubbeet al., 2009). Several studies at regional scales have alsonoted the association of craniofacial variation withbroad and dichotomous diet categories, with hunter-gatherers having bigger and more robust skulls thanfarmers (Carlson and van Gerven, 1977; Stynder et al.,2007; Perez and Monteiro, 2009; Paschetta et al., 2010).This has been attributed to differences in masticatoryforce or loading due to the consumption of items thatdiffer in their mechanical properties, with farmers rely-ing on softer and more processed diets than hunter-

gatherers (e.g., Carlson and van Gerven, 1977;Paschetta et al., 2010). In this context, a reduction inthe mechanical loading will result in an underdevelop-ment of masticatory muscles and the associated cranialstructures. Others have stressed the role of diet compo-sition as a factor with systemic effects on the skull(Stynder et al., 2007; Perez and Monteiro, 2009).Because diets poorer in proteins characteristic of farmergroups (Larsen, 2006) tend to decrease overall growth,this might also result in cranial differences due to allo-metric changes (Perez and Monteiro, 2009). The

Additional Supporting Information may be found in the onlineversion of this article.

Grant sponsor: Consejo Nacional de Investigaciones Cientficas yTecnicas; Grant number: PIP 112-200901-00132 (20102012); Grantsponsor: Universidad Nacional de La Plata PPID N002 (20112013);Grant number: PI 11/N691 (20122013).

*Correspondence to: Lumila P. Menendez, Paseo del Bosque S/N.La Plata 1900, Buenos Aires, Argentina.E-mail: [email protected]

Received 9 September 2013; accepted 16 June 2014

DOI: 10.1002/ajpa.22560Published online 1 July 2014 in Wiley Online Library

(wileyonlinelibrary.com).

2014 WILEY PERIODICALS, INC.

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 155:114127 (2014)

influence of these two dimensions of diet on cranial var-iation is well supported by a large body of experimentalevidence on rodents, hyraxes, and nonhuman primates(Beecher et al., 1983; Pucciarelli and Oyhenart, 1987;Dressino and Pucciarelli, 1997; Lieberman et al., 2004).However, both might have similar effects on cranialmorphology (i.e., reduction in size and robusticity ofmalar and maxillar bones with the consumption of arti-ficially softened foods, as well as with diets poor in pro-teins), and thus, their role on cranial variation inparticular human populations is difficult to assess basedon morphometric data only.

The lack of comparative studies that explicitly takeinto account and quantify independently the multipledimensions in which diet can be decomposed, such astype and amount of nutrients and the physical proper-ties of food items, have limited the comprehension of theprocesses underlying the observed association betweendiet and cranial morphology. Southern South America isan excellent region for investigating the process of cra-nial diversification at regional scales in relation to die-tary factors because these populations occupied a widerange of environments, which differ in the amount andtype of the available resources. This ecological variationincreased during the last 3,000 years as a consequenceof the development of food production (i.e., agriculture)and processing technologies (i.e., pottery and grindingtools) leading to changes in diet composition and masti-catory loads (Harlan, 1971; Pearsall, 1992, 2008). Inaddition, the exceptionally high levels of cranial varia-tion found among South American populations, compara-ble to geographically larger regions worldwide (Sardiet al., 2005; Bernal et al., 2006), might have arisenlocally since the molecular evidence suggests that theydescend from a single ancestral population, which colon-ized the region 13,000 years BP (Garca-Bour et al.,2004; de Saint Pierre et al., 2012). Previous studies con-cerned with the causes of these high levels of cranialvariation among native populations from southern SouthAmerica disagree on the influence of carbohydrate/pro-tein intake and masticatory loads, although these twodimensions of the diet have not been thoroughly eval-uated (Gonzalez-Jose et al., 2005; Perez and Monteiro,2009; Perez et al., 2011).

The main objective of this study is to investigate thecontribution of diet composition and bite forces on thecraniofacial diversification of late Holocene human popu-lations from the southern region of South America. Par-ticularly, we expect that if the pattern of craniofacialvariation is related to differences in masticatory loading,the morphometric variation will be correlated with biteforce estimations. Alternatively, if craniofacial variationis related to diversity in diet composition, the pattern ofmorphometric variation among populations should becorrelated with differences in the proportion of carbohy-drates and proteins consumed. Localized effects in theskull are expected if the masticatory loading rather thandiet composition was the main factor underlying the cra-nial diversification in the region under study (Pucciarelliand Oyhenart, 1987; Lieberman et al., 2004). This wasevaluated by analyzing the whole skull and three previ-ously hypothesized modules that differ functionally anddevelopmentally: face, vault, and base (Cheverud, 1995;Lieberman et al., 2000; Sperber, 2001; Hallgrmssonet al., 2007; Perez and Monteiro, 2009). We quantifiedcranial morphometric variation (form, size, and shape)using coordinates of reference points (i.e., landmarks

and semilandmarks) and geometric morphometric tech-niques (Adams and Otarola-Castillo, 2013). We meas-ured diet composition (i.e., relative percentage ofcarbohydrate and protein) using dental caries and car-bon isotopes; and infer masticatory loading using biteforce estimations based on cross-sectional areas of themain jaw adductor muscles and their respective in-levers about the temporomandibular joint (TMJ; Kiltie,1984; Thomason, 1991; Christiansen and Wroe, 2007).Because cranial variation among neighbor populationsin this geographic region displays spatial structure dueto the effect of neutral processes (Perez et al., 2010,2011; Bernal et al., 2010; de Saint Pierre et al., 2012),the association of cranial variation with diet compositionand bite force was tested by using spatial comparativemethods (Legendre and Legendre, 1998; Diniz-Filhoet al., 2007).

MATERIAL AND METHODS

Samples

For this study, we analyzed skulls with no evidence ofartificial cranial modifications from 12 samples (N indi-viduals5 474) of male and female adults. The 12 sam-ples come from three geographic regions (Fig. 1):Northwestern Argentina (NW, N5 183), SoutheastPampa/Northeastern Patagonia (SP/NP, N5127), andCentral Western Argentina (CW, N5164). Samples aredeposited in Museo de La Plata, Museo Etnografico J.B.Ambrosetti of Buenos Aires, Museo de Ciencias Natu-rales y Antropologicas Juan Cornelio Moyano in Men-doza, Museo Regional de Malargue, Museo de HistoriaNatural in San Rafael and Instituto de InvestigacionesArqueologicas y Museo Prof. Mariano Gambier. Table 1shows more details about the samples studied by geo-graphical region.

Sex and age determinations were done using osteologi-cal criteria (Buikstra and Ubelaker, 1994). Given thatthe samples belong to museum collections, the majorityof individuals were represented by skulls only, andtherefore the determinations were restricted to cranialtraits. Age estimation was based on inspection ofspheno-basilar suture closure and eruption of M3 (Buik-stra and Ubelaker, 1994). Individuals were assigned toone of two categories, subadult and adult, consideringadults as those individuals that had M3 erupted and thespheno-basilar suture already closed. Individuals withmalformations, pathologies or severely reabsorbed alveo-lar ridge due to ante-mortem tooth loss, among others,were excluded from this study. Sex estimation was donethrough geometric morphometric methods, which provedto be more useful to describe subtle differences or inter-mediate morphologies in sexually dimorphic structures(Gonzalez et al., 2011). Coordinates of landmarks andsemilandmarks were obtained to describe the glabella,malar, frontal, mastoid, and zygomatic processes. Thesestructures were chosen because they exhibit high levelsof preservation in archaeological contexts and the factthat previous studies suggest that these structures aresexually dimorphic in our region of study (Gonzalezet al., 2011). The procedure of landmarks digitizationand preliminary data management will be describedlater. The software used to analyze sexually dimorphicstructures was Morphologika 2.5 (OHiggins and Jones,2009).

DIET AND CRANIAL VARIATION IN SOUTH AMERICA 115

American Journal of Physical Anthropology

Morphometric analyses

A total of 84 three-dimensional (3D) anatomical points(42 landmarks and 42 semilandmarks) located over thewhole skull were defined for this study (Fig. 2). Land-marks were selected according to Howells (1973) andBuikstra and Ubelaker (1994). Semilandmarks wereplaced mainly along craniofacial sutures and contours.Data were collected employing a MicroScribe G2X digit-izer. The 3D points for each specimen were aligned usingan extension of the Generalized Procrustes Superimposi-tion (GPS) method (Bookstein, 1997; Adams et al., 2004).This method aligns the configurations of landmarks andsemilandmarks using a least square criteria, and mini-mize the differences tangent to the contours in the caseof semilandmarks (Bookstein, 1997; Perez et al., 2006).Specifically, semilandmarks were slid along the curvesby minimizing the Procrustes distances between thepoints, until they coincided with the positions of the cor-responding points along the contour in the reference con-figuration. For the purposes of our study, this procedureis done because the curves or contours should be homolo-gous among individuals, although not necessarily theirindividual points need to be (Mitteroecker and Gunz,2009).

We then studied three geometric morphometric prop-erties: shape, size, and form as defined by Rohlf andSlice (1990) and Mitteroecker et al. (2004). Shape wasanalyzed using Procrustes coordinates, that is, theCartesian coordinates after GPS alignment. Size wasmeasured as the logarithm of centroid size (CS), whichwas calculated as the square root of the sum ofsquared distances of all points from its center of grav-ity. Cranial form was described as the Procrustes coor-dinates plus logCS (Mitteroecker et al., 2004;Mitteroecker and Gunz, 2009). A principal componentanalysis (PCA) was performed over all the Procrustescoordinates (shape variables), and over Procrustescoordinates plus logCS (form variables), to obtain low-dimensional axes of shape and form variation, respec-tively. All analyses were performed on the mean valueof each sample, using the average of individual CS andthe consensus configuration based on Procrustescoordinates.

The morphometric analyses were done for the entireskull and for three modulesface, vault and basethat

TABLE 1. The 12 samples from which the South American human populations were studied, together with their abbreviations,sample sizes (N), mean14C dates, d13C, CI, and geographic region

Geographic region Sample Abrev. N mean14C d13C CI

Northwestern Argentina (NW) Quebrada de Yacoraite QY 27 700 6 60 11.2 15Andalgala Valley AnV 22 400 6 50 12.4 13.1Puna Pu 45 700 6 60 15.8 12.16Calchaqu Valley CalV 46 821 6 40 11.8 9.34Quebrada de Humahuaca QH 43 910 6 50 11.2 12.5

Central Western Argentina (CW) South San Juan SJ 59 559 6 40 13.3 10North Mendoza NM 46 1080 6 45 14.8 2.75South Mendoza SM 59 1766 6 50 16.6 3.87

Southeast Pampa/NortheasternPatagonia (SP/NP)

San Blas SB 32 1028 6 45 16.85 1.1Pampa Pa 35 1300 6 40 16.5 9.8Negro Valley NV 24 523 6 45 17.98 0.9Chubut Valley ChV 36 1326 6 65 16.6 3.5

Radiocarbon age (AMS) and d13C determination for South San Juan was held at the Arizona Radiocarbon AMS Facility (Universityof Arizona) from collagen of a rib bone fragment.

Fig. 1. Southern South America Map depicting the centralgeographic location of the 12 samples analyzed (Pu: Puna, QY:Quebrada de Yacoraite, QH: Quebrada de Humahuaca, CalV:Calchaqu Valley, AnV: Andalgala Valley, SJ: South San Juan,NM: North Mendoza, SH: South Mendoza, Pa: Pampa, SB: SanBlas, NV: Negro Valley, and ChV: Chubut Valley). More detailedinformation of the samples is shown in Table 1.

116 L. MENENDEZ ET AL.


represent developmentally and functionally units thatvary with relative independence from one another (Che-verud, 1995; Lieberman et al., 2000; Perez and Mon-teiro, 2009). Particularly, the face is related to foodprocessing and then it is expected to display the closestassociation with environmental variables as diet (Che-verud, 1995; Sperber, 2001; Gonzalez-Jose et al., 2005),whereas the cranial base would be the structure leastrelated to these factors (Lieberman et al., 2000; Sperber2001; Harvati and Weaver, 2006; but see Rosemanet al., 2010).

A subsample of 361 individuals were used for theanalysis of the entire skull, face, vault, and base. Facialanalyses were repeated using the total of 474 individu-als. This discrepancy in the number of individualsresponds to differences in bones preservation. There wasan attempt to increase the number of individualsincluded in the facial analysis due to the importance ofthis module for the present study. Morphometric analy-ses were done with MorphoJ (Klingenberg, 2011) andpackage Geomorph of R software (Adams and Otarola-Castillo, 2013; R Developmental Core Team, 2013).

Fig. 2. Landmarks (L), Semilandmarks (Sl) and muscles registered for the present study by cranial region. (a) Face landmarks:n: nasion, nf: nasofrontal, ss: subespinal, al: alar, d: dacrion, sf: supraorbital foramen, so: superior orbital, afm: anterior frontoma-lar, ec: ectoconchion, zo: zigoorbital, pr: prosthion, c: canine, em: ectomolar, p: palatine, alv: alveolon, tfm: temporal frontomalar,stm: superior temporomalar, itm: inferior temporomalar, iitm: intern inferior temporomalar, azm: anterior zygomaxillar, and pzm:posterior zygomaxillar; (b) Vault landmarks: g: glabella, f: frontex, b: bregma, l: lambda, op: opistocranion, ft: frontotemporal, stf:stefanion, fsp: frontosphenoparietal, tsp: temporosphenoparietal, and as: asterion, (c) Base landmarks: o: opisthion, ba: basion, so:sphenooccpital, lso: lateral sphenooccipital, to: temporooccipital, ts: temporosphenoides, tm: temporomandibular, au: auricular, am:anterior mastoides, im: inferior mastoides, pm: posterior mastoides. Masseter muscle (gray area) demarcated by landmarks, mass-eter inlever arm (m), outlever arm (Mo), and centroid of masseter (Mc); (d) Temporal muscle (gray area) demarcated by landmarksand semilandmarks, temporal inlever arm (t), and centroid of the temporal muscle (Tc).



Diet variables

Diet composition. Diet composition was measuredusing two independent variables widely used in archaeo-logical and bioarchaeological studies, caries and carbonisotope data. While caries inform about the amount ofcarbohydrates consumed, the isotope data used (collagend13C) informs about the protein fraction of the diet. Forthis reason, we considered these two variablesseparately.

Stable isotopes of the inorganic and organic fractionsof bone provide a good record of dietary intake over atleast the last 57 years of an individuals life, and thuscan be used to infer the average diet in human prehis-toric populations (Ambrose and Krigbaum, 2003; Tykot,2006). Particularly, carbon isotope (d13C) values of colla-gen reflect the origin of the protein component of thediet and allow to assess the contribution of C3 or C4plant foods. In this work, stable isotopic values wereobtained from published data as well as data obtained inthis study (Table 1; Bernal et al., 2008; Beron et al.,2009; Gil et al., 2009, 2011; Beguelin, 2011; Gordon,2011; Killian Galvan and Samec, 2013). Only d13C colla-gen values were employed because there is a wider col-lection of this data available.

Dental caries are the result of a demineralization pro-cess on tooth tissues due to the production of organicacids from bacterial fermentation (e.g., Streptococcusmutans) of carbohydrates (Hillson, 2001). Consequently,they provide valuable information about the proportionof carbohydrates, sugar, and sticky nutrients in the dietof prehistoric populations (Larsen et al., 1991; Lukacs,1992). In this work, the presence of carious lesions wasrecorded by us (V.B., P.N., and L.M) following Buikstraand Ubelaker (1994). Finally, the Carious Index (CI) wascalculated as a function between the number of cariousteeth and the total number of teeth available (Hillson,2001).

Dental wear (DW). Given that the prevalence ofcaries in a sample could be affected by the degree of DW,the latter was recorded to get an indirect control of thequantification of the carious lesions. DW is a continuouslong-lasting process that results from the mutual contactof opposite crowns as well as from their contact withfood or abrasive material incorporated into food (Smith,1984), that might remove both fissures of eruptingmolars before they became carious and the carious tissueas soon as a lesion appears (Hillson, 2000). Considering

the natural eruption sequence of permanent molars, inwhich there is an approximate 6-year interval betweenthe eruptions of the first, second and third molars in allhuman populations, it is possible to derive age-independent rates of wear with acceptable confidence(Scott and Turner, 1988). According to this, rates of wearwere estimated based on the degree of wear followingthe ordinal scale proposed by Scott (1979) for M1 andM2. Molars showing no exposed dentine or total loss ofenamel were excluded from analysis. A principal axisanalysis was applied to estimate wear rates between M1

and M2. The slope obtained was then used as an indica-tor of the relationship between adjacent molars, andtherefore, wear rate (Scott and Turner, 1988).

We also discriminate DW related to masticatory func-tion from the wear generated by use teeth as tools (i.e.,parafunctional use of teeth). We recorded macroscopi-cally the degree, direction and form of individual toothwear as proposed by Smith (1984) for incisors, caninesand premolars, and Scott (1979) for molars. Since theteeth function as a unit, harmonic DW would be attrib-utable to the masticatory function, while atypical toothwear could indicate the use of teeth as a tool (Larsen,1997). Therefore, the presence of teeth that are outlierswith respect to adjacent ones were considered as prob-ably employed in paramasticatory functions (Molnar,1971) and the individuals were excluded from the follow-ing analyses.

The relationship between DW and CI was exploredusing correlation analyses. These results show two axesof independent variation given by CI and DW (Table 2),which means that carious lesions are not related to DW(r520.28). CI is associated to latitude, with the highestvalues occurring in the NW groups and Pampa, interme-diate values in CW samples and the lowest values in theSP/NP group. Conversely, DW does not follow a geo-graphic pattern, presenting the highest values in SouthSan Juan and South Mendoza samples, and the lowestvalues in Chubut Valley, North Mendoza, and QuebradaYacoraite.

Bite force (BF). Bite force was estimated using a com-bination of two models based on cranial anatomy andlever mechanics, proposed by Kiltie (1984) and Thoma-son (1991), with some modifications to adapt such mod-els to human head anatomy, as well as to 3D geometricmorphometrics. By this technique we inferred bite forceusing (a) an estimation of the size of the major jawadductors, the temporalis and the masseter-medial pter-ygoideus muscles complexes, (b) approximate measure-ments of the muscle moment arms about the TMJ, and(c) moment arms of upper second molar (M2) about TMJ.The major assumptions made when using these modelsare that the true physiological cross-section of muscles,which have a complex 3D architecture, can be simplifiedand that contralateral muscles are assumed to contractequally forcefully (Ellis et al., 2008). We also assumethat for humans the upward force of the adductormuscles is balanced by downward forces at three places:the two condyles and the bite point, which for modernhuman is located between M2 and M3. These threepoints constitute the triangle of support for masticationin Homo sapiens (Lieberman, 2011). However, it isimportant to remark that different versions of thismethod could generate differences in the bite force esti-mations (Ellis et al., 2008).

TABLE 2. Principal Axis results for evaluating the associationbetween DW and CI

Groups r2 P Intercept Slope Angle

QY 0.89 0 0.089 0.752 36.955AnvV 0.6 0.01 3.234 0.659 33.407Pu 0.5 0 3.287 0.554 29.029CalV 0.27 0.05 9.453 0.338 18.707QH 0.8 0 26.860 1.170 49.482Pa 0.52 0 21.685 0.902 42.053SSJ 0.43 0 26.498 1.172 49.540NM 0.65 0 0.546 0.685 34.412SM 0.68 0 210.760 1.121 48.290SB 0.72 0 22.030 0.860 40.702NV 0.55 0 219.640 1.485 56.054ChV 0.44 0.01 3.681 0.617 31.680



To estimate the muscles size (a), we used a combina-tion of Kiltie (1984) and Thomason (1991) methods, as3D landmarks allow us to estimate a global measure-ment of temporalis (Tcs) and masseter-medial pterygoi-deus (Mcs) muscles size using CS values (see Fig. 2).The moment arm of the temporalis and massetermuscles (b) was estimated following Thomason (1991).According to this, the direction of each force vector wasassumed to act perpendicularly to the plane of the area,through its centroid. The in-lever arms of each vector fortemporalis (t) and masster (m), were defined as the dis-tance between the centroid of temporalis (Tc) and mass-eter (Mc) muscles and the TMJ. The centroid of eachmuscle area was calculated considering the anatomicalpoints displayed in Figure 2 (Mc, Tc). Finally, the out-lever arm to the bite location, was defined as the dis-tance from the TMJ to the junction of M2 on the maxilla(Mo; Thomason 1991). A factor of 2 was also added onthe assumption that both sides were contracting maxi-mally (Ellis et al. 2008).

In summary, the bite force (BF) was calculated usingthe masseter and temporalis CS (Mcs, Tcs), the in-leverarms from the centroid of each muscle to the TMJ (m, t)and the out-lever arm from the TMJ to the M2 (Mo), allthe above doubled to include both sides of the head:

BF52 Mcs m 1 Tcs t =Mo

The anterior dentition, which was included in Kiltie(1984) and Thomason (1991) calculations, was used herewith comparative proposes only because in humanbeings, its principal function is to cut and tear and notto masticate as the molars do. Therefore, were-calculated BF with an out-lever arm from the TMJ tothe canine (C).

Finally, it was evaluated if there is a linear relationbetween bite force and cranial size, because these varia-bles are usually related (Kiltie, 1984; Christiansen andWroe, 2007; Ellis et al., 2009; Herrel et al., 2014). Theresults of the linear regression between the bite forceand logCS for our dataset show that there is no associa-tion between there variables in this sample (R25 0.04;P50.59). Therefore, all subsequent analyses were per-formed with BF values without size standardization.

Spatial data and analysis

Statistical analyses were performed in order todescribe the spatial structure of morphometric and eco-logical variables (Diniz-Filho et al., 2007; Dormannet al., 2007; Perez et al., 2010). Geographic origin of thesamples was obtained from already published data andthen the geographical coordinates of each local samplewere transformed to a geodesic system (decimal degreesof latitude and longitude).

Firstly, Morans I Correlograms were calculated forthe ecological variables (d13C, CI, and BF) and the mor-phometric data (CS, shape PC, and form PC) for theentire skull and each of the three modules (face, vault,and base). A correlogram is a graph in which autocorre-lation Morans I values (i.e., a measurement of data sim-ilarity based on both data geographic locations and datavalues simultaneously), calculated at different distanceclasses, are plotted on the ordinate against distanceclasses among sampling localities on the abscissa (Sokaland Oden, 1978; Legendre and Fortin, 1989). Morans Iformula behaves mainly like Pearsons correlation coeffi-

cient, with value near 11.0 indicating geographic clus-ter of data while a value near 21.0 indicatinggeographic dispersion of data. The shape of the correlo-gram could be related to the type of spatial structureand evolutionary processes underlying the studied vari-ables (Sokal, 1979). Sokal and Oden (1978) and Legen-dre and Fortin (1989) gave a series of typicalcorrelogram patterns and the corresponding evolution-ary and ecological interpretation. By example, correlo-grams display a characteristic asymptoticallydecreasing shape when the pattern of morphometricvariation is related to the joint effect of drift and geo-graphically limited gene flow, that is, isolation by dis-tance model (Barbujani, 2000). When other factors areinvolved, correlograms can show a random pattern, acline, or a depression. Particularly, clines affecting theentire study area, or only a part of it, can be related todeterministic factors such as natural selection and phe-notypic plasticity.

As the main objective of the present work is to eval-uate whether bite force and/or diet composition arerelated to cranial morphology, we performed regres-sion analyses including d13C, CI, and BF as the inde-pendent variables, and CS, shape PC and form PC asthe dependent variables. Also, because previous stud-ies pointed out the importance of temperature on cra-nial variation, this variable was tested in ouranalyses. Similar to previous studies in the region, wefound no association between cranial variation andtemperature (r50.00, P5 0.9). One of the assumptionsof ordinary least-squares regression model is the inde-pendence of observations. This model can be describedas y5Xb1E, where y is the vector that describes mor-phometric variation, X is the matrix of independentdiet variables, b is the vector of regression coefficients,E is the error term with a covariance matrix C amongresiduals C5r2I, where r2 is the variance of the resid-uals, and I is an identity matrix (Perez et al., 2010).When data are spatially autocorrelatedthat is, obser-vations that are closer to each other in space havemore similar values as in our study (see below),autoregressive models instead of the ordinary regres-sion should be used (Legendre and Legendre, 1998).Two autoregressive models were employed here: simul-taneous autoregressive model (SAR) and conditionalautoregressive model (CAR; Dormann et al., 2007;Perez et al., 2010). In the SAR model, the covariancematrix C among residuals is described as

CSAR5r2 I2qW Th i21

I2qW 21, where W is a weightingmatrix based on the inverse of geographic distance and qis an autoregressive coefficient for morphometric variable.The CAR model is a modification of the SAR model,where the covariance matrix C among residuals is

described as CCAR5 r2Wi1

I

I2qW 21. Therefore, thesemethods model spatial effects within the error term,which is predefined from a neighborhood matrix, andthen parameters are estimated using a generalized least-squares framework (Dormann et al., 2007; Perez et al.,2010). Here, we used simultaneous autoregressive errormodels with all first order neighbors with equal weight-ing of all neighbors. The fit of the models was comparedby R2 and AIC values for SAR and CAR.

Prior to regression analysis, Pearsons correlationanalyses were performed to test the association amongthe ecological variables. This was done to avoid problemsof multicollinearity. If variables are collinear, or nearly



so (i.e., highly correlated), the results obtained are diffi-cult to interpret because the independent effect of eachpredictor cannot be estimated correctly (Legendre andLegendre, 1998). Pearson correlations indicated that thecorrelation between BF and CI (r520.224) and betweenBF and d13C (r5 0.229) was low. Conversely, the correla-tion between d13C and CI was high (r520.769). Becauseof collinearity between d13C and CI, subsequent regres-sion analyses were performed separately for the lattervariables. Spatial analyses were done with SAM soft-ware (Rangel et al., 2010).

RESULTS

Shape, size, and form

The distribution of samples along shape PC1 follows ageographic pattern, with NW and SP/NP samples on theopposite extremes and CW samples in an intermediateposition (Fig. 3). Shape PC2 also separates CW samplesfrom the others. When the entire skull is considered(Fig. 3a), SP/NP samples locate in the positive extremeof shape PC1, NW samples in the negative shape PC1extreme, while CW samples stay in the middle and differ

Fig. 3. The results of the distribution of the 12 sample along the first two principal shape PCA axes by cranial region: (a) entireskull, (b) face, (c) vault, and (d) base. In brackets is shown the percentage of variation explained by each principal component.



from the other samples along the PC2. The dispersion ofthe samples in the shape PCA for the facial and vaultmodules is similar (Fig. 3b,c). The PCA of cranial base(Fig. 3d) displays the highest superposition between NWand CW samples. NW and CW samples locate on thepositive extreme of shape PC1, while SP/NP group onthe negative one.

The wireframes shown in Supporting Information Fig-ure S1 illustrate the main trend of shape changes alongPC 1 axis. Most important changes in the face arerelated to nasal bones, orbit, and maxilla (SupportingInformation Fig. S1a). Skulls located at the negativeextreme, from the SP/NP group, display a relativeincrease along the supero-inferior axis in orbits andnasal bones, and along antero-posterior dimensions inthe maxilla. Vault configurations at the positive extreme,from the NW samples, show an increase in the supero-inferior axis and a decrease in the antero-posteriordimensions compared with the samples at the negativeextreme (Supporting Information Fig. S1b). The base isthe module with fewer changes: in general terms, cra-nial bases at the negative extreme show a relativeincrease in antero-posterior dimensions (SupportingInformation Fig. S1c).

Variation in cranial size among samples shows a simi-lar geographical pattern as shape (Fig. 4). NW and CW

samples have on average the smallest skulls, while theNP samples display the largest CS values. Form PC alsodisplays a spatial pattern, but the samples are not clus-tered as strong as in shape PCA (Supporting Informa-tion Fig. S2). CW samples are closer together when theentire skull, face and vault were analyzed, and as inshape plots, they tend to locate in an intermediate posi-tion between NW and SP/NP samples. In the form PCAfor the cranial base, the samples from NW and CW aremixed, different to the shape PCA of cranial base.

The spatial correlograms of size and shape variationare shown in the Figure 5. The Supporting InformationFigure 3 depicts the correlogram of form variation.These correlograms show a monotonic decrease in Mor-ans I coefficient with geographic distance, suggesting aclinal pattern in the spatial distribution of the studiedsamples. Neighbor samples are more similar than thedistant ones, showing a strong morphometric similarityat small geographic scales. This pattern is stronger forthe entire skull, vault, and face shape data. Morans Ivalues are high but not significant, which might be dueto the number of samples studied.

Diet variation

CI and d13C were plotted with the objective of explor-ing their relationship to each other. As can be seen from

Fig. 4. Boxplots showing variation in CS and BF per sample, which are grouped according to geographical region.



Figure 6, in general terms, these variables show a linearrelationship. In the extremes of the distribution, NorthPatagonian samples (NV, SB, and ChV) have the lowestd13C and CI values while North West samples (QY, QH,AnV, and CalV) have the highest d13C and CI values.CW samples are distributed between these two groups.An exception of this association constitutes Pa and Pusamples, with both having high CI and low d13C values.Because d13C and CI values probably describe differentdiet properties, we decided to analyze both variablesindependently in the subsequent analyses in order toprevent the loss of information.

Bite force boxplot (Fig. 4) shows great variationbetween extreme values and does not display the samegeographical pattern as other variables measured. Ingeneral, samples from NW have the lowest values and

CW samples have the highest values. However, SP/NPsamples display a range of values that includes theextreme values of NW and CW samples (Fig. 4). Partic-ularly, SM, SJ, and NV have the highest values, whileChV, NM, and QH have the lowest ones. In addition, Pasample has similar bite force values to NW samples.CW samples have the greatest dispersion. Among NWsamples, there are slight differences between theextreme values, with CalV having the highest bite forcevalues and QH the lowest ones. However, a great differ-ence exists between extreme values of SP/NP with NVhaving the highest values and ChV the lowest ones.Correlograms showed that, contrary to BF, d13C, and CIdisplay a clinal geographical pattern (Fig. 7), but thispattern is not as strong as for morphometric shapedata.

Fig. 5. Morphological Correlograms for CS and Shape by cranial region. Each correlogram mapped a morphological variable toexplore its spatial structure and assess spatial autocorrelation. Distance units (geodesic) are shown in the horizontal axes andincrease to the right side. Morans I (autocorrelation coefficient) values are shown on the vertical axes.



Regression models

The regression results (Supporting Information TableS1) are ordered by cranial module and the predictorsaccording to AIC values from the lowest (best fit andsimple model) to the highest one. The R2 values (fitdegree) are also displayed. Overall, our results indicatethat size (CS) is significantly associated with the ecologi-cal variables analyzed here. When the entire skull isconsidered, diet composition (measured as CI) is thevariable that best predicts size variation. Particularly,CI explains 59% of variation in skull size. The sameassociation was found for form variation, displaying58.9% of fit for entire skull. Similar results wereobtained for facial and vault variation, CI being the vari-able that best predicts their variation in form and size(with values ranging from 30 to 55%). The percentagesof variation in the entire skull, facial, and vault shapeaccounted for the ecological variables were much lower(between 0 and 28%) and nonsignificant. Finally, varia-tion in cranial base size and form was better explainedby CI, which accounted for 40 and 30% of total variation,respectively. Conversely, none of the ecological variablesaccounted for a large amount of variation in cranial baseshape. Overall, the samples with highest CI values aremore gracile and display the smallest skulls, while thesamples with lower CI values are more robust and havethe largest skulls. BF alone explains a very low propor-tion of size, form and shape variation (ca. 0%) when biteforces are estimated based either on canine or secondmolar (Supporting Information Table S1).

DISCUSSION

The main objective of this study was to evaluate thecontribution of diet composition and masticatory loadingon craniofacial diversification of late Holocene humanpopulations from the southern region of South America.Particularly, we tested whether bite forces, inferred bycross-sectional areas for the major jaw adductors, wereassociated with variation in size, shape, and form ofboth the whole skull and the three main functional anddevelopmental modules in which this structure is usu-ally divided. Our results point out that, contrary to theexpectation set forth by the biomechanical hypothesis,morphometric variation in cranial traits in the regionunder study is not correlated with bite forces (Support-ing Information Table S1). It is important to remarkthat the results of this work do not indicate that masti-catory loading has no effect on craniofacial morphology,but this was not the most important factor in modelingthe pattern of variation among southern South Ameri-can populations.

These findings might be seen as contradicting previousstudies that hypothesized that a reduction in the masti-catory muscles activity and a concomitant decrease inmechanical loading of the skull induce a reduction inmuscle size, as well as morphometric changes amonghuman populations (Carlson and van Gerven, 1977;Gonzalez-Jose et al., 2005; Paschetta et al., 2010; vonCramon-Taubadel, 2011). However, most of these studieswere based on the assumption that farmers relied onsofter diets because of the type of items consumed andthe food processing techniques used. The main argumentused is that consistency of food produces a mechanicalstress that acts differentially on the masticatory com-plex, so that hunter-gatherers are expected to have

Fig. 6. Scatterplot for evaluating the association between CIand d13C.

Fig. 7. Ecological Correlograms for d13C, CI, and BF. Eachcorrelogram maps an ecological variable to explore its spatialstructure and assess spatial autocorrelation. Distance units(geodesic) are shown in the horizontal axes and increase towardthe right. Morans I (autocorrelation coefficient) values areshown on the vertical axes.



larger facial structures. The approach usually followedto test this hypothesis was to compare morphologicaltraits among populations previously assigned to onebroad category that describes its subsistence (e.g., Carl-son and van Gerven, 1977; Gonzalez-Jose et al., 2005;Sardi et al., 2006; Paschetta et al., 2010). Nevertheless,differences in masticatory forces were rarely measureddirectly, and thus this factor was not tested independ-ently from other confounding factors such as food compo-sition. Here, we showed that the magnitude of biteforces and the type of subsistence (farmers vs. hunter-gatherers) is not related in the region under study. Sam-ples from Northwest Argentina, previously described asfarmers (Gonzalez Jose et al., 2005; Perez and Monteiro,2009), exhibited low bite forces, while hunter-gatherersfrom Patagonia were characterized by a large variationranging from very low values comparable to those offarmers to high values not found in other samples. Thisis particularly striking in the sample of hunter-gatherers from Chubut, which displays the lowest BFvalues despite having large and robust skulls (Fig. 6).Several studies have shown that the use of food process-ing and cooking techniques, particularly grinding stonetools and pottery, was a widespread strategy across thegeographical region during the late Holocene (ca. 2,000years ago; Bernal et al., 2007, 2010). These practicescould have led to the consumption of softer diets, inde-pendent of whether the resources were domesticated ornot.

We also tested for the association of diet compositionand craniometric variation using two independent varia-bles; the CI as a measure of the proportion of carbohy-drates consumed and d13C values as a measure of thesource of proteins. Our results support the hypothesisthat variation in overall cranial size and form amongpopulations in the region under study is related to thevariation in diet composition. Around 60% of variationin whole cranial size and form was explained by thevariable that measure the proportion of carbohydratesconsumed. The association between this ecologicaldimension and morphology was still significant afteraccounting for the geographic structure of both types ofdata. Particularly, samples with lower CI display biggerskulls, meaning that diets with a reduced proportion ofcarbohydrates are associated with skulls of larger size.Conversely, populations that incorporated a higheramount of carbohydrates in their diets have smallerskulls. Experimental and comparative data demonstratethat the consumption of diets with a relatively largerproportion of carbohydrates than proteins induce adecrease in body and skull size with the correlated allo-metric shape changes (Pucciarelli and Oyhenart, 1987;Stynder et al., 2007; Frisancho, 2009; Stock et al., 2011;Auerbach, 2011). This effect of nutrient intake on bonegrowth is mediated by the regulation of the GH-IGFaxis, which has a central role in stimulating the prolifer-ation and differentiation of chondrocytes and bone for-mation (Thissen et al., 1991; Bonjour et al., 2001;Nijhout, 2003). Particularly, diets with a lower propor-tion of proteins impair both the production and action ofIGF-I and induce GH resistance that in turn causegrowth arrest of skeletal structures.

When the three cranial modules were analyzed sepa-rately, we found that size and form variation of face andvault were also associated with CI, although the per-centage of variation accounted by this ecological variablewas lower than the value obtained for the whole skull.

For the cranial base, the only association found wasbetween size and CI (Supporting Information Table S1).The fact that the size of cranial structures not directlyrelated to masticatory function is also associated withdiet composition suggests the effect of systemic factorson skeletal growth. Similarly, a significant relationshipbetween diet and dental size variation among SouthAmerican populations has been reported in previousstudies (Bernal et al., 2010). Populations that relied ondiets rich in carbohydrates have the smallest teeth,while terrestrial hunter-gatherers are characterized bylarger dental size. In the context of population studies,the association between morphological characteristicsand ecological variables is commonly viewed as theresult of nonrandom factors such as selection and pheno-typic plasticity (Hendry and Kinnison, 1999). Given therecent divergence of groups with markedly differentdiets (50100 generations; Harlan, 1971; Pearsall, 1992,2008; Perez and Monteiro, 2009) and the relatively highpopulation density in the Late Holocene, natural selec-tion does not seem to be the most probable factor behindmorphological diversification among these populations.In large populations, natural selection can influence phe-notypic changes only when the environmental factors(representing selective pressures) have acted throughlong periods of time, and thus, several generations havebeen exposed to them. Selection can act over short time-scales when population size is small and thus adaptivepeaks can be reached by chance (Wade and Goodnight,1998; Hendry and Kinnison, 1999). Conversely, pheno-typic plasticity that occurs at the ontogenetic time scalecan be though as the most plausible factor driving mor-phological diversification at short evolutionary time-scales in large populations (Carroll et al., 2007; Perezand Monteiro, 2009).

The results obtained in this work show that 3D cranio-facial shape variation among southern South Americanpopulations, unlike size and form variation, is independ-ent from the ecological dimensions analyzed here andexhibits a strong spatial structure (as is depicted by cor-relograms with asymptotically decreasing autocorrela-tion values; Fig. 5). Consequently, evolutionary neutralor random factors (e.g., genetic drift) may be central forunderstanding shape variation in the region understudy. This is in agreement with the strong adjustmentto geographical distance of dental shape and molecularvariation observed in the region of study (Moraga et al.2000; Schurr, 2004; Bernal et al., 2010). Moraga et al.(2000) and Bernal et al. (2010) suggested that the spa-tial pattern of molecular and morphological shape varia-tion in South America might have resulted either fromgene flow restricted by geographical distance (i.e., isola-tion by distance model; Barbujani, 2000; Hey andMachado, 2003) or from a serial founder effect (Hey andMachado, 2003). The geographical structure of humanpopulations generates more gene flow between nearbypopulations, leading to high phenotypic and genetic sim-ilarities between populations that are geographicallyclose and differences between geographically distantpopulations by genetic drift. A similar pattern canemerge as the result of the colonization of an areathrough successive dispersion events of populations witha small number of individuals, which leads to severalrandom sampling or serial founder events (Hey andMachado, 2003). This second process is highly supportedby molecular and dental data, which show a linearincrease in distances in the main direction of peopling



that is, from North to South(Moraga et al., 2000; Ber-nal et al., 2010; de Saint Pierre et al., 2012).

In summary, the results obtained in this study pointout the complexity of the relationship between morpho-logical diversification and environment. Traditionally,studies intended to elucidate the factors driving the evo-lution of human populations have been conducted underthe assumption that variation in craniometric datamainly reflects neutral processes. Even though the effectof environmental factors has been recognized, only inrecent years they have been incorporated to the study ofhuman diversification (Roseman, 2004; Gonzalez-Joseet al., 2005; Stynder et al., 2007; Perez et. al., 2011).This article is, to the best of our knowledge, the firstwork that has systematically quantified two relevantvariables associated with diet, masticatory loading, andfood composition, for disentangling their contribution tocraniofacial variation among populations at short tempo-ral scales. Our results suggest that food productionincreased the availability of carbohydrate enriched dietscreating novel environmental conditions that in turncontributed to changes in morphology among popula-tions from southern South America. Further studies thatexplicitly incorporate quantitative data of multiple eco-logical dimensions into the analyses will continue toshed light on the factors that shape morphologicalchanges at different temporal and geographic scales andenvironmental settings in human populations.

ACKNOWLEDGMENTS

The authors are grateful to Paula Gonzalez, AJPA Asso-ciate Editor, and two anonymous reviewers for their com-ments on the manuscript. They thank the staff at theInstitutions in Argentina for granting access to the humanskeletal collections under their care: Mariano del Papa,Andres Di Bastiano, Cristina Mu~ne (Museo de La Plata),Beatriz Ca~noman (Museo Regional de Malargue), LuisBallarini, Miriam Ayala, Adolfo Gil, Gustavo Neme (MuseoHistoria Natural de San Rafael), Clara Abal de Russo(Museo de Ciencias Naturales y Antropologicas "Juan Cor-nelio Moyano", Mendoza), Claudia Aranda (MuseoEtnografico "J.B. Ambrosetti", Buenos Aires), and TeresaMichieli (Instituto de Investigaciones Arqueologicas yMuseo "Prof. Mariano Gambier").

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