Anatomy and Neurobiology, Washington University School of Medicine,. St. Louis ... particular types of deformation, but that these effects have little impact on the.
AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 90:3548 (1993)
Cranial Deformation and Nonmetric Trait Variation LYLE W. KONIGSBERG, LUCI A.P. KOHN, AND JAMES M. CHEVERUD Department of Anthropology, University of Tennessee, Knoxville, Tennessee 37996 (L.W.K.); Department ofdnthropology, Field Museum of Natural History, Chicago, Illinois 60605 (L.W.K.); Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110(L.A.P.K., J.M.C.)
KEY WORDS cans
Biological distance, Skeletal biology, Native Ameri-
ABSTRACT Cranial deformation is known to influence many traditional craniometric variables, but its effects on nonmetric trait variation are not well characterized. In this study, we examine the effects of three types of deformation (annular, lambdoid flattening, and fronto-occipital) on nonmetric traits, using a large sample of protohistoric and prehistoric crania. Our results indicate that a few traits are increased or decreased in relative frequency by particular types of deformation, but that these effects have little impact on the calculation of biological distances between groups. o 1993 Wiley-Liss, Inc. Artificial cranial deformation has obvious and quantifiable effects on human cranial morphometry (Blackwood and Danby, 1955; Moss, 1958; Bjork and Bjork, 1964; McNeill and Newton, 1965; Hellmuth, 1970; Anton, 1989; Kohn and Cheverud, 1991; Cheverud et al., 1992). In contrast to these unambiguous effects on metric variation, influence of deformation on cranial discrete or nonmetric trait frequencies has not been clearly resolved in the literature. Nearly 100 years ago, Dorsey (1897) suggested that the high relative frequency of coronal ossicles in a sample of Kwakiutl crania at the Field Museum was caused by the annular deformation practiced by this group. In contrast, Sullivan (1922) could find no clear association between deformation and discrete trait frequencies for North American samples. However, these two early studies did not compare nonmetric trait frequencies between deformed crania within individual populations, and consequently it was impossible to tell whether trait frequency differences were due to deformation or to between-population variation. In more recent studies, researchers have contrasted trait frequencies across deformation status (or some proxy measure) within populations 0 1993 WILEY-LISS,
INC
(e.g., Bennett, 1965; Ossenberg, 1970; Buikstra, 1976; El-Najjar and Dawson, 1977; Gottlieb, 1978; Shipman et al., 1990). Further, Pucciarelli (1974) has shown in experimental studies of rats that deformation can increase the relative frequency of wormian bones. Nonetheless, there is no current consensus on the importance of artificial cranial deformation in influencing nonmetric trait frequencies among humans. That cranial deformation could affect the frequencies of nonmetric traits follows from the empirical (and presumably developmental) relationship between metric and nonmetric cranial variation. Corruccini (19761, Cheverud et al. (1979), and Richtsmeier et al. (1984) have documented the interdependency of nonmetric and metric variation in the human and rhesus cranium. Given their empirical findings, as well as the presumption that there are environmental correlations among morphological aspects of the cranium, it is possible that deformation
Address reprint requests to Lyle W. Konigsberg, Department of Anthropology, 252 South Stadium Hall, University of Tennessee, Knoxville,Tennessee 37996-0720. Received August 8,1991; accepted June 28,1992.
36
L.W. KONIGSBERG ET AL
could act jointly on both nonmetric and metric variation, at least a t a local level within the cranium. There are varied reasons why the effect of cranial deformation on nonmetrics is unresolved. First, any simple contrast of “deformed versus ‘hot-deformed” does not take into account the fact that there are different types of deformation, nor the fact that intensity of deformation can vary widely. In this study we consider the effects of three different types of deformation: annular, lambdoid flattening, and fronto-occipital, in order to examine whether they may have differential effects on nonmetric trait frequencies. Additionally, we treat deformation a s a n ordinal categorical variable so as to account for differing intensity of deformation. Second, the classification of “deformed” versus “not-deformed” within past studies is often very ambiguous, and is rarely supported by any examination of repeatability of the scoring. In contrast to the previous situation, we begin our examination of the effects of cranial deformation by discussing a n intraobserver repeatability study of deformation scoring. Finally, the statistical methods for assessing the effects of deformation have not always been appropriate. For example, Buikstra (1976) criticized a n earlier study of Ossenberg (1970) for not accounting for sideto-side correlation in bilateral traits. I n the following analysis, we attempt to circumvent some of these previous statistical pitfalls by using more appropriate methods, including both probit analysis and a multivariate distance measure which properly accounts for correlations between traits (Blangero and Williams-Blangero, 1991). MATERIALS AND METHODS We scored deformation status, age, gender, and presencelabsence of 39 nonmetric cranial traits (see Table 2 for a listing of traits) using a sample of 447 crania from the Hopi, Nootka, Kwakiutl, and a prehistoric Peruvian series. The Hopi skeletal collection is from the site of Old Walpi (also known as Kuchaptavela, or Ash Hill). By association with ceramics and ethnohistoric accounts, the material from this site can be dated to 1300-1680 A.D. The skeletal material was collected by C.L. Owen in 1901 and is main-
tained at the Field Museum of Natural History (see e.g., Dorsey and Voth, 1902; Martin and Willis, 1940; and various Field Museum Annual Reports for passing references to Owen’s excavation). Although the collection has been used in previous studies (e.g., ElNajjar, 1978; Cheverud et al., 1979) there is no detailed archaeological account available. The Nootka and Kwakiutl skeletal collections derive from protohistoric or very early historic groups, and were obtained primarily by Franz Boas, George Dorsey, George Hunt, and C.F. Newcombe (see Cybulski, 1975; Cole, 1985 for sample and historical information, respectively). These collections are split between the Field Museum and the American Museum of Natural History. The prehistoric Peruvian material is from the necropolis at Ancon, and was collected by George Dorsey in 1892 (Dorsey, 1895). Ancon is a multicomponent site, so the material a t Field Museum dates to a broad time range. Artifactual associations indicate that the material is from the Huari Empire dating from 600 A.D. to 1450 A.D. (Menzel, 1977). The mode and form of deformation varies substantially across these different collections. For the Hopi, children were cradleboarded and this often resulted in lambdoidal flattening (Dennis and Dennis, 1940). The Kwakiutl and Nootka practiced annular deformation, although the intensity of the deformation varied between these two groups (Cybulski, 1975) and probably across regions within cultural groups a s well (Boas, 1921). Among the crania from Ancon, frontooccipital deformation is the most common form of cranial modification (Reichlen, 1982). All material at the Field Museum (380 crania) was scored by Konigsberg and Cheverud, while the material at the American Museum (67 crania) was scored by Kohn. Cranial deformation was scored on a n ordinal scale a s “not deformed (n.d.), “slightly deformed” (s.d.1, “deformed (d.), or “much deformed (m.d.1. We use “not deformed to refer to any cranium where there is no indication that artificial deformation has reshaped the cranial vault. “Slightly deformed refers to crania where there is some indication t h a t the vault has been reshaped,
CRANIAL. DEFORMATION AND NONMETRIC TRAITS
but where the deformation might be overlooked if we did not know the provenience (and hence the culturally associated deformation practices) for the skull. We use “deformed for crania where there is no question but that the cranial vault has been artificially reshaped, and “much deformed” for crania where the reshaping is extreme. In addition to the deformation scoring for the Field Museum material by Konigsberg and Cheverud made in 1989, there was also an independent scoring of deformation made by Konigsberg between 1985 and 1987 while he was conducting an inventory of the Field Museum skeletal collection. These two independent ordinal categorical scorings will be used to determine the repeatability of the deformation classifications. We measure repeatability by finding the polychoric correlation between the 19851987 and the 1989 scorings of deformation. Other models of repeatability are available for ordinal categorical data (see Agresti, 1988), but we use polychoric correlation because it allows for an underlying normal distribution of deformation status. Polychoric correlation is the multiple-class analog of the more commonly known tetrachoric correlation (Olsson, 1979). The tetrachoric correlation measures the underlying bivariate correlation on which thresholds are placed parallel to both the ordinate and abscissa to produce four probability density regions. The densities in these regions give the expected proportions of cases for a 2 x 2 contingency table. Thus, a tetrachoric correlation expresses the correlation between two binary traits. A polychoric correlation extends this idea by allowing more than one threshold parallel to the ordinate andor the abscissa (see Fig. 1). With four classes of deformation (n.d., s.d., d., and m.d.1 the cross-tabulation of the 1989 against the 1985-1987 scorings produces a 4 x 4 contingency table. Assuming that cranial deformation is a normally distributed variable which we observe as 4 ordered classes, maximum likelihood estimation can be used to find the standard bivariate normal distribution describing the relationship between the two scorings. The model can be parameterized using three thresholds for the 1985-1987 scoring, three
37
Comparison of 1985 and 1989 Scoring of Deformation
-3
-2
I
I
I
I
I
-1
0
1
2
3
1985 Scoring Fig. 1. Graphic representation of polychoric correlation between 1985-1987 and 1989 scorings of cranial deformation. Axes are labelled in standard deviation units, and there are three thresholds in each direction cutting the graph into 16 regions. The bivariate correlation (r = 0.891 is shown with isolines at every 0.02 units.
thresholds for the 1989 scoring, and a Pearson correlation. Equality of the 1985-1987 and 1989 thresholds is examined to determine whether there was any change in the scoring procedure across time. In other words, this test determines whether there was any change in the frequency of crania assigned to each of the deformation classes in 1985-1987 as versus 1989. If the thresholds are equal, then the polychoric correlation can be used as a measure of intraobserver repeatability for the deformation scoring. The polychoric correlation and threshold values were estimated using the FORTRAN function BIVNOR (Baughman, 1988) and GEMINI (Lalouel, 1979) to maximize the log-likelihood function. Because the underlying continuous distribution for the scoring of deformation suggested that the mean within-group deformation scores were approximately linearly arranged (see Results), we coded the deformations as “not deformed” equal 0; “slightly deformed equal 1; “deformed”equal 2; and “much deformed equal 3. Age was coded as 1 (less than 16 years); 2 (16 to 30 years); 3 (30 to 50 years); and 4 (over 50 years), while gender was coded as 1 (male); 2 (female); or
38
L.W. KONIGSBERG ET AL
likelihood is asymptotically distributed as a chi-square statistic with 1 degree of freedom. Because different forms of deformation are represented within this sample, we also test whether the deformation effects are equal across samples by reparametrizing equation 1 to include deformation-by-population interaction. This saturated model (see Agresti and Agresti, 1979 for a definition of this concept) then contains 6 parameters: age and sex effects which are assumed to be equal across the four populations and deformation effects within each of the four populations. The restricted model contains the age effect, sex effect, and a common deformation effect across all populations. The loglikelihood ratio test comparing these two models has three degrees of freedom. All probit log-likelihood functions were maximized using GEMINI (Lalouel, 1979), with the normal integrals evaluated using the FORTRAN function ALNORM (Hill, 1973). To examine the effect of deformation on the calculation of biological distances we calculated the morphological distances between the Hopi, Nootka, Ancon, and Kwakiutl samples separately for the nondeformed sample (n.d.1 and the deformed sample (s.d., d., or m.d.1. The distance measure we used is an extension of Mahalanobis’ D2 to cover discrete traits caused by thresholds on polygenwhere P(t = 1)is the probability that an in- ically determined liabilities (Blangero and dividual will display a particular nonmetric Williams-Blangero, 1991). The distance betrait, +[f(x)] is the standardized normal in- tween two samples can be written as: tegral from negative infinity to f(x), ci is a constant, and the beta weights are regressions on sex, age, and deformation status. The sum of the product of the observed counts (number of individuals in a particu- where (z, z2) is a column vector of differlar classification of age, sex, deformation ences between t threshold values for two status, and trait presencelabsence) with the samples, and R is a matrix of pooled withinnatural log of P(t = 1)or P(t = 0 ) is propor- group tetrachoric correlations between traits. The thresholds for the z vectors were tional to the log-likelihood. Given the current method of coding, a pos- estimated using the probit regression shown itive beta weight for deformation indicates in equation 1, which assumes homogeneous that the trait frequency increases with de- age and sex effects across samples. In this formation, while a negative weight indicates case, ci becomes a vector of threshold values that the trait frequency decreases with de- (i.e., z) within populations after controlling formation. To test whether these regres- for common age and sex effects. The tetrasions are significantly different from zero, choric correlations were estimated using biwe reestimate the probit parameters above variate probit analyses (see Ashford and with the deformation effect set equal to zero. Sowden, 1970) with separate age and sex Negative two times the difference in the log- effects for each pair of traits. The bivariate
9 (unknown). The nonmetric cranial traits were scored as 0 (absent), 1 (present), or 9 (unobservable). Seventeen bilateral traits and 5 midline traits were scored for a total of 39 traits. To determine whether cranial deformation has an effect on the occurrence of individual cranial nonmetric traits we used univariate probit analyses. For these analyses we disregard the fact that many of the traits are intercorrelated, and that there are often strong correlations between left and right sides for the bilaterally scored traits. In a previous study (Konigsberg et al., 1991) we used bivariate probit analyses to control for leftlright correlations in bilateral traits, but given the complexity of interpreting the results from these analyses, we have not included them here. We solely use the univariate probit analyses to look at individual effects on relative trait frequencies, and do not try to use these analyses to make an omnibus statistical statement (which is reserved for the following multivariate analyses). For the univariate probit analyses, the probit takes the following form:
~
CRANIAL DEFORMATION AND NONMETRIC TRAITS
39
TABLE 1 Repeatabclrty of cranral deformation scoring Observed 1985-1987 and 1989 scorings 1989 scoring
nd nd sd d md
1985-1987 scoring
sd
134 20 2 0
30 51 29 0
Expected values, separate 1985-1987 and 1989 thresholds (polychoric correlation = 0.8890): 1989 scoring nd sd
md
0 0 4 8
d
md
~~
~~
1985-1987 scoring
d 4 21 76 1
nd sd d md
134 28 20 42 2 27 0 00
32 01 49 82 26 15 0 01
Expected values, equal 1985-1987 and 1989 thresholds (polychonc correlation = 0 8868) 1989 scoring n.d. s.d. n.d. 134.32 26.02 26.02 50.13 s.d 1985-1987 scoring 23.07 2.56 d. m.d. 0.00 0.01 ~~
2 67 19 88 78 32 2 70
0 00 0 01 5 99 5 47
d. 2.56 23.07 78.15 4.22
m.d. 0.00 0.01 4.22 5.63
-
0.1520 for the slightly deformed, 1.0294 for the deformed, and 2.3284 for the much deformed classification (see Kelley, 1924 for the method of converting threshold values to RESULTS within-class means). Because these 4 Table 1 contains the observed cross-tabu- within-class means are remarkably close to lation of the 1985-1987 and 1989 scorings of being on a linear scale, we simplify the cranial deformation for the Field Museum univariate probit analysis by coding deforskeletal material. In addition, this table con- mation as 0 (n.d.1, 1 (s.d.1, 2 (d.), and 3 tains the expected frequencies from the (m.d.). Table 2 contains the deformation beta polychoric correlation analysis with unequal thresholds across the two marginal distribu- weights for each sample from the univariate tions, and the analysis with equal marginal probit analyses. Although the age and sex thresholds. The log-likelihood ratio xz for beta weights are not shown here, these pacomparison of these two models (unequal rameters were also estimated within each versus equal marginal thresholds) is 4.6641, sample when possible. From a cursory exwith 3 degrees of freedom (P = 0.1981). This amination of Table 2, it is apparent that denonsignificant result indicates that the formation has a significant effect on the ocmarginal distributions for 1985-1987 and currence of only a minority of traits. 1989 are equal, so that there was no “tempo- Specifically, in the Hopi, deformation acts to ral drift” in the deformation scoring proce- decrease the relative frequency of left dure. Additionally, the high positive poly- masto-occipital ossicles, and increases the choric correlation (r = 0.8868, P < 0.0001) relative frequency of the right foramen spiindicates the considerable repeatability of nosum open, right foramen of Huschke, and sagittal ossicles. Among the Nootka, deforscoring for deformation. The three thresholds from Table 1 were mation increases the relative frequency of estimated at -0.1796, 0.4955, and 1.9439. left and right coronal ossicles, while for AnThese thresholds correspond to deviations of con, deformation acts to increase the relathe class means from the grand mean equal tive frequency of sagittal ossicles. Among to -0.9157 for the not deformed class, the Kwakiutl, deformation decreases the
integrals for the bivariate probit analyses were evaluated using the FORTRAN function BIVNOR (Baughman, 1988).
40
L.W. KONIGSBERG ET AL.
TABLE 2. Probit remession coefficients of trait fresuencres on deformation status (left sides listed before right) Trait
Epipteric bone Asterionic bone Parietal notch hone Larnhdoid 0s. Masto-occipital 0s. Coronal 0s Infraorhital suture Supraorbital foramen Access. infraorb. f. Divided hypoglossal Postcondylar canal F. wale open
F. spinosurn open F. Huschke Mastoid f. exsutural Ohelionic foramen Access. less. palat. Metopic suture Bregmatic bone Apical hone Inca bone Sagittal hone I
Houi
Nootka
Ancon
Kwakiutl
-0.2343 0.0205 -0.0794 0.0105 -0.4054 -0.2444 0.3266 0.1275 -0.4055* -0.1868 0.5339' 0.3420 -0.0437 0.3420 -0.0437 0.1302 0.2438 0.0327 0.0149 - 0.4663 -0,2582 0.2623 0.2398 0.1160 0.01861,2 1.0282l* 0.1416 0.5262* 0.2350 0.1119 -0.1803 0.0386 -0.0645 -0.0104 0.1170 0.0766 0.0108'
0.5996 0.2077 0.1652 0.2985 0.0406 -0.1187 -0.0609 -0.0868 0.2507l 0.1678 0.6001" 0.9588 -0.2503 0.2658 -0.3729 -0.2331 -0.1640 -0.0897 0.2038 -0.0348 -0.0559 0.3589 -0.2253 -0.2898 0.0367 -0.1259
0.0395 0.1528 0.2716 0.0573 0.1333 -0.0755 0.2191 0.1047 -0.4366 0.0971 0.7214l
-0.6904* -0.3731 0.1107 0.1016 0.1539 0.2731* 0.0815 -0.0477 0.2841 0.5735* 0.8008+ 0.5191* 0.0531 -0.0026 -0.0067 -0.1422 -0.0613 -0.1082 -0.0751 0.0138 -0.0515 0.0630 0.1628 0.3611 0.0279l -0.2703 -0.1401 -0.2614 0.2462 0.0849 -0.0042 0.1015 -0.0577 0.0335
-
____
0.7200"
-
____ ____
-0.1433 -0.2013 0.2413 0.4065 -0.0009 0.1547 -0.2617 -0.1300
-
__--
-0.0110 -
_-__
-0.0110 -0.0906 -0.1235 -0.0893 -0.0579 0.0917 -0.0006 -0.1166 0.1435 -0.1938 0.0308 -
-
__--
0.0602 -0.2071 0.2028 0.2026 -0.1714 0.0546 -0.0095 0.1573 0.0766 0.2172 0.6566'.' 0.71811,' 0.4279*
-
____ -___
-0.1142 -0.1121 -0.0769
Sex effect could not be estimated.
'Age effect could not be estimated.
*Significantly different from zero at P < 0.05(two-tailed)
relative frequency of left epipteric bones, while increasing the relative frequency of right parietal notch bone, right masto-occipital ossicles, and left and right coronal ossicles. These changes in trait frequencies can be observed in Appendix Figure A, which contains the trait frequencies tabulated across undeformed crania (n.d.1 and deformed crania (s.d., d., and m.d.1. The sample sizes in Appendix Figure A are slightly larger than those used for the probit analyses, because crania of unknown sex were eliminated from the probit analyses. Aside from the question of individual effects of deformation within populations, there is also the question of population-bydeformation interaction effects on the occur-
rence of nonmetric traits. Because there are different forms of deformation across the populations (occipital flattening in the Hopi, fronto-occipital deformation for Ancon, and annular deformation among the Nootka and Kwakiutl), any interaction of deformation with population indicates differential effects of these various forms of deformation on trait presence. Table 3 lists the probability values from likelihood ratio chi-square tests comparing three different models. The first model is saturated, and includes a separate deformation-within-population effect for each population. The second model is a homogeneous effects model, in which there is one deformation parameter across populations. In other words, the homogeneous
CRANIAL DEFORMATION AND NONMETRIC TRAITS
41
TABLE 3. P-values from comparisons of saturated to homogeneous deformation effects model, saturated to no deformation effects model, and homogeneous effects to no effects model Trait Epipteric bone Asterionic bone Parietal notch bone Lambdoid 0s. Masto-occipital 0s. Coronal 0s. (-AP Infraorbital suture (-A) Supraorbital foramen Access. infraorb. f. Divided hypoglossal Postcondylar canal F. ovale open F. spinosum open
F. Huschke (-N) (-N) Mastoid f. exsutural Obelionic foramen Access. less. palat. Metopic suture Bregmatic bone Apical bone Inca bone (-H, -N) Sagittal 0s. (-A)
Saturated vs. homog.
Saturated vs. no deform.
Homog. vs. no deform.
0.0477" 0.6163 0.4382 0.8307 0.4209 0.1276 0.5003 0.7404 0.0239" 0.0440* 0.8547 0.5296 0.8354 0.5660 0.7026 0.5557 0.3148 0.9117 0.9629 0.2980 0.8280 0.3131 0.3662 0.0996 0.9985 0.0267* 0.5864 0.0195" 0.4834 0.6008 0.3756 0.5482 0.9928 0.8128 0.6532 0.5522 0.4967 0.1556 0.0583
0.0916 0.7232 0.3369 0.6500 0.4579 0.2046 0.1635 0.8426 0.0500" 0.0555 0.00011' 0.0214* 0.9092 0.6500 0.7530 0.5076 0.4523 0.8774 0.9793 0.4183 0.8218 0.3670 0.4903 0.1572 0.9998 0.0505 0.7676 0.0332" 0.1015 0.5205 0.5159 0.3152 0.9824 0.7620 0.7930 0.5191 0.6252 0.3641 0.0001"
0.7758 0.6001 0.1754 0.2071 0.3661 0.6267 0.0415" 0.6924 0.8303 0.2866
