ACTA BIOL. SZEGED. 4 2 . p p . 8 1 - 8 8 ( 1 9 9 7 )
P H E N O G R A M S D U E T O D I F F E R E N T S E T O F N O N - M E T R I C T R A I T S
Z s . J U S T ' a n d M . F I N N E G A N2
'Department of Anthropology. Jozsef Attila University. H-6701 Szeged. Hungary
1Department of Sociology. Anthropology and Social Work. Kansas State University.
Manhattan. 6650} Kansas. USA (Received: Januar 20, 1997)
Abstract
904 crania representing 11 Arpadian-age populations were subjected to non-metric trait analysis in order to highglight the biological affinities among them.
A phenogram generated from the frequencies of the parent set of variables, consisting of 41 non- metric traits, differs from those phenograms which were built up on the basis of reduced and specialized subsets of non-metric traits. One generated subset of variables involves sutural variations, the other is limited to foramen variations.
Key words: affiliated or subset of non-metric traits, biological distance or mean measure of divergence (MMD), Arpadian-age
Introduction
A study of 11 Arpadian-age populations concerning the occurrence of 41 non- metric cranial traits and a generated biological distance analysis (JUST, 1996) provides the opportunity for further investigations on the features of non-metric traits.
Two reduced and specialized subsets of non-metric traits were selected from the above 41 non-metric traits: a set of foramen variables (Table 2) and a set of sutural variables (Table 2).
This paper examines whether the phenograms derived from these subsidiary non-
metric data sets differ from each other and from the phenogram generated from the
frequencies of the original non-metric traits. In other words this research compares the
mean measure of divergence (MMD) or biological distance produced by different sets
of non-metric traits using the same sample.
8 2 ZS. JUST and M. FINNEGAN
Materials and method
A sample of 904 crania representing II Arpadian-age ( I l t h - I 4 t h A D century) populations w a s scored for 41 non-metric cranial traits in a previous study (JUST, 1996) Sample names, abbreviations, sample size and the dale of the sample are presented in Table I
From the a b o v e 41 non-metric variables two subsets of variables were separated: a subset of the 11 sutural variables includes sutural ossicles and persisting sutures and a foramen subset consisting of 12 trails, including accessory foramen and foramen with multiple alternative expression
The frequency data of these subsets were separately subjected lo the GREWAL-SMITH statistics (GREWAI . 1962; FINNEGAN and COOPRIDER. 1978: FINNEGAN el al.. 1993) which transforms the basic trail frequencies into a mean measure of divergence ( M M D ) or biological distance among all population pairs.
Each matrix was moved to a statistical package (ROLF el al.. 1974) which generated a p h e n o g r a m . Each matrix was subjected to a T A X O N analysis which provided a sequential agglomerative. hierarchical cluster analysis in which w e employed the unweighted pair-group method using arithmetic averages and dictated that the lowest values were considered for similarity. T h e routine M X C O M P , which c o m p u t e s the cophenetic values for each matrix position, was employed and the resultant cophenetic value matrix was compared to the original matrix for congruence. (SOKAL and SNEAfH, 1963)
Table /. Sample names, dale of samples by centurties, abbreviations of sample names, sample sizes by the number of studied crania.
site century abbrev. size
Hékés-Povádzug I0-I2.SZ. bep 58
Cegléd-Borzahegy * 11-I3.SZ. ccb 37
Cegléd-Madarászhalom * 11-I3.SZ. cem 9 4
Csálalja-Vágotthegy 11-I3.SZ. csát 4 3
Csongrád-Felgyö 10-1 l.sz. csof 29
1 lódmezövásárhely-Kardoskíil 11-I2.SZ. hvk 123
Jászdózsa-Kápolnahalom * 11-I4.sz. ják 41
Kiszombor B I0-I2.SZ. kisz 80
Orosháza-Rákóczitelep I0-I2.SZ. ort 157
Szatymaz-Vasútál lomás I0-I2.SZ. szva 70
Szegvár-Oromdűlő I0-12.SZ. szeg 172
total szeg
904
Results and discussion
A list of the chosen variables and their frequencies for each population sample is presented in Table 2. Frequencies presented in this table served as basic data for the
G R E W A L - S M I T I I
distance statistic. MMDs (biological distances) among all population
pairs generated from the frequencies of sutural variables are given in Table 3; the same
for foramen variables are presented in Table 4. Underwritten figures are estimates of the
variance. Significant differences between pairs at the level of p < 0.05 are indicated
with a + while an * indicates significant differences at the level of p < 0.01. Most of the
biological distances calculated from sutural trait frequencies are not significant, while
most of the biological distances based on foramen variables are significant (p < 0.05) or
very significant (p < 0.01). Some population sample pairings produced negativ
biological distance values. This is an artifact of the
G R E W A L - S M I T Hstatistic, produced
when frequency differences between population pairs are very small. In order to avoid
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PHENOGRAMS DUE TO DIFFERENT SET OF NON-METRIC TRAITS 8 5
negativ distance values during the cluster analysis, all MMD distance measures were increased by 0.010 before the matrix was submitted to the cluster programs.
The phenogram in Fig. 1 presents population clusters based on the distance matrix of sutural variables. The phenogram in Fig. 2 shows clustering of the population samples using the foramen distance matrix. In comparing phenograms from the sutural matrix to the phenogram from the sutural cophenetic matrix, a correlation of 0.739 was achieved while the foramen variables generated a correlation of 0.625. For the limits of this study, these internal correlations may be considered significant.
-0.010 0.010 0.030 0.050 0.070 0.090 0.110 0.130
bep hvk ort ceb к is/
szva cem jak szeg csat csof
Fig. I. Phenogram based on ihe clustered distance matrix of sutural variables. Abscissa is scaled in relative population distances.
In analysing specific diffrences between the sutural and foramen generated phenograms, we find that four major clusters develop on the sutural phenogram: union of ВЕР, HVK and ORT compose the first cluster, СЕВ, KISZ and SZVA form the next, while the third cluster includes the samples of СЕМ, JAK and SZEG. If 0.040 (an arbitrary, but logical value based on the original distance matrix) is considered to be a cluster identity level, CSAT and CSOF come together above this level.
The phenogram drawn from the foramen distance matrix divides into three major clusters: ВЕР. СЕВ. CSAT and CSOF form the first unit, CEM, SZEG, KISZ and JAK compose the next cluster and than the triad of HVK, ORT and SZVA joins as the third cluster. JAK meet the second cluster at a higher level, somewhat above the 0.040 identity level.
In comparing sutural, foramen and parent phenograms (Fig. 3) only two pairs of
samples consistently cluster together: HVK with ORT and CSOF with CSAT seem to be
inseparable. The close relation between HVK and ORT can be supported by their geo-
graphical proximity, however the same situation does not hold true for CSAT and CSOF.
8 6 ZS. JUST and M . FINNEGAN
Table 3. Measures of divergence (biological distance) based on sutural variables between population samples used in this study Underwritten figures in italics are estimates of the variance Levels of significance: + (p<05): * (p< OI)
ВЕР СЕВ СЕМ CSAT CSOF IIVK JAK KIS/ OR 1 SZEG
CEB 0.010
0 016 CEM 0 045 - 0.010
0.055+
ООН CSAT 0.041
0 017 0.019 0.021
0 0 8 8 0015 CSOF 0.102+
0.020 0.052
0.023 0,083+
0.017 0.040 0.024 IIVK 0.003
0 010 0.029 0.013
0.017 0.007 0.048
0.014 0.065 0 016 JAK 0.078+ 0.108+ -0.005 0.132* 0.108- 0.040+
0.015 0.019 0.013 0 020 0 022 0012
KISZ 0.030 0.035 0.001 0.060+ 0.0841 0.020 0.011 ООП 0.014 ООО fi 0.015 ООП 0.007 0 013 ORT 0.007 0.013 0.013 0.026 0.066+ -0.002 0.039+ 0.008
0.009 0.012 0 006 0 013 0.016 0.005 0.011 0.007
SZEG 0.033+ 0.083* 0.001 0 0 9 9 * 0.114* 0.018+ 0.000 0.022+ 0.024+
0.009 0.012 0.006 ООН 0.016 0.006 0.011 0.007 0.005
SZVA 0.012 -0.004 0.007 0.042 0.074+ 0.007 0.039 -0.008 -0.003 0.028+
0.012 0.015 0.009 0 016 0.0! X ОМОН омы 0.009 0.007 O.OOX
-0.010 0.010 0.030 0.050 0.070 0.090 0.110 0.130
bep ceb csál csof cem szeg kisz iák hvk
3
Fig. 2. Phenogram based on ihe clustered distance matrix of foramen variables. Abscissa is scaled in relative population distances.
On the sutural phenogram HVK and ORTjoin to ВЕР; on the phenogram based on the clustered distance matrix of foramen variables they meet SZVA. Comparing these subset phenograms to the original, parent phenogram based on Ihe 41 non-metric trait set, ВЕР. HVK, ORT and SZVA are all found in the same cluster. Similarly, CEM and JAK cluster in the sutural phenogram while in the foramen phenogram CEM and KISZ meet below the 0.040 identity level and JAK joins this group above this identity level.
In the parent phenogram these three population samples belong to the same cluster.
PHENOGRAMS DUE TO DIFFERENT SET OF NON-METRIC TRAITS 8 7 Table 4. Measures of divergence (biological distance) based on foramen variables between population
samples used in this study. Underwritten Figures in italics are estimates of the variance. Levels of significance: + (p<05); * (p< OI)
BEP CEB CEM CSAT CSOF IIVK JAK KISZ ORT SZEG
CEB 0.037+
0.011 CEM 0.036+
0007
0.149*
0.009 CSAT 0.039+
0.013
-0.006 0.015
0.123*
0.011 CSOF 0.000
0.014
-0.008 0.016 0.102*
0.012
-0.009 o.oix IIVK 0.026+
0 007 0.094*
0 009 0.027+
0.005
0.104*
HO 11
0.089*
0.012
JAK 0.140* 0.295* 0.027+ 0.294* 0.245* 0.071*
0.011 0.013 0.00X 0.014 0.015 o.oox
KISZ 0.059* 0.117* 0.032* 0.085* 0.077* 0.084* 0.103*
0.007 0.009 0.005 0.011 0.012 0 005 0.009
ORT 0.068* 0.143* 0.054* 0.178* 0.152* 0.014+ 0.071* 0.140*
0 007 0.00X 0.004 0.010 0.011 0.004 O.OOX 0.004
SZEG 0.023+ 0.086* 0.002 0.070* 0.063' 0.033* 0.071 * 0.024+ 0.061*
0.006 0.00X 0.004 0.010 0.011 0.004 O.OOX 0 004 0.004
SZVA 0.092* 0.140* 0.084* 0.188* 0.151* 0.047* 0.094* 0.166* 0.005 0.081*
0.00X 0.010 0.006 0 012 0.013 0.006 0.010 0.006 0.006 0.005
0.010 0.030 0.050 0.070 0.030 0.110 0.130 I . 1 . 1 1 1 1 » 1 * 1
szeg bep hvk szva ort iák kisz cem csát csof ceb
Zb
D -
Fig. 3. Phenogram based on the clustered distance matrix of the original 41 non-metric traits. Abscissa is scaled in relative population distances.
Although, these parallels can be discovered among the phenograms, inconsistency
exists as far as biological distances are concerned. For example, HVK and ORT meets
at a higher level on the foramen phenogram than on the sutural phenogram and the
relative MMD between CSAT and CSOF is higher on the sutural phenogram than on
the foramen phenogram.
8 8 ZS. JUST a n d M. FINNEGAN
Because of the inconsistencies seen in the suturai, foramen and parental phenograms, it is currently not possible to ascertain the precise contribution either the suturai or foramen subset of variables provides to the separation of clusters seen in these phenograms.
Acknowledgments
The authors are indebted to a number of colleagues who assisted in various phases of this research. Particularly, Dr.
KLNGA E R Ywas instrumental in codifying our thoughts about comparing subset data within this list of popular non-metric traits.
References
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