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Statistical analysis using multistate qualitative variables

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Archeologia e Calcolatori

15, 2004, 239-255

STATISTICAL ANALYSIS USING MULTISTATE QUALITATIVE VARIABLES

APPLIED TO THE HUMAN DENTAL MORPHOLOGICAL TRAITS

IN THE BRONZE AGE (GRANADA, SPAIN, 1300-1500 B.C.)

1. INTRODUCTION

The study of genetic diversity using dental morphology is recent and

constitutes an element providing a great quantity of information in anthro-

pological investigations. The Arizona State University Dental Anthropology

System uses a set of traits that allows us to measure the presence/absence

dichotomy and obtain replicability of results among observers. The ASU stand-

ard obtains the maximal and the minimal trait expression, and various grada-

tions between these points.

Although the set of traits proposed by the ASU standard is very large,

in this paper we use a subset of them, due to the characteristics shown by the

archaeological remains, using the following criteria (TURNER, NICHOL, SCOTT

1991):

– the selected traits are the most easily and reliably observed;

– they persist for many years even if the subject had a harsh lifestyle. This

case is the most usual in archaeological samples;

– most traits have low or no sex dimorphism. This feature is very important

because usually it is very difficult to obtain prehistoric archaeological re-

mains having sexual distinction.

– those traits evolve very slowly and permit a distinct characterization of

very well the populations for affinity studies.

2. THE ASU SIMPLIFIED DENTAL ANTHROPOLOGY SYSTEM

The scoring procedures in the ASU system are focused mainly on the

morphological features of the crowns and roots mainly, having special fea-

tures in function of the type of the root. Since the conditions in which the

archaeological remains appear make it very difficult determine in a reliable

way the degree of the traits, in this paper we propose to use the presence or

absence of each feature to get the minimal and maximal trait representation,

for greater reliability in the information obtained.

These simplified sets of morphological features of the crown and roots

are obtained from the qualitative scoring proposed by TURNER, NICHOL, SCOTT

(1991) and they are focused on upper teeth, mainly showing the following

traits.

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2.1 Crown characters of incisors and canines

The traits used are winging with reference to the upper central incisors

(ENOKI, DAHLBERG 1958; TURNER 1970); Labial Convexity relative to upper

incisors (NICHOL, TURNER, DAHLBERG 1984; SCOTT, TURNER 1997); Shoveling

is relative to upper incisors, canine and lower incisors (HARDLICKA 1920;

DAHLBERG 1956; SCOTT, TURNER 1997); Double-Shoveling occurs in upper

incisors, canine and lower incisors (DAHLBERG 1956); the Interruption Groove

appears in upper incisors (SCOTT, TURNER 1997); the Tuberculum Dentale

feature is present in upper incisors and canines (NICHOL, TURNER 1986); the

Canine Mesial Ridge or Bushman canine is located in upper and lower ca-

nines (MORRIS 1975; SCOTT, TURNER 1997); Canine Distal Accessory Ridge

appears in upper and lower canines (MORRIS 1975; SCOTT, TURNER 1997); the

Peg-Shaped character occurs in the upper lateral incisors (SCOTT, TURNER 1997);

Congenital Absence character appears in the upper lateral and lower central

incisors (MONTAGUE 1940; SCOTT, TURNER 1997); the Canine Root Number is

present in lower canines (SCOTT, TURNER 1997).

2.2 Crown characters of premolars

The traits studied include the Double-Shoveling located in the first premo-

lar (DAHLBERG 1956); the Premolar Mesial and Distal Accessory Cusps occurs

in the upper premolars (TURNER 1967); Tricusped Premolars is a very rare trait

located in the upper premolars (SCOTT, TURNER 1997); the Distosagittal Ridge

or “Uto-Aztecan Premolar” appears in the first upper premolar (MORRIS et al.

1978); Enamel Extensions are present in the upper premolars (PEDERSEN 1949);

Premolar Root Number is measured in the upper premolar (SCOTT, TURNER

1997); the presence of Odontome occurs in the upper and lower premolars

(PEDERSEN 1949; ALEXANDARSEN 1970; SCOTT, TURNER 1997); Congenital Ab-

sence character appears in the upper lateral and lower second premolars

(MONTAGUE 1940; SCOTT, TURNER 1997); Tomes’ Root is present in lower first

premolars (TOMES 1923; SCOTT, TURNER 1997).

2.3 Crown characters of molars

The traits considered are the Metacone and the Hypocone characters

located in the upper molars (DAHLBERG 1951; TURNER 1979); Cusp 5 or

Metaconule trait appears in upper molars (HARRIS 1977); the Carabelli’s

trait appears in the upper molars (DAHLBERG 1956; SCOTT, TURNER 1997);

the Parastyle character is located in upper molars (BOLK 1916; SCOTT, TURNER

1997); Enamel Extensions are present in the upper molars (PEDERSEN 1949);

the Upper Molar Root Number is measured in the upper molars (SCOTT,

TURNER 1997); the Peg-Shaped molar character occurs in the upper third

molar (SCOTT, TURNER 1997); Premolar Lingual Cusp Variation is very sen-

Statistical analysis using multistate qualitative variables

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sitive to wear and occurs in the lower premolars (PEDERSEN 1949; KRAUS ,

FURR 1953; SCOTT, TURNER 1997); the Congenital Absence character ap-

pears in the upper lateral and lower third molars (MONTAGUE 1940; SCOTT,

TURNER 1997); the Anterior Fovea trait is located in the lower first molar

(HARDLICKA 1924; SCOTT, TURNER 1997); the Groove Pattern feature ap-

pears in the lower molars with three scorings: X, Y and + (HELLMAN 1928;

JORGENSEN 1955; SCOTT, TURNER 1997); the Cusp Number scores the cusp

number in the lower molars (GREGORY 1916; SCOTT, TURNER 1997); Deflect-

ing Wrinkle appears in lower first molar (SCOTT, TURNER 1997); Distal Trigo-

nid Crest occurs in the lower molars (HARDLICKA 1924; HANIHARA 1961;

SCOTT, TURNER 1997); Protostylid character is located in the lower molars

(DAHLBERG 1956; SCOTT, TURNER 1997); Cusp 5, Cusp 6 and Cusp 7 are

located in lower molars (SCOTT, TURNER 1997); Lower Molar Root Number

is present in lower molars (TURNER 1971; SCOTT, TURNER 1997).

This set of morphological features is stored by means of a database

developed in ACCESS database management using a form of data recording

that includes the set of simplified morphological dental features (Fig. 1).

Fig. 1 – The ASU system. Simplified database.

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This database form developed is easy to use, and includes the list of the

teeth (see diagram) and the list of the jaws, and features verification fields to

avoid possible mistakes. It also includes other optional information about the

archaeological site: the settlement key (in Spain this codification is composed

of two alphabetical characters belonging to the province, two alphabetical char-

acters belonging to the town, and a number (with three digits) corresponding

to the settlement in the town, the place-name, the chronology and the UTM

coordinates of the settlement. This information is important for comparing

two or more different settlements, but it is possible to save each archaeological

site data set in an independent file and merge the files later.

The information recorded in the database can be exported to another soft-

ware like EXCEL, SPSS, MATLAB, the interchange format ASCII, and so. This

data set file can be analysed by means of standard statistical procedures, MATLAB

routines, etc., or can be written using WORD or other software packages.

3. THE MMD (MEDIAN MEASURES DIVERGENCE) METHOD

The previous method allows us to convert the variables in dicotomized

qualitative variables, and it makes the assumption that there is only a single

genotype for any specific trait, and that, when asymmetry is present, the

antimere exhibiting the greater degree of trait expression is the more accu-

rate indicator of the genotype. The score used is the highest grade of expres-

sion observed between the two sides (TURNER, NICHOL, SCOTT 1991). How-

ever, the statistical MMD method is established for general qualitative

multistate variables, and not presence/absence variables only.

The statistical procedure assumes that the variables are independent

variables with no correlation between them, and each one follows a binomial

distribution because the global population is very large. Then, for each trait

the expectation of the proportion p and the variance of p is:

being P the proportion of the trait in the total population (an unbiased esti-

mator of P, that has the least variance and is a sufficient estimator) and N is

the number of individuals in the sample. However, if N is small for a variable

there may be great discrepancies between p y P, and all the proportions do

not have equal importance. Therefore, it is essential to transform the propor-

tion p in a new variable with no dependent variance of the population pro-

portion P; this new quantitative variable is obtained using the inverse sine

transformation, measured in radians, by means of the formula (GREWAL 1962):

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This variable has the advantage that the variance is approximately 1/N

instead 820.7/N (this is the variance obtained using the more usual transfor-

mation Θ = arcsin

√

−

p) (BERRY, BERRY 1967; SJØVOLD 1973).

The test of differences between two populations was established by

BERRY (1963) according to:

under the hypothesis that there is no genetic difference between the popula-

tions compared, the proportions p1 and p2 in two samples must be equal

apart from sampling fluctuations. Then, the difference Ø1 - Ø2 is approxi-

mated by a normal distribution with variance (1/N1 + 1/N2), and the statistic

is distributed by means of a normal distribution N(0,1). Therefore, the ex-

pression

follows a distribution, and the expression

will be approximately distributed according to . And it can be

demonstrated (SJØVOLD 1973) that the expectation of X is E(X)=0 and the

variance of X is

The Mean Measure Divergence between the samples 1 and 2 using the

mean of X extended to all traits is defined by:

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being:

θ1i=angular transformation of the proportion p1 of elements in the sample 1

for the trait i;

θ2i=angular transformation of the proportion p2 of elements in the sample 2

for the trait i;

N1i=number of elements in the sample 1 having no missing the trait i;

N2i=number of elements in the sample 2 having no missing the trait i;

V=number of traits that can be evaluated in the samples.

The variance of MMD is obtained by means of the expression (BERRY,

BERRY 1967):

The p1i and p2i proportions can take extreme values distorting the an-

gular transformation. In this case the previous values must be replaced by the

following values (SJØVOLD 1973):

– if the proportions are near 0 we must take and

– if the proportions are near 1 we must take and

The MMD value is distributed according to , but it is

usual to use the following rule proposed by BERRY, BERRY, UCKO (1967):

if is bounded for each i, the MMD is asymptotically normally dis-

tributed and is significant at the 0.05 probability level when the MMD is

twice than its standard deviation (SJØVOLD 1973; SJØVOLD 1977; AL-ABBASY,

SARIE 1997; BAILEY 2000).

4. THE CLUSTER ANALYSIS ALGORITHM

Cluster analysis constitutes a very important statistical method to de-

tect grouping in a data set, and many techniques are developed to make

classifications, groups of objects, etc., based on quantitative variables. How-

ever, an important part of the archaeological data is defined by means of

Statistical analysis using multistate qualitative variables

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qualitative variables with several states, but there are no procedures adapted

for grouping these data except for presence/absence data (SNEATH, SOKAL 1973;

ROMESBURG 1984; KRZANOWSKI 1988).

The methods of analysis for qualitative variables usually codify each

state of each variable as a new presence/absence variable, loosing the linkage

between the states in the same variable. This problem has been approached

by several scientific theories such as image recognition, string of symbols

recognition, database management, archaeological pattern recognition of in-

complete data, etc. (MICHALSKI, STEPP 1983; BEN-BASSAT, ZAIDENBERG 1984;

CHIU, WONG 1986; ESQUIVEL 1988; FUKUNAGA 1991).

According to the Shannon theory, in a mathematical communication

model the information is determined by a statistic parameter associated with a

probability scheme, and it must indicate a measure related to the uncertainty

according to the occurrence of a particular message in a set of messages:

We propose that the information carried out by the xik state with ex-

perimental probability pik and ni number of states, and the associated entropy

to the Xi variable are defined as:

The entropy H(Xi) minimizes the influence of the rare cases; but

this influence is very important to study the association between elements

because: «the agreement in rare states is less probable that the agreement

between frequent states and it must be more valued» (SNEATH, SOKAL 1973).

The total entropy of the Xi (ESQUIVEL 1988) is accord to the previous

ideas:

The total entropy D(X i) measures the byass or distortion that pro-

duces a non regular variable in the space of elements.

Being G={A1, A2, ... An} the set of elements defined by the set of quali-

tative multistate variables V={X1, X2, ... Xv}, and ni the number of states of

variable Xi. Each element has linked a mathematical object defined by the n-

pla of measures (DUBOIS, PRADE 1980) m(Ai)={(m1(Ai), m2(Ai), ... mv(Ai)},

k=1,...,v and i=1,...,n, being mk(Ai)=xkj if j is the index of the state of vari-

able Xk that is in Ai. The set of mathematical objects defined by this proce-

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dure is named the “pattern space” S, and using pi(A) as experimental fre-

quency of the xij state, the distortion that produces an element is defined by

the total uncertainty originated by that element in the pattern space and is

formuled by the mathematical expression:

The interaction between elements determines the clustering by means of

the similarity characteristics of each one. Using a similar terminology to the

physical sciences and used by the pattern recognition theory, the information

measurement of attraction between elements will decide that units can be clus-

tered and the intensity of clustering. The attraction measure between two ele-

ments must reveal the common information in the variables shared by them,

while the dissimilarity must quantify the difference between them.

These concepts need the previous definitions:

– Common information between two elements. Is defined according to the

mathematical set intersection

– Joint information between two elements. Is defined according to the math-

ematical set union

The information values allowed by the common and the joint informa-

tion verify the basic relations established by PAL, DUTTA MAJUMDER (1985) to

quantify the degree of fuzzyness of a data set.

This theorem allows us to obtain the joint information provided by the

elements of the group Gn={A1, A2, ..., An} according to the boolean logic

rules (ESQUIVEL 1988; ESQUIVEL 1999):

In a space pattern, the study of the relations needs a parameter that

quantifies the distance between groups of elements, or conversely a similarity

measure because the basic relation is d(A,G)=1-S(A,G) (in this paper we use

a similarity measure). In the case of similarity between two elements, the

affinity measure is the common information between them. The relation be-

tween the affinity function and the information of elements is established by

the following expression (ESQUIVEL 1988; ESQUIVEL 1999):

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This definition verifies the properties established by BACKER, JAIN (1981)

that must verifiy each affinity measures:

1) the affinity measure element-group should not be smaller if the element is

a member of the group that if it is not contained in him;

2) the affinity will be near 0 when the element is spurious to the group;

3) the affinity will be an absolute maximum if the group is constituted by an

single element having the same localization that the previous element.

Two basic forms are possible to establish a similarity measure:

This measure verifies the conditions established by BACKER, JAIN (1981)

and only quantifies the uncertainty provided by the states that appear in all

and each one of the elements of the groups.

The S2 measure doesn’t verify the conditions imposed by Backer and

Jain since it takes into account all the occurrences among the elements of the

groups, and therefore the uncertainty is added to the total value increasing.

Also, it is less strict than the previous measure.

5. DATA SET AND RESULTS OF ANALYSIS

The data have been obtained from the remains of burials belonging to

three contemporary settlements belonging to different cultures and with great

geographic proximity (about 150 km). The Castellón Alto and the Fuente

Amarga settlements are prototypes of the Argaric culture and they are lo-

cated in Galera and Huéscar (Granada, Spain), on the left bank of the river

Galera, on a steep terraced hill. In the construction of the cottages, the in-

habitants took advantage of the natural terraces. Their main cultural charac-

teristics are the production of a special pottery, a diet based on agriculture,

and the burial of the inhabitants with their trousseau under the houses or

inside tiny caves in the walls of the hill.

The La Navilla settlement is a megalithic dolmen with multiple burial

that contains the remains of 54 human bodies, most conserved in an incom-

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plete state. It is contemporary to the other locations and it is located in the

right bank of the river Cacín. Culturally it is quite different from the Castellón

Alto and Fuente Amarga settlements since it belongs to the Copper Age. There

are no burials under the cottages or in the little caves, and they were stock-

breeders but not farmers; however the pottery found in the burials is an

Argaric typology, maybe due to the geographical proximity and to

contemporary acculturation phenomena that occured.

The data set is composed of the teeth of 54 individuals in the mega-

lithic cemetery of La Navilla and of 114 individuals, generally incomplete,

found in the Argaric settlements of Castellón Alto and Fuente Amarga in a

good conservation state. The dental morphological variables have been stud-

ied according to the methodology of the ASU system and other pathologies

have been analyzed (caries, toothloss before death and dental waste).

We carried out previous morphological studies to obtain the distribu-

tion of the different characters of the maxillary and the jaw for sides and sex,

to determine that differences do not exist due to the sex or the laterally using

χ2 tests. We have distinguished males, females and undetermined (all non

adults and those older than 20 years for whom it has not been possible to

determine the sex are included in this category).

In all the cases the non existence of statistically significant differences

with a significance level p <0.05 was determined, which is in agreement

with the results obtained in previous works (TURNER, NICHOL, SCOTT 1991;

HILLSON 1996; SCOTT, TURNER 1997), indicating that dental morphological

characters usually exhibit a high grade of symmetry. In some studies already

completed, the agreement between both sides exceeds 95% and only 7% of

fellows sample some asymmetry, generally in expression grades (SCOTT, TURNER

1997).

Since many characteristics are not represented and in order to assign

greater value to the possible differences than to the similarities, 36 features

have been selected among those whose frequencies diverge more (see Tables

1 and 2). The values of the MAD and their standard deviation, as much for

the maxillary one as for the jaw, do not reflect statistically significant differ-

ences at p<0.05 significance level. In this study we have distinguished be-

tween the maxillary and the jaw cases to obtain greater detail in the results;

also most of the characteristics of the jaw do not appear in the maxillary.

The global MMD value for traits in maxillary is 0.184 with standard

deviation Ã=0.059, showing no statistically significant differences between

the Argaric settlements and the non-Argaric Navilla settlement, with a sig-

nificance level p<0.05 because the MMD value is not twice its standard

deviation. Also, the analysis of the jaw provides a global MMD value 0.125

with standard deviation Ã=0.038, showing no statistically significant differ-

ences between the Argaric settlements and the non-Argaric Navilla settle-

Statistical analysis using multistate qualitative variables

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ment, with a significance level p<0.05 because MMD<2 Ã (SJØVOLD 1973;

SJØVOLD 1977; AL-ABBASY, SARIE 1997; BAILEY 2000).

The analysis using the MMD test indicates that significant differences

do not exist for the total of dental variables between the sample of the Navilla

and that of the Argaric settlements in Galera. Although variables with more

differences were selected for this analysis, the result has been negative. Ac-

cording to the historical data, the two population samples studied descend

biologically from the populations that inhabited East Andalucia in the Cop-

per Age and, therefore, they do not differ from each other, too very little

time has passed for evolutionary phenomena to occurr. Some of these traits

present significant differences and could correspond to endogamy phenom-

ena and/or drift genetics, which demonstrates that, inside the generality, each

group presents some distinctive characteristics.

In general, both populations, as well as those of the Neolithic and Cop-

per Age in Andalucia (GALLARDO 2001), although among them some small

differences appear, fit the general profile that defines the populations from

Europe and Western Asia for dental morphological variables.

These measures get very good results from clustering a data set defined

by qualitative multistate variables. The previous cluster analysis algorithm

has been applied to the data set and confirms the results provided by the

MMD test, i.e., there is no separation between the Argaric and non-Argaric

populations, and the teeth of both populations appear mixed in the groups

and subgroups obtained in the dendrogram tree.

Traits MMD

Canine Distal Accessory Ridge 0.290

Premolar Lingual Cusp Variation P20.039

Anterior Fovea 0.170

Groove Pattern X M20.232

Groove Pattern Y M10.084

Groove Pattern X M3-0.005

Cusp Number M10.084

Cusp Number M2-0.009

Distal Trigonid Crest M20.396

Protostylid M10.058

Protoslylid M30.021

Protostylid M3-0.076

Cusp 5 M2-0.009

Cusp 5 M30.341

Cusp 7 M10.232

Canine Root Number -0.011

Congenital Absence I10.170

Congenital Absence M30.238

Tab. 1 – MMD results for the present traits in the maxillary between the Navilla settlement and

the Argaric settlements.

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Due to the great quantity of data available, in this paper the results

described are those obtained when applying the algorithm to the teeth of the

maxillary. The categories used have been the tooth type (M1, M2 and M3)

and those specific variables of the maxillary ones: Metacone, Hypocone,

Cusp 5, Carabelli’s Trait, Parastyle, Enamel Extensions and Root Number.

The teeth numbered 1-25 are the non-Argaric. The dendrogram shows the

following results (Fig. 2):

– There are no isolated groups for Argaric and non-Argaric settlements us-

ing the teeth of the maxillary.

– Each group is composed of teeth belonging to both cultures, without dis-

tinction between them.

– There is not a specific typology that allows us to determine if a tooth

belongs to one or another culture.

6. CONCLUSIONS

In this work we have studied the remains of maxillary and jaws belong-

ing to 168 individuals with a total of 1313 pieces belonging to the multiple

megalithic graves of La Navilla (necropolis of the Pantano de los Bermejales,

Granada) and of the Argaric settlements of Castellón Alto and Fuente Am-

arga (near Galera, Granada), sites that are near to each other geographically.

This material has been analyzed on the basis of the dental morphological

characteristics using the ASU (Arizona State University) system.

Tab. 2 – MMD results for the present traits in the jaw between the Navilla settlement and the

Argaric settlements.

Traits MMD

Canine Distal Accessory Ridge 0.290

Premolar Lingual Cusp Variation P20.039

Anterior Fovea 0.170

Groove Pattern X M20.232

Groove Pattern Y M10.084

Groove Pattern X M3-0.005

Cusp Number M10.084

Cusp Number M2-0.009

Distal Trigonid Crest M20.396

Protostylid M10.058

Protoslylid M30.021

Protostylid M3-0.076

Cusp 5 M2-0.009

Cusp 5 M30.341

Cusp 7 M10.232

Canine Root Number -0.011

Congenital Absence I10.170

Congenital Absence M30.238

Statistical analysis using multistate qualitative variables

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Fig. 2 – Dendrogram obtained by means of cluster analysis algorithm using qualitative multistate variables.

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Although the study was very complicated due to the high grade of

deterioration of the Argaric teeth, and the mixed disposition of the material

of La Navilla, satisfactory results have been obtained.

The previous analyses do not show statistically significant differences

between jaw and maxillary or between sexes, which coincides with the char-

acteristics of the phenotypic distribution of the dental morphological vari-

ables. Therefore, the morphological features of the ASU system are an excel-

lent indicator for the comparison between biological populations that are

not affected by environmental factors such as diet, disease, etc.

The application of the MMD test of differences shows that statistically

significant differences do not exist among contemporary and very near geo-

graphically Argaric and non-Argaric populations in Granada, showing that

the people studied belong to the same biological population. Therefore, any

differences among these locations will be due to cultural factors, etc.

These results have been confirmed by means of the application of an

algorithm of cluster analysis developed to use multistate qualitative variables,

showing that independent groups do not exist between the Argaric and non-

Argaric populations studied.

The trait frequencies of the ASU system indicate that the populations

studied belong biologically to that which SCOTT and TURNER (1997) have de-

fined as populations from Western Eurasia. That is to say, they form part of

the group of populations from Europe, North Africa and Southwest Asia.

Some characteristics present different frequencies from those of West-

ern Europe, but we cannot determine if they are or are not characteristic

features of Mediterranean populations, since the moment the number of stud-

ies of this type in the North valley is very small and therefore a biological

characterization of these populations has not been made based on their den-

tal morphological variables.

The frequencies obtained are very similar to the signal ones recorded

by GALLARDO (2001) for the populations of the Neolithic Age and the Copper

Age in Granada. Therefore, it is possible to affirm that a biological continu-

ity exists in the region from the Neolithic to the Bronze Age.

JOSÉ ANTONIO ESQUIVEL

Departamento de Prehistoria y Arqueología

Instituto Andaluz de Geofísica

Universidad de Granada

IHAB AL OUMAOUI

Departamento de Prehistoria y Arqueología

Universidad de Granada

SILVIA JIMÉNEZ-BROBEIL

Departamento de Ciencias Morfológicas

Laboratorio de Antropología Física

Universidad de Granada

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ABSTRACT

The study of dental morphological traits in prehistoric populations is a new method

of analysis and allows us to determine important characteristics of different human popu-

lations. In this paper we study the dental feature traits proposed by the ASU System

(developed by Turner et al. in Arizona State University) by means of an alphanumeric and

graphic database recording the dental morphological characteristics and the possible dental

diseases (caries, dental wear, etc.). These traits are easily and reliable observed, and per-

sist many years in dentally harsh life styles, evolving very slowly or without sex dimor-

phism.The multivariate data set obtained using the ASU System is defined by means of

multistate qualitative variables, and the methodology of statistical analysis is the following:

– The MMD test (Mean Measures of Divergence) was developed by SJOVOLD (1977) to

observe the differences between two or more previously established and defined groups

by means of multistate qualitative variables. It is also possible to test if existing differ-

ences among populations are ethnic, cultural, etc.

– A Cluster Analysis algorithm developed by one of the authors (ESQUIVEL 1988) that

enables us to build a grouping using qualitative multistate variables by means of specific

developments in Information Theory established by Claude Shannon. Therefore, it is

possible to determine the similarities of dental morphological traits between human

groups, and compare these results with other previous information from archaeological

data.

This methodology has been applied to analyze human genetic diversity using ex-

clusively dental morphological characteristics to determine the diffusion of the culture of

the Argar, a prehistoric culture which existed in 1300-1500 B.C. The analysis has been

applied to the teeth of 116 subjects belonging to the Argaric culture in the neighbouring

settlements of Castellón Alto and Fuente Amarga (Granada, Spain), and the teeth of 58

subjects belonging to the non-Argaric settlement of La Navilla, also 1300-1500 B.C.,

about 150 Kms. distant. The results show a biological continuity, endogamy phenomena

and genetic drifts. Finally, the study of the maxillar pathology like cavities and dental

wear tells us about dental health, food and food preparation.

J.A. Esquivel, I. al Oumaoui, S. Jiménez-Brobeil

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