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presented by

Valerie N. Yavornitzky

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A TEST OF NON-METRIC ANCESTRY DETERMINATION IN FORENSIC
ANTHROPOLOGY: SHOULD THE CURRENT CATEGORIZATION OF
INDIVIDUALS OF EUROPEAN DESCENT BE RECONSIDERED?

By

Valerie Nicole Yavomitzky

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF ARTS
Department of Anthropology

2002

ABSTRACT
A TEST OF NON-METRIC ANCESTRY DETERMINATION IN FORENSIC
ANTHROPOLOGY: SHOULD THE CURRENT CATEGORIZATION OF
INDIVIDUALS OF EUROPEAN DESCENT BE RECONSIDERED?
By

Valerie N. Yavomitzky

In the field of forensic anthropology, analyses of non—metric skeletal traits play a
vital role in the determination of ancestry from the human skull. However, there remains
a need for more data and research on the heritability of specific non-metric skeletal traits
and their distribution in worldwide populations to fully support their use in the
determination of ancestry or “race“ from human skeletal remains. Current ancestral
categories used by forensic anthropologists to define people are too broad and are
primarily limited to European, African, and Asian. In this study, I test a portion of the
trait list for the “American Caucasoid” category devised by Rhine in his widely-used
study on the ancestral classification of non-metric traits, “Non-Metric Skull Racing”
(Rhine, 1990), on a series of crania from the Miitter Museum of the College of Physicians
of Philadelphia. This study aims to statistically evaluate whether the broad category of
European ancestry can be narrowed so as to account for the variation present in European
subpopulations. It is my goal to propose a more geographically specific classification

system for determining ancestry in forensic anthropology from non-metric skeletal traits.

TABLE OF CONTENTS

 

 

 

 

 

List of Tables _ .......... - - - .................. - v
Appendices Table Key.------ .......... - - ....... - vi
Chapter 1: Statement of Problem ............ -- - - .....1
Introduction ................................................................................................................ 1
Statement of Problem ................................................................................................ 2
Anticipated Results and Contributions to Anthropology ........................................... 7
Chapter 2: The Use and Value of Non-Metric Skeletal Variants ........................ 10
The Definition of Non-metric Skeletal Variants ........................................................ 10
The Genetic Basis of Non-metric Skeletal Variants ................................................... 13
Non-metric Skeletal Variants and Biodistance Analysis in

Physical Anthropology ................................................................................... 18
Limitations to Non-metric Trait Studies .................................................................... 19
Current Direction of Non-metric Studies in Physical Anthropology ......................... 23
Chapter 3: Materials - - .................. - - - - ...... 25
The Hyrtl Skull Collection ......................................................................................... 25
Political Geography of 19th Century East Central Europe ........................................ 27
Ethnolinguistic Relationships of 19th Century East Central Europe ......................... 28
Chapter 4: Methods ................................................................................................. 30
Data Collection Methods ........................................................................................... 30
Statistical Treatment of Entire Dataset ...................................................................... 34
Expected Results for Statistical Treatment of Entire Dataset .................................... 35
Statistical Treatment of Geographic and Ethnolinguistic Subgroups ........................ 36
Expected Results of Statistical Treatment of Geographic and

Ethnolinguistic Subgroups ............................................................................. 38
Chapter 5: Results of Statistical Tests ..... - 39
Results of Chi-Square Analyses for Entire Dataset ................................................... 39
Results of Chi-Square Analyses for the Geographic Subgroups ............................... 41
Results of Chi-Square Analyses for the Ethnolinguistic Subgroups ......................... 41
Results of Mean Measure of Divergence Tests ......................................................... 41
Summary of Hypotheses Testing ............................................................................... 43
Chapter 6: Discussion..-.-.---......- - - ...... -- -- ..-45

 

iii

CONTINUED

 

Chapter 7: Conclusions- - - - - - - -- 49
Summary .................................................................................................................... 49
Future Research ......................................................................................................... 50
Appendix A: Original Data Collection Sheet ........................................................... 53
Appendix B: Chi-Square Results for Entire Dataset ................................................ 55
Appendix C: Chi-Square Results for Geographic Subgroups .................................. 58
Appendix D: Chi-Square Results for Ethnolinguistic Subgroups ............................ 63
Appendix E: Hierarchical Cluster Analyses of MMD Values ................................. 76
Literature Cited -- - - ....... - - ............. . 79

 

LIST OF TABLES

 

 

 

 

 

 

Table 1: Regional Groups of Europe .......................................................................... 28
Table 2: Ethnolinguistic Groups of Europe ................................................................ 29
Table 3: Classification System for Nasal Index ......................................................... 34
Table 4: Rhine’s (1990) Maxwell Collection Sample ................................................ 39
Table 5: Mean Measure of Divergence Values Geographic Groups .......................... 41
Table 6: Mean Measure of Divergence Values Ethnolinguistic Groups .................... 42
Table 7: Expected Traits per Ethnic Group from Rhine (1990:Table 3) .................... 53
Table 8: Chi-Square Analyses for the Entire Miitter Museum Sample as

Compared with Rhine’s (1990) Expected Values .............................. 54
Table 9: Chi-Square Analyses for the Geographic Groups Comparison for

Austro-Hunggy .................................................................................. 55
Table 10: Chi-Square Analyses for the Geographic Groups Comparison for

The Balkans ........................................................................................ 56
Table 11: Chi-Square Analyses for the Geographic Groups Comparison for

The Baltics .......................................................................................... 57
Table 12: Chi-Square Analyses for the Geographic Groups Comparison for

The Italian Peninsula .......................................................................... 58
Table 13: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

East Slavic Grog) ............................................................................... 59
Table 14: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

West Slavic Grog; .............................................................................. 61
Table 15: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

South Slavic Group ............................................................................. 63
Table 16: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

Finno-Ugric Group ............................................................................. 65
Table 17: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

Romance Group .................................................................................. 67
Table 18: Chi-Square Analyses for the Ethnolinguistic Groups Comparison for

German Group .................................................................................... 69

 

APPENDICES TABLE KEY

 

 

 

 

 

 

 

Symbol Meaning

* Statistical results are significantly different than expected outcome at the 95%
confidence level.

+ Indicates that Miitter sample data is statistically different than Rhine’s (1990)
dataset, however results in table correlate with what Rhine (1990:Table 3)
lists as expected per ethnic group.

F Fisher’s Exact (2-Sided) Test of Significance Replaces Chi-Square Test

V Violates Chi-Square assumption of expected count of at least five; results

remain significant

 

vi

 

Chapter 1
STATEMENT OF PROBLEM

Introduction

“’Races’ have no more or less reality than ‘chairs’ since both are human
informational constructs which will linguistically and conceptually persist only as long as
they serve the purposes of their users” (Kelso, 1970294). Despite the acknowledgement by
most physical anthropologists that typological races are defunct as a biological concept,
forensic anthropologists contend that socially-defined races may still be of practical use to
the field (Gill & Rhine, 1990; Sauer, 1992). Forensic anthropologists continue to assign
race or ancestry based on specific characteristics of the human skeleton to unidentified
skeletal remains. While this practice seemingly contradicts the idea that discrete
biological races do not exist, forensic anthropologists insist that the determination of race
or ancestry from skeletal remains for identification purposes is both useful and necessary
(Gill, 1998; Brace, 1995; Kennedy, 1995; Sauer, 1992; Gill, 1986). Because society
continues to group people into racial or ancestral categories in census data, polls, research
studies, and medico—legal documents, the social concept of race persists in modern
societies (Gill, 1998; Brace, 1995; Kennedy, 1995; Sauer, 1992; Gill, 1986). Although
forensic anthropologists may be perpetuating “a myth that human races are natural
entities within our species” by employing ancestry determination methods
(Kennedy,l995:798), forensic anthropologists use this social reality to advance their goal:
the identification of unknown human remains.

The job of the forensic anthropologist is to ascertain from human remains sex,

age-at-death, ancestry, stature, body form, pathology, antemortem and postmortem

trauma, occupational stresses, and any information that may help to understand the
manner of or time since death. The purpose of creating this biological profile is to aid in
the identification of an individual from his or her remains. Often the ability to identify an
individual hinges on the ability to communicate with law enforcement or the general
public. It is for this reason that racial or ancestral categories are used in the biological
description of human remains. Because American culture continues to identify and
categorize people by race as based on the social constructs of the concept, it is often
helpful for ancestry information to be provided in the biological description of an
unidentified individual, assuming that such information can be ascertained from the
remains. The more information forensic anthropologists can provide law enforcement
regarding the appearance of an individual during life, the better the attempt to identify
missing individuals will be.
Statement of Problem

While there is no genetic evidence to support the existence of various subspecies

in Homo sapiens, there is data to support that certain skeletal traits are particular to given

 

geographic regions (Brace, 1995; Sauer, 1992; Kennedy, 1995). However, what
delineates a geographic region and the skeletal traits specific to it have neither been
clearly defined nor exhaustively explored. Non-metric analyses (Gill and Rhine, 1990)
and metric analyses (Giles and Elliot, 1962; Howells, 1995) have been used to determine
ancestry from the human skeleton, but currently used methods need to be further
researched and the broadly defined categories used to classify ancestry with non-metric
traits — European, African and Asian — should be reevaluated (Brues, 1990; Rhine, 1990).

Because there is a significant amount of non-metric and metric human skeletal

variation just within the United States, a problem compounded by gene flow and
immigration, classifying individuals into the broadly defined ancestry categories used in
the US. — European, African, and Asian — has become impractical. Most people in the
US. and much of the rest of the world have more than one ancestral background, and
many of these people would classify themselves outside of these three major ancestral
categories. Further, the increasing involvement of forensic anthropologists abroad
necessitates an understanding of human variation in other regions of the world (Worden,
1995; Simmons and Lagrou, 2001). Individuals within the United States that would be
classified as “European” may, and most likely do, look different than people living in
Europe. A better understanding of how human skeletal variation should be classified to
best support the goal of forensic anthropology is necessary and justified (Ousley and
Billeck, 2001; Kennedy, 1995).

The largest publication to report on the use of non-metric traits in the forensic
identification of race, “Skeletal Attribution of Race” (Gill & Rhine, 1990) documents
several studies that examine the validity of non-metric methods. These studies include a
discussion of the distribution of Shovel-shaped incisors and of alveolar prognathism, the
use of the visibility of the oval window through the external auditory meatus, the
inversion of the lower border of the mandible, and of a small study examining forty-five
non-metric traits across four ancestral groups (Hinkes, 1990; Brooks et. al., 1990; Napoli
& Birkby, 1990; Angel & Kelley, 1990; Rhine, 1990). Of these studies, the latter, “Non-
metric skull racing”, provides a list of non-metric traits currently used in forensic
anthropology laboratories to aid in the determination of ancestry as well as the data used

in the original study (Rhine, 1990). Although Rhine’s (1990) study “Non-metric skull

skull racing” provides a fairly exhaustive list of non-metric traits considered by the
Mountain, Desert, and Coastal Forensic Anthropologists membership to be of the most
value in the determination of “race” from the human skull, the small sample size (Total
N=87), the use of casts to represent approximately 1/3 of the “Black” sample, and the use
of prehistoric Native Americans used in the study brings into question the accuracy of the
study’s conclusions (Rhine, 1990:10-12). While previously published studies have
discussed the use of non-metric skeletal traits in the evaluation of “race” or ancestry, none
take a sampling of several ancestral groups from within the United States and compare
the frequency of forty-five trait occurrences across the groups as does Rhine (1990)
(Hooton, 1920). Additionally, Rhine’s (1990) work aims to examine the validity of the
method not to determine biological distance between groups or the heritability of traits as
do many other anthropological studies using non-metric traits as part of their analyses
(Ullinger, 2002; Praymak et. al., 2001; Lane, 1978; Saunders & Popovich, 1978;
Ossenberg, 1977). For these reasons, I have chosen to compare my data to Rhine’s (1990)
study and further the examination of the methodology used to determine ancestry from
human skeletal remains.

I intend to re-examine the ancestry category “Eur0pean” using non-metric data
collected on 122 male skulls from the Hyrtl Skull Collection, a collection of 139 skulls
primarily from Central and Eastern Europe located at the Mtitter Museum in Philadelphia,
Pennsylvania. This study will compare the frequencies of nineteen non-metric traits
evaluated from the Hyrtl sample to Rhine’s (1990) “Anglo” sample (n=53), “Hispanic”
sample (n=15), “Indian” sample (n=12), and “Black” sample (n=7) from the Maxwell

Museum’s Skeletal Collection (Rhine, 1990:9). The non-metric traits used to evaluate the

from “Non-Metric Skull Racing” (Rhine, 1990210). Specific traits used in the Hyrtl skull
study were selected for their case to record, their tendency to be less subjective (reduction
of inter-observer error), and their previously determined value in evaluating ancestry from
the human skull.
Considering this, three questions are proposed of the collected data. From these

questions, hypotheses to be tested are set forth.
Question 1: Are the current practices for the determination of “European” ancestry in
forensic anthropology accurate? Does the Hyrtl skull data support the expected trait
frequencies for “Caucasoids” in Rhine (1990) study?
The following two hypotheses will allow me to test the validity of Rhine’s “European“
ancestry category:

Null Hypothesis 1 - European Definition Hypothesis: The

non-metric data collected will demonstrate there to be no

statistically significant difference in the distribution of

discrete trait frequencies between the Hyrtl Skull Sample

and Rhine’s (1990) “Anglo” sample from the Maxwell

Museum Collection.

Null Hypothesis 2 - European Distinction Hypothesis:

The non-metric data collected will demonstrate there to

be no statistically significant differences in the

distribution of discrete trait frequencies among the Hyrtl

Skull Sample and any of Rhine’s (1990) three non-

European samples from the Maxwell Museum Collection.
If the European Definition Hypothesis is rejected then a successful argument can be made
for changing the current description of Europeans in the non-metric cranial trait literature.
If the European Distinction Hypothesis is rejected then an argument can be made for

maintaining the current non-metric cranial trait distinction among European people and

people of a different ancestral lineage.

people and people of a different ancestral lineage.
Question 2: Does the data indicate that certain traits can be distinguished among regional
or ethnolinguistic groups located within Europe? Can bivariate statistical tests
demonstrate statistical differences among specific traits among such groups? The Hyrtl
collection will be sorted in two fashions: (1) geographically by region according to the
Federation of East European Family History Societies (2001) electronic maps; and (2)
ethnolinguistically according to the “Historical Atlas of East Central Europe” (1993).

Null Hypothesis 3 - European Trait Consistency

Hypothesis: Traits in the dataset will not be statistically

different among the created subgroups.
If the European Trait Consistency Hypothesis is rejected, this will indicate that certain
traits are better at differentiating the subgroups from one another and are of more value in
the forensic determination of ancestry from the human skull in this collection. Further, the
statistical results will Show which method of grouping the subjects is most reliable. This
will explicate which of the two grouping methods - geographic or ethnolinguistic -
provides a more direct correlation with distinguishing skeletal features.
Question 3: The third objective of this study is to discover whether ancestry can be more
specifically determined from non-metric traits of the human skull. Does the data support
the narrowing of the currently used ancestry classification system?
Considering this, the following null hypothesis is proposed:

Null Hypothesis 4 - European Regions Hypothesis: The

means of the data will not be statistically different among

regional/ethnolinguistic groups within the Hyrtl sample,

supporting the notion that there is no statistically

significant variation in the distribution of non-metric or
metric traits among people with European ancestry.

The examination of this intrapopulational variation is to determine if ancestry
categories can be made more geographically specific, because the current classification
system - European, African and Asian - is limited in its use and oftentimes inaccurate. If
it can be demonstrated that certain non-metric traits, currently used to predict European
ancestry, have statistically different means among regional groups of Europeans, then
Null Hypothesis 4 will be rejected.

While this dataset does not incorporate individuals from the whole of Europe,
rather only the Central/Eastem European region, it does provide an opportunity to
examine differences existing among some European peoples. While the multivariate
statistics may not demonstrate as large of differences among the subgroups as they might
if more individuals within the dataset originated in other parts of Europe, this dataset does
have documented place of birth, which makes this Skeletal collection a rarity and of great
value to physical anthropological research. It is anticipated that even' within this small
European region, statistically significant differences will exist for specific traits among
subgroups.

Anticipated Results and Contribution to Anthropology

Previous studies on non-metric craniofacial and dental morphology have failed to
determine where the geographic lines of distinction lie for craniofacial and dental
morphology. This is to say that no study has demonstrated craniofacial and dental traits to
be solely specific to the area encompassing all of Europe and the Middle East. These
geographicilines have been drawn to a certain degree arbitrarily, and it cannot be said that
once these geographic boundaries have been crossed, morphology is distinctly different.

Despite this, it has been proposed that individuals who appear to be “White” or generally

of European descent tend to share suites of non-metric traits. If this is true, it is possible
that the morphological variation of the skull will demonstrate suites of non-metric
features to be specific to more narrowly defined geographic regions.

There has been no known test of Rhine’s (1990) study that has examined the
validity of his list for expected non-metric traits associated with “American Caucasoids”
or individuals with European ancestry. Because Rhine’s (1990) number of individuals for
“Anglos” is only 53, less than half the size of the Hyrtl sample, it is possible that further
research, including this study, will demonstrate there to be a different correlation between
European ancestry and the given expected traits listed in Rhine (Rhine,1990:9-1 1).

In addition to such data-restricted reasons for the null hypotheses rejections, a few
anthropologists have alluded to the fact that more geographically specific criteria for the
determination of ancestry would be useful and is already being practiced. Kennedy (1995)
notes that he is fairly confident of assigning ancestry based on the non-metric variation in
individuals from the Indian subcontinent and Sri Lanka according to geographic sectors
due to his extensive paleodemographic work in the area for many years. Further, he states
that he would feel less confident doing the same with skeletons from another area of the
world (Kennedy, 19952799). Howells’ (1995) metric analyses of crania have divided
Europe into four ancestral categories: Norse, Zalavar, Berg, and Egypt. He has further
divided other major geographic areas of the world into twenty-four distinct ancestral
categories based on cranial metric variation (Howells, 1995:4-5). This study supports the
view that geographically specific regional variation does exist for Homo sapiens, and that
forensic anthropologists are justified in using more discriminating ancestry categories in

the determination of ancestry from the human skull.

anthropology. While biological race has long been rejected as a valid concept by most
physical anthropologists, the idea that variation in human morphology can tell
anthropologists about past peoples is still vital. In forensic anthropology, applied
methodology uses human variation to help graph the appearance of an individual during
life from the morphology of their skeletal remains. The forensic application of non-metric
methods for the determination of ancestry has not been adequately researched or
discussed in the published literature. Rhine’s (1990) study, “Non-Metric Skull Racing”,
only looks at a sample of 87 individuals and is one of the largest studies to date to
examine a cross section of non-metric traits used in forensic anthropology (Rhine,
l990:9; Gill & Rhine, 1990:9-46). Research in the area of non-metric ancestry
determination will strengthen forensic anthropologists’ understanding of craniofacial and
dental morphological variation as well as better their ability to identify individuals from
human remains. Further research is necessary to assess the validity of the methods being
used in forensic anthropology since human variation can best be understood by looking at
larger samples and through the conducting of more studies. My study will aim to not only
look at Rhine’s classification of “Europeans” but also to assess whether the methodology
being used is applicable to the Hyrtl Skull Sample. If Rhine‘s (1990) conclusions seem to
be sample specific and not indicative of racial or ancestral features, forensic
anthropologists should consider using methods other than non-metric traits in the

determination of ancestry.

Chapter 2

THE USE AND VALUE OF NON-METRIC SKELETAL VARIANTS
The Definition of Non-metric Skeletal Variants

In the 19305 Hooton remarked, “morphological features which can be observed
and described but cannot be measured are probably of greater anthropological
significance than diameters and indices” (Brues, 1990:2). Over seventy years have passed
since Hooton remarked on the importance of non-metric skeletal traits to physical
anthropology, and non-metric analyses continue to play a vital role in the field today
(Duray et al. 1999, Gill and Rhine 1990, Gill 1998 Hunley & Cabana 2001,Villmoare
2001 , Durband et. a1. 2002, Steininger 2002). Non-metric skeletal variants have been
used in physical anthropology particularly to determine biological distance among
populations of people (Ossenberg 1977, Molto 1979, Buikstra & Frankenberg 1990),
ethnic or “racial” affinity (Rhine 1990, Gill and Rhine 1990, Gill 1998, Duray et. al.
1999), sex (Phenice 1969), age (Suchey et. al. 1986, Lovejoy et. a1 1985), as well as
identify markers of stress on the skeleton (Gruneberg 1952, Ossenberg, 1970). The value
of non-metric traits in the determination of ancestral affinity and biological distance
assessment has been argued as being greater than that of metric variables (Hooton 1930,
Molto 1979, Brues 1990, Gill 1998). By discussing how non-metric skeletal variants
have been used in science and their genetic basis, a better understanding of their value
and practical use in physical anthropology can be gained.

Non-metric traits are discrete or most often quasi-continuous skeletal
manifestations that cannot be measured and thus are scored. In the literature such traits

have also been termed discrete traits, discreta, discontinuous traits, epigenetic traits,

10

minor variants, and quasicontinuous traits (Buikstra & Ubelaker 1994: 85). Scoring for
such traits may be on the presence/absence scale, however trends in the field of physical
anthropology are leaning towards graded scoring forms that account for multiple types of
a manifestation (e. g. nasal form, nasal aperture) or for the degree of variance or number
of skeletal manifestations (e. g. base angle, nasal spine length; supraorbital foramen, extra
ossicles, respectively) (Rhine 1990, Buikstra & Ubelaker 1994). This trend supports the
idea purported by Gri'tneberg (1952) that non-metric skeletal variants are not discrete
entities and that recording forms need to consider the quasi-continuous nature of such
traits if possible.

Three major classes of quasi-continuous non-metric skeletal variants have been
identified: foraminal, fusion, and hypostotic/hyperstotic. The first, foraminal, include the
degree of closure and number of foramina present at a particular bony location. Buikstra
and Ubelaker (1994) outline in Standards a number of varying human skeletal foramina
of “primary importance“ (Buikstra and Ubelaker, 1994: 87-93): suprascapular foramen,
stemal foramen, jugular foramen, ethmoidal foramina, infraorbital foramina, foramen
ovale, spinosum foramen, mental foramen, zygomatico-facial foramina, supratrochlear
foramen, accessory lesser palatine foramen, accessory transverse foramina in C3-C7
vertebrae, parietal foramen, and the mastoid foramen.

The second class, fusion, is the degree to which two bones have fiised at the
sutural line. This class includes differential fusion of bones (e. g. metopic trace) but also
deficiencies in bone formation (e.g. septal aperture of the humerus). The third class of
traits, hypostotic/hyperstotic, refers to the underdevelopment or failure of ossification

(hypostosis), or the over-development or abnormal formation of bone (hyperostosis).

11

This latter class is less specific in nature and generally includes the presence or absence
of extra cranial ossicles. Buikstra & Ubelaker (1994) list the more widely identified extra
cranial ossicles: epipteric bone, coronal ossicle, bregmatic bone, sagittal ossicle, apical
bone, lambdoid ossicle, asteronic bone, ossicle in the occipito-mastoid suture, and
parietal notch bone.

Scored traits that were previously mentioned, such as nasal form and nasal spine
length, could fall within the hypostotic/hyperstotic class of non-metric traits, or
potentially an additional fourth class of traits is widely being used in physical
anthropology today. These other traits include differences in the manifestation of bony
structures, such as the form that nasal bones assume or the shape of the bony nasal bridge.
Rhine (1990) examines thirteen scored traits including: the length of the base chord, the
base angle, bony orbit shape, nasal opening size, nasal bone formation, nasal spine length,
nasal sill form, dental arcade shape, shape of chin, profile of chin in F rankfitrt plane, the
formation of the lower border of the mandible, and the thickness and angle of the
ascending rarnus of the mandible.

In addition to the non-metric skeletal traits involving bone, dental non-metric
traits are also widely used in the determination of ancestry from the skeleton and in
biodistance studies (Rhine 1990, Berry 1978). Dentition is particularly useful for
biodistance studies and determining ethnic affinity because they preserve better than bone
in the archaeological record. Ansorge also notes, “characters of the teeth are very
promising, as they remain very stable during late ontogenesis” (Ansorge, 2001 :3).

Since Hooton devised the first fairly comprehensive list of human non-metric

skeletal variants in the 19303, the types of non-metric traits being recorded and their

12

determined genetic basis has changed throughout the decades. Studies have found that
hypostotic/hyperstotic traits have a larger genetic component than forarninal and fusion
traits (Cheverud and Buikstra 1981, 1982; Richtsmeier and McGrath 1986). Considering
this, recent studies in ancestry determination from the skeleton and in biological distance
have moved to more varied non-metric trait lists (Rhine 1990, Buikstra & Ubelaker
1994). While the genetic basis for non-metric traits continues to be an issue of contention
in the sciences, heritability for certain traits has been successfully demonstrated through
experimentation.
The Genetic Basis of Non-metric Skeletal Variants

Non-metric cranial and dental variation in animals became a prominent subject of
research in the 19503 when researchers began to examine the genetic inheritance of non-
metric traits (Griffin MC, 1989:Chapter 3). A consensus exists among scientists that non-
metric traits are “epigenetic polymorphisms” and therefore are affected by both
enviromnental and genetic influences (Griffin MC, 1989:Chapter 3). Debate continues on
the degree to which non-metric skeletal traits are affected by environmental factors.
Several early studies on mice argue for a strong genetic component to non-metric traits
(Gruneberg 1952, Berry 196). While several considerations such as the influence of age
and sex on the manifestation of traits are primarily left out of the early studies, these
studies still successfully demonstrate patterns of inheritance in non-metric traits and
identify evolutionary forces affecting their prevalence in certain populations. Three
primary animal studies are relevant to research on human non-metric skeletal variation

Gritneberg (1952) carried out one of the earliest studies on the inheritance of non-

metric skeletal variants entitled, “Genetical Studies on the Skeleton of the Mouse”. For

13

this study, Griineberg crossbred two different strains of laboratory mice to observe the
genetic inheritance of behavior and vertebral and pelvic skeletal variation. He concluded
that non-metric variation in the skeleton is inherited multifactorally and is affected by the
environment. After this experiment, Griineberg introduced the principle of “quasi-
continuity” which states that the inheritance of a discontinuous trait is due to the
segregation of multiple genes in conjunction with the “threshold of manifestation”
(Griffin, 1993: Chapter 3). This means that skeletal traits of a quasi-continous nature
must be influenced by a certain number of genes in order to be expressed; it takes so
many genetic factors to create the threshold at which a certain trait will manifest. The
principle of quasi-continuity is particularly relevant to the method a researcher uses in
scoring non-metric traits: recording techniques should record not only presence/absence
but also numbers and degrees because the latter are under genetic control as well.

Berry’s (1963) study, “Epigenetic Polymorphism in Wild Populations of Mus
musculus”, is perhaps the most notable and widely cited study to examine whether or not
non-metric skeletal variants are under genetic control. His objective was to find more
reliable and easier means for identifying small wild animals in a population without
having to mark animals and track home ranges. For this, he set out to test whether
populations of house mice sharing a common gene pool in nature possess a suite of non-
metric skeletal traits. Approximately 50 house mice skeletons were taken from various
parts of the world, including ten different localities in the United Kingdom. However, the

bulk of the study and the basis of Berry’s (1963) conclusions derives from the

14

examination of fifteen different populations of house mice living on separate corn r'icksl
within a large farm in Hampshire, England. For each skeleton, Berry (1963) recorded 35
cranial and postcranial non-metric skeletal traits, including sites of bony fusion, the
presence/absence of foramina, size and closure of foramina, bony fusion of vertebral
elements, and presence of all vertebrae. Chi—square analyses were performed for each trait
among rick populations, and the CAB. Smith Mean Measure of Divergence tests were
performed among the whole data for each of the populations.

By focusing on the corn rick populations at the Hampshire farm, Berry was able to
identify the sources of variation, because all fifteen rick populations originated from one
main population of house mice. At the outset, Berry (1963) identified four ecological
variables that could be contributing to the variation found in the mice skeletons: type of
food mice were eating, geographic location, the number of animals invading each rick,
and the selection for resistance to the poison Warfarin. Age and sex were not considered
as contributing variables to variation in the mice skeletons. Based on his observation of
the distribution of skeletal variation and statistical analyses, Berry (1963) concluded that
there was a small diet effect on the group, but that the main source of variability among
the ricks was geographic in nature (Berry, 1963). His data suggest that genetic drift
occurred as the original population split and divided up among the ricks to form new
populations. Furthermore, stabilizing selection was occurring in reaction to the
introduction of the poison Warfarin into the rick populations. Berry’s (1963) study was

the first to demonstrate heritability patterns for non-metric skeletal markers.

 

’ Ricks are stacks of hay or straw which have been covered and blocked off for protection from the weather.

15

A third study on mice carried out by Richtsmeier and McGrath (1986) examined
the effects of differential fostering among mice to see how the postnatal environment
affects the expression of non-metric traits. The results of this study found
hypostotic/hyperstotic traits to be significantly more heritable than other classes of non-
metric traits (fusional and foraminal) that are impacted by the development patterns of
other tissues. For instance, the number and distribution of foramina is dependent upon the
number and distribution of blood vessels and nerves. Therefore, forarrrina are less of an
indicator of genetic relationships and more an indicator of development patterns. Such
observations led Richtsmeier and McGrath (1986) to introduce the Functional Matrix
Hypothesis, which states as the number of developmental resources increases, the
heritability of a trait decreases. This means that if traits are arising during the
development of the bone or other structures interacting with the bone during human
growth, the likelihood that the trait has a genetic basis is lessened. F oramina are a good
example of traits influenced by developmental processes because their number or
placement is directly related to those structures developing through them (i.e. arteries,
nerves). Further, Richtsmeier and McGrath (1986) are among the many researchers to
elucidate the effect non-genetic factors have on the manifestation of non—metric skeletal
traits.

In addition to the many genetic studies on non-metric traits in non-primates,
several more recent studies have examined skeletal variation in non-human primates for
the purpose of calculating population biodistance (Berry and Berry 1971, Cheverud and
Buikstra 1981, 1982). Berry and Berry (1971) examined the distance among subspecies of

several primate species, including Gorilla gorilla, Hylobates lar, Pan troglodytes, and

16

Pongo pymaeus. As Berry (1963) found with the geographic separation of the house mice
in England, systematic skeletal variation existed among the subspecies of each primate
species on the basis of geographic separation and thus reproductive isolation. With this
study, Berry and Berry further asserted that a strong genetic basis for non-metric traits in
the skeleton does exist.

Cheverud and Buikstra (1979, 1981, 1982) supported the use of non-metric traits
as genetic markers in their study of Rhesus macaques. Using 297 individuals of both
sexes and a range of ages, Cheverud and Buikstra calculated heritability values from
correlation statistics for 24 non-metric variables. Their results demonstrated that
hypostotic and hyperstotic traits have higher heritability variables than foramina] traits.
This conclusion prompted the Richtsmeier and McGrath (1986) study on the heritability
of traits in mice that led to the Functional Matrix Hypothesis. Again, the differential
manifestations of those traits that are closely related to developmental processes involved
in human growth are less likely to be genetically linked.

The previously discussed studies successfully create a genetic link with non-
metric skeletal variants in animals. While environmental influences affect the expression
of some non-metric traits, genes more heavily influence hypostotic/hyperstotic traits.
Despite continued disagreement over the extent to which environmental factors play a
role in the expression of non-metric traits, anthropologists have widely accepted that a
genetic component to many non-metric traits exists (Larsen 1999). Some traits have been
shown to have less heritability than others, however the heritability of non-metric skeletal
variants matters less than the randomness of environmental influence over such traits

(Larsen 1999:304). The large number of biological distance studies in the field that utilize

l7

non-metric trait analyses evinces their value in the evaluation of biological distance
(Bennett 1965, Ossenberg 1969, Korey 1970, Buikstra 1980, Birkby 1973, Blakely 1973,
Berry 1974, Corruccini 1974, Finnegan and Faust 1974, Saunders and Popovich 1978,
Finnegan 1978, Kennedy 1981, Hennenberg et. al. 2001, Ullinger & Sheridan 2001,
Praymak et. a1. 2001, Cucina et. al. 2002, Nystrom 2002, Ullinger 2002, Wrobel 2002).
Non-metric Skeletal Variants and Biodistance Analysis in Physical Anthropology

The history of recording non-metric traits from the human skeleton traces back to
Blumenbach in 1776 (Buikstra & Ubelaker, 1994: 85), but anthropologists did not begin
looking at their possible genetic basis until the late 19th century (Shepherd 1893;
Symmers 1895). The 1970s proved to be a period in physical anthropology when non-
metric variation in the human skeleton was given much attention (Corruccini 1974,
Finnegan 1974, Czarnetzki 1975, Finnegan 1978, Lane & Sublett 1978, Perizonius 1979,
Anonymous 1979, Cheverud et. al. 1979, Cosséddu et. al. 1979, Finnegan & McGuire
1979). However, during the 19803 and 19903, biological distance studies, which comprise
the majority of studies on non-metric traits, tapered off.

Biological distance analysis, “a measurement of population divergence based on
polygenic traits”, was the dominant type of skeletal research in the 19‘h century (Buikstra
et. al., 1990:1). Research in biological distance, according to Buikstra et. al. (1990),
declined after the 19703 and has remained a less popular area of study in recent times.
Investigations into the health and diet of past peoples have become the more prevalent
form of research in physical anthropology since the 19703, but researchers focusing on
biological distance assert it is a necessity for biodistance analyses to be incorporated into

paleopathology and paleoepidemiological research (Buikstra et. al., 1990).

18

Buikstra et. al. (1990) outline three motivations for continuing to examining
biodistance in past populations as a means of addressing anthropological questions. First,
biodistance can answer basic questions about evolutionary history. Population movement
and migratory behaviors, the effects of genetic drift and gene flow, and factors that
stabilize selection can be identified through the analysis of biodistance from the human
skeleton. Secondly, biodistance analysis can be incorporated into archaeological
research, assisting with defining regional groups and residence patterns. Further,
biodistance analysis should serve as the background for all paleopathology and
paleoepidemiological research. Because inquiries into past people’s health, diet and
morbidity/mortality rates are often used comparatively among populations, the biological
distance among such populations is relevant to the research.

Despite the decline in biological distance studies since the 19703, non-metric
skeletal research continues in physical anthropology today (Powell & Neves 1999, Stefan
1999, Hennenberg et. al. 2001, Praymak et. al. 2001, Steadman DF 2001, Ullinger &
Sheridan 2001, Corruccini & Shimada 2002, Cucina et. al. 2002, Nystrom 2002, Ullinger
2002, Wrobel 2002). Further, the focus on non-metric studies both in biological distance
analysis and in the sub-area of ethnic/“racial” affinity determination has had a resurgence
in the 19903 (Gill and Rhine 1990, Gill 1995, Gill 1998, Aubin 2002, Heffner 2002,
Ousley 2002). These studies have focused on troubleshooting methods employing non-
metric trait analyses: evaluating what traits are more reliable in determining biological
distance or ethnic affinity and examining the limitations to the methodology.
Limitations to Non-metric Trait Studies

While many factors that can sway the results of non-metric trait data were largely

l9

ignored in earlier (19303-19503) studies, much research has occurred on the limitations of
non-metric trait analysis in more recent years (Finnegan & Cooprider 1978, Molto 1979,
De Stefano et. al. 1984, Gualdi-Russo et. a1. 1999Ansorge 2001). These limitations
include subjectivity of scoring (interobserver/intraobserver error), sex and age related
differences in the human skeleton, asymmetry of non-metric traits, and inter-trait
correlation.

The subjectivity of non-metric trait analysis will always remain a complication of
this method. Interobserver error can oftentimes be linked to the experience of researchers
recording the traits, and can be avoided by having a sole researcher record all traits (De
Stefano et. al. 1984, Gualdi-Russo et. al. 1999). While this is not always possible, it is of
note that non-metric interobserver error is less systematic than the interobserver error
experienced with metric analyses, thus the impact interobserver error has on the
interpretation of results in non-metric studies is less significant (Molto 19792333). The
reason metric analyses are more prone to systematic interobserver error is because
researchers will tend to measure landmarks in a similar way throughout the measuring
process. If a researcher consistently measures in an incorrect manner, all measurements
will be off. Whereas with non-metric traits, interobserver error occurs more frequently
with only one or so traits that are misunderstood by the researcher. While interobserver
error is associated with all types of data collection, interobserver error in the recording of
non-metric traits is largely related to the exactness and clarity of non-metric trait
descriptions. It is therefore imperative that researchers meticulously describe their data
and methods in non-metric trait studies.

Intraobserver error is more difficult to avoid. While researchers can, if possible,

20

correct their own data through multiple analyses and discarding those traits with low
precision scoring, the issue of intraobserver error is oftentimes less obvious. Furthermore
even if the precision of scoring by one researcher is quite high, it is difficult to evaluate
whether one researcher would label a manifestation of a trait the same as another. For
instance, what one researcher would consider to be a “guttered nasal sill” another might
record as “blurred nasal sill”; while the intraobserver precision of scoring may be high,
the manner in which the trait is recorded may be different from one researcher to the next.
This again can only be ameliorated through detailed descriptions of traits. Despite the
possibility of intraobserver error, Molto suggests that intraobserver error is less
systematic in nature and therefore less affective in the analysis of non-metric datasets
(Molto 1979:342). Finnegan supports this claim and suggests that “interobserver error is
much more critical in the published literature and more difficult to test than intraobserver
error” (Finnegan 1978:393). This is a serious issue for non-metric studies, particularly for
those in forensic anthropology. Interobserver error could be swaying the results of non-
metric analyses of skeletal remains as many anthropologists attempt to employ methods
devised and perhaps only clearly understood by a few people.

One of the primary arguments against Berry’s (1963) study results, “Epigenetic
Polymorphism in Wild Populations of Mus musculus”, was his failure to analyze the
effects of sex and age on the sample. Finnegan and Cooprider (1978) note that several
researchers have reexamined Berry’s claims that sex and age do not affect non-metric trait
analyses. Such studies have found that certain traits are impacted by sexual dimorphism,
however age has largely no impact on non-metric analyses. Further studies have shown

that sex and age have a statistically insignificant impact on such analyses (Perizonius

21

1979; Cosseddu et. al. 1979). A small number of traits have typically been excluded from
non-metric analyses of ancestry or biological distance in humans because of their
tendency to be sex dependent (e.g. depression at nasion, gonial angle).

Asymmetry of non-metric traits and the method for scoring bilateral traits has
been addressed in the literature (Cosseddu et. al. 1979, Ansorge 2001). Asymmetry is a
result of environmental noise and is perhaps one of the developmental influences for
which Richtsmeier and McGrath’s (1986) model accounts. A recent look at asymmetry by
Ansorge (2001) finds fluctuating asymmetry to be the impact of the environment over
genetics (Ansorge 2001 :7). However because fluctuating asymmetry is random, many
researchers have dealt with this problem by being consistent in their methodology
(Cosseddu et al. 1979, Ansorge 2001): recording all traits as bilateral sets or selecting one
side to record consistently.

Another issue related to the heritability of traits and their use as independent
markers of biodistance is that of inter-trait dependency. If the expression of one trait were
contingent upon the expression of another, the use of both traits as genetic markers would
be problematic. Fortunately, researchers have found correlations among traits to be so
small that significance is irrelevant to the statistical analyses of non-metric trait data
(Benfer 1969, Finnegan & Cooprider 1978 ) “Consensus thus holds that few traits can be
shown to be statistically correlated; those that are can either be explained biologically, or
they show a very low degree of association” (Finnegan & Cooprider 1978: 3). It can
therefore be assumed that the genetic model holds true; independent genes result in
disassociated traits (Finnegan & Cooprider 197823).

Considering these possible limitations, researchers have moved in the direction of

22

identifying traits of heritable significance and providing better descriptive information on
non-metric traits.
Current Direction of Non-metric Studies in Physical Anthropology

A search of the 2001 and 2002 American Association of Physical Anthropologists
(AAPA) meeting presentations show continued research interest in non-metric skeletal
variation. Non-metric studies deal with the following issues: ontogenetic development
(Busby 2002, Hattman 2001, Lovej oy et. al. 2002), biodistance and settlement patterns
(Cucina et. al. 2002, Hennenberg et. al. 2001, Praymak et. al. 2001, Ullinger & Sheridan
2001, Nystrom, 2002, Ullinger, 2002, Wrobel, 2002;), paleoanthropology (Hunley &
Cabana 2001,Villmoare 2001, Durband et. al. 2002, Steininger 2002), and skeletal stress
markers (Imber & Aiello 2001). Other current research in non-metric trait analysis
focuses on the issue of race in forensic anthropology (Gill and Rhine 1990, Aubin 2002,
Heffner 2002).

In addition to their continued use in establishing biodistance in archaeological
populations, the subfield of forensic anthropology has heavily focused on the use of non-
metric traits in the determination of “race” or ethnic affinity from the skeleton. Recent
developments in this area include broadening the range of “racial” differences identifiable
through non-metric analyses and better descriptions of traits. The nasal region, inner ear
and palate are becoming the most reliable skeletal areas in determining ethnic affinity
from the skull (Gill, 1998). Gill (1998) reiterates Rhine’s statement that “an important
step for making non-metric approaches more useful to forensic anthropologists, and other
human osteologists, is the systematic testing of their utility in diagnosing racial affinity”

(Gill 1998:310). Many researchers have followed through with this important step (Duray

23

et. al. 1999, Aubin 2002, Heffner 2002).

In summary, non-metric traits are valuable tools for examining the lives and
relationships of past peoples. While environment does contribute to the expression of
non-metric traits, differential heritability exists among non-metric skeletal traits, and
random environmental factors do not heavily influence non-metric analyses in
determining biological distance.

Biodistance along with research on determining ethnic affinity of individuals from
the human skeleton continue to be of value and a research focus in physical anthropology.
Future research concerning non-metric human skeletal variants will further the field of
physical anthropology’s ability to examine a wide array of archaeological, evolutionary

and biological questions

24

Chapter 3
MATERIALS
The Hyrtl Skull Collection

The 139 skulls from the Joseph Hyrtl Collection, housed in the Mfitter Museum at
the College of Physicians of Philadelphia, were acquired by the renowned European
anatomist between 1848 and 1869 (Worden, 1995). During this period, Hyrtl served as
professor of anatomy at the University of Vienna, Austria and conducted comparative
anatomical studies at the J osephinum museum in Vienna. At the latter, Hyrtl collected a
variety of specimens including anatomical preparations, as well as fish and amphibian
skeletons, auditory ossicles from numerous species, and human skulls. Around the time
of Hyrtl’s retirement from the University of Vienna, his collections were dispersed, and
the Miitter Museum purchased the skulls from Hyrtl in 1873 (Worden, 1995).

The 139 skulls were collected primarily from Central and Eastern Europe and all
have identity cards, which have been translated into English from German, Latin or
Italian. Place of birth, name, age, religion, occupation, cause of death and donor are
recorded for most of the individuals in the collection. Some of this information was
originally inscribed on the skulls themselves; however, much of the ink inscription has
been lost to cleaning (Worden, 1995: 108). There are fourteen female and 125 male
individuals within the collection, and their ages range from eight to eighty years old with
the most prevalently listed causes of death being trauma or disease. This collection is
unique from many other collections in the United States today because a record has been
kept for place of birth; thus specific ancestry information can be ascertained from the

identity cards.

25

This uniqueness has made the Hyrtl Skull Collection of interest to those studying
how ancestry can be determined from the human skull, including Joseph Hyrtl himself
who maintained “individuals subsumed within [a] generic racial category did not
necessarily have identical characteristics in the same combinations” (Kemper, 1970:56).
The collection was shown to be anthropologically useful in the mid-19903 when two
anthropologists from the Smithsonian examined and measured several Croatian and
Serbian skulls in preparation for training Croatian anthropologists how to identify skeletal
remains being excavated from mass graves in the Balkans (Worden,1995:110). My
research continues to explore human skeletal variation within the Hyrtl Skull collection
so as to further understand the presence of intrapopulational variation in Europe.

Of the 139 skulls within the Hyrtl Skull Collection, 122 will be included in this
study. The total number of individuals has been reduced to include only adult males (to
rule out sexual dimorphism as a contributing factor), and then further reduced to exclude
a few individuals in the collection not of European or Middle Eastern origin. Because the
information on each individual was collected during the mid-18003, place of birth reflects
a political geography that precedes both the Soviet Union and Yugoslavia and is distinctly
different from modem-day political geography. Therefore many of the geographic
locations specified on the identification cards have been subsumed by other modem-day
nations. In an attempt to clarify origin, some individuals have been assigned, if possible, a
modern origin; however, within the dataset, the originally stated place of birth is
parenthesized next to the modern origin.

In addition to the data collected by the researcher, materials will also include data

from a previous study conducted by Stanley Rhine, Ph.D. on a sample of skulls from the

26

Maxwell Museum Collection (1990). His study examined 45 non-metric traits on an
“Anglo” sample (n=53) in addition to skulls of “Hispanic”, “Indian” and “Black”
ancestry. His total sample size is 87 individuals, 78 are contemporary and of known
identity (Rhine, 1990:9). While the Hyrtl sample is not American in origin as is the
Maxwell sample, individuals classified as “Anglo” would be classified as “European” in
the classification system for ancestry used in forensic anthropology today. Therefore, the
Hyrtl sample serves as a good comparison for the “Anglo” sample in Rhine’s (1990)
study.

Political Geography of 19th Century East Central Europe

The Hyrtl Collection is divided into geographic groups according to the
Federation of East European Family History Societies Map Index (2001). This on-line
map index (http://feeflrs.org/maps/indexmaphtml) provides an extensive collection of
19th century maps of Europe and accounts for all of the geographic places listed as places
of birth in the Hyrtl Skull collection. Table 1 shows the division of the data.

The Geographic Grouping excludes two individuals with an unknown place of
European birth, one individual listed as “Jewish”, and one individual from Finland.
Because the data does not divide into evenly distributed geographic groups, two of the
groups are discarded in the analyses of the Geographic groupings: Switzerland (Region 4)
and The Middle East (Region 6). Analyses of the crania in the Geographic groupings will

include comparisons among all of the remaining groups.

27

Table 1: Regina] Groups of Europe

 

 

Regional Group Name Geographic Areas Encompassed Number of
by Region Individuals in
Regional Group
(n=118)
Austro-Hungary —- Austria, Bohemia, Czechoslovakia 38
Region 1 Galicia, Hungary Moravia, Slovakia,

Stovenia, Trieste

 

The Balkans — Region 2 Albania, Bosnia-Herzegovina, 35
Bulgaria, Carpathian Mountains,
Crete, Croatia, Greece, Moldavia,
Romania, Serbia

 

 

 

 

 

The Baltics — Region 3 Belarus, Caucasus, Crimea, Poland, 17
Russia, Ukraine

Switzerland — Region 4 Switzerland 4

Italian Peninsula — Italy, Malta 19

Region 5

The Middle East — Region Turkey, Armenia, Kurdistan, 5

 

 

 

Regional Groups of Europe, Federation of East European Family History Societies (2001)
Ethnolinguistic Relationships of 19th Century East Central Europe
The data were also divided into seven ethnolinguistic groups based on The

Historical Atlas of East Central Europe (Sugar & Treadgold,1993). This atlas divides the
region of East Central Europe into most closely related groups of people as based on the
history of their languages and ethnic backgrounds. Table 2 shows the division of the
individuals according to ethnolinguistic background.

. All of the crania in the ethnolinguistic groupings with the exception of the “other”
group are compared against one another in the Chi-Square tests and Mean Measure of

Divergence Tests.

28

 

Table 2: Ethnolinguistic Groups of Europe

 

 

 

Ethnolinguistic Group Geographic Areas Encompassed Number of
Name by Group Individuals in
Ethnolinguistic
Group (n=122)
Germanic Group Austria, Switzerland 18
East Slavic Group Bohemia, Czechoslovakia Galicia, 18

Hungary Moravia, Slovakia,
Stovenia, Trieste

 

West Slavic Group Belarus, Caucasus, Crimea, 14
Poland, Russia, Ukraine

 

South Slavic Group Albania, Bosnia-Herzegovina, 17
Bulgaria, Carpathian Mountains,
Crete, Croatia, Moldavia, Serbia

 

 

 

 

F inno-Ugric Group Finland, Hungary 12
Romance Group Italy, Malta, Romania 27
Other Turkey, Armenia, Kurdistan, 16
Lebanon, Greece, Jewish,
Unknowns

 

 

 

Ethnolinguistic Grouping of Europe: The Historical Atlas of East Central Europe (Sugar
& Treadgold,1993)

29

 

Chapter 4
METHODS
Data Collection Methods

A total of nineteen non-metric traits will be used in this study. Eighteen of the
non-metric traits were evaluated from the Hyrtl sample (n=122) at the Mfitter Museum of
the College of Physicians of Philadelphia during the week of August 12, 2001-August 18,
2001. These eighteen traits appear in Rhine’s (1990) list of 45 non-metric traits (Rhine,
1990:Table 2). The original number of traits used in Rhine’s (1990) study was reduced to
eighteen to reflect those traits deemed to be of significance to the “American Caucasoid”
individuals in Rhine’s (1990) study. These eighteen traits were also chosen for their
predetermined value in the determination of ancestry from the human skull (Gill, 1998);
ease to collect and correctly evaluate; and tendency to be less subjective in nature. An
attempt to balance these three factors was made, and a few traits originally collected have
been discarded because the trait was perhaps better evaluated metrically (i.e. base chord,
see Rhine (1990:19)) or not included in the Rhine (1990) study (i.e. parietal foramen,
supraorbital foramen, and occipital shelf). Some of the latter traits, particularly the
presence or number of foramina, have been de—emphasized in recent studies of ancestry
determination from the human skull (Gill, 1986; Gill, 1998)

The following is a description of each non-metric trait used to evaluate the Hyrtl
sample and the manner in which the trait was evaluated for this research. These
descriptions are derived from Rhine’s (1990) list of traits (Rhine, 19902Appendix, 19-20).
An attempt was made to replicate Rhine’s (1990) method for evaluating the following

non-metric traits:

30

Metopism: Partial or complete metopism; an incomplete/complete persistence of
the metopic suture in the area immediately superior to nasion. Any presence of
metopism (complete/incomplete) is listed as present.

Wormian Bones: Extra ossicles formed by the complexities of the sutural course,
particularly in the lambdoidal and sagittal sutures. Any extra ossicles found along
the lambdoid suture is listed as present.

Canine F ossa: Depression in the maxillae at the root of the canine. Any degree of
depression is listed as present.

Inion Hook: An inferior projection of the external occipital protuberance. Any
degree of projection is listed as present.

Nasal Spine: Amount of outward projection of the nasal spine. Scored as being
long, medium or short.

Nasal Sill: Area where the vertical maxillae create a sharp ridge separating the
nasal cavity from the maxillae. If the ridge is high, it is scored as “deep”; if
shallow, it is scored as “shallow”; if a sharp ridge is lacking, it is scored as
“blurred”; and if a smooth curve leading into the nasal aperture without
interruption is present, it is scored as “guttered”. Scored according to criteria:
deep, shallow, blurred, or guttered.

Base Angle: This is the angle formed by the basion-opisthion plane compared to a
plane projected posteriorly from the palate. Scored as high or low by laying a

3 mm wide, 12-inch instrument through the described planes.

Depression at Nasion: Depression just inferior to nasion at the deepest point of

curvature of the nasal bones. Scored as depressed or flat.

31

Nasal Form: General form of the nasal bones as being high and steeply angled
(Steepled), wider and slightly concave (Tented), or low and smoothly rounded
(Quonset Hut). Scored according to criteria as one of the following tented,
steepled, or quonset hut.

Maxillary Prognathism: Slight alveolar projection of the maxilla. Scored as
present or absent.

Carabelli’s Trait: The bilateral presence of either a full or partial (trait)
supemumerary cusp on the first molars. Any presence of Carabelli’s trait
(cusp/trait) is listed as present.

Molar Crenulations: A complex wrinkling of the mandibular molar crowns.
Deviation of the mandibular molars from a classic Y-S pattern listed as present.
Incisor Shoveling: Slight lingual indentation of incisors. Scored as present or
absent.

Undulating Lower Border of Mandible: A deviation of the lower border of the
mandible upwards from a plane when mandible is placed on a flat surface. Scored
as undulating, slightly undulating or flat.

Ascending Ramus Angle: Angle of ascending ramus; either vertical with the
posterior border near 90° or slanted with an angle greater than 90°. Scored as
either straight or slanted.

Oval Window: Small oval opening located inside the ear canal and either visible
or not visible through the external auditory meatus. Scored as visible bilaterally or
not visible bilaterally.

Zygomaticomaxillary Suture: Suture joining the medial aspect of the zygomatic

32

bone with the lateral aspect of the maxilla is either S-shaped or angled. Scored as
curved or angled.

Venous Markings: Grooves present on the frontal bone seen slightly superiorly
to the temporal lines, superior to the orbits. Any number of venous markings on
the frontal bone listed as present.

For additional information on the methodology, Appendix A is an example of the
original data collection sheet.

The researcher and an assistant transported approximately twenty skulls at a time
from the display case to an examination room where each skull was scored/evaluated for
the given non-metric traits. This evaluation was blind as the skulls were not arranged in
the case by ancestry nor were the skulls themselves marked with ancestry information.

The nineteenth trait, nasal opening, was derived from the research of Jennifer
Eberly, a graduate student of Temple University in Philadelphia, PA. She collected metric
data from the Hyrtl Skull Collection on nasal height (nasion-nasospinale) and nasal
breadth (alare-alare) and from these measurements calculated a nasal index. The formula
for the nasal index is the dividend of nasal breadth and nasal height multiplied by 100
(Thomas, 1997:1269). These data were collected using sliding calipers that measure
distance in millimeters. These metric data have been converted into categorical data with
three classifications for the range of the nasal index and are now included in the non-
metric dataset. The three classifications represent the size of the nasal aperture and are

listed with their respective index parameters in Table 3.

33

Table 3: Classification System for Nasal Index2

 

 

 

 

 

 

 

Classifications for the Nasal Index Range Corresponding Classifications for
Nasal Index Range Nasal Opening from Rhine (1990)
Leptorrhiny <47.99 mm Narrow
Mesorrhiny 48.00 mm — 52.99 mm Medium
Platyrrhiny >53.00 mm Wide

 

The conversion of the quantitative data has been done to maintain consistency in
the analysis of the data and for comparative purposes since these data can now be
compared to Rhine’s (1990) frequencies for nasal opening size. Permission to use
Eberly’s data has been granted for this study by the researcher. Statistical analyses have
been applied to the data as a whole (one European sample) and to the data of
regional/ethnolinguistic groups present within the Hyrtl sample.

Statistical Treatment of Entire Dataset

One type of analysis was performed on the entire Hyrtl sample dataset: Chi-
Square statistics among the Hyrtl sample counts and the expected percentages listed in
Rhine (1990) for each of Rhine’s (1990) four subgroups. To test Null l-The European
Definition Hypothesis and Null 2-The European Distinction Hypothesis, a Chi-Square
statistic was computed for each of the nineteen traits four times using Rhine’s (1990)
frequencies for his four anceStry groups — “Anglo”, “Hispanic”, “Indian”, and “Black” -
as expected values (Rhine, 1990:Table 2). When p<0.05, the trait frequencies for each
will be considered statistically different between the Hyrtl sample and Rhine’s (1990)
“Anglo” sample, and Null l-The European Definition Hypothesis rejected for that trait. If

Chi-Square analyses computed among the Hyrtl sample and Rhine’s (1990) three non-

 

2 Nasal lndex definition from Moore-Jansen et. al. (1994)

34

 

European ancestral groups — “Hispanic”, “Indian”, and “Black” — are not significantly
different (p>0.05) for any given trait, then the Null 2-The European Distinction
Hypothesis will be accepted for that trait.

Table 7, Appendix B shows Rhine’s (1990) expected outcome for each trait among
Rhine’s subsamples (Anglo, Hispanic, Indian and Black) and the relationship among
these subsamples as they pertain to each given trait. Because each of the traits being
examined may not all be expected as “present” in a European sample, Table 7, Appendix
B is used in the determination of whether a given trait rejects Null l-The European
Definition Hypothesis.

If Null l-The European Definition Hypothesis is rejected for a given trait and Null
2-The European Distinction Hypothesis accepted, then the particular non-metric trait’s
significance in the determination of European ancestry should be devalued since the
variation seen among ancestral groups for that non-metric trait may be due to random
events and not geographically specific morphological variation.

Expected Results for Statistical Treatment of Entire Dataset

For those variables which are listed as expected for individuals classified as
“American White” by Rhine (1990), it is anticipated that presence for these variables will
be high in the Hyrtl sample. For those variables listed as being more prevalent among
other ancestral groups (i.e. Hispanic, Indian or Black by Rhine (1990)), a reverse trend is
expected.

Chi-Square analyses among the Hyrtl sample and those expected frequencies
listed by Rhine (1990) for his 4 subgroups - Anglo, Hispanic, Indian and Black —- should

reveal a pattern. If Rhine’s (1990) percentages for non-metric traits (Rhine, 1990:Table 2)

35

truly reflect expected trait frequencies in the populations, then Chi-Square analyses of
traits found to be “expected” in American Caucasoids should indicate no significant
statistical difference (P>0.05) between Rhine’s Anglo subgroup and the Hyrtl sample.
Further there should be statistically significant differences found among those traits
deemed exclusive to any particular ancestral group(s) other than Anglo (Hispanic, Indian
or Black). If any trait is expected for one particular ancestral group or pair of ancestral
groups, then Chi-Square analyses should show statistically significant differences
(P>0.05) among this/these groups and those ancestral groups for which the trait is not
expected. For example, the non-metric trait “metopism” is expected, according to Rhine
(1990), to appear in both “Anglos” and “Hispanics“, however not in “Indians” or
“Blacks” (Rhine, 1990116). Therefore, Chi-Square tests should show statistically
insignificant P-values among the Hyrtl Sample and Rhine’s “Anglo” and “Hispanic”
samples. Statistically significant P-values would be expected among the Hyrtl sample and
Rhine’s “Indian” and “Black” samples.
Statistical Treatment of Geographic and Ethnolinguistic Subgroups

Three types of analyses were performed on the geographic/ethnolinguistic
subgroups: descriptive statistics, bivariate statistics, and a multivariate analysis for each
grouping. For descriptive analysis, clustered bar charts of each non-metric variable were
created for the six main ethnolinguistic subgroups and the four main geographic
subgroups.

Two-way tables, Chi-Square Analyses, and where cells violated the Chi-Square
assumption of a minimum count of 5, Fisher’s Exact (2-Sided) tests were performed as

bivariate analyses. All of these analyses were performed on the Geographic Subgroups

36

and then the Ethnolinguistic Subgroups separately. Instead of performing an overall
comparison of all subgroups in a grouping for a given trait, more precise analyses were
performed. For each non—metric trait, a Chi—Square or Fisher’s Exact Test, where
appropriate, were computed to examine whether a statistically significant difference
exists between any one subgroup and each of the other subgroups. For example, in the
Geographic Grouping, a Chi-Square Table exists for “Austro-Hungary”. Chi-Square
results in this table show the Chi-Square Test Statistic, P-Value and degrees of freedom
for each trait as compared among Austro-Hungary and the three other subgroups
individually. This allows the researcher to examine between which two subgroups
statistically significant results appear.

For both the Geographic Grouping and the Ethnolinguistic Grouping, a F reeman-
Tukey transformation was performed on all of the variable counts to obtain means for the
datasets. Green and Suchey (1979) suggest that non-metric cranial data be transformed
according to the F reeman—Tukey transformation (Green and Suchey, 1979:67), which is
as follows:

New Value = V2 sin" (1-2k/(n+1)) + '/2 sin" (1-2(k+1)/(n+1))

This transformation stabilizes the variance to approximately 1/(n+ 1/2) and best reduces the
likelihood of falsely rejecting the null hypothesis (Green and Suchey, 1979:65). From
these means, the CAB. Smith’s Mean Measure of Divergence (MMD) was performed to
examine the distance among each of the subgroups. Geographic subgroups were run
against all of the other geographic subgroups, and Ethnolinguistic subgroups were run
against all other Ethnolinguistic subgroups. From the distance measures, a Hierarchical

Cluster Analysis, using Ward’s Method, was performed for both the Geographic

37

Grouping and the Ethnolinguistic Grouping.

Expected Results for Statistical Treatment of Geographic and Ethnolinguistic
Subgroups

For bivariate analyses, if all geographic subgroups should be classified under one
ancestral category, “European”, then there should be no statistically different results
among any of the subgroups either within the geographic or the ethnolinguistic groupings.
Particular note will be taken of how great the fluctuation in P-values is for the Chi-Square
statistics among the different geographic/ethnolinguistic subgroups. If particular traits do
have statistical differences between certain groups, it could be concluded that these traits
are of greater significance in the determination of ancestry than others.

For multivariate analyses, the distance between each of the subgroups in both
groupings are not expected to represent the true distance between these populations.
Because the MMD measure is sensitive to sample size and the sample sizes of most
subgroups being compared are less than 20, the results may appear less significant than
perhaps they truly are. Despite this, it is expected that distance measure will demonstrate

which of these populations are most closely related.

38

Chapter 5
RESULTS OF STATISTICAL TESTS

Results of Chi-Square Analyses for Entire Dataset

Chi-Square was performed for each variable in the Mutter dataset four separate
times, comparing each variable to Rhine’s Anglo subgroup, Hispanic subgroup, Indian
subgroup, and Black subgroup. Rhine’s (1990) dataset is problematic for Chi-Square
because some of his results do not meet the minimum cell frequency assumption
necessary to run Chi-Square. This is in part due to his small subgroup sample sizes (see
Table 4), but also due to very low frequency of certain traits. For example with the
Rhine’s (1990) Anglo subgroup, 100% of the individuals express a low base angle;
therefore the expected frequency for high base angle is less than five individuals, thus
violating the assumption (Rhine, 1990).

Table 4: Rhine’s (1990) Maxwell Collection Sample

 

 

 

 

 

Rhine’s (1990) Subgroup Number of Individuals
Anglo 53
Hispanic 15
Indian 12
Black 7

 

 

 

 

It is questionable if any statistic can robustly examine Rhine’s (1990) data for the smaller
subgroups; this is one of the problems with utilizing Rhine’s (1990) study to justify
determination of ancestry from the human skull. I will discuss the results of all statistical
tests, yet have found that the Anglo subgroup allows for the most reliable results of the
four subgroups due to the larger sample size. Table 8, Appendix B in this section shows
the results of the comparative analysis among the frequency of the given variables in the

Hyrtl sample and the corresponding frequencies given in Rhine (1990). Traits that

39

produced error results were discarded from the final analysis. Disregarding these
variables, fourteen variables were tested and the table shows that six variables show a
statistical difference between Rhine’s “Anglo” data and the Miitter data: metopism,
nasion depression, slight prognathism, Carabelli’s Cusp, zygomaticomaxillary suture, and
venous markings. Another four variables shown do not reflect the pattern of expected
traits listed in Rhine’s Figures 1,2,3 (Rhine,1990:10-12) and are statistically similar to
Rhine’s non-European subgroups: nasion depression, Carabelli’s Cusp,
zygomaticomaxillary suture, and venous markings. When examining the statistical
results, it is important to note that Hispanic is a new ancestry category and has been
researched very little. Ultimately, Rhine includes Hispanics in his “Caucasoid” sample
count and does not distinguish between “White” and “Hispanic”. It is also of note that
some of the statistical results demonstrated significant differences between the Rhine
(1990) study and the Hyrtl study, however the null hypothesis could not be rejected for
some of these traits. The reasoning behind this conclusion is that the Hyrtl sample better
supported Rhine’s (1990) conclusions than his own data. For many traits listed as
expected by Rhine (1990), his data indicated reverse trends and so therefore the Hyrtl data
conflicts with his results. This scenario occurs for both the traits nasal opening and nasal
spine. For example with the nasal opening trait, Rhine (1990) indicates that a narrow
nasal opening is expected for Caucasoids however less than half of his sample expresses a
narrow nasal opening. Further an equal number have what Rhine classifies as a “medium”
nasal aperture (Rhine, 1990:14-16). Approximately 67% of the Hyrtl skulls express a

narrow nasal opening, resulting in a statistically significant P-value for the trait.

4O

Rt’Sl

of 1'

Co:

the

Ill-L

Results for Chi-Square Analyses for The Geographic Subgroups

The set of Tables found in Appendix C show the Chi-Square results among each
of the subpopulations for 18 traits. Seven of these traits reject Null 3-The European Trait
Consistency Hypothesis at a P-Value equal to or less than 0.05 on at least one occasion
(between at least one set of populations): nasion depression, nasal sill, undulating
mandible, ascending ramus, oval window and Wormian bones.
Results for Chi-Square Analyses for The Ethnolinguistic Subgroups

The set of Tables found in Appendix D show the Chi-Square results among each
of the subpopulations for 18 traits. Seven of these traits reject Null 3-The European Trait
Consistency at a P-value equal to or less than 0.05 on at least one occasion (between at
least one set of populations): metopism, nasion depression, Carabelli’s Cusp, venous
markings, nasal sill, nasal opening, and venous markings.
Results for Mean Measure of Divergence Tests

As anticipated, Null Hypothesis 4 cannot be rejected. The results of the Mean
Measure of Divergence tests for the Geographic and Ethnolinguistic Subgroups appear in
Table 5 and Table 6 respectively. Rejection of the hypothesis was determined by the
statistical software program.

Table 5: Mean Measure of Divefimnce Values Geographic Groups

 

 

 

 

 

 

 

 

Population Population MMD Standard Deviation Test df
Group 1 Group 2 for MMD Statistic
Austro-Hungary The Balkans 0.011506 0.019266 22.37 18
Austro-Hungary The Baltics -0.004325 0.031109 18.29 18
Austro—Hungary Italian Peninsula 0.012798 0.027088 21.46 18
The Balkans Italian Peninsula -0.021993 0.027744 13.67 18
The Balkans The Baltics -0.009377 0.031784 17.11 18
Italian Peninsula The Baltics 0.031328 0.039542 24.18 18

 

 

 

 

 

 

41

 

For the Geographic Grouping, the Austro-Hungary and The Baltics Subgroups are

most closely related and are distinct from the Italian Peninsula Subgroup and The Balkans

Subgroup which are significantly less related than the former pair. Appendix E shows the

cluster analysis which demonstrates the relatedness of the above four Geographic groups.

Table 6: Mean Measure of Divergence Values Ethnolinguistic Groups

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Population Group Population Group MMD Standard Test df
l 2 Deviation for Statistic
MMD
Romance Group F inno-Ugric Group 0.32459 0.049827 18.48 15
Romance Group East Slavic Group -0.030197 0.036243 10.87 15
Romance Group West Slavic Group -0.034418 0.041051 10.75 15
Romance Group German Group -0.015694 0.035835 13.21 15
Romance Group South Slavic -0.004566 0.036071 14.75 15
Finno-Ugric Group German Group 0.004512 0.057028 15.99 15
Finno-Ugric Group East Slavic Group -0.045711 0.057295 9.71 15
Finno-Ugric Group West Slavic Group 0.021658 0.061922 16.63 15
Finno-Ugric Group South Slavic Group 0.037822 0.056922 18.74 15
South Slavic Group German Group -0.000569 0.043434 15.49 15
South Slavic Group East Slavic Group 0.023549 0.043898 18.33 15
South Slavic Group West Slavic Group -0.032750 0.048769 11.69 15
West Slavic Group East Slavic Group 0.000638 0.048864 15.08 15
West Slavic Group German Group 0.006760 0.048414 16.59 15
East Slavic Group German Group -0.000092 0.043621 15.51 15

 

 

 

 

 

 

 

For the Ethnolinguistic Grouping, the German Subgroup and the West Slavic

Group are most closely related, and share a close relationship with the South Slavic and

Italian Subgroups. These four subgroups are less related to the East Slavic and Finno-

Ugric Subgroups which share a relationship with one another. Appendix E shows the

cluster analysis which demonstrates the relatedness of the above six Ethnolinguistic

groups.

42

Summary of Hypotheses Testing
Hypotheses

Null Hypothesis 1 - European Definition Hypothesis: The non-metric data collected
will demonstrate there to be no statistically significant difference in the distribution of
discrete/quasi-continuous trait frequencies between the Hyrtl Skull Sample and
Rhine’s (1990) “Anglo” sample from the Maxwell Museum Collection.

Null Hypothesis 2 - European Distinction Hypothesis: The non-metric data collected
will demonstrate there to be no statistically significant differences in the distribution
of discrete trait frequencies between the Hyrtl Skull Sample and any of Rhine’s (1990)
three non-European groups.

Null Hypothesis 3 - European Trait Consistency Hypothesis: None of the traits in the
dataset will be statistically different between the created subgroups.

Null Hypothesis 4 - European Regions Hypothesis: The means of the data will not be
statistically different between regional/ethnolinguistic groups within the Hyrtl sample,
supporting the notion that there is no statistically significant variation in the
distribution of non-metric or metric traits among people with European ancestry.

° Null Hypothesis 1 can be rejected for the following non-metric traits (14 traits ran
valid tests):

Metopism

Nasion Depression

Slight Prognathism

Carabelli’s Cusp

Zygomaticomaxillary Suture

Venous Markings

000000

0 Null Hypothesis 2 can be accepted for the following non-metric traits (14 traits
ran valid tests):
0 Nasion Depression
0 Carabelli’s Cusp
o Zygomaticomaxillary Suture
o Venous Markings

43

Null Hypothesis 3 can be rejected for the following non-metric traits among the
Ethnolinguistic Grouping (18 traits ran valid tests):
0 Metopism

Nasion Depressed

Carabelli’s Cusp

Venous Markings

Nasal Sill

Nasal Opening

OOOOO

Null Hypothesis 3 can be rejected for the following non-metric traits among the
Geographic Grouping (18 traits ran valid tests):
0 Nasal Sill

Nasion Depressed

Undulating Mandible

Ascending Ramus Profile

Oval Window

Wormian Bones

OOOOO

Null Hypothesis 4 is accepted. No statistically significant difference exists among
the means of the Hyrtl sample subgroups.

44

'l‘l

r'rfi

Chapter 6
DISCUSSION

The major trends in this dataset indicate that differences do exist among
subpopulations in Europe. Despite the small size of the study collection and its
geographic limitations, this study rejects several traits as being universal to all European
groups. It is interesting to note that many of these traits are specific to the nasal region, a
newer morphological area of focus in studies of human variation and the determination of
ancestry, and that such traits may be of more significance in the differentiating among
groups of people.

A review of my statistical tests and results indicates that Null l-The European
Definition Hypothesis and Null 3-The European Trait Consistency Hypothesis can be
rejected for certain traits. While Null 2-The European Distinction Hypothesis can be
accepted for a number of traits, and Null 4 - The European Regions Hypothesis cannot be
rejected due largely to small sample size.

Null l-The European Definition Hypothesis is rejected for six of the fourteen
valid traits, meaning that these six traits were statistically different among their frequency
of occurrence in the Hyrtl Sample and the Rhine (1990) “Anglo” sample. Four of these
six traits also violate Rhine’s conclusions by being more similar to “non-European”
ancestral groups. Null 2-The European Distinction Hypothesis is accepted for these four
traits, indicating that not only were the frequency of occurrence statistically different
between the Hyrtl Sample and Rhine’s (1990) “Anglo” sample, but that there were
statistically similar to the three other “non-European” ancestral groups. Therefore, these

four traits - Nasion Depression, Carabelli’s Cusp, Zygomaticomaxillary Suture and

45

Venous Markings - do not correspond with Rhine’s (1990) conclusions that they
distinguish “Europeans“ from other ancestral groups not sharing the specific trait
expressions. These traits were not reliable in the evaluation of ancestry from the Hyrtl
sample, and their use in other studies or forensic anthropology cases as indicators of
ancestry should be questioned.

Null 3-The European Trait Consistency Hypothesis is tested twice, once with the
Ethnolinguistic Subgroups and again with the Geographic Subgroups. For both tests,
eighteen non-metric traits ran valid tests. The Ethnolinguistic Subgroup Chi-Square
analyses show that among at least two of the six subgroups a significant P-value was
obtained for six of the non—metric traits - metopism, nasion depressed, Carabelli’s Cusp,
venous markings, nasal sill and nasal opening. The statistical differences among the
subgroups for these six traits indicate that these traits may be better traits for
distinguishing European groups from each other on the basis of ethnolinguistic lines. The
Geographic Subgroup Chi-Square analyses show that among at least two of the four
subgroups a significant P-value was obtained for six of the non-metric traits - nasal sill,
nasion depressed, undulating mandible, ascending ramus profile, oval window and
Wormian bones. The statistical differences among the subgroups for these six traits
indicate that these traits may be better traits for distinguishing European groups from each
other on the basis of geographic lines. While different traits were rejected for Null
Hypothesis 3 for each of the groupings, Ethnolinguistic and Geographic, both groupings
show that for the Hyrtl data, Europe could be divided along geographic and
ethnolinguistic lines with equal validity. There is no evidence in this study to suggest that

narrowing the ancestral group “European” based on ethnolinguistic divisions would be

46

any more valid that narrowing it based on geographic distance. Considering this, there is
evidence that differences among the created subgroups do exist for six of the eighteen
traits tested. On five such occasions, the non-metric traits distinguishing the subgroups
involves the nasal region, supporting Gill’s (1998) discussion on the value of the nasal
region in determining ancestry from the human skull (Gill, 1998:304-305). Therefore,
based on the results of this study, there is evidence to suggest that the area of Europe
could be better defined for non-metric ancestry determination purposes.

Null 4-The European Regions Hypothesis set out to use the CAB Smith’s Mean
Measure of Divergence (MMD) test to demonstrate a statistical difference among the
total mean trait frequencies among subgroups so at to further support the narrowing of the
ancestral category “European”. The MMD test is sensitive to sample size differences and
unfortunately the division of the Hyrtl sample into four geographic subgroups and six
ethnolinguistic subgroups left small, unbalanced samples to test. Null Hypothesis 4 could
not be rejected, and the MMD results do not support the division of the ancestral category
“European” into smaller ancestral groups. More data is needed for the narrowing of the
ancestral category “European”.

Previous studies on non-metric craniofacial and dental morphology have failed to
determine where the geographic lines of distinction lie for craniofacial and dental
morphology. This is to say that no study has demonstrated craniofacial and dental traits to
be solely specific to the area encompassing all of Europe and the Middle East. These
geographic lines have been drawn to a certain degree arbitrarily, and it cannot be said that
once these geographic boundaries have been crossed, morphology is distinctly different.

Despite this, it has been proposed that individuals who appear to be “White” or generally

47

of EurOpean descent tend to share suites of non-metric traits. To a certain extent, the
study shows this to be true for eight of the traits listed as expected by Rhinenhowever, it
is clear from the Chi—Square analyses that firrther analyses must be undertaken.

There has been no known test of Rhine’s (1990) study that has examined the
validity of his list for expected non-metric traits associated with “American Caucasoids”
or individuals with European ancestry. Because Rhine’s (1990) number of individuals for
“Anglos” is only 53, less than half the size of the Hyrtl sample, it is possible that further
research will continue to identify the problems with Rhine’s (1990) original expected trait
list (Rhine,1990:9-1 1).

It is expected that future research will provide better indicators for the
determination of ancestry from the skeleton. A few anthropologists have alluded to the
fact that more geographically specific criteria for the determination of ancestry would be
useful and is already being practiced. Kennedy (1995) notes that he is fairly confident
assigning ancestry based on the non-metric variation in individuals from the Indian
subcontinent and Sri Lanka according to geographic sectors due to his extensive
paleodemographic work in the area for many years. Further, he states that he would feel
less confident doing the same with skeletons from another area of the world (Kennedy,
1995:799). Howells’ (1995) metric analyses of crania have divided Europe into four
ancestral categories: Norse, Zalavar, Berg, and Egypt. He has further divided other major
geographic areas of the world into twenty-four ancestral categories based on cranial
metric variation (Howells, 1995:4-5). This demonstrates that geographically specific
regional variation does exist for Homo sapiens, and that anthropologists are justified in

using more discriminating ancestry categories in the determination of ancestry.

48

Chapter 7
CONCLUSIONS
Summary

This study contributes to the general understanding of human variation in physical
anthropology. While biological race has long been rejected as a valid concept by most
physical anthropologists, the idea that variation in human morphology can tell
anthropologists about past peoples is still vital. Forensic anthropologists take our
knowledge of human variation and apply it to help depict the appearance of an individual
during life from the morphology of their skeletal remains.

Techniques used for determining ancestry from the human skull non-metrically
have not been sufficiently documented. Research in the area of non-metric ancestry
determination will strengthen forensic anthropologists’ understanding of craniofacial and
dental morphological variation. These statistical analyses address the questions proposed
at the outset of this project. While there is little control over the collection sizes available
to researchers, the sample size for this project did impact the results. I hope in future
studies that sample size will have less of an impact on the statistical results.

Despite research problems related to Sample size, this study in addition to research
I have recently conducted at the Smithsonian Institution’s National Museum of Natural
History on 600 skulls of known “Asian” ancestry brings into question the validity of the
current assignment of expected non-metric traits to the three main ancestry categories -
European, Asian and African. Statistical tests on the Hyrtl sample indicate that a good
portion of the trait list found by Rhine to be expected in his fifty-three individuals of

“Caucasoid” descent were not present in the same frequency in the Hyrtl samples’ 122

49

individuals. As anticipated, the trait list proposed by Rhine (1990) is most probably
population or collection specific. The follow-up question to this problem would be
whether the methodology on a whole is truly dependent upon population variation and is
not as wide-sweeping as perhaps depicted in non-metric trait studies (Gill & Rhine,
1990). Based on my research, the former seems true; non-metric cranial traits vary from
population to population and cannot describe an entire group of populations deriving
from a general region such as Europe or Asia. This is important particularly for forensic
anthropologists employing Rhine’s (1990) methodology for the determination of ancestry.
One of the following is needed in the field of forensic anthropology in order for non-
metric trait methodology to be a viable medico-legal method for the determination of
ancestry: 1) additional research on non-metric trait variation which can substantiate the
inclusion of several populations into one ancestral category, or 2) an abandonment of the
use of non-metric traits as a sole means for determining ancestry from the human skull.
Future Research

Further research on the use of non-metric skeletal variants in the determination of
ancestry from human skeletal remains is currently being carried out by the author at the
Smithsonian Institution, National Museum of Natural History (N MNH). The current
project will examine the accuracy of Rhine’s expected non-metric skeletal trait list for
“Southwestern Mongoloids” or individuals with Asian ancestry on a sample of human
skeletal remains of Asian or Native American descent housed at the NMNH. From these
collections, my research sample includes 600 adult individuals of both sexes: seventy-six
Chinese, sixty-two Alaskan Inuit, sixteen Japanese, five modern Asian, 204 Mongolian,

seventy-one Native American’s from the Southwest region (Arizona & New Mexico),

50

seventy-three Native American’s from the Midwest region (Illinois), fifty-three
Peruvians, and forty Siberians.

Rhine’s (1990) skeletal sample for individuals of Asian ancestry is restricted to
the US. Southwest region. Rhine acknowledges, “this sample represents only a very
small fragment of the continuum of variability which makes up the Mongoloid group”
(Rhine, 1990: 1 3). Despite this, Rhine’s (1990) list of skeletal traits provides a good
foundation for the examination of non-metric traits in skeletal populations. Furthermore,
the literature in the United States on the distribution of non-metric skeletal traits for
Asian populations is not as extensive as for African or European populations, and Asian
individuals in general are often compared to those non-metric traits expected for Rhine’s
(1990) “Southwestern Mongoloid” category.

The goal of this study is to continue to examine the value of non-metric traits in
distinguishing individuals from one another along more narrowly defined ancestral
borders. If the results from this research find that, as with this project, several traits are
more valuable in distinguishing subgroups of individuals currently classified as being of
“Asian” ancestry, anthropologists should consider revising the current classification

system for ancestry as it is used in forensic anthropology today.

51

APPENDICES

52

APPENDIX A

Original Data Collection Sheet

53

The Miitter Museum: Hyrtl Skull Collection Data Sheet

Institution: The Mfitter Museum/College of Physicians of Philadelphia
Researcher: Valerie N. Yavornitzky

Association: Michigan State University

Skull Number:

Data Sheet Number:

 

 

Non-Metric Trait Presence/Absence Completeness/Degree Comment

 

l. Metopism

 

2. Wormian Bones

 

3. Canine Fossa

 

4. Supraorbital Struc.

 

5. Inion Hook

 

6. Nasal Spine

 

7. Nasal Sill

 

8. Parietal Foramen

 

9. Base Chord

 

10. Base Angle

 

11. Occipital Shelf

 

12. Nasion

 

13. Nasal Shape

 

14. Prognathism

 

15. Carabelli’s Cusp

 

16. Molar Crenulation

 

17. Incisor Shoveling

 

18. L. Border Mandibl

 

l9. Ascending Ramus

 

20. Oval Window

 

21. Zygomatico Sut.

 

 

 

 

 

22. Venous Markings

 

54

 

APPENDIX B

Chi-Square Results for Entire Dataset

55

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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H L.de \rooN H 8th 5th H oood womb H ecccdv NH©.Hm mung—«SH 25:3»
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H ooodv KNOWN H 43%.: 036 H LEN... NNmH H nmnd god 855 SHE—.83
H ooodv NHNNm H ooodv N H NHm H ocodv 3H .3 m H 425.: womb amigo—Z

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57

APPENDIX C

Chi-Square Results for Geographic Subgroups

58

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N “ohm Sum N Sum 8cm N Sum 82m win—00¢ Hana Z
H 00N.0 SNH H 00 H .0 HmnH H 0K0 000.0 0.0.5.002 mac=0>
H $.00 02.0 H ammm0 - H 0N00 20.0 0.525 00:080wa
H 0.20.0 «H We H .2000. H - H 3 H0 vovN 300:5» .05
H 0000. H - H 3.03.0 - H 0200 - 2.500— 052.003.
N 0Nm.0 onNN N Sum 5th N 45000 0H H0 03.052 20.325
H 0000.H - H 2000.H - H 0000.H - 0.520:— 00.02::
H H HN.0 vcmH H a000.H - H Nmmd 000.0 Egan—5.000 EH02
H 30.0 Hm H .0 H 5R0 voH.0 H 0000 30.0 02.0 03:00an
H 003.0 - H 0000.H - H H80 0mN.0 505350.: 32.0
H 000.0 08.0 H 00m .0 05N0 H 005.0 000.0 2.2: H532
H me0 H000 H 0.30.0 mNN.v H 80.0 N000 00202.0: =0Hmaz
m 82m 8an m 8an 8th m >000.0v 0H H Ham :5 Hamel
H mNm.0 H.000 H a000.H - H $00 NH0.0 2:00 .0002
H 03.0 53.0 H 02.0 08.0 H 00N.0 NNH.H 0.00: :35
H E000.H - H “.000.H - H H H00 NH0.0 080: 2:55
H vad 20.0 H 00> 30.0 H mmH.0 30N 028m 5:503
H 3&0 H000 H 0mN.0 HNmH H NWO N000 803302
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N Sum. .05. N .0th Sum. N 8:0 8...“. 00.500 .037.
H 000.0 H000 H 0NHO 000.N H 0000 000.0 0003.02 2.2.0.»
H 20.0 0000 H NN00 00N0 H 0N00 $0.0 25:0 8.22.8wa
H 3N0 N00. H H .00 H .0 - H E .0 v0v.N 322:3 .20
H .000.H - H .00 H .0 - H .0000 - 2.53. 05.2.33.
N 8th 8:0. N SE. 5:0 N i000 0H .0 0.0.05.2 22.6.5
H .000. - H .000. - H 000. - 28.2.. 3.32.0
H 000.0 00 H .0 H .000. H - H N000 000.0 5.3.2.20 .23.».
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H $00 0000 H 02.0 N000 H 0000 000.0 90.2.0 .230
H N000 0000 H .0000 NON... H 000.0 N000 088.02. E:27.
0 >500 000.: 0 >300 H00.mH 0 >000.0v 0H H00 ...0 .330
H .0 H .0 - H .030 - H 0000 N H 0.0 2:00 .032
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H .000. - H .000. - H H H00 N H00 .28... 2......0
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H 000.0 30.0 H .000. - H .800 N000 50.0322
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H .. .N0.0 000.0 H .00 H .0 - H .000. - 300...?» .600
H .030 - H .00 H .0 - H .0000... - 0.2.0”. 00.000000.
N 8:0 5...: N Sum. .80. N 8th Sum. 0.0.05.5. 0.0.50.5
H .000. - H .000. - H .000. - 0.80.0... 00.0>....0
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H .0000 - H .N000 - H .000. - E0....2.wo.... 50.0
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N .Obm. .obm N .Ohm .obm. N .Obm— .Obm. wflmflomo _a0az
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62

APPENDIX D

Chi-Square Results for Ethnolinguistic Subgroups

63

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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78

LITERATURE CITED

79

LITERATURE CITED

Anonymous. 1979. Non-metric traits — Bibliography. Journal of Human Evolution.
82705-708.

Angel JL and Kelley JO. 1990. “Inversion of the Posterior Edge of the Jaw Ramus: New
Race Trait.” In Skeletal Attributes of Race: Methods for forensic anthropology. Gill GW,
and Rhine S. (eds.) Albuquerque, New Mexico: Maxwell Museum. pp. 33-40.

 

Ansorge H. 2001. Assessing non-metric skeleton characters as a morphological tool.
Zoology 104:000-000.

Aubin MM. 2002. Nonmetric traits of the cervical vertebrae: Methods for data collection
and comparison. American Association of Physical Anthropology 71St Annual Meeting.
Poster presentation.

Barnett SA. 1970. The human species; a biology of malt. Gloucester, MA: P. Smith.

Bass WM. 1995. Human Osteology: A laboratory and field manual of the human
skeleton. Fourth edition. Columbia: Missouri Archaeological Society.

Benfer RA. 1970. Associations among cranial traits. American Journal of Physical
Anthropology 32:463.

Bennett KA. 1965. The Etiology and genetics of worrnian bones. American Journal of
Physical Anthropology 23 :25 5-260.

Berry AC. 1974. The Use of nonmetrical variations of the cranium in the study of
Scandanavian population movements. American Journal of Physical Anthropology
40:345-358

Berry AC and Berry RJ. 1971. "Epigenetic polymorphism in the primate skeleton," in
Comparative Genetics in Monkeys, Apes and Man. Edited by AB. Chiarelli, pp. 13-42.
New York: Academic.

Berry RJ. 1963. Epigenetic polymorphism in wild populations of Mus musculus.
Genetical Research 4: 193-220.

Birkby WH. 1973. Discontinuous Non-metric Traits of the Skull as Population Markers
in the Prehistoric Southwest. Ph.D. Dissertation, University of Arizona.

Blakely RL. 1973. Biological Variation Among and Between Two Prehistoric Indian
Populations at Dickson Mounds. Ph.D. Dissertation, University of Indiana.

80

Brace CL. 1995. Region does not mean “Race” — Reality versus convention in forensic
anthropology. Journal Forensic Science 40: 171-1 75.

Brace CL and Hunt KD. 1990. A nonracial craniofacial perspective on human variation:
A(ustralia) to Z(uni). American Journal of Physical Anthropology 82:341-360.

Brooks S, Brooks RH, and France D. 1990. “Alveolar Prognathism Contour, an Aspect of
Racial Identification.” In Skeletal Attributes of Race: Methods for forensic anthropology.
Gill GW, and Rhine S. (eds.) Albuquerque, New Mexico: Maxwell Museum. pp. 41-46.

Brues AM. 1990. The once and future diagnosis of race. In Skeletal Attributes of Rafi
Methods for forensic anthropology. Gill GW, and Rhine S. (eds.) Albuquerque, New
Mexico: Maxwell Museum. pp. 1-7.

Buikstra J E. 1980. "Epigenetic distance: A study of biological variability in the lower
Illinois river region," in Early Native Americans: Prehistoric Demography, Economy, and
Technology. Edited by David L. Browman, pp. 271-299. New York: Mouton Publishers.

Buikstra J E, Frankenberg SR, and Konigsberg LW. 1990. Skeletal biological distance
studies in American physical anthropology: Recent trends. American Journal of Physical
Anthropology 82:1-8.

Buikstra JE, and Ubelaker DH. (eds.) 1994. Standards for Data Collection from Human
Skeletal Remains. Arkansas Archaeological Survey Research Series. 44.

Busby A. 2002. Ontogenetic development of the axillary border of the scapula in
Neanderthals. American Association of Physical Anthropology 71St Annual Meeting.
Abstract.

Carpenter J C . 1984. A comparative study of metric and non-metric traits in a series of
modern crania. American Journal of Physical Anthropology 45:337-344.

Cheverund J M, Buikstra JE, and Twitchell E. 1979. Relationships between non-metric
skeletal traits and cranial size and shape. American Journal of Physical Anthropology
50:191-198.

Cheverud J M and Buikstra J E. 1978. A study of intragroup biological change induced by
social group fission in Macaca mulatta using discrete cranial traits. American Journal of
Physical Anthropology 48:41 -46.

Cheverud J M and Buikstra JE. 1981. Quantitative genetics of skeletal nonmetric traits in
the Rhesus macaques on Cayo Santiago. II. Phenotypic, genetic and environmental
correlations between traits. American Journal of Physical Anthropology 54:51-58.

81

Cheverud J M and Buikstra J E. 1982. Quantitative genetics of skeletal nonmetric traits in
the Rhesus macaques on Cayo Santiago. III. Relative heritability of skeletal nonmetric
and metric traits. American Journal of Physical Anthropology 59:151-155.

Corruccini RS. 1974. An examination of the meaning of cranial discrete traits for human
skeletal biological studies. American Journal of Physical Anthropology40z425-446.

Corruccini RS, Shimada I. 2002. Dental relatedness corresponding to mortuary patterning
at Huaca Loro, Peru. American Journal of Physical Anthropology 117:113-121.

Cosseddu GG, Floris G, and Vona G. 1979. Sex and side differences in the minor non-
metrical cranial variants. Journal of Human Evolution 82685-692.

Cucina A, Tiesler Blos V, Sierra Sosa T. Dental anthropology, settlement pattern and
social structure at the Maya site of Xcambo, Yucatan. American Association of Physical
Anthropology 71St Annual Meeting. Abstract.

Czarnetzki A. 1975. On the question of correlation between the size of the epigenetic
distance and the degree of allopatry in different populations. Journal of Human Evolution
4:483-489.

DeStefano GF, Hauser G, Guidotti A, Rossi S, Russo EG, and Gualandi PB. 1984.
Reflections on interobserver differences in scoring non-metric cranial traits (with
practical examples). Journal of Human Evolution 13:349-355.

Duray SM, Morter HB, and Smith F J . 1999. Morphological variation in cervical spinous
processes: potential applications in the forensic identification of race from the skeleton.
Journal Forensic Science 44:937-944.

Duband A, Kidder J, and J antz RL. 2002. A multivariate examination of the Hexian
Homo erectus calvarium. American Association of Physical Anthropology 71St Annual
Meeting. Abstract.

Eberly J. Nasal Index of Hyrtl Skull Collection. Unpublished study. Received from
Worden G of the Mutter Museum of the College of Physicians of Philadelphia.

Federation of East European Family History Societies. 2001. FEEF HS Map Room —
Background and Map Index. Electronic Document.
http://feefhs.org/maps/indexmap.html, accessed February 18, 2002.

Finnegan M. 1974. Cranial and infracranial non-metric traits: those traits which are most

important and how they may be handled. American Journal of Physical Anthropology
41 :478-479 (abstract).

82

Finnegan M. 1978, An analysis of intra-observer error in non-metric studies. American
Journal of Physical Anthropology 48:393 (abstract).

Finnegan M J and Cooprider K. 1978. Empirical comparison of distance equations using
discrete trait. American Journal of Physical Anthropology 49:39-46.

Finnegan MJ and Faust MA. 1974. Bibliography of human and nonhuman nonmetric
variation. Research Reports, 14. Department of Anthropology, University of
Massachusetts, Amherst.

Finnegan M, and McGuire SA. 1979. Classification systems for discrete variables used in
forensic anthropology. American Journal of Physical Anthropology 51:547-553.

Giles E, and Elliot 0. 1962. Race Identification from Cranial Measurements. Journal of
Forensic Sciences 7: 147-56.

Gill GW. 1986. Craniofacial criteria in forensic race identification. In Forensic
Osteology: Advances in the identification of human remains. Reichs KJ (ed.) Springfield,
IL: Charles C. Thomas. pp. 143-159.

Gill GW, Hughes SS, Bennett SM, and Gilbert BM. 1988. Racial Identification from the
midfacial skeleton with special reference to American Indians and Whites. Journal of
Forensic Sciences 33:92-99.

Gill GW, and Rhine S. (eds.) 1990. Skeletal Attributes of Race: Methods for forensic
anthropology. Albuquerque, New Mexico: Maxwell Museum.

 

Gill GW. 1995. Challenge on the frontier: Discerning American Indians from Whites
Osteologically. Journal of Forensic Sciences 40:783-788.

Gill GW. 1998. Craniofacial criteria in the skeletal attribution of race. In Forensic
Osteolcgy: Advances in the identification of human remains. Reichs KJ (ed.) Springfield,
IL: Charles C. Thomas. pp. 293-315

Green RF, and Suchey JM. 1976. The use of inverse sine transformations in the analysis
of non-metric cranial data. American Journal of Physical Anthropology 45:61-68.

Griffin MC. 1989. Internet. Dental Variation of Native Populations fiom Northern
Spanish Florida. M.A. Thesis. Department of Anthropology, Northern Illinois University,
DeKalb. http://online.sfsu.edu/~mgriffin/mgthesis.html

Grfineberg H. 1952. Genetical studies on the skeleton of the mouse. IV. Quasi-continuous
variations. Journal of Genetics 51 :95-1 14.

83

Gualdi-Russo E, Tasca MA, and Brasili P. 1999. Scoring of nomnetric cranial traits: a
methodological approach. Journal of Anatomy 195:543-550.

Hattman K. 2001. Morphological variability through ontogeny of the modern human chin.
American Association of Physical Anthropology 70St Annual Meeting. Abstract.

Hauser G, and De Stefano GF. 1989. Epigenetic Variants of the Human Skull. Stuttgart:
E. Schweizerbart’sche Verlagsbuchhandlung (N agele u. Obermiller).

Heffner, JT. 2002. An assessment of craniofacial nonmetric traits currently used in the
forensic determination of ancestry. American Academy of Forensic Sciences 54Lh Annual
Meeting. Presentation.

Hennenberg RJ, De Migeul D, Castany J, Bemades J, F abregas L, Calderer J, Townsend
G, and Hennenberg M. Identical twins from the Neolithic Site Llord. American
Association of Physical Anthropology 70St Annual Meeting. Abstract.

Hinkes MJ. 1990. Shovel shaped incisors in human identification. In Skeletal Attributes
of Race: Methods for forensic anthropology. Gill GW, and Rhine S. (eds) Albuquerque,
New Mexico: Maxwell Museum. pp. 21-26.

Hooton, EA. 1920. Peabody Museum of Harvard University - Cranial Observations and
Indices. In Skeletal Attributes of Race: Methods for forensic anthropology. Gill GW, and
Rhine S. (eds.) Albuquerque, New Mexico: Maxwell Museum. pp. 3-4.

Hooton, EA. 1930. The Indians of Pecos Pueblo. New Haven: Yale University.

Howells. 1995. Who’s Who in Skulls: Ethnic identification of crania from measurements.
Papers of the Peabody Museum of Archaeology and Ethnology. Vol. 82. Cambridge, MA:
Harvard.

Hunley KL and Cabana GS. 2001. One or many species? A method for comparing Middle
Pleistocene and modern human morphological variation. American Association of

Physical Anthropology 70St Annual Meeting. Abstract.

Imber CE and Aiello LC. 2001. Postcranial robusticity and rugosity patterning in modern
humans. American Association of Physical Anthropology 70St Annual Meeting. Abstract.

Jantz RL, and Jantz LM. 2000. Secular change in craniofacial morphology. American
Journal of Human Biology 12:327-338.

Kelso, AJ. 1970. Physical anthropology; an introduction. Philadelphia: Lippincott. pp. 94.

Kemper MJ. 1970. Whatever happened to Joseph Hyrtl? M.A. Thesis. Temple University.

84

Kennedy B. 1981. Marriage patterns in an archaic population: A study of skeletal remains
from Port au Choix, Newfoundland. Archaeological Survey of Canada, National Museum
of Man, Mercury Series, Paper No. 104.

Kennedy KAR. 1995. But Professor, why teach race identification if races don’t exist?
Journal Forensic Science 40:797-800.

Korey KA. 1970. Characteristics of the Distributions of Non-metric Variants of the Skull.
M.A. Thesis, University of Chicago, Illinois.

Lane RA and Sublett AJ. 1972. Osteology of social organization: Residence pattern.
American Antiquity 37: 1 86-201.

Lane RA. 1978. Non-metric osteological variation as a function of genetic kinship.
American Journal of Physical Anthropology 48:413 (abstract).

Larsen CS. 1997. Bioarchaeology: Interpreting behavior from the human skeleton.
Cambridge: Cambridge University Press. pp. 302-332.

Livingstone PB. 1964. On the non-existence of human races. In The Concept of Race.
Montagu, A. (ed.) New York: The Free Press of Glencoe. pp. 46-60.

Lovejoy CO, Meindl RS, Pryzbeck TR, Mensforth RP. 1985. Chronological
Metamorphosis of the Auricular Surface of the Ilium: A new method for the
determination of adult skeletal age at death. American Journal of Physical Anthropology
68:15-28.

Lovejoy CO, Reno PL, Kriz MA, Rosenman BA. 2002. Developmental field fluctuation:
a potential basis for skeletal morphological variation. American Association of Physical
Anthropology 71St Annual Meeting. Abstract.

Molto JE. 1979. The assessment and meaning of intraobserver error in population studies
based on discontinuous cranial traits. American Journal ofPhysical Anthropology
51 :333-344.

Moore-Jansen PM, Ousely SD, and Jantz RL. 1994. Data Collection Procedures for
Forensic Skeletal Material. Report of Investigations No. 48 Department of Anthropology,
University of Tennessee, Knoxville.

Napoli ML, and Birkby WH. 1990. Racial differences in the visibility of the oval window
in the middle ear. In Skeletal attributes of race: Methods for forensic anthropology. Gill
GW, and Rhine S. (eds) Albuquerque, New Mexico: Maxwell Museum. pp. 27-32.

85

Nystom KC. 2002. Ethnicity and the ancestors: A bioarchaeological investigation of
ethnic boundaries. American Association of Physical Anthropology 71St Annual Meeting.
Abstract.

Ossenberg NS. 1969. Discontinuous Morphological Variation in the Human Cranium.
Ph.D. Thesis, University of Toronto.

Ossenberg NS. 1970. The influence of artificial deformation on discontinuous
morphological traits. American Journal of Physical Anthropology 33:357-372.

Ossenberg NS. 1977. Congruence of distance matrices based on cranial discrete traits,
cranial measurements, and linguistic-geographic criteria in five Alaskan populations.
American Journal of Physical Anthropology 47:93-98.

Ousley S, and Billeck W. 2001. Assessing tribal identity in the plains using nontraditional
craniometrics (interlandmark distances). Presented at the 70th Annual Meeting of the
American Association of Physical Anthropologists, Kansas City, MO. March 28-31.

Perizonius WRK. 1979. Non-metric cranial traits: Sex difference and Age Dependence.
Journal of Human Evolution 8:679-684.

Phenice TW 1969. A newly developed visual method of sexing the os pubis. American
Journal of Physical Anthropology 30:297-302.

Powell JF, Neves WA. 1999. Craniofacial morphology of the first Americans: Pattern and
process in the peopling of the New World. American Journal of Physical Anthropology
110:153-188.

Praymak RC, Graver AM, Parr RL, Molto JE. 2001. A heritability study of the non-
metric trait fronto-temporal articulation in the Dakhleh Oasis, Egypt using mitochondrial
DNA analysis. . American Association of Physical Anthropology 70St Annual Meeting.
Abstract

Rhine S. 1990. Non-metric skull racing. In Skeletal Attributes of Race: Methods for
forensic anthropology. Gill GW, and Rhine S. (eds.) Albuquerque, New Mexico:
Maxwell Museum. pp. 9-20.

Richtsmeier JT and McGrath J W. 1986. Quantitative genetics of cranial nonmetric traits
in randombred mice: heritability and etiology. American Journal of Physical
Anthropology 69:51-58.

Sauer NJ. 1992. Forensic anthropology and the concept of race: If races don’t exist, why
are forensic anthropologists so good at identifying them? Social Science and Medicine

34:107-111.

Saunders SR and Popovich F. 1978. A family study of two skeletal variants: Atlas
bridging and clinoid bridging. American Journal of Physical Anthropology 49:193-204.

86

Schwartz JH. 1995. Skeleton Keys: An introduction to human skeletal morphology,
development, and analysis. New York: Oxford University Press. pp. 257-291.

Shepherd FJ. 1893. Symmetrical Depressions on the Exterior Surface of the Parietal
Bones. Journal of Anatomy and Physiology 27:501-504.

Simmons T, and Lagrou MK. 2001. Age indicators are population specific — Caution!
American Academy of Forensic Sciences Proceedings, Vol. 7.

SPSS. 1999. Statistical Package for Social Sciences, version 10.0. Chicago: SPSS, Inc.

Steadman DW. 2001. Mississippians in motion? A population genetic analysis of
interregional gene flow in west-central Illinois. American Journal of Physical
Anthropology 114261-73.

Stefan VH. 1999. Craniometric variation and homogeneity in prehistoric/protohistoric
Rapa Nui (Easter Island) regional populations. American Journal of Physical
Anthropology 1 10:407-419.

Steininger, C. 2002. Variation in the face of Australopithecus robustus. American
Association of Physical Anthropology 71St Annual Meeting. Abstract.

Suchey J M, Wisely DV, and Katz D. 1986. Evaluation of the Todd and McKem-Stewart
methods of aging the male 0s pubis. In Forensic Osteology: Advances in the
identification of human remains Reichs, K (ed.). Springfield, IL: Thomas. Pp. 33-67.

Sugar P and Treadgold R. 1993. Historical Atlas of East Central Europe. University of
Toronto Press.

Symmers WSC. 1895. A skull with enormous parietal foramina. Journal of Anatomy and
Physiology 29:329-330.

Thomas CL (ed.). 1997. Taber’s Cyclopedic Medical Dictionm. 18th ed. Philadelphia:
F .A. Davis Company. pp. 1269.

Ubelaker DH. 1997. Latest developments in skeletal biology and forensic anthropology.
In Biological Anthropology: The state of the science. Boaz NT, and Wolfe LD. (eds.)
Second ed. Corvalis, OR: Oregon State University. pp. 101-115.

Ullinger J and Sheridan SC. 2001. “Let us go to the house of the Lord”: Biocultural
reconstruction of pilgrimage. American Association of Physical Anthropology 70St
Annual Meeting. Abstract.

87

Ullinger J. 2002. A dental reconstruction of biological relationships in the Late Bronze
and Early Iron Ages of the Southern Levant. American Association of Physical
Anthropology 71St Annual Meeting. Abstract.

Villmoare B. 2001. Randomization method to analyze variation in non-metric characters,
and the single species concept for Homo erectus. American Association of Physical
Anthropology 70St Annual Meeting. Abstract.

Weiss E. 2002. The mystery of muscle markers: Aggregation and construct validity.
American Association of Physical Anthropology 71St Annual Meeting. Poster
Presentaiton.

Worden G. 1995. The Hyrtl Skull Collection. Transactions & studies of the College of
Physicians of Philadelphia.

Wrobel GD. 2002. Intertrait association: measuring correlations among dental discrete

traits within and between tooth classes in a Maya skeletal collection. American
Association of Physical Anthropology 71st Annual Meeting. Abstract.

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