ChapterPDF Available

Chapter 5. Ancestry Estimation


Abstract and Figures

Ancestry is the most controversial aspect of the biological profile due to the vast societal impact the practice of dividing people into groups on the basis of physical characteristics has had. Regardless of the controversy however, ancestry will continue to remain part of the biological profile assessed by biological anthropologists due to its social meaning and utility for positive identification in forensic cases. This chapter reviews anthropology’’s history with regards to the race concept and presents the state of current thought with regards to human variation. Current research using advanced statistical methods for the analysis of cranial metric and nonmetric traits such as discriminant function analysis is presented. Keywords: ancestry estimation, race, scientific racism, eugenics, discriminant function analysis, craniometrics, morphoscopic traits, nonmetric traits, FORDISC, ordinal regression, logistic regression
Content may be subject to copyright.
Ancestry Estimation
Elizabeth A. DiGangi, Joseph T. Hefner
Why are biological anthropologists interested in studying ancestry when the topic is mired
with controversy due to the ways race research has been used socially and politically? The
answer is simple: anthropology is at its heart the study of humankind and all its aspects,
both cultural and biological. Therefore, many early physical anthropologists were concerned
with ordering or classifying human groups into categories, in part as a way to more fully
understand humankind. As we will uncover in the pages ahead, much of this early effort
was typological,
assuming that different groups of people conformed to types, and in
many cases their research assumed a certain hierarchical arrangement of the various races.
Current thought regarding ancestry conversely takes a population perspective and focuses
on two primary objectives: (1) understanding the distribution of human variation; and (2)
using that variation during human identification for medicolegal purposes.
As stated above, ancestry is arguably the most controversial topic we must contend with in
biological anthropology in general, and more specifically, during the construction of the
biological profile from human skeletal remains. While this controversy has existed for
decades, we have only recently fully accepted that while race does not exist from a true
biological standpoint, it does exist from a social standpoint, a realization that must be
acknowledged. Further, while race is not biological per se, we are nevertheless able to estimate
ancestry (given the social categories in use
) from a number of skeletal features, most notably
from the skull.
All bolded terms are defined in the glossary at the end of this volume.
It is important to note that the social categories in use for race are cultural constructions and therefore
arbitrary. Each culture will have its own unique system to categorize what it views as the different races.
For example, the 2010 U.S. Census recognized 15 different categories (Humes et al., 2011), and technically
six of those are nationalities (e.g., Japanese, Filipino). Conversely, the Brazilian census recognizes five
categories in total (Instituto Brasileiro de Geografia e Estatistica, 2010). While the majority of these
categories are arbitrary with no basis in biology, it nonetheless happens that we can estimate ancestral
origin from the skull for four major categories (African, Asian, European, and Indigenous/Native
American), as will be discussed in this chapter.
E.A. DiGangi and M.K. Moore: Research Methods in Human Skeletal Biology Copyright Ó2013 by Elsevier Inc. All rights reserved.
You must be well versed on the history of race in anthropology before embarking on
research in ancestry estimation. Therefore, this chapter will first review the different eras
of thought and practice in anthropology regarding race and will demonstrate the social
and political implications resulting from race research conducted by anthropologists and
others in the past two centuries. A discussion of modern era ancestry estimation using robust
statistics and a case study demonstrating a novel approach to ancestry estimation will be pre-
sented. After reading this chapter, you will have a better understanding of the history of
race/ancestry estimation in anthropology as well as a modern scientific foundation upon
which to base the exploration of your own questions.
A Note on Terminology
Throughout this chapter, the terms race and ancestry are used interchangeably. We will
use the term “race” when discussing the history of the concept or when referring to how
human groups have been classified, from either a supposedly biological or social stand-
point. The term “ancestry” will be used in reference to modern thought about human
variation. Additionally, we will only use the “-oid” terms (i.e., Caucasoid, Mongoloid,
and Negroid) when referring to a specific taxonomic schemata used in the past. When
talking about ancestry estimation today, the terms currently in vogue are European,
Asian, and African, because these exclusively refer to a major geographic region of ances-
tral origin,
rather than to a taxonomic classification engorged with underlying social
This section is not meant to be an exhaustive review of the history of the race concept in
anthropology and cannot mention every important player in the development of the concept.
It will, however, set the basic background from which the reader can embark on further
exploration of the topics raised. There are a number of books dedicated to the history of
race in anthropology, notably Man’s Most Dangerous Myth by Ashley Montagu, The Mismeas-
ure of Man by Stephen J. Gould, and “Race” is a Four-Letter Word by C. Loring Brace, among
many others. A recent dissertation by Algee-Hewitt (2011) comprehensively covers the
subject as well. We encourage you to read these and others for a detailed background if
you wish to embark on a study in ancestry estimation.
Further, the following sections will discuss several specifics from anthropology’s history
that may make many readers uncomfortable. It is important that you try to understand
history within its own contextdi.e., recognize that each scientist works within the bound-
aries and established viewpoints set by their own culture. Likewise, anthropologists today
are confined by our own culture, even if we are self-aware of this fact and yet struggle to
While these categories are still somewhat race-based (Mukhopadhyay and Moses, 1997) there is no
consensus on what better alternative terminology would be.
break out of these imposed boundaries. Remember, every scientist (including you) is
a product of his or her own time and culture.
The practice of dividing humans into discrete groups dates back to the fifteenth century,
when European explorers were encountering people who looked and acted very differently
from themselves. The prevailing thought was that there must be a reason for these clear
differences, and explaining them as distinct races made sense. Carolus Linnaeus is credited
with creating the binomial nomenclature system of Genus and species still used today. He
wrote in Systema Naturae (1759) that while humans represent one species, Homo sapiens, there
are nevertheless subspecies of humans, which he subdivided based on geography and phys-
ical characteristics as well as personality characteristics. He called these subdivisions the afri-
canus,americanus,asiaticus, and europaeus types. His classification of humans into subspecies
effectively set the stage for the emphasis on classification and taxonomy that would dominate
research on human differences for the next two centuries (Stanton, 1960).
Following Linnaeus, the German anatomist Johann Blumenbach was the first to lay out
five different human races in the eighteenth century. As he saw them, the Caucasian, Mongo-
lian, Ethiopian, American, and Malayan types captured the whole pattern of human races.
He was the first to propose the use of the terms “Caucasoid” and “Mongoloid” with reference
to the classification of peoples from Europe and Asia (Brace, 2005). While the categories
proposed by Blumenbach were subject to change over the centuries to come, the terms
Caucasoid and Mongoloid nevertheless continue to be used, albeit unadvisedly.
Monogenism and Polygenism
During this period in race research, there were attempts to explain the differences between
the various races and to understand how these seemingly disparate races came to be. Two
primary schools of thought existed: one group believed that that all human groups dated
back to the Biblical Adam and Eve, and following that “perfect” coupling, environmental
changes as well as population shifts occurred that led to the various races beyond Caucasoids
(Brace, 2005). This monogenistic view stemmed from the belief in The Great Chain of Being, the
idea first developed by ancient Greek philosophers and later revisited in Europe during
medieval times. The Great Chain of Being posits that all living things are arranged in a hier-
archy, with the Christian God at the top and human beings directly below (Lovejoy, 1936).
This view fit well with the story of creation from Genesis, and therefore was compatible
with a religious viewpoint that fit with the “scientific” view of the different races. It also hier-
archically arranged the races in a way that provided religious support for their ordered
Conversely, the polygenists believed that each race had its own unique origin. According
to the polygenists, the Caucasoid race was oldest and therefore was the most evolved;
conversely, the Negroid race was youngest and therefore was the least evolved. This view-
point was popular in the nineteenth century, especially in the United States, which led
For example, Kaszycka et al. (2009) demonstrated that the disparate views on race held by contemporary
European anthropologists are both dependent on education and influenced by sociopolitical ideology.
European anthropologists to dub the anthropology in that country as “The American School
of Anthropology”(Brace, 2005). Under this umbrella fits the research of Samuel George
Morton was an anatomist with an interest in craniometry working in Philadelphia. The
movement towards an emphasis on skull measurement was an extension of typological
theory, since it attempted to show from a biological and evolutionary standpoint the hierar-
chical arrangement of the races (Gould, 1981). Morton collected human skulls from around
the world to measure. He was particularly interested in cranial (braincase) capacity, because
he felt that particular measure would show unequivocally the correct racial hierarchical
arrangement. His results demonstrated that Caucasoids had the highest cranial capacity;
however, reanalysis of his data a century later by Gould demonstrated that Morton had either
deliberately or subconsciously manipulated the data to fit his preconceived notions (Gould,
1978). Interestingly, Gould’s work has recently been reanalyzed as well, and like Morton’s,
may have been inadvertently biased (Brace, 2005; Lewis et al., 2011).
Morton produced several volumes on craniometry describing in detail the relationship
between the races as explicitly “proven” by science (e.g., Morton, 1839). Unsurprisingly
based on his predetermined ideas, his results showed that white males were superior to
others in terms of cranial capacity, with white females and people of other races lagging
behind. His work not only was used as support for social policies of the time (e.g., justifica-
tion of slavery in the United States), but also has been used by other researchers looking for
biological justification for hierarchical classification of the races (e.g., Rushton, 1995). Another
researcher doing similar work was Paul Broca, surgery professor and founder of the Anthro-
pology Society of Paris in the mid-nineteenth century. He was interested in the physical
weights of brains in order to establish a link between race, brain size, and intelligence (Gould,
1981). While his work was inconclusive, it has not stopped others from attempting similar
comparisons, even up to the relative present day (i.e., Herrnstein and Murray, 1994).
Three of the most important historical figures in the development of American physical
anthropology are Ale
s Hrdli
cka, Franz Boas, and Earnest Hooton. Their differing viewpoints
on race continue to impact the field today. While Hrdli
cka and Hooton had similar views,
Boas occupied a different camp entirely. Their scientific differences can be summed up
into two opposite viewpoints on how to explain human variation: (1) as a result of separate
evolutionary pathways leading to different races (Hrdli
cka and Hooton) versus (2) emphasis
on the influence of environmental variables (i.e., culture, nutrition, stress, climate, etc.) on
variation (Boas).
The former viewpoint is typological and focuses on creating categories based on arbitrary
physical characteristics (skin color, facial features, etc.), which become linked to cultural char-
acteristics. As such, typology inherently includes aspects of biological determinismdthe
concept that biology dictates not only physical traits, but sociocultural traits as well (e.g.,
“level” of civilization, language, intelligence, and so on). In other words, biological deter-
minism states that an individual is destined for a certain fate socially and culturally
depending on things such as country of birth, skin color, head shape and size, and other
physical or cultural traits. It assumes a priori that categories exist and therefore attempts to
classify each group and each physical and cultural characteristic. In contrast, the viewpoint
emphasizing the importance of the environment is a holistic one that explores and tests the
different processes leading to human variation (Caspari, 2009). Rather than making assump-
tions about the existence of discrete categories, it attempts to tease apart the complex envi-
ronmental variables that have impacted the expression of biology (Caspari, 2009).
Ales Hrdlicka
s Hrdli
cka immigrated to the United States from Eastern Europe as a child in the late
nineteenth century. After training as a medical doctor, he went to France for a brief stay
where he received his first formal instruction in anthropology (Brace, 2005). Following a posi-
tion as a field anthropologist at the American Museum of Natural History in New York, he
moved to the Smithsonian Institution where he would spend the bulk of his career as a phys-
ical anthropologist (Brace, 2005). Given his inspiration by Paul Broca’s Anthropology Society of
Paris, Hrdli
cka tried for years to set up a similar organization in the United States, which ulti-
mately culminated in the creation of the American Journal of Physical Anthropology (AJPA) in
1918 and the later founding of the American Association of Physical Anthropologists (AAPA)
in 1929 (Spencer, 1981; Brace, 2005). His position of authority within the museum, the journal,
and the organization allowed Hrdli
cka to manage how anthropology could inform public
discourse about racedthe social meaning of race is implicit here (Caspari, 2009). For
example, he personally played a role in influencing public policy on immigration in the
United States by testifying before Congress in 1922 about his views on the hierarchical
arrangement of the races and biological determinism (Oppenheim, 2010).
cka essentially viewed the different races as “stems of humanity” (as many of his
contemporaries did) and focused his questions on the number of races that existed (Brace,
2005; Caspari, 2009; Oppenheim, 2010:93). Hrdli
cka defined physical anthropology as the
study of comparative racial anatomy and emphasized a comparative, descriptive approach
towards the study of the three main racial groups as he classified them (white, black, and
yellow/brown); this emphasis was to best understand the white race (Blakey, 1987; Caspari,
2009; Oppenheim, 2010). Inherent in his thinking were elements of biological determinism.
He believed that the social differences between the races were due to different evolutionary
histories, and that the white race was superior (Blakey, 1987; Oppenheim, 2010). While
cka did not produce students in his position at the Smithsonian, his founding of the
two major organs of our discipline (the AJPA and AAPA) and his extensive scholarship
solidified his status as one of the fathers of physical anthropology, deterministic views
Franz Boas
Franz Boas held a PhD in physics from a German university but began practicing anthro-
pology in the late nineteenth century. He took a full-time faculty position at Columbia
University in New York in 1905 after spending several years at the American Museum of
Natural History (Spencer, 1981; Caspari, 2009). Early on, he did ethnographic research
with the Eskimo in the Canadian Arctic, an experience that provided him an understanding
of the crucial role that culture plays in impacting biology and behavior, and not the other way
around (Erickson, 2008). Rather than being based on typology and biological determinism,
his thought system on human groups was geared instead toward investigating links between
environment, culture, and the resulting biological variation (Erickson, 2008; Caspari, 2009).
His experiences as an ethnographic fieldworker with different indigenous groups in Canada
solidified his view that race is not a causal factor of cultural traitsdi.e., that racial traits (skin
color, head shape, etc.) do not cause or influence cultural features such as language (Erickson,
2008). Boas was an example of this: he was white, yet learned the language of the groups he
studied in Canada and whenever possible even partook in their culture (Erickson, 2008).
Boas’ landmark publication in 1910, Changes in Bodily Form of Descendants of Immigrants,
proposed that the cranial index of the children of immigrants born in the United States
was different from the cranial index of their siblings born overseas.
His argument was
that this biological change was due to the differing environments of the United States and
the home countries of the immigrant parents. Perhaps nutrition had improved, or there
was increased access to medical care for expectant mothers, but whatever the reason, his
conclusions were evidence against biological determinist arguments. Even though Boas’
conclusions have recently been modified (Sparks and Jantz, 2003; Jantz and Logan, 2010),
his research was key in the early twentieth century towards undermining racial typologies
and demonstrating that biological deterministic thinking has no scientific basis (Gravlee,
2003). Further, his emphasis on the importance of testing hypotheses about human variation
rather than making broad assumptions about race is clear: “Nobody had tried to answer the
questions why certain measurements were taken, why they were considered significant, [or]
whether they were subject to other influences” (Boas, 1936; as quoted in Montagu, 1964a:16,
emphasis added).
Boas’ influence on the field in terms of his perspectives on race (rejecting types and
embracing culture and environment as holding answers to human variation questions)
and his stress on the importance of bringing an overall, holistic anthropological viewpoint
to bear on problems in physical anthropology cannot be overstated (Caspari, 2009). In addi-
tion to being major professor of 20 students, many of whom went on to be influential in the
field themselves (Erickson, 2008), his position on the importance of culture and environment
in human variation research is the foundation for research questions today and it is clear that
this emphasis will continue to shape the future of the discipline. In addition, his stress on the
importance of metric traits to reveal secular change
dhow time brings about changes in
This publication was part of the Dillingham Commission, resulting in a 41-volume report on immigrant
assimilation in the United States. See Lund (1994) for more information.
At the time of Boas’ publication, the United States was experiencing (and had been experiencing) an influx
of immigrants from European countries and others. As a result, “native” born Americansdthose white
Americans already in the country for several generationsdsaw the Irish, the Italians, the Chinese, and
others as different races from themselves. Immigrant groups with white skin did not start to be considered
socially white until the 1920s (Jacobson, 1998). Immigrant groups with non-white skin of course today
remain socially as non-white.
See discussion on secular change in Moore and Ross (Chapter 6); and McKeown and Schmidt (Chapter 12),
this volume.
biology due to changing variables in the environmentdalso continues today (e.g., Jantz and
Jantz, 1999; Jantz and Jantz, 2000; Jantz, 2001).
Earnest Hooton
Earnest Hooton and Franz Boas, while personally cordial with each other, were profes-
sional adversaries, at least in terms of their very different philosophies in physical anthro-
pology. Hooton was trained in the classics but became interested in anthropology during
time spent at Oxford (as a Rhodes Scholar) prior to earning his PhD from the University of
Wisconsin in 1911 (Spencer, 1981; Brace, 1982). In stark contrast to Boas, Hooton’s ideas about
race were polygenic and typological (Caspari, 2009). Furthermore, similar to Hrdli
cka, deter-
ministic ideas were mired in his thoughts about race (Brace, 1982; Caspari, 2009).
Hooton was interested in the use of cranial nonmetric traits (e.g., presence/absence of the
infraorbital suture) for classificatory purposes. He created the Harvard Blanks as a standard
for recording of nonmetric traits, general cranial observations, and cranial measurements he
deemed useful for answering his research interests about body form (Brues, 1990).
Many of his students shared his typological ideas, and several published books and arti-
cles that looked at race from a largely deterministic or typological point of view. Hooton’s
own publications ranged from the clearly typological (On Certain Eskimoid Characters in
Icelandic Skullsd1918) to eccentric applications of typology and determinism (Crime and the
d1939). Ironically, Hooton seemingly was antiracist and participated in antiracism
activities, e.g., attempting to create an antiracism group in anthropology (Caspari, 2003),
yet his typological analyses supported a hierarchical arrangement of the races. From a histor-
ical perspective, even taking into account this history, Hooton is one of the most significant
figures for the development of physical anthropology (Shapiro, 1954).
Hooton was incredibly influential in the growth of the field in large part because physical
anthropology did not exist as an established discipline when he began at Harvard in 1913 and
his program was the first to produce PhDs rapidly in the discipline (28 overall). Many of his
students went on to lead physical anthropology programs in anthropology departments at
major universities in the United States (Spencer, 1981; Giles, 1997). A large majority of
currently practicing biological anthropologists (including both authors on this chapter) can
trace their educational pedigree via thesis or dissertation committee members three or four
steps back to Hooton, illustrating his overall impact on the field (Caspari, 2009).
Scientific racism is defined as the use of science to justify discrimination against groups of
people based on perceived inherent differences. These inherent differences typically begin
with skin color and other phenotypic traits that have been used to classify races and are
expanded to include culture, intelligence, and morals (Nash, 1962; Blakey, 1999). Scientific
This book in particular showcases Hooton’s beliefs that people could be categorized almost ad infinitum
(he has groups such as “native white” and “Old American,” the difference between which depends upon
how many generations their families have been in the United States) and that this typology determines
one’s propensity to commit certain types of crimes.
racism as a construct developed in the West over the past three centuries from two ideas: (1)
scientific knowledge is authoritative; and (2) groups of people can be separated taxonomi-
cally on the basis of both physical and cultural characteristics (Marks, 2008). The history of
taxonomically separating groups of people is mired with inquiry as to what defines or sepa-
rates one group from another. Beginning with Linnaeus, groups were separated not just on
the basis of obvious physical differences (skin color, hair color, etc.) but also on character
traits (such as level of intelligence and culture) that were arbitrarily chosen to be definitive.
With scientific racism, science is used to validate these co-associations. The problem is three-
fold: (1) as will be discussed later on, there is no biological basis for separating human groups
on the basis of race; (2) character traits such as intelligence are complexly influenced by both
genetics and environment and are not exclusive to one group of people versus another; and
(3) given science’s authority with the public, a scientific proclamation stating that physical
and character differences are related is very difficult to retract.
Social Darwinism and The Origin of Species
The most famous publication in the nineteenth century was Charles Darwin’s thesis on
how species come into being (Darwin, 1859). While the concept of evolution was not new
and several others had tried (and failed) to explain its mechanism, Darwin’s explanation
of natural selection was the first to logically elucidate a mechanism of evolution. Essentially
natural selection states that those organisms having beneficial adaptations (beneficiality
depends on the environment in which an organism lives) are more likely to survive, repro-
duce, and pass on those advantageous traits, while those organisms with nonbeneficial traits
will not survive to reproduce, or at least not in significant numbers. The result is that advan-
tageous traits will appear in organisms at a higher proportion than non-advantageous traits
for a particular environment. Natural selection, along with other evolutionary forces (see
Cabana et al. [Chapter 16], this volume) can lead to the formation of new species.
The book was revolutionary for the field of biology for obvious reasons, but it also spurred
unforeseen effects in other fields and for society at large. While Darwin only briefly
mentioned the implication of his theory for human beings in the last chapter of that volume,
other scholars latched onto the idea and extrapolated natural selection to include cultural
achievement and development (Nash, 1962; Blakey, 1999; Graves, 2001). Each race, therefore,
was believed to have its own specific level of intellect and culture by which it could be char-
acterized (Nash, 1962; Blakey, 1999).
Herbert Spencer, a contemporary of Darwin, was the first to coin the now-famous phrase,
“survival of the fittest” (Stanton, 1960; Shipman, 1994; Graves, 2001). When this phrase was
applied to human beings, an individual’s “fitness” was defined as intelligence, attractive-
ness, education, wealth, and cultural accomplishments, in addition to other characteristics.
Survival of the fittest was used to justify and rationalize social institutions like capitalism and
Darwin discussed the origins of humans and the issue of race in a later publication, The Descent of Man
It is important to point out that each of these categories is culturally bound, i.e., each culture has its own
definition of intelligence, wealth, of what it means to be civilized and educated, and so on. In this case, it
was the privileged sector of Western culture setting the categories and definitions.
colonialism, two systems that favor certain groups of people over others (Marks, 2008). It was
considered that those not fit enough were simply not destined to survive and reproduce. This
fate was not the fault of any person or institution, but through the destiny set forth by science.
Evolution was therefore unfortunately used to explain and validate the differences between
the classes, the races, and the sexes.
Social Darwinism also led to the belief that social and cultural traits (e.g., poverty, propen-
sity to criminality) were inherited as were physical traits. Francis Galton (Darwin’s cousin)
coined the term “eugenics” in 1883 (from the Greek for “well born”) to espouse his ideas
of artificial selection for human beings (Shipman, 1994). In his view, “undesirable” traits
were inherited, and therefore breeding programs for humans could be designed to combat
the propagation of undesirable traits by allowing only “desirables”
to mate (Montagu,
1964b; Gould, 1981; Shipman, 1994:111; Graves, 2001; Paul, 2008). Galton argued that it
was society’s responsibility to control human reproduction so that the lower classes
(including criminals and the poor) would be prevented from passing on their defects
(Paul, 2008). Clearly, there was no consideration or even acknowledgment of any effect the
environment had on the development of various “undesirable” characters. Of course, the
environment in large part included the social conditions that the higher classes had imposed
to create the lower classes.
Ernst Haeckel, who was a contemporary of Darwin’s and a German biologist, further
developed ideas such as these. He argued incorrectly that evolution was progressive and
goal-directed leading to an ideal form (he argued for the Aryan type) and that social and
cultural traits were freely inherited without influence from the environment (Graves,
2001). Similar to many of his contemporaries, Haeckel believed in a social hierarchy. For
example, Haeckel wrote (disturbingly by today’s standards) in 1905 (emphasis added),
“These lower races .are psychologically nearer to the mammals (apes or dogs) than to civi-
lized Europeans; we must, therefore, assign a totally different value to their lives.”
Charles Davenport, an influential American biologist in the early twentieth century, was
responsible for introducing eugenics to the United States
(Shipman, 1994; Marks, 2008).
His book, Heredity in Relation to Eugenics (1911), fed on the fears of the ruling class Ameri-
cans, mainly that the influx of immigrants with certain undesirable traits (poverty, propen-
sity towards criminality, homosexuality, chronic illness, etc.) would lead to the downfall of
society (Davenport, 1911; Marks, 2008). Davenport’s main thesis was that biology was
behind the development or downfall of civilization. Five years later, Madison Grant used
these ideas to argue that the answer to the problem lay in the sterilization of people deemed
to be unfit (1916). This book, and other eugenic writings,
led to eugenics laws with 30
One of the key dangers here is in who gets to decide which traits are “desirable” and which traits are not.
Interestingly, while eugenic ideas spread worldwide, each country or region focused on one aspect more
heavily than another (e.g., class differences in one country versus race differences in another) (Marks, 2008)
thereby illustrating the pervasive nature of culture to even influence emphasis of racist thought.
Not every scientist agreed with the tenets of eugenics, including Franz Boas (e.g., see Boas, 1918a), but
few came out publicly to denounce it in the early days of the movement (Marks, 2008).
different states in the U.S. sterilizing people involuntarily over the next two decades (Suzuki
and Knudtson, 1989; Marks, 2008). The state of California
alone for example forcibly ster-
ilized 20,000 people before World War II on the basis of perceived mental disability, criminal
history, or other undesirable traits
(Suzuki and Knudtson, 1989; Larson, 1996; Marks,
2008). Shamefully, these American laws helped to form the basis for genocidal practices
in Nazi Germany (Suzuki and Knudtson, 1989), following a progression from forced steril-
ization to human extermination. The onset of the Great Depression in the United States
redirected focus on domestic economic problems while eugenic ideas took hold and flour-
ished in Germany during the same time period (Bozeman, 1997; Marks, 2008). While
some involuntary sterilizations continued after WWII in the United States, the horror of
the Holocaust and the fact that it was the end result of eugenic ideas discredited such
programs. The laws were gradually removed, albeit quietly in many instances (Blakey,
1999; Marks, 2008).
Although it would seem that this should signal the end of the use of science to justify
group inequality, recent examples of scientific racism continue to include proclamations
that intelligence and race are linked, that athletes from certain groups are naturally better
at particular sports than others, that different races are more prone to certain diseases than
others, and that genes for different human behaviors are connected with race (see Gould,
1996; Armelagos and Goodman, 1998; Goodman, 2000; Graves, 2001; Smedley and Smedley,
2005; Sternberg et al., 2005; Marks, 2008; Gravlee, 2009). These beliefs persist in society at
large regardless of the fact that none of these assertions can be or has been validated from
a scientific standpoint. Furthermore, these stereotypes fail to account for socioeconomic
and environmental factors (see for example discussion in Cartmill, 1999). In addition, the
very existence of stereotypes that reinforce the popular view of race and biology act via
culture to actually establish measurable differences in health between different racial groups
(Gravlee, 2009). Gravlee (2009) has adapted Kuzawa’s (2008) model of health inequalities to
demonstrate how this occurs, in addition to a superb discussion. The reader is encouraged to
refer to this paper for more information.
Obviously, scientific racism can have, and has had, very severe and tangible consequences.
As a 21st century anthropologist contemplating an ancestry project, it is essential that you
realize this discussion is not purely of academic interest. Our discipline has discarded the
concept of biological race and with it the ideas that character traits are associated with phys-
ical traits. However, the fact remains that race is a social construct. Consequently, there is
a societal cost especially for those perceived to be members of the so-called inferior races
(Moses, 2004; Smedley and Smedley, 2005). Anthropology’s past assertions have contributed
to the solidification of societal ideas about race (Harrison, 1995, 1999) and therefore we need
to decide how to manage the consequences. As Harrison notes, “.there is no theoretical,
methodological, or political consensus shared across any of the subdisciplines on how to
interpret and explicate the social realities that constitute race” (1999:610). Montagu described
race as an “event” that is experienced (1964b:117) and our discipline has yet to systematically
In early 2012, the state of North Carolina resolved to financially compensate its approximate 7500 living
victims of involuntary sterilization, the first restitution for such cases in the United States (Severson, 2012).
Feeblemindedness, alcoholism, and epilepsy were included as well (Suzuki and Knudtson, 1989).
examine the cost of this event socially or biologically (Armelagos and Goodman, 1998; Har-
rison, 1995, 1999; Lieberman and Kirk, 2004; Gravlee, 2009).
It is a reality that social race exists and that forensic anthropologists can estimate
geographic ancestral origin from different bones. But this same reality makes ancestry esti-
mation a delicate endeavor indeed. On the one hand, biological anthropologists say, “race
does not exist biologically”; yet on the other, they say, “however, we can estimate ancestry
from the skeleton.” This contradictory message confuses the public, in part because they
are not aware of the nuances of the evolutionary forces that have led to certain skeletal
features, but also because such statements would seem to reinforce societal views about
different race categories. Blakey refers to the conundrum of continued racial categorization
as a “tangled web” (1999:42) and it is clear that untangling the web to move past categoriza-
tion is an impossible task since we rely on traditional categories to estimate ancestry from
skeletons. The point is that as anthropologists who estimate ancestry, it is our responsibility
both to the discipline and to society to recognize the societal implications of doing ancestry
estimation. We must consider two major interests of society: (1) victim/decedent identifica-
tion, and (2) combating racism. How do we decide which interest is more important and how
do we convincingly and clearly explain to the public the difference between social race cate-
gories and the characteristics of different geographic populations that we can see and
measure from the skeleton?
“To give up all general racial classifications would mean for anthropology freeing
itself from blinkers it has too long worn, and focusing all its energy on its actual goal:
the understanding of human variability, as it really is.” Jean Hiernaux (1964:43e44)
One could argue that Franz Boas first laid the overall foundation for our current conception
of ancestry just before the turn of the twentieth century (Caspari, 2009). His publications
showcase his interest in human variation outside of race, rejecting both biological determinist
and typological explanations (Caspari, 2009; and for example Boas, 1918b). He additionally
focused on the concept of culture and the effect of the environment on human variation,
perhaps being at least partially influenced by Edward Tylor’s famous definition of “culture,”
still in use today: “that complex whole which includes knowledge, belief, art, law, morals,
custom, and any other capabilities and habits acquired by man as a member of society” (Tylor,
1871; Caspari, 2009). During his career, he continued to publish on similar ideas; however,
typological and racial determinist ideas continued to compete (Littlefield et al., 1982).
This began to change in 1951 with Sherwood Washburn’s seminal paper, The New Physical
Anthropology. In it, Washburn, a Hooton student, defined a new direction for the discipline:
a movement away from applied typology and toward studies examining evolutionary
change, population genetics, and human variation (1951). Essentially, this paper set up the
framework for modern thought in biological anthropology, i.e., a focus on the population
from an evolutionary perspective. The population in this sense can be defined as a group
of contemporary human individuals living in relatively the same geographic area who
have a shared culture that includes language, traditions, and belief systems and who tend
to find mates from within the same group.
Several scholars contributed to this major shift in
the discipline, including Ashley Montagu (a Boas student) and Frank Livingstone (a Harvard
student who took classes from Hooton). These scholars argued that race does not exist
because it does not explain the scope of human variation (Livingstone, 1962), it ignores evolu-
tionary forces, and it is imbued with social meaning (Montagu, 1964; Washburn, 1964).
Further, Comas (1961), in refute of biological determinism, additionally emphasized the
importance that the environment plays in influencing trait expression.
While the discipline was changing focus, change nevertheless came slow. Littlefield et al.
(1982) demonstrated that the majority of physical anthropology textbooks published between
1932 and 1969 took the position that human beings were divided into races. Interestingly, this
trend began to decrease after 1970, with more texts arguing that races do not exist (Littlefield
et al., 1982). Despite this gradual development over the past 40 years, Caspari (2003) contends
that while the discipline may have discarded the race concept idea, certain aspects of racial
thinking continue. This includes essentialism (viewing each racial taxonomic category as
having certain essential features that define it which are due to a separate evolutionary
history) and cladistic thinking (viewing the relationships between races as clades, with
each race separate from the others having its own branch on a tree diagram) (Caspari, 2003).
While this is appropriate for illustrating evolutionary relationships between species,
which by definition are reproductively isolated from each other, separating human groups
into clades is not an appropriate way to explain human variation because (1) all modern
humans belong to the same species and therefore we successfully mate with each other;
and (2) it suggests that different human groups had separate evolutionary histories (evolving
from separate ancestors), which is not the case. Today biological anthropologists study pop-
ulations rather than races, but the definition of “population” still often incorporates these
essentialist and cladistic aspects (Caspari, 2003). Future research should move beyond this
type of antiquated thinking.
Human Variation
Recently, the American Journal of Physical Anthropology published a special issue on race and
human variation (2009, 139(1): 1e107). The papers cover the range of agreement and
disagreement regarding how the field currently conceptualizes human variation, in terms
of its differences and patterns. The papers demonstrate that general agreement centers
around several points: (1) variation exists within and between populations; (2) the environ-
ment, including culture and geography, has exerted considerable influence on variation; (3)
race is neither a useful nor correct way to describe populations; and (4) research in human
variation holds implications for society and fields such as forensics and medicine (Edgar
and Hunley, 2009).
Conversely, disagreement centers on how the geographic patterns of variation are orga-
nized. One school of thought is that human variation is clinally distributed, and that more
genetic variation exists within a population than between all populations (Livingstone, 1962;
Lewontin, 1972; Edgar and Hunley, 2009). The concept of a cline was introduced by British
This mate choice tendency is not absolutedall humans are members of the same species because there is,
has been, and will continue to be gene flow between populations.
evolutionary biologist Sir Julian Huxley in 1938 and refers to a gradual change of a character
or feature in a species over a geographical area (Lieberman, 2008). For example, Relethford
(2009) argues that human phenotypic features such as skin color and craniometrics are
patterned clinally (e.g., skin color being darkest near the equator and gradually lightening
as latitude increases), and that natural selection controls traits such as expression of skin
color from environmental pressure. Similarly, craniometrics are partially controlled by
natural selection in some instances and selectively neutral in others, although craniometric
differences still demonstrate more variation within local populations than between them.
Relethford (2009) argues that while geographic patterning is evident in these traits, placing
them into broad racial categories masks the true diversity of human variation.
The other school of thought explains variation as resulting from complex factors that
contribute to evolutionary forces, such as migration, bottlenecks, and population divisions
(e.g., Hunley et al., 2009). These complex factors interrupt gene flow as larger populations
are split up. This leads to the founder effect, where the genes of a smaller segment of the
larger population become overly representative of the parent population, resulting in genetic
While workers such as Hunley and colleagues (2009) and Long and colleagues (2009)
contend that the pattern of human variation is nested (the diversity in one population is
a subset of the diversity found in another) rather than clinal, they come to the same conclu-
sion as those in the cline camp: namely, that the traditional racial classification system is not
adequate for explaining human variation.
Regardless of what the actual geographic pattern of variation turns out to be, the impor-
tance of the environment in expression of phenotypic traits cannot be overstated. There is
a complex interplay between genotype,phenotype, and environment, which will never be
fully teased apart. Remember that the environment consists of sociocultural and physical
aspects to which an individual is exposed, beginning in utero and including but not limited
to postnatal factors such as nutrition, diet, exposure to pathogens, climate, education, and
physical and psychosocial stress. The environment essentially influences which traits will
be beneficial, harmful, and neutral, factors that change as the environment changes.
Cartmill (1999) contends that culture also plays a strong role in how genes and the envi-
ronment interact. The reader is encouraged to refer to that paper for an excellent discussion
on the interplay between environment and heredity. Lieberman and Kirk (2004:137)
further emphasize that one of the reasons the race concept has been rejected is due to the real-
ization that cultures are “a dynamic expression of their history and ecologya
`la Boas.
Research that examines human variation must account for environmental factors and
acknowledge that it is likely that not all of the different aspects of the environment’s influence
on trait expression will be uncovered.
Therefore, to restate the overall research problems currently under investigation: (1) What
is the true nature of human population history that has led to the range of existing variation?
(2) How can geographic patterns explain human variation? (3) How does geography and
evolutionary forces contribute to the patterning of phenotypic and genotypic variation?
Many avenues are being used to address the numerous questions inherent to these prob-
lems, including from the field of DNA and from a biological distance perspective. Refer to
See Cabana et al. (Chapter 16), this volume for further definition and discussion of evolutionary forces.
Cabana and colleagues (Chapter 16), this volume, for a discussion on what DNA analysis has
revealed about human variation. Biological distance can be defined as how closely related or,
alternatively, divergent populations are from one another. Given that one of the assumptions
with biodistance analysis is that changes in allele frequencies due to evolutionary forces such
as genetic drift and gene flow affect changes in phenotypic traits, including skeletal features
(Stojanowski and Schillaci, 2006), studies of biological distance are relevant for the ancestry
problem. Several workers have addressed the problem of human variation (especially within
bioarchaeological studies) using biological distance models, with perhaps the work of Rele-
thford and Blangero, and Konigsberg and colleagues being the most central (e.g., Relethford
and Lees, 1982; Relethford and Blangero, 1990; Relethford, 1991; Konigsberg, 1990, 2000;
Konigsberg et al., 1993; Konigsberg and Ousley, 1995). Refer to Konigsberg (2006) for a review
and see McKeown and Schmidt (Chapter 12), this volume for more information on
Ancestry is the third component of the biological profile, after age-at-death and sex esti-
mations. In any society with a diverse population like the United States, part of the recovery
of decomposed, damaged, and/or skeletonized human remains from a medicolegal purview
(e.g., from clandestine disposal to mass disaster) will often include questions by law enforce-
ment regarding the race, or ancestry, of the victim.
When the skull (and more importantly the facial skeleton) is complete, the likelihood of
estimating ancestry accurately is assumed to be high. We state this with a caveat however,
as correct ancestry estimation depends on (1) the availability of an appropriate reference
sample (discussed below), and (2) the analyst’s ability and experience with the measurement
techniques and his or her ability to correctly understand and visually assess the cranial
nonmetric features associated with various ancestral groups.
However, as Sauer (1992:107) questioned, “If races don’t exist, why are forensic anthropol-
ogists so good at identifying them?” The answer to this question lies in the fact that concor-
dance exists between social race categories (i.e., Black, White) and cranial morphology
(Ousley et al., 2009). Evolutionary forces (e.g., gene flow, genetic drift) have led to a discor-
dance of skeletal traits (and other phenotypic traits) between populations enabling us to
measure and analyze that data. This leads to ancestry estimations based on our knowledge
of trait frequency in each major population group ee.g., variation in cranial morphology
is structured by geography (Kennedy, 1995; Relethford, 2009). Sauer (1992) and Konigsberg
et al. (2009) further reason that we must use the same terminology for ancestry categories
used by the medicolegal community in order to make a contribution to the identification
of remains.
There are two generally accepted methods of ancestry estimation in forensic anthropology:
(1) metric analysis of cranial and postcranial measurements, and (2) nonmetric (morpho-
scopic) traits of the cranium. There are advantages and disadvantages to each of these
approaches (outlined below). Prior to an in-depth look at each, a general comparison of
the statistical treatment for each is warranted.
Metric traits are measured on a continuous scale (e.g., maximum cranial breadth can be,
at least theoretically, any value between 0 and N)whereasmorphoscopic traits are
measured categorically. The statistical treatment of continuous data has several advantages
over categorical data, which is assigned a value that is in one of several possible categories.
Categorical variables do not always have a numerical meaning. In other words, variables
like skin color (light, dark) and nasal aperture width (narrow, intermediate, wide) do not
necessarily have an explicit numerical equivalent. Because of this feature, categorical
data are not appropriately treated with the same statistical methods as their continuous
(i.e., metric, numerical) counterparts. The statistical methods used to evaluate continuous
data (see examples below) are more widely used and are generally better understood
than those used for categorical methods. Therefore, we will briefly outline the methods
used to treat continuous variables and spend the majority of our discussion on the treat-
ment of categorical data. As stated, this is a brief overview and several statistical concepts
will be introduced in the upcoming section. A certain knowledge of statistics is assumeddif
any of these concepts are unfamiliar, we recommend taking graduate level statistics courses
to catch up.
Metric Methods
Analyzing craniometric (and more recently postcranial metric) data to assess ancestry has
a long history in anthropology. To analyze craniometric data, the first and therefore most
important step is the proper collection of craniometric data. In the past, such data were
collected using sliding and spreading calipers, craniofor, and mandibulometers, among other
tools. However, a large number of laboratories are switching to three-dimensional digitizers
for data collection (e.g., see McKeown and Schmidt [Chapter 12], this volume). No matter the
method of data acquisition, the theoretical underpinnings are the same: the collection of land-
mark data and interlandmark distances for use in data analysis. The landmarks used by
forensic anthropologists are rooted in the earlier work of several prominent (though often
infamous) physical anthropologistsdrecall Morton’s early craniometric data collection.
However, Martin (1914) and Howells (1973, 1989, 1995) are considered the “gold standards”
for landmark descriptions, illustrations, and definitions and should be consulted regularly by
both inexperienced and experienced anthropologists. Of course, reading the literature and
landmark definitions is no substitute for mentoring. Find an experienced anthropologist,
pester them to no end and watch over their shoulder as they explain the nuances of data
collectiondit worked for us, it will work for you, as well. The interlandmark distances
used in a final analysis (such as FORDISCddiscussed below) are outlined in Martin
(1914);Howells (1973, 1989, 1995) and Buikstra and Ubelaker (1994).
FORDISC and Discriminant Function Analysis
Once the data have been appropriately collected the next step is finding and using an
appropriate known reference sample. In the United States, this is most often the Forensic
Anthropology Databank (Jantz and Moore-Jansen, 1988) and the computer program
FORDISC 3.0 (FD3) (Jantz and Ousley, 2005). One part of properly utilizing FD3 is appreci-
ating what, exactly, FD3 is doing. Fordisc uses discriminant function analysis (DFA) to clas-
sify an unknown individual into one of several reference populations and is, by and large, the
most widely used classification statistic in forensic anthropology, particularly when the data
are continuous.
Giles and Elliot (1962, 1963) first used a DFA on crania to determine sex and race for Amer-
ican White, American Black, and Amerindian
crania. Linear discriminant function analysis
was developed as a means to classify a target individual (e.g., unknown crania) into one of
several reference groups by incorporating a similar mathematical approach to regression
analysis (Krzanowski, 2002). Whereas regression analysis uses a weighted combination of
predictor variables to calculate some object’s value (e.g., stature from measurements of the
postcranial skeleton), DFA uses a weighted combination of those predictor variables to clas-
sify an unknown object into a reference group based on a distance statistic. The discriminant
function score is a derived variable (Krzanowski, 2002), which is equal to the weighted sum
of values for each variable.
The most common distance statistic employed in forensic anthropological research and
classification is Mahalanobis distance (D
), which is a distance measure similar in practice
to Euclidean distance (the “ordinary” distance between two points as one would measure
with a ruler), but that is not affected by scale or correlation (Krzanowski, 2002). Unlike
Euclidean distance, D
is based on the covariance between variables and is used to measure
the similarity (as the distance from a group centroid
) between unknown and known indi-
viduals. When interpreting the D
value, smaller distances equate to more similar
The statistical assumptions associated with DFA include multivariate normality and homo-
geneity of variances/covariances. Multivariate normality is one of the most common assump-
tions in statistics, as many tests and statistics are related to the normal distribution (think
bell curve here). Generally, testing for multivariate normality is testing for univariate and
bivariate normality, that is, testing to see that each variable is normally distributed and, like-
wise, that all pairs of variables are bivariate normal using one- and two-dimensional plots
(i.e., histograms and scatterplots). In practice, this is generally sufficient for testing for multi-
variate normality, especially when using DFA as that method is relatively robust against
deviations from multivariate normality. Other more robust methods to test for multivariate
normality exist, but are beyond the scope of this work (cf., Mardia’s statistic of multivariate
skewness/kurtosis [Mardia, 1970] or the Doornik-Hansen multivariate normality test [Door-
nik-Hansen, 2008]).
The second assumption involves whether there is homogeneity of variances/covariances
(or, testing that the level of variation in each group is relatively similar) and testing for this is
also relatively straightforward. There are a variety of tests for homogeneity. In FD3, homoge-
neity among samples is tested using the Kullback (1959) test for homogeneity. If the level of
These were the terms originally used by Giles and Elliot and also are terms used by FORDISC and the
Forensic Databank. We will use the same terms when referring to FORDISC in this section to stay consistent
with its terminology.
The group centroid is the point that represents the mean for all variables in the multivariate space defined
by the variables in the model.
heterogeneity within groups is high the analyst is encouraged to explore other statistical
procedures, such as logistic regression (Jantz and Ousley, 2005).
Two additional considerations in DFA are outliers and multicollinearity. Discriminant func-
tion analysis is sensitive to the inclusion of outliers (individuals or measurements falling far
outside the collective distribution of all other individuals or measurements). The researcher
should carefully consider the data through graphs (plots) and descriptive statistics to identify
potential outliers. If outliers are found, the cause for each should be identified, when
possible. Remember, transcription errors (e.g., 24 entered as 42), incorrect data entry
(entering maximum cranial breadth (XCB) for maximum cranial length (GOL)), and
measurements that are just wrong (XCB measured as 145 when it is in fact 120) may lead
to outliers. When these types of errors are identified the data should be corrected. If no expla-
nation can be found, the individual may be dropped from the analysis unless there is good
reason to suspect he or she is just an expression of the variation seen in that population.
Multicollinearity is the same as trait interdependence (correlation). When two variables
are highly correlated (or one is the sum of other dependents) the parameter estimates behave
erratically when the model (or the variables) undergoes even minute changes. While this
does not affect the overall model, it does affect classifications based on that model. In other
words, collinearity also means the standardized discriminant function coefficients cannot
reliably assess the relative importance of the predictor variable(s), decreasing the overall
strength of the final discriminant function for classification purposes. As with outliers,
graphs (two-dimensional plots) of the variables will assist in identifying highly correlated
Two additional statistics that can be obtained from the discriminant function analysis
provide further information about the classification. The FORDISC 3.0 help file (Jantz and
Ousley, 2005) goes into great detail about posterior and typicality probabilities, but a brief
explanation will help the reader better understand some of the analyses described below.
Posterior probability is the probability that the unknown belongs to any one of the popula-
tions selected for in the analysis and is based on the relative distances the unknown has
(calculated using Mahalanobis distance, or D
) to each population. Because it is the proba-
bility of belonging to any one of the populations used in the analysis, the posterior proba-
bility will always sum to 1. A major assumption (of classification statistics in general) is
that the unknown individual truly belongs to one of the reference groups (hence the need
for strict guidelines when selecting reference samples), because a DFA will always “force”
a classification.
We can use another statistic, typicality probability, as a measure of how likely it is that the
unknown does, in fact, belong to any one of those populations. Typicality probability is based
on the absolute distances of the unknown from all groups, rather than the relative distances.
Please note that the typicality probability is essentially equivalent to a univariate t-test. In
other words, it is a measure of how many other individuals in a population would be
expected to be as far or farther from that population’s centroid than the unknown individual.
As Jantz and Ousley (2005:np) point out “[typicality probabilities] below 0.05 (5%), or
certainly 0.01 (1%) for a group .indicate questionable probability of membership in that
group or the possibility of measurement error.” This means that the typicality probability
can essentially be ignored if the value is greater than 0.05, since such values do not indicate
a statistically significant difference in the suite of measurements. When the value is less than
0.05, carefully consider the measurements entered and the populations (reference samples)
included in the analysis.
Case Study: Using FORDISC
Identifying the appropriate reference sample is one of the more daunting aspects of
ancestry assessment using FD3. FD3 has two major samples to which an unknown may
be compared. The first is the Forensic Databank (FDB) (Jantz and Moore-Jansen, 1988),
which had approximately 3400 cases and growing as of early 2011 (Ousley et al., 2011).
The FDB consists of identified individuals from forensic cases originating predominantly
from the United States. The second is the Howells database of 2504 individuals from 28 pop-
ulations around the world compiled by W.W. Howells. When doing an analysis using
FORDISC, the user chooses to which groups the unknown cranium should be compared.
However, the challenge is in deciding at what point one group or another should be
excluded from the comparison. The general consensus is to begin with a broad approach.
In FD3, this would include all possible groups in the first analysis, and then, based on those
results, removal of populations that are not probable. For example, if your results in the first
analysis suggest a male individual, with all values being highest for males regardless of the
population, then all females should be removed and the analysis processed again.
For the sake of example, let us assume that in the second analysis the values in Table 5.1 are
obtained. Clearly, this individual is not a white male (D
¼28.6; Post. Prob. ¼0.000). In fact, it
is highly unlikely (improbable) that the cranium in the example above belongs to Hispanic
males, Amerindians, or Black males, based on the low posterior and typicality probabilities.
Once those groups are removed and the analysis is recalculated, we get the following results
in Table 5.2.
Although the classification (Vietnamese male) has not changed, we have narrowed down
the potential list of ancestral groups to four populations. Again, we could continue to narrow
the populations down to just two groups (as suggested in the FD3 manual). However, for our
purposes we can feel confident that this individual is most likely Vietnamese, though not to
the exclusion of other reasonable possibilities. Wait a minute! That doesn’t seem good
enough. If you are like us, you will see that this is not enough information to make a final
estimation of ancestry. Even if we reduce the number of variables (Table 5.3 using a stepwise
selection esee the FD3 manual for a detailed discussion of this process) we are no closer to
a final determination.
In fact, these results further muddy the issue because now the VM and GTM results are
nearly identical. This is not an uncommon situation and it clearly demonstrates that a proper
understanding of human variation, metric analysis, and nonmetric traits is necessary not
only to correctly assess ancestry, but also to correctly interpret FD3 results and properly select
reference samples.
So what are we to make of the example case described above? All of the assumptions for
discriminant function are met, so the DFA appears to be performing well. Other chapters in
this volume describe the importance of context when interpreting results from skeletal
analyses. Perhaps the context (i.e., situation in which they were found) of these remains
can assist in making our final decision.
This example was taken from the FORDISC 3.0 help file (Jantz and Ousley, 2005) and is
identified therein as Example 2. The measurements are from a University of Tennessee
TABLE 5.1 Multigroup Classification of Example 1 Using FD3
Group Classified Distance (D
) Post. Prob. TypF
VM **VM** 10.2 0.368 0.941
GTM 11 0.242 0.883
CHM 11.2 0.225 0.875
JM 12.7 0.107 0.783
HM 14.3 0.046 0.597
AM 17.5 0.010 0.648
BM 20.2 0.003 0.343
WM 28.6 0.000 0.036
Example 1 is closest to VMs.
VM ¼Vietnamese Males
GTM ¼Guatemalan Males
CHM ¼Chinese Males
JM ¼Japanese Males
HM ¼Hispanic Males
AM ¼Amerindian Males
BM ¼Black Males
WM ¼White Males
TABLE 5.2 Reduced Multigroup Classification of Example 1 Using FD3
Group Classified Distance (D
) Post. Prob. TypF
VM **VM** 10.7 0.669 0.927
GTM 13.5 0.164 0.769
CHM 14.5 0.101 0.716
JM 15.4 0.065 0.628
Example 1 is closest to VMs.
TABLE 5.3 Stepwise-Selected, Reduced Multigroup Classification of Example 1 Using FD3
Group Classified Distance (D
) Post. Prob. TypF
VM **VM** 2.4 0.465 0.82
GTM 2.6 0.433 0.791
CHM 6.8 0.051 0.288
JM 6.9 0.051 0.276
Example 1 is closest to VMs, however note that VM and GTM results here are nearly identical.
forensic case that was positively identified as a Laotian male. The reader is encouraged to
utilize the help file and the tutorials within to further explore this example.
Of course,
another approach is nonmetric (morphoscopic) data, which have been used to verify metric
analyses or to refute their outcome. In the following section, we explore morphoscopic traits
and their use in the assessment of ancestry.
Nonmetric Methods
Nonmetric traits have a long history in anthropology, particularly as they relate to ancestry
assessment. However, there are two distinct types of cranial nonmetric traits: epigenetic vari-
ants and morphoscopic traits. While the focus of this chapter is on the latter, one should
understand both types. Traditional cranial nonmetric, or discrete, traits (“epigenetic vari-
ants” following Hauser and DeStefano, 1989) are defined following Buikstra and Ubelaker
(1994) as “dichotomous, discontinuous, epigenetic traitsdnon-pathological variations of skeletal
tissues that can be better classified as present or absent (or as a point on a morphological
gradient, e.g. small to large) rather than quantified by a measurement.”
There are five major categories of epigenetic variants in the cranium: (1) extrasutural bone
(e.g., Inca bone); (2) proliferative ossifications (e.g., pterygo-alar bridging); (3) ossification
failure (e.g., septal aperture); (4) suture variation (e.g., metopic suture); and (5) foramina vari-
ation (e.g., zygomatico-facial foramen number) (Buikstra and Ubelaker, 1994). As discussed
earlier, the roles played by the genome and the environment in the inheritance of cranial
nonmetric traits (or any phenotypic traits) are poorly understood. However, these traits
are routinely used in biological distance studies as a measure of relatedness within and
between populations (i.e., Sjøvold 1977, 1984, 1986) and as a proxy for identifying familial
relationships within cemeteries (Pilloud, 2009). In a forensic context, the traits used to assess
ancestry are not necessarily the same characters as epigenetic variants, because of the unique
history of morphoscopic traits in forensic anthropology (Hefner, 2009).
Morphoscopic Traits
Ousley and Hefner (2005) first used the term “macromorphoscopic” to describe the cranial
nonmetric traits used in forensic anthropological research. They considered macromorpho-
scopic traits to be quasicontinuous variables of the cranium that can be reflected as soft-tissue
differences in the living (cf., Brues’ [1958]: second class of traits “due to the contour of bone in
areas where it closely follows the surface .apparent in both skeleton and living”). Later,
Hefner (2009) simplified the term to morphoscopic traits but maintained the original character-
ization of the variables. These traits fall into one of five classes: (1) assessing bone shape (e.g.,
nasal bone structure); (2) bony feature morphology (e.g., inferior nasal aperture
morphology); (3) suture shape (e.g., zygomaticomaxillary suture); (4) presence/absence of
data (e.g., post-bregmatic depression); and (5) feature prominence/protrusion (e.g., anterior
nasal spine) (Hefner, 2009).
FORDISC 3.0 is available from the Forensic Anthropology Center at the University of Tennessee <http://>; additionally, any forensic anthropology laboratory in the U.S. and Canada will
have a copy.
Morphoscopic traits are used to assess the ancestry of a single individual for the purpose
of identification. The morphoscopic traits more commonly employed to assess ancestry can
be found in Hefner (2009). These traits are drawn predominantly from trait lists found in
Rhine (1990) and most introductory forensic anthropology textbooks. For an in-depth discus-
sion on historical aspects of morphoscopic trait analysis not covered herein, see Hefner and
colleagues’ (2012) discussion of morphoscopic traits and the assessment of ancestry.
When assessing ancestry from an unknown set of skeletal remains we caution against the
use of typological trait lists that supposedly typify the skull of an individual derived from
a specific ancestral group, sensu Rhine (1990). A more methodologically sound approach
involves focusing on individual traits (characters) and the variable expression of those traits
(character states) within and between populations. Remember, no single trait is found exclu-
sively in only one population, as with other phenotypic traits. As one of the authors has
demonstrated elsewhere (Hefner et al., 2012), shovel-shaped incisors are often cited as an
Asian-specific trait. While shoveling occurs in 70e85% of Asians worldwide (Scott and
Turner, 1997) (not 100% as may be believed), the same trait is also found in almost all other
populations, though in much lower frequencies (3e10% of Europeans; 8e11% of Africans
[Scott and Turner, 1997]).
Nonmetric traits are not discrete or isolated within one population due to multiple factors.
In fact, the variation results from very specific evolutionary mechanisms. Mechanisms such
as the genetic effects of selective pressures from particular environments, the effects of gene
flow between groups, and the random effects of drift and founding (Lahr, 1996) all play a role
in the expression of variation within and between groups. Of course, by definition the
different levels of gene flow, selection, and drift acting to establish this variation between
groups is closely linked to geography (Lahr, 1996), and it is this geographic division that
accounts for the high degree of variation observed among humans today.
Therefore, we need to understand the frequency of state expressions and meaningfully
combine them into suites of significant traits using appropriate statistical methods. In that
way we (anthropologists) can begin to see the necessary patterns of variation that permit
valid assessments of ancestry using morphoscopic data. As with the craniometric data
described earlier, this requires adequate reference data, standardized protocols for scoring
the variables, and appropriate statistics for categorical data analysis.
To that end, Hefner (2003, 2007, 2009) collected data on the expression of a large number
of morphoscopic traits from multiple skeletal populations and provided a series of simple,
direct illustrations of each character state. The complete list of traits (characters and char-
acter states) and populations are fully described elsewhere (Hefner, 2009). By collecting
such data, Hefner documented intergroup variation without making assumptions about
so-called racial groups, and developed empirically supported methods for assessing
How can these variables be combined to assess ancestry? The answer is via classification
statistics appropriate for categorical data. A number of statistical approaches have proven
useful to analyze morphoscopic data. Two of these are summarized below, and others
(k-nearest neighbor, canonical analysis of principal coordinates, and discriminant function analysis)
are discussed in Hefner et al. (2012) and Hefner (2013). Two additional methods are outlined
below to give the reader a sense of the many ways this type of data can be used during
forensic anthropological analysis.
Ordinal Regression
Ordinal regression analysis (ORA) measures the association of an ordinal response vari-
able (a categorical variable with orderingdi.e., small, medium, large) to a set of predictor
variables (a variable used to predict the value of another variable). In traditional linear
regression, the sum-of-squared differences between a continuous dependent variable
and the weighted combination of the independent variables are minimized prior to calcu-
lating regression coefficients. This is not the case when the dependent variable is ordinal.
Ordinal regression calculates coefficients based on the assumption that the response vari-
able is a categorical response with some underlying continuous distribution. In most
cases, there is a valid theoretical basis for assuming this underlying distribution.
However, even when this assumption is not met, the model can still theoretically produce
valid results.
Rather than predicting the actual cumulative probabilities, an ORA predicts a function of
those values using a process known as a link function. Simplistically, the link function links
the model specified in the design matrix to the real parameters of the dataset. After initial
model development, the predicted probability of each response category can be used to
assign an unknown individual to a group. An ORA can be expressed as
link ðgijÞ¼qj½b1ci1þb2ci2þbpcij(5.1)
where link( ) is the link function for the current analysis, g
is the cumulative probability of
the jth category for the ith case, q
is the threshold for the jth category, pis the number of
regression coefficients, c
are the values of the predictors for the ith case, and b
are the regression coefficients. One of the benefits of ORA, and a similarity of ORA to analysis
of variance (ANOVA), is the ability to assess the significance of individual response variables
and to test for any interaction between all response variables. For example, ORAs allow one
to determine if sex, ancestry, or the interaction of sex and ancestry significantly affect the
expression of inferior nasal aperture morphology.
Ordinal regression analysis can be carried out using the PLUM function in SPSSÒ. The
purpose of the ORA in ancestry research is twofold. First, as mentioned above, the ORA
can be used to determine the significance of sex and ancestry, and the interaction of the
two, on the expression of each morphoscopic trait. Significance is assessed at the a¼0.05
level using the Wald statistic, a measure similar to the F-value in a traditional ANOVA.
Each of these parameter estimates is then assessed for significance. As an example, the
ORA parameter estimates for interorbital breadth are presented in Table 5.4. Once all signif-
icant traits are determined, we can apply the ORA with all significant traits set as the
TABLE 5.4 Parameter Estimates and Significance Levels for Interorbital Breadth
Ind. Variable Estimate Std. Error Wald df Sig.
Ancestry 2.492 0.340 53.723 1 0.000
Sex 1.113 1.250 0.792 1 0.373
Ancestry*Sex 0.929 1.299 0.512 1 0.420
predictor variables to assess ancestry for the entire sample. As Table 5.5 shows, the ORA
works well, separating a sample of American Blacks and Whites (data collected by JTH) in
a two-way analysis correctly nearly 90% of the time. Table 5.5 also presents the classification
matrix for the two-group analysis.
Multiway ORAs are not as successful. In a three-way analysis the ORA correctly classified
approximately 70% of the sample of American Whites, American Blacks, and Amerindians
(Table 5.6). As more groups are added to the model the classification rate is drastically
reduced. This may be because ORAs are somewhat sensitive to sample size. Yet the method
is promising and merits further scrutiny and research.
Logistic Regression
Logistic regression (LR) is a statistical method similar to linear regression since LR finds an
equation that predicts an outcome for a binary variable, Y, from one or more response vari-
ables, X. However, unlike linear regression the response variables can be categorical or
continuous, as the model does not strictly require continuous data. To predict group member-
ship, LR uses the log odds ratio rather than probabilities and an iterative maximum likeli-
hood method rather than a least squares to fit the final model. This means the researcher
has more freedom when using LR and the method may be more appropriate for nonnormally
distributed data or when the samples have unequal covariance matrices. Logistic regression
assumes independence among variables, which is not always met in morphoscopic datasets.
However, as is often the case, the applicability of the method (and how well it works, e.g., the
classification error) often trumps statistical assumptions. One drawback of LR is that the
method cannot produce typicality probabilities (useful for forensic casework), but these
values may be substituted with nonparametric methods such as ranked probabilities and
ranked interindividual similarity measures (Ousley and Hefner, 2005).
TABLE 5.5 Classification Matrix for the ORA Two-Group Analysis
Black White Total % Correct
Black 203 15 218 93.12
White 22 124 146 84.93
Total 225 139 364 89.03
¼190.709; p <0.000
TABLE 5.6 Classification Matrix for the ORA Three-Group Analysis
Amerindian Black White Total % Correct
Amerindian 206 46 10 262 78.63
Black 59 130 29 218 59.63
White 10 33 103 146 70.55
Total 275 209 142 626 69.60
¼287.765; p <0.000
Logistic regression analysis can also be carried out in SPSSÒusing the NOMREG proce-
dure. We suggest a forward stepwise selection procedure. When we ran that analysis on
a sample of data collected by JTH (2009) the LR stepwise selected five variables: (1) inferior
nasal aperture, (2) interorbital breadth, (3) nasal aperture width, (4) nasal bone structure,
and (5) post-bregmatic depression. The likelihood ratio test (Table 5.7) is significant and
demonstrates that the reduced model is equivalent to the final LR model. The Cox and Snell
pseudo R-squared statistics (not shown) (0.553) imply that approximately 56% of the vari-
ation in morphoscopic trait expression is explained by ancestry. This LR model is accurate
for nearly 90% of the individuals in the sample (Tab l e 5 .8 ).
Each of these presented methods has advantages and disadvantages and each is suited to
a particular task. We present these two statistics not to suggest they are the best or most appro-
priate methodsbut to demonstrate the flexibility of statistical methods to handle categorical data
and to encourage the reader to explore these and other statistics for use in their own projects.
In statistical models morphoscopic traits perform as well as a metric analysis (Hefner 2007,
2009). The following section guides the reader through a typical analysis of morphoscopic
traits, and presents reporting strategies to use following data analysis.
TABLE 5.8 Classification Matrix for Two-Way Logistic Regression
Black White % Correct
Black 200 17 92.17
White 19 117 86.03
Total 89.80
TABLE 5.7 Likelihood Ratio Tests for the Two-Way Logistic Regression
Model Fitting Criteria Likelihood Ratio Tests
e2 Log Likelihood of Reduced Model Chi-Square df Sig.
Intercept 183.2665 0 0 e
INA 238.2002 54.9337 5 0.00000
IOB 207.4619 24.1953 2 0.00001
NAW 193.9302 10.6637 2 0.00484
NBS 199.0345 15.7680 4 0.00335
PBD 191.4299 8.1634 2 0.01688
INA ¼inferior nasal aperture
IOB ¼interorbital breadth
NAW ¼nasal aperture width
NBS ¼nasal bone structure
PBD ¼post-bregmatic depression
The first phase of morphoscopic trait analysis is the selection of the character states that
best match the configuration exhibited by the unknown specimen (see example below).
This is completed for each observable trait. Following this stage, appropriate classification
statistics (e.g., ordinal regression, logistic regression, canonical analysis of principal coordi-
nates (CAP), k-nearest neighbor (k-nn), discriminant function analysis) and suitable refer-
ence populations are selected. Once the statistical analysis has placed the unknown
specimen into a population, the probability of group membership for the unknown spec-
imen, and the overall error rate (misclassification rate or classification accuracy) of the model
are reported along with the assigned group membership. This approach is likely familiar, as
it is the same reporting strategy used in metric analyses.
The computer program Macromorphoscopics, designed specifically for the collection of mor-
phoscopic trait data, is available from Hefner and Ousley (2005) and is also incorporated in
the computer program Osteoware that is available at no charge from the Smithsonian Insti-
tution ( These programs facilitate data management and also
standardize trait descriptions. To put the entire process in perspective the following example
cranium is presented (Figure 5.1).
The morphoscopic traits for this cranium were scored following Hefner’s (2009) illustra-
tions and definitions. The following trait scores are noted: (1) the anterior nasal spine is
well-developed and markedly protrudes from the face (ANS ¼3); (2) the inferior nasal aper-
ture is consistent with the straight morphology (INA ¼3); (3) interorbital breadth is interme-
diate (INA ¼2); (4) nasal aperture width is intermediate (NAW ¼2); (5) the nasal bones
exhibit steep lateral walls, with an accompanying broad surface plateau (NBC ¼2); (6) nasal
overgrowth is pronounced (NO ¼1); and (7) no post-bregmatic depression is observed
(PBD ¼0). These visual observations alone are enough for the experienced forensic anthro-
pologist to make an educated guess of ancestry.
However, recall that stopping at this point is unempirical and therefore not scientific. We
must go a step further and apply a statistic to assess the overall classification. In this example,
originally conducted for a medical examiner ’s office, I (JTH) used a novel classification
FIGURE 5.1 Cranium for case study: assessing ancestry for an unknown.
statistic soon to be published: an artificial neural network (aNN). The algorithm (and a graph-
ical user interface) will be available in early 2013. Using the aNN, which contains data on
over 1100 individuals collected by JTH (American Black, American White, Hispanic, East
Asian, etc.), the cranium described above was correctly placed in the Hispanic category.
This cranium, which originated along the U.S.eMexico border, was later identified as
a male from the northern Mexico state of Sonora who perished while crossing the border.
Discussion of the problems inherent with the use of the category “Hispanic” is necessary
here. See Box 5.1.
BOX 5.1
The most recent census in the United
States (2010) revealed that the group desig-
nated as Hispanic was the fastest growing
minority group, with an increase in 15.2
million people over the last census in 2000.
This has resulted in a total Hispanic pop-
ulation of 50.5 million, 16% of the total U.S.
population (Humes et al., 2011). The census
does not define “Hispanic” as a “race,”
considering that it is a separate concept from
race. Therefore, individuals were able to self-
report Hispanic origin and racial origin. Of
those who identified as Hispanic, over half
self-reported White as their race. About one
third reported being in the category “some
other race” alone while the rest reported
being in other racial categories alone (e.g.,
Black, Asian (Humes et al., 2011).
These facts are telling of the problems
inherent with ancestry estimation using
a category designator such as “Hispanic.”
The fact that the Hispanic population has
rapidly grown makes this an issue of
importance for forensic anthropologists in
the United States who must estimate the
biological profile, as Spradley and colleagues
(2008) have noted. The undocumented
border crosser deaths issue in the
Southwestern United States has additionally
highlighted the importance of this subject
(Anderson, 2008; Anderson and Parks, 2008;
Fulginiti, 2008; Birkby et al., 2008). Further, as
several workers have discussed, “Hispanic”
is a linguistic category, applied to individuals
who speak Spanish as their native language
(Slice and Ross, 2004; Ross et al., 2004;
Spradley et al., 2008; Birkby et al., 2008;
Hurst, 2012). The language being spoken has
nothing to do with actual features measured
from bones that may indicate ancestry;
however, we persist in utilizing this category.
The 2010 U.S. Census defined Hispanic or
Latino as “a person of Cuban, Mexican,
Puerto Rican, South or Central American, or
other Spanish culture or origin regardless of
race” (Humes et al., 2011:2). The problem
here is twofold: (1) we are using Hispanic as
a racial category for forensic identification
even in the face of evidence from sources
such as the census data that individuals
considering themselves to be Hispanic also
consider themselves to be some other race;
and (2) persons speaking Spanish as their
native language (excluding Spain) originate
from two continents and numerous coun-
tries, which include hundreds of millions of
To conclude, it seems that we have barely revealed the tip of the iceberg when it
comes to the old race concept, modern thought on human variation, and ancestry
BOX 5.1 (cont’d)
people. The existing diversity is therefore
intrinsic. To illustrate this further, both
Spradley and coworkers (2008) and Hurst
(2012) have demonstrated that variability (in
terms of skeletal features) in the Hispanic
group is higher than was once thought.
Workers such as Ross et al. (2004) and
Spradley et al. (2008) have noted the
complexity of population history in Latin
America leading to phenotypic heteroge-
neity. Each country in Latin America has its
own particular history that in general began
with indigenous groups, later conquest by
Europeans, in several cases, importation of
enslaved Africans, and later immigation of
people from Europe and/or Asia. The extent
to which these three latter events and their
consequences (i.e., war, disease) affected
each country’s population history varies.
Consequently, the majority of people in Latin
America who speak Spanish as their native
language do so as a result of European
conquest but may ancestrally be European,
African, indigenous, or a mixture of the
Therefore, Spradley et al. (2008) and Ross
et al. (2004) both emphasized the need for
population-specific standards with the latter
authors especially stressing the necessity of
regional studies to build metric and
nonmetric trait information for Hispanics. As
a brief example of such an attempt, Lo
and colleagues (2012) recently presented
results from a preliminary study in Colombia
to assess ancestry. As with many other South
American countries, Colombia has a hetero-
geneous population as a result of European
conquest, importation of enslaved Africans,
and the resultant mating with the indigenous
people. As ancestry has never been system-
atically studied in Colombia, Lo
´pez et al.
(2012) used two modern samples of skeletons
from different parts of the country (Bogota
and Medellı
´n) to look at heterogeneity. Using
craniometric interlandmark distances, bio-
logical distance plots revealed that the
grouped Colombian data (Bogota
´n) fell closest to the Hispanic group
and far from the Guatemalan group from the
Forensic Databank (FDB). However, when
the Bogota
´and Medellı
´n samples were
separated, Medellı
´n fell closest to the FDB
European-American group with Bogota
remaining close to the FDB Hispanics (Lo
et al., 2012). This only begins to illustrate the
heterogeneity within one country alone and
underscores the need for more work in this
As the Hispanic population continues to
grow in the United States, we predict that
this issue will increasingly become a hot
topic for research in forensic anthropology.
We strongly suggest that students contem-
plating an ancestry project consider research
on Hispanic populations and in particular
travel to Latin American countries for data
collection and analysis on modern samples.
estimation. There is much more to be explored. Allow the references herein to be points
of departure.
There are numerous possibilities for research in this area. Biological distance studies
have the potential to reveal more about the evolutionary forces and therefore unique pop-
ulation histories that have characterized Homo sapiens asawhole,bothinthepastandinthe
present. Increased and improved use of classification and exploratory statistics play a role
in how we identify patterns of human variation and how we can use that variation to iden-
tify skeletonized remains. As Spradley and colleagues (2008:21) assert, “The formulae used
by forensic anthropologists are only as good as the data that are used to derive them.”
Therefore, improving our datasets with craniometric and nonmetric data from modern
populations all over the world will ultimately enhance our ability to estimate ancestry in
addition to increasing our understanding of human variation.
Moreover, do not neglect to consider research regarding how race is culturally constructed
(Gravlee, 2009) or how the race concept has affected individuals socially and biologically
(Armelagos and Goodman, 1998; Harrison, 1995, 1999). Look to collaborate with anthropol-
ogists in other subdisciplines (Mukhopadhyay and Moses, 1997), as the sociocultural aspect
of race is just as important as applying ancestry estimation to skeletons. We are holistic
anthropologists first, united by the Culture concept with the other subdisciplines in anthro-
pology. Remember, gone are the days of typology and biological determinism. Today, anthro-
pologists must document human variation, its social consequences, and understand the
global patterns of variation as they actually exist.
We are grateful to Dr. Natalie Shirley, whose thoughtful comments and suggestions on a draft
of this chapter improved its contents. We would also like to thank Dr. Bruce Anderson for
providing and granting permission for the use of the cranium photographs in the case study.
Thanks are further due to Dr. Jonathan Bethard for providing the results from the Lo
´pez and
colleagues (2012) presentation.
Algee-Hewitt, B.F.B., 2011. If and How Many ‘Races’? The Application of Mixture Modeling to World-Wide Human
Craniometric Variation. Unpublished PhD dissertation. The University of Tennessee, Knoxville, TN.
Anderson, B.E., 2008. Identifying the dead: Methods utilized by the Pima County (Arizona) Office of the Medical
Examiner for undocumented border crossers: 2001e2006. Journal of Forensic Sciences 53 (1), 8e15.
Anderson, B.E., Parks, B.O., 2008. Symposium on border crosser deaths: introduction. Journal of Forensic Sciences
53 (1), 6e7.
Armelagos, G.L., Goodman, A.H., 1998. Race, racism, and anthropology. In: Goodman, A.H., Leatherman, T.L.
(Eds.), Building a New Biocultural Synthesis. University of Michigan Press, Ann Arbor, pp. 359e377.
Birkby, W.H., Fenton, T.W., Anderson, B.E., 2008. Identifying southwest Hispanics using nonmetric traits and the
cultural profile. Journal of Forensic Sciences 53 (1), 29e33.
Blakey, M.L., 1987. Skull doctors: intrinsic social and political bias in the history of American physical anthropology
with special reference to the work of Ales Hrdlicka. Critique of Anthropology 7 (2), 7e35.
Blakey, M.L., 1999. Scientific racism and the biological concept of race. Literature and Psychology 45 (1/2),
Boas, F., 1910. Changes in Bodily Form of Descendants of Immigrants. US Immigration Commission, Washington,
DC. Senate Document No. 208, 61st Congress. US Government Printing Office.
Boas, F., 1918a. Review of The Passing of the Great Race; or The Racial Basis for European History by Madison
Grant. American Journal of Physical Anthropology 1 (3), 363.
Boas, F., 1918b. Notes on the anthropology of Sweden. American Journal of Physical Anthropology 1, 415e426.
Bozeman, J., 1997. Technological Millenarianism in the United States. In: Robbins, T. (Ed.), Millenium, Messiahs, and
Mayhem: Contemporary Apocalyptic Movements. Routledge, New York.
Brace, C.L., 1982. The roots of the race concept in American physical anthropology. In: Spencer, F. (Ed.), A History of
American Physical Anthropology, 1930e1980. Academic Press, New York.
Brace, C.L., 2005. Race Is a Four Letter Word: The Genesis of the Concept. Oxford University Press, Oxford.
Brues, A.M., 1958. Identification of skeletal remains. Journal of Criminal Law, Criminology, and Police Science 48
(5), 551e563.
Brues, A.M., 1990. The once and future diagnosis of race. In: Gill, G.W., Rhine, S.J. (Eds.), Skeletal Attribution of
Race. Maxwell Museum of Anthropology, Albuquerque, New Mexico, Anthropological Papers No. 4, pp. 1e8.
Buikstra, J.E., Ubelaker, D.H., 1994. Standards for Data Collection from Human Skeletal Remains. Arkansas
Archeological Survey, Fayetteville, AR.
Cabana, G.S., Hulsey, B.I., Pack, F., 2013. Molecular methods. In: DiGangi, E.A., Moore, M.K. (Eds.), Research
Methods in Human Skeletal Biology. Academic Press, San Diego.
Cartmill, M., 1999. The status of the race concept in physical anthropology. American Anthropologist 100 (3),
Caspari, R., 2003. From types to populations: a century of race, physical anthropology, and the American
Anthropological Association. American Anthropologist 105 (1), 65e76.
Caspari, R., 2009. 1918: Three perspectives in race and human variation. American Journal of Physical Anthro-
pology 139, 5e15.
Comas, J., 1961. Scientific racism again? American Anthropologist 2 (4), 303e340.
Darwin, C., 1859. On the Origin of Species by Means of Natural Selection. John Murray, London.
Darwin, C., 1871. The Descent of Man, and Selection in Relation to Sex. John Murray, London.
Davenport, C., 1911. Heredity in Relation to Eugenics. H Holt & Company, New York.
Diamond, J., 1999. Guns, Germs, and Steel: The Fates of Human Societies. W.W. Norton & Co. New York.
Doornik, J.A., Hansen, H., 2008. An omnibus test for univariate and multivariate normality. Oxford Bulletin of
Economics and Statistics 70, 927e939.
Edgar, H.J.H., Hunley, K.L., 2009. Race reconciled? How biological anthropologists view human variation. Amer-
ican Journal of Physical Anthropology 139, 1e4.
Erickson, P.A., 2008. Franz Boas. In: Moore, J. (Ed.), Encyclopedia of Race and Racism. Macmillan Reference USA,
Fulginiti, L.C., 2008. Fatal footsteps: murder of undocumented border crossers in Maricopa County, Arizona.
Journal of Forensic Sciences 53 (1), 41e45.
Galton, F., 1892. Hereditary Genius: An inquiry into its Laws and Consequences, 2nd ed. Macmillan, London.
Giles, E., 1997. Earnest Albert Hooton. In: Spencer, F. (Ed.), History of Physical Anthropology, Vol. I. Garland, New
York, pp. 499e500.
Giles, E., Elliot, O., 1962. Race identification from cranial measurements. Journal of Forensic Sciences 7, 147e157.
Giles, E., Elliot, O., 1963. Sex determination by discriminant function analysis of crania. American Journal of
Physical Anthropology 21, 53e68.
Goodman, A.H., 2000. Why genes don’t count (for racial differences in health). American Journal of Public Health
90 (11), 1699e1702.
Gould, S.J., 1978. Morton’s ranking of races by cranial capacity. Science 200, 503e509.
Gould, S.J., 1981. The Mismeasure of Man. W.W. Norton & Company, New York.
Gould, S.J., 1996. The Mismeasure of Man, Revised Edition. W.W. Norton & Company, New York.
Grant, M., 1916. The Passing of the Great Race. Scribner, New York.
Graves, J.L., 2001. The Emperor’s New Clothes: Biological Theories of Race at the Millennium. Rutgers University
Press, New Brunswick, New Jersey.
Gravlee, C.C., 2003. Boas’s changes in bodily form: the immigrant study, cranial plasticity, and Boas’s physical
anthropology. American Anthropologist 105 (2), 326e332.
Gravlee, C.C., 2009. How race becomes biology: embodiment of social inequality. American Journal of Physical
Anthropology 139, 47e57.
Haeckel, E., 1905. The Wonders of Life: A Popular Study of Biological Philosophy. Harper and Brothers Publishers,
New York.
Hauser, G., De Stefano, G.F., 1989. Epigenetic Variants of the Human Skull. Schweizerbart, Stuttgart.
Harrison, F.V., 1995. The persistent power of race in the cultural and political economy of racism. Annual Review of
Anthropology 24, 47e74.
Harrison, F.V., 1999. Introduction: Expanding the discourse on race. American Anthropologist 100 (3), 609e631.
Hefner, J.T., 2003. Assessing Nonmetric Cranial Traits Currently used in the Forensic Determination of Ancestry.
Unpublished Master’s thesis. The University of Florida, Gainesville, FL.
Hefner, J.T., 2007. The Statistical Determination of Ancestry Using Cranial Nonmetric Traits. Unpublished PhD
dissertation, Department of Anthropology, The University of Florida, Gainesville, FL.
Hefner, J.T., 2009. Cranial nonmetric variation and estimating ancestry. Journal of Forensic Sciences 54 (5),
Hefner, J.T., 2013. Cranial morphoscopic traits and the assessment of American Black, American White, and
Hispanic. In: Berg, G.E., Ta’ala, S.C. (Eds.), Biological Affinity in Forensic Identification of Human Skeletal
Remains: Beyond Black and White. Taylor and Francis, New York.
Hefner, J.T., Ousley, S.D., 2005. Macromorphoscopics [computer program]. Beta version. Hefner and Ousley,
Kaneohe, HI.
Hefner, J.T., Ousley, S.D., Dirkmaat, D.C., 2012. Morphoscopic traits and the assessment of ancestry. In: Dirkmaat, D.
(Ed.), A Companion to Forensic Anthropology. Wiley-Blackwell Hoboken, N.J., pp. 287e310.
Herrnstein, R., Murray, C., 1994. The Bell Curve. Free Press, New York.
Hiernaux, J., 1964. Concept of race and taxonomy of mankind. In: Montagu, A. (Ed.), The Concept of Race. The Free
Press of Glencoe, Collier-Macmillan, London.
Hooton, E.A., 1918. On certain Eskimoid characters in Icelandic skulls. American Journal of Physical Anthropology
1, 53e76.
Hooton, E.A., 1939. Crime and the Man. Greenwood Press, New York.
Howells, W.W., 1973. Cranial variation in man: a study by multivariate analysis of patterns of difference among
recent human populations. In: The Museum, Vol. 67. Papers of the Peabody Museum of Archaeology and
Ethnology, Harvard University, Cambridge, MA.
Howells, W.W., 1989. Skull shapes and the map: Craniometric analyses in the dispersion of modern Homo. In: The
Museum, Vol. 79. Papers of the Peabody Museum of Archaeology and Ethnology, Harvard University, Cam-
bridge, MA.
Howells, W.W., 1995. Who’s who in skulls: ethnic identification of crania from measurements. In: The Museum,
Vol. 82. Papers of the Peabody Museum of Archaeology and Ethnology, Harvard University, Cambridge, MA.
Humes, K.R., Jones, N.A., Ramirez, R.R., 2011. Overview of Race and Hispanic Origin: 2010. US Department of
Commerce, Economics and Statistics Administration, US Census Bureau.
Hunley, K.L., Healy, M.E., Long, J.C., 2009. The global pattern of gene identity variation reveals a history of long-
range migrations, bottlenecks, and local mate exchange: implications for biological race. American Journal of
Physical Anthropology 139, 35e46.
Hurst, C.V., 2012. Morphoscopic trait expressions used to identify southwest hispanics. Journal of Forensic Sciences
57 (4) 859e865.
Instituto Brasileiro de Geografia e Estatistica (2010). <
Jacobson, M.F., 1998. Whiteness of a Different Color: European Immigrants and the Alchemy of Race. Harvard
University Press, Cambridge.
Jantz, R.L., 2001. Cranial change in Americans. Journal of Forensic Sciences 46 (4), 784e787.
Jantz, L.M., Jantz, R.L., 1999. Secular changes in long bone length and proportion in the United States, 1800e1970.
American Journal of Physical Anthropology 110, 57e67.
Jantz, R.L., Jantz, L.M., 2000. Secular change in craniofacial morphology. American Journal of Human Biology 12
(3), 327e338.
Jantz, R.L., Logan, M.H., 2010. Why does head form change in children of immigrants? A Reappraisal. American
Journal of Human Biology 22, 702e707.
Jantz, R.L., Moore-Jansen, P., 1988. A Database for Forensic Anthropology: Structure, Content, and Analysis.
Department of Anthropology, University of Tennessee. Report of Investigations No. 47.
Jantz, R.L., Ousley, S., 2005. FORDISC 3: Computerized Forensic Discriminant Functions. The University of Ten-
nessee, Knoxville, TN.
Kaszycka, K.A., Strkalj, G., Strzalko, J., 2009. Current views of European anthropologists on race: Influence of
educational and ideological background. American Anthropologist 111 (1), 43e56.
Kennedy, K.A.R., 1995. But professor, why teach race identification if races don’t exist? Journal of Forensic Sciences
40 (5), 797e800.
Konigsberg, L.W., 1990. Analysis of prehistoric biological variation under a model of isolation by geographic and
temporal distance. Human Biology 62, 49e70.
Konigsberg, L.W., 2000. Quantitative variation and genetics. In: Stinson, S., Bogin, B., Huss-Ashmore, R.,
O’Rourke, D. (Eds.), Human Biology: An Evolutionary and Biocultural Perspective. Wiley-Liss, Hoboken, NJ,
pp. 135e162.
Konigsberg, L.W., 2006. A post-neumann history of biological and genetic distance studies in bioarchaeology. In:
Buikstra, J.E., Beck, L.A. (Eds.), Bioarchaeology: The Contextual Analysis of Human Remains. Academic Press,
San Diego, pp. 263e279.
Konigsberg, L.W., Ousley, S.D., 1995. Multivariate quantitative genetics of anthropomorphic traits from the Boas
data. Human Biology 67, 481e498.
Konigsberg, L.W., Cheverud, J., Kohn, L.A.P., 1993. Cranial deformation and nonmetric trait variation. American
Journal of Physical Anthropology 90, 35e48.
Konigsberg, L.W., Algee-Hewitt, B.F.B., Steadman, D.W., 2009. Estimation and evidence in forensic anthropology:
sex and race. American Journal of Physical Anthropology 139, 77e90.
Krzanowski, W.J., 2002. Principles of Multivariate Analysis: A User’s Perspective. Oxford University Press, London.
Kullback, S., 1959. Information Theory and Statistics. Wiley, New York.
Kuzawa, C.W., 2008. The developmental origins of adult health: Intergenerational inertia in adaptation and disease.
In: Trevathan, W.R., McKenna, J.J. (Eds.), Evolutionary Medicine and Health: New Perspectives. Oxford
University Press, New York.
Lahr, M.M., 1996. The Evolution of Modern Human Diversity: A Study of Cranial Variation. Cambridge University
Press, Cambridge.
Larson, E., 1996. Sex, Race, and Science: Eugenics in the Deep South. Johns Hopkins University Press, Baltimore.
Lewis, J.E., DeGusta, D., Meyer, M.R., Monge, J.M., Mann, A.E., et al., 2011. The mismeasure of science: Stephen Jay
Gould versus Samuel George Morton on skulls and bias. PLoS Biology 9 (6).
pbio.1001071, e1001071.
Lewontin, R.C., 1972. The apportionment of human diversity. Evolutionary Biology 6, 381e398.
Lieberman, L., 2008. Clines and continuous variation. In: Moore, J. (Ed.), Encyclopedia of Race and Racism. Mac-
millan Reference USA, Detroit, pp. 341e346.
Lieberman, L., Kirk, R.C., 2004. What should we teach about the concept of race? Anthropology & Education
Quarterly 35 (1), 137e145.
Linnaeus, C., 1758. Systema Naturae. 10th revised edition of 1758 [1956]. Laurentii Salvii, Stockholm.
Littlefield, A., Lieberman, L., Reynolds, L.T., 1982. Redefining race: the potential demise of a concept in physical
anthropology. Current Anthropology 23 (6), 641e655.
Livingstone, F.B., 1962. On the non-existence of human races. Current Anthropology 3 (3), 279e281.
Long, J.C., Li, J., Healy, M.E., 2009. Human DNA sequences: More variation and less race. American Journal of
Physical Anthropology 139, 23e34.
´pez, M.A., Casallas, D.A., Castellanos, D., Soto, F.V., Bethard, J.D., 2012. Unveiling ancestry in Colombia through
morphometric analysis. Proceedings of the American Academy of Forensic Sciences 18, 418.
Lovejoy, A.O., 1936. The Great Chain of Being. Harvard University Press, Cambridge.
Lund, J., 1994. Boundaries of Restriction: The Dillingham Commission. University of Vermont History Review,
Vol. 6. <>.
Mardia, K.V., 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 36,
Marks, J., 2008. History of scientific racism. In: Moore, J. (Ed.), Encyclopedia of Race and Racism. Macmillan
Reference USA, Detroit, Vol. 3, pp. 1e16.
Martin, V.R., 1914. Lehrbuch der Anthropologie in systematischer Darstellung mit besonderer Beru
¨cksichtigung der
anthropologischen Methoden, fu
¨r Studierende, A
¨rzte und Forschungsreisende. Textbook of anthropology
systematically presented with special emphasis on anthropological methods. Gustav Fischer, Jena.
McKeown, A.H., Schmidt, R.W., 2013. Geometric morphometrics. In: DiGangi, E.A., Moore, M.K. (Eds.), Research
Methods in Human Skeletal Biology. Academic Press, San Diego.
Mielke, J.H., Konigsberg, L.W., Relethford, J.H., 2011. Human Biological Variation, 2nd ed. Oxford University Press,
New York.
Montagu, A., 1964a. The concept of race. In: Montagu, A. (Ed.), The Concept of Race. The Free Press of Glencoe,
Collier-Macmillan, London, pp. 12e28.
Montagu, A., 1964b. Man’s Most Dangerous Myth: The Fallacy of Race, 4th ed. The World Publishing Company,
Cleveland and New York.
Moore, M.K., Ross, A.H., 2013. Stature estimation. In: DiGangi, E.A., Moore, M.K. (Eds.), Research Methods in
Human Skeletal Biology. Academic Press, San Diego.
Morton, S.G., 1839. Crania Americana: Or, A Comparative View of the Skulls of Various Aboriginal Nations of
North and South America; To Which is Prefixed an Essay on the Varieties of the Human Species. J Dobson,
Moses, Y.T., 2004. The continuing power of the concept of race. Anthropology & Education Quarterly 35 (1),
Mukhopadhyay, C.C., Moses, Y.T., 1997. Reestablishing race in anthropological discourse. American Anthropologist
99 (3), 517e533.
Nash, M., 1962. Race and the ideology of race. Current Anthropology 3 (3), 285e302.
Oppenheim, R., 2010. Revisting Hrdlicka and Boas: asymmetries of race and anti-imperialism in interwar anthro-
pology. American Anthropologist 112 (1), 92e103.
Ousley, S.D., Hefner, J.T., 2005. Morphoscopic traits and the statistical determination of ancestry. Proceedings of the
American Academy of Forensic Sciences 11, 291e292.
Ousley, S.D., Jantz, R.L., Fried, D., 2009. Understanding race and human variation: Why forensic anthropologists are
good at identifying race. American Journal of Physical Anthropology 139, 68e76.
Ousley, S.D., Spradley, M.K., Jantz, R.L., 2011. Fordisc 3.1 Workshop February 2011, Chicago, IL.
Paul, D.B., 2008. History of eugenics. In: Moore, J. (Ed.), Encyclopedia of Race and Racism. Macmillan Reference
USA, Detroit, pp. 441e447.
Pilloud, M.A., 2009. Community Structure at Neolithic C¸ atalho
¨k: Biological Distance Analysis of Household,
Neighborhood, and Settlement. Unpublished PhD dissertation. The Ohio State University, Columbus, Ohio.
Pounder, C., Adelman, L., Cheng, J., Herbes-Sommers, C., Strain, T., Smith, L., Ragazzi, C., 2003. Race: The Power of
an Illusion. California Newsreel, San Francisco.
Relethford, J.H., 1991. Genetic drift and anthropomorphic variation in Ireland. Human Biology 63, 155e165.
Relethford, J.H., 2009. Race and global patterns of phenotypic variation. American Journal of Physical Anthropology
139, 16e22.
Relethford, J.H., Blangero, J., 1990. Detection of differential gene flow from patterns of quantitative variation.
Human Biology 62, 5e25.
Relethford, J.H., Lees, F.C., 1982. The use of quantitative traits in the study of human population structure.
American Journal of Physical Anthropology 25, 113e132.
Rhine, S., 1990. Non-metric skull racing. In: Gill, G.W., Rhine, S. (Eds.), Skeletal Attribution of Race: Methods for
Forensic Anthropology Albuquerque: Maxwell Museum of Anthropology Anthropological Papers No. 4.
Ross, A.H., Slice, D.E., Ubelaker, D.H., Falsetti, A.B., 2004. Population affinities of 19th century Cuban crania:
implications for identification criteria in South Florida Cuban Americans. Journal of Forensic Sciences 49 (1),
Rushton, J.P., 1995. Race, Evolution, and Behavior: A Life History Perspective. Transaction, New Brunswick, NJ.
Sauer, N.J., 1992. Forensic anthropology and the concept of race: if races don’t exist, why are forensic anthropol-
ogists so good at identifying them? Social Science & Medicine 34 (2), 107e111.
Scott, G.R., Turner II, C.G., 1997. The Anthropology of Modern Human Teeth: Dental Morphology and Its Variation
in Recent Human Populations. Cambridge University Press, Cambridge.
Severson, K., 2012. Payment set for those sterilized in program. The New York Times (January 11 2012 p. A13), New
Shapiro, H., 1954. Earnest Albert Hooton 1887e1954. American Anthropologist 56, 1081e1084.
Shipman, P., 1994. The Evolution of Racism: Human Differences and the Use and Abuse of Science. Harvard
University Press, Cambridge.
Sjøvold, T., 1977. Nonmetrical divergence between skeletal populations: the theoretical foundation and biological
importance of C. A. B. Smith’s mean measure of divergence. Ossa 4 (Suppl. 1), 1e133.
Sjøvold, T., 1984. A report on the heritability of some cranial measurements and nonmetric traits. In: van Vark, G.N.,
Howells, W.W. (Eds.), Multivariate Statistical Methods in Physical Anthropology. D. Reidel, Dordrecht, pp.
Sjøvold, T., 1986. Infrapopulation distances and genetics of nonmetrical traits. In: Herrmann, B. (Ed.), Innovative
Trends in der Pra
¨historischen Anthropologie: Mitteilungen der Berliner Geselhchaft fu
¨r Anthropologie, Eth-
nologie und Urgeschichte. Verlag Marie Leidorf, Berlin, pp. 81e93.
Slice, D.E., Ross, A.H., 2004. Population affinities of Hispanic crania: Implications for forensic identification.
Proceedings of the American Academy of Forensic Sciences 10, 280e281.
Smedley, A., Smedley, B.D., 2005. Race as biology is fiction, racism as a social problem is real. American Psychologist
60 (1), 16e26.
Sparks, C.S., Jantz, R.L., 2003. Changing times, changing faces: Franz Boas’s immigrant study in modern
perspective. American Anthropologist 105 (2), 333e337.
Spencer, F., 1981. The rise of academic physical anthropology in the United States (1880e1980): a historical overview.
American Journal of Physical Anthropology 56, 353e364.
Spradley, M.K., Jantz, R.L., Robinson, A., Peccerelli, F., 2008. Demographic change and forensic identification:
problems in metric identification of Hispanic skeletons. Journal of Forensic Sciences 53 (1), 21e28.
Stanton, W.R., 1960. The Leopard’s Spots: Scientific Attitudes Toward Race in America, 1815e1859. University of
Chicago Press, Chicago.
Sternberg, R.J., Grigorenko, E.L., Kidd, K.K., 2005. Intelligence, race, and genetics. American Psychologist 60 (1),
Stojanowski, C.M., Schillaci, M.A., 2006. Phenotypic approaches for understanding patterns of intracemetery bio-
logical variation. Yearbook of Physical Anthropology 49, 49e88.
Suzuki, D., Knudtson, P., 1989. Genethics: The Clash Between the New Genetics and Human Values. Harvard
University Press, Cambridge.
Tylor, E.B., 1871. Primitive Culture. Harper, New York. Reprinted 1958.
Washburn, S.L., 1951. The new physical anthropology. Transactions of the New York Academy of Sciences, Series II
13 (7), 298e304.
Washburn, S.L., 1964. The study of race. In: Montagu, A. (Ed.), The Concept of Race. The Free Press of Glencoe,
Collier-Macmillan, London, pp. 242e260.
American Anthropological Association. RaceeAre We So Different?Ò
Diamond, J., 1999. Guns, Germs, and Steel: The Fates of Human Societies. W.W. Norton & Co. New York.
Mielke, J.H., et al., 2011. Human Biological Variation, 2nd ed. Oxford University Press, New York.
Pounder, C., et al., 2003. Race: The Power of an Illusion (film series). California Newsreel, San Francisco.
... 14 Other currents of thought such as Eugenics emerged in the 20th century having an influence on social and scientific theories, and historical events. 15 Through the evolution of both science and history, there has been a number of definitions assigned to the word "race". 16 The recent scientific advances, more notably in the field of population genetics, have provided the impulse to reject and expand beyond the boundaries of old typological approaches that can be misleading. ...
... Anthropologists such as Aleš Hrdlička in the 20th century interlinked human variation with evolutionary theories, physical characteristics and other related factors such as environmental influences. 15 In addition, molecular scientists found that there are more genetic similarities between populations compared to similarities noted within populations. 19 Thus, a tendency of the eradication of the term "race" and a call for a throughout revision of both typology and classification approaches related to human variation is noted. ...
... [25][26][27] Traditionally, that includes both metric and morphological approaches, most commonly performed on the cranium. 15 Recent techniques make use of advanced statistical analyses, novel technologies and powerful computational systems, providing statistical robustness to the assessment. The present paper offers a discussion on the considerations, advantages and limitations of ancestry estimation reviewing traditional metric and morphoscopic skeletal methods as well as more sophisticated approaches such as geometric morphometrics and machine learning models. ...
... To predict group association, LR uses the log odds ratio rather than probabilities and an iterative maximum likelihood technique rather than least squares to fit the final model. This means the researcher has more freedom when using LR and the method may be more appropriate for non-normally distributed data or when the samples have unequal covariance matrices which is one of the assumptions for running regression analysis [7]. Logistic regression assumes independence among variables, which is not always met in some datasets. ...
Full-text available
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62 billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can be generalized to other fraud types. This paper compares the performance of logistic regression, decision trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines (SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature of the data and compared the performance of the supervised models on the oversampled data to the raw data. From the results, the Random Forest classifier outperformed the other models with a higher AUC score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest performance.
... Evolutionary history (in the generations) and ontogeny (in an individual's life course) shape the space of human variation, through contingent events and adaptations. The sources of variation are multiple and include sex and ancestry [1], [2] as well as cultural practices, lifestyle and socioeconomic conditions [3], [4]. The interplay between all these factors, produces complex patterns of variation, often difficult to disentangle. ...
Full-text available
Abstract:Human skeletal remains are an immense source of data to describe human biodiversity with an intrinsic complexity due to the multifactorial origin of human variability. Evolution and ontogeny produced complex patterns of variation through contingent events and adaptations. Multivariate approaches have been widely adopted in physical anthropology; however, at present, Artificial Intelligence algorithms have scarcely been applied to such datasets. Data analysis techniques based on Artificial Intelligence algorithms have shown to be suitable in many different fields, from engineering and medicine up to cultural heritage and Egyptology. In this work we aim to show how Machine Learning algorithms can be applied in the field of anthropology, using the W.W. Howells dataset of cranial measurements, limited to the analysis of African populations. Principal Component Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), Spectral Embedding and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction, along with supervised and unsupervised methods to explore and quantify the differences due to ancestry and sex in the skulls of African populations. Algorithms such as Support Vector Machines and the unsupervised DBSCAN were applied to the data in order to quantify this similarity. This strategy allows a discrimination of sex and ancestry (about 85% of accuracy for both) in human remains, ultimately opening up new routes for anthropological research.
... For statistical results, some assumptions were not confirmed, such as multivariate normality of the second to fifth metatarsals of the left side and the first and fifth metatarsals of the right side. The accuracy of formulae based on these metatarsal variables should be treated with caution, although discriminant function analysis is relatively robust against deviations from multivariate normality (81). It should be further highlighted that the accuracy of estimation may be influenced by the characteristics of the selected bone, population, sample size, and age. ...
Full-text available
The aim of the present paper is to determine the sex of the individual using three-dimensional geometric and inertial analyses of metatarsal bones. Metatarsals of 60 adult Chinese subjects of both sexes were scanned using Aquilion One 320 Slice CT Scanner. The three-dimensional models of the metatarsals were reconstructed, and thereafter, a novel software using the center of mass set as the origin and the three principal axes of inertia was employed for model alignment. Eight geometric and inertial variables were assessed: the bone length, bone width, bone height, surface-area-to-volume ratio, bone density, and principal moments of inertia around the x, y, and z axes. Furthermore, the discriminant functions were established using stepwise discriminant function analysis. A cross-validation procedure was performed to evaluate the discriminant accuracy of functions. The results indicated that inertial variables exhibit significant sexual dimorphism, especially principal moments of inertia around the z axis. The highest dimorphic values were found in the surface-area-to-volume ratio, principal moments of inertia around the z axis, and bone height. The accuracy rate of the discriminant functions for sex determination ranged from 88.3% to 98.3% (88.3%–98.3% cross-validated). The highest accuracy of function was established based on the third metatarsal bone. This study showed for the first time that the principal moment of inertia of the human bone may be successfully implemented for sex estimation. In conclusion, the sex of the individual can be accurately estimated using a combination of geometric and inertial variables of the metatarsal bones. The accuracy should be further confirmed in a larger sample size and be tested or independently developed for distinct population/age groups before the functions are widely applied in unidentified skeletons in forensic and bioarcheological contexts.
Sex estimation is an essential step for identification of unidentified human remains. Pelvis, skull, and long bones are commonly used because of their distinct sexual dimorphism. However, these bones are incomplete or missing in some circumstances and other potential bones are needed. Several studies have shown the sexual dimorphism of the patella in specific populations by using an anthropometry method with statistical modeling. The patella has been recognized to be resistant to post-mortem changes. We developed discriminant function equations for sex estimation from measurements of the patella in a modern Central Thai population. Six variables of the patella were measured on 130 skeletons derived from Central Institute of Forensic Science, Thailand. Results showed prediction accuracies of 90% on the left side and 93% on the right side. This study illustrated the potential usage of the patella for sex estimation and indicated size variations of the patella among Thai sub-populations.
Population affinity estimation is an important step in the identification of unknown individuals. To ensure accurate results, validation studies of newly developed methods must be performed using different target populations and skeletal elements. This research aims to determine the accuracy and reliability of population affinity estimation on a modern Spanish sample using two online software applications. The sample consists of 114 adult individuals (51 males, 63 females) using 38 measurements and one angle from the skull and mandible. AncesTrees was used for craniometric measurements and (hu)MANid for mandibular variables with different classification models and probability thresholds being evaluated. The required parameters were inputted for each individual and statistics were generated to assess the accuracy of the estimation. AncesTrees performed with the greatest accuracy as the program correctly classified the sample as Southwestern European or European, with highest accuracies being 54.56% (trial 1), 86.05% (trial 2), 82.61% (trial 3), 34.55% (trial 4) and 100.00% (trial 5). (hu)MANid correctly classified the sample as being from white origin with accuracies ranging from 70.59 to 80.00% without considering correct sex estimation, while accuracy ranged between 62.75 and 80.00% accounting for estimated sex. Population affinity estimation may determine subsequent methods used in the construction of the biological profile. Our results demonstrate varying accuracy rates depending on the element and method, offering a critical view in relation to software applicability and validity. Reference populations and intrinsic and extrinsic factors can potentially influence the method accuracy and reliability. Future research should focus on the inclusion of underrepresented groups.
Full-text available
This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model.
We investigate the impact of cultural dimension on energy poverty—a topic hitherto overlooked in the literature—employing panel fixed effects, logistic, and heteroskedasticity identified endogenous variable regression estimators. The panel framework incorporates 103 countries over a period of 1971–2018. Using five different proxies representing the cultural dimensions and other demographic and macroeconomic control variables, the empirical analyses reveal that power distance and masculinity (as opposed to femininity) worsen the conditions of energy poverty while individualism (as opposed to collectivism) and long/short-term orientation (i.e., pragmatism vs. traditionalism/conservatism) tend to lessen the probability of energy deprivation. We find the effect of uncertainty avoidance on energy poverty ambiguous. Our research findings have profound policy implications in reducing not just energy poverty but also eradicating poverty in general. In light of our results, we suggest policy reforms and global initiatives that are gender sensitive, incorporate the multidimensional impact of culture on national behavior particularly aiming at reversing the cultural acceptance of a higher degree of unequally distributed power, and create a more inclusive society with pragmatism, leading to the achievement of the sustainable development goals of the 2030 Agenda.
Full-text available
Scorrer J, Faillace KE, Hildred A, Nederbragt AJ, Andersen MB, Millet M-A, Lamb AL, Madgwick R. 2021 Diversity aboard a Tudor warship: investigating the origins of the Mary Rose crew using multi-isotope analysis. R. Soc. Open Sci. 8: 202106. The great Tudor warship, the Mary Rose, which sank tragically in the Solent in 1545 AD, presents a rare archaeological opportunity to research individuals for whom the precise timing and nature of death are known. A long-standing question surrounds the composition of the Tudor navy and whether the crew were largely British or had more diverse origins. This study takes a multi-isotope approach, combining strontium (87Sr/86Sr), oxygen (�18O), sulfur (�34S), carbon (�13C) and nitrogen (�15N) isotope analysis of dental samples to reconstruct the childhood diet and origins of eight of the Mary Rose crew. Forensic ancestry estimation was also employed on a subsample. Provenancing isotope data tentatively suggests as many as three of the crew may have originated from warmer, more southerly climates than Britain. Five have isotope values indicative of childhoods spent in western Britain, one of which had cranial morphology suggestive of African ancestry. The general trend of relatively high �15N and low �13C values suggests a broadly comparable diet to contemporaneous British and European communities. This multi-isotope approach and the nature of the archaeological context has allowed the reconstruction of the biographies of eight Tudor individuals to a higher resolution than is usually possible.
Full-text available
Assessing ancestry using cranial morphoscopic (cranial nonmetric) traits gives the impression of a straight forward approach—pick up a cranium, observe the trait values according to ancestry from published trait lists, and classify the individual’s ancestry according to the observed values. In reality, these assessments are not so clear-cut; instead, they are clouded in misunderstandings on the nature of human variation and hindered by the experience-based approach that relies on typological trait lists (cf. Rhine 1990). Like other contributions to this volume, the purpose of this chapter is to provide the reader with an effective and relatively straightforward method of ancestry assessment. Te original intention was the presentation of a large suite of morphoscopic traits to which the researcher could refer and incorporate in an analysis. However, after focusing more or less exclusively on the analytical value of slight variations in cranial form over the past decade, I have noticed (and I hope the reader will develop an acute appreciation for this proposal, as well) that more is not always better (contra Gill 1998). In fact, as more variables are considered, the number of individuals in the reference sample expressing so-called “expected” trait values (derived from trait lists) reaches nearly zero (Hefner 2003, 2007, 2009)
Cultures under stress often give rise to revitalization movements in which leaders urge followers go "back to basics" to solve societal and religious problems. While usually thought of in the context of religious fundamentalism, this work suggests that technology can also offer the promise of social restoration and the advent of a new, future, golden age. Examples can be found in the American eugenics movement, the space colonization movement, the American cryonics movement, and the transhumanist movement. It is argues that such technologically-minded movements are no less millenarian in nature than religiously-oriented ones.
Historically, anthropology has occupied a central place in the construction and reconstruction of race as both an intellectual device and a social reality. Critiques of the biological concept of race have led many anthropologists to adopt a “no-race” posture and an approach to intergroup difference highlighting ethnicity-based principles of classification and organization. Often, however, the singular focus on ethnicity has left unaddressed the persistence of racism and its invidious impact on local communities, nation-states, and the global system. Within the past decade, anthropologists have revitalized their interest in the complex and often covert structures and dynamics of racial inequality. Recent studies shed light on race’s heightened volatility on contemporary sociocultural landscapes, the racialization of ethno-nationalist conflicts, anthropology’s multiple traditions of antiracism, and intranational as well as international variations in racial constructions, including the conventionally neglected configurations of whiteness.