NON-METRIC AND METRIC DENTAL ASSESSMENT OF ANCESTRY
IN CONTEMPORARY MEXICAN INDIVIDUALS: AN AID TO
IDENTIFYING UNDOCUMENTED BORDER CROSSERS
______
A Thesis
Presented
to the Faculty of
California State University, Chico
______
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
in
Anthropology
______
by
Rebecca George
Spring 2015 NON-METRIC AND METRIC DENTAL ASSESSMENT OF ANCESTRY
IN CONTEMPORARY MEXICAN INDIVIDUALS: AN AID TO
IDENTIFYING UNDOCUMENTED BORDER CROSSERS
A Thesis
by
Rebecca George
Spring 2015
APPROVED BY THE DEAN OF GRADUATE STUDIES AND VICE PROVOST FOR RESEARCH:
______Eun K. Park, Ph.D.
APPROVED BY THE GRADUATE ADVISORY COMMITTEE:
______Guy Q. King, Ph.D. Eric J. Bartelink, Ph.D., Chair Graduate Coordinator
______Beth S. Shook, Ph.D. ACKNOWLEDGMENTS
This thesis would not have been possible without the work and support of so many people. First, thank you to my committee chair, Dr. Eric Bartelink. Without your assistance and guidance, this thesis simply would not have happened. Thank you to my second committee member, Dr. Beth Shook. Your encouragement during my entire career at CSU, Chico helped to shape the academic I have become. I thank both of you for the amount of time and faith you have invested in me and this thesis.
Special thanks are necessary to all the people I encountered along my travels to complete this thesis. Dr. Heather Edgar, thank you for taking the time to train me, as well as opening up your collection for my usage. Your students, especially Lexi, Anna,
Kate, and Corey, made my time in Albuquerque an enjoyable experience. Lexi and Mo
O’Donnell, thank you for making me a part of your family during this process. Thank you to Drs. Traci van Deest and Bruce Anderson at the PCOME for graciously allowed me access to your laboratory and accommodating every imaginable request. Thanks to
Dr. Laura Fulginiti and Avery at the MCOME for letting me take over your laboratory for a morning. Thank you to Dr. Kate Spradley at Texas State University for opening the doors to all of your collections while I was on campus, and for working your hardest to help me gain access to additional collections. Thanks to Dr. Abigail Meza at the IAA,
UNAM, Mexico City, for helping me with collection access. Many thanks to Sebastián
Santamaria and David Roldan, also at the IAA, for helping me navigate Mexico City and
iii for being some of the best people I have come across in my travels. A very special thank you to Paty Perez for opening your home to me and being a friend in Mexico City when I needed one the most. Thank you to Dr. Jorge Gómez-Valdes and your students at the
Facultad de Medicina, UNAM, Mexico City for granting me access to your collection. I look forward to working with you in the future as you continue to build the relationship between Mexican and American researchers.
My family’s support in this process has been critical, as it has been for my entire life. Thank you, Mom and Dad, for encouraging me to follow my crazy dreams by helping me move across the country, travel where I need to, and supporting me along the way. Thank you, Grandma, for helping me when necessary and being there always.
Thanks, also, to my brother Matt. I love you all.
Numerous of the other professors at CSU, Chico, have provided support and guidance during my time here. Dr. Colleen Milligan, thank you for being a source of advice and friendship through the MA process. Dr. P. Willey, thanks for the honest dose of reality that many of us graduate students need from time to time. To the friends that I have made since being at CSU, Chico, thank you for your encouragement and guidance.
My physical cohort mates, Aoife Kilmartin and Julia Prince, have been my sounding board and closest friends during this process – thank you for just being there. Heather
MacInnes, thanks for saving my data! If I have not named you here, know that it is not a lack of thought or love, I just cannot write a thesis in the acknowledgements to all of you!
Finally, thank you to the Office of Graduate Studies, the Karen Gardner
Research Award, and the Human Identification Laboratory account for funding of this
iv thesis. This financial support was just as crucial as any emotional or physical support received to make this thesis a reality.
v TABLE OF CONTENTS
PAGE
Acknowledgments ...... iii
List of Tables...... viii
List of Figures...... x
Abstract...... xii
CHAPTER
I. Introduction...... 1
Research Hypothesis Overview...... 3 Organization of Thesis ...... 6
II. Background...... 7
Introduction ...... 7 Undocumented Border Crossers...... 7 Population Variation in the Human Dentition...... 21 Summary...... 24
III. Materials and Methods ...... 26
Introduction ...... 26 Skeletal and Cast Collections ...... 27 Data Collection Methods...... 29 Statistical Methods ...... 45 Summary...... 50
IV. Non-Metric Results...... 52
Introduction ...... 52 Intraobserver Error ...... 52 Interobserver Error ...... 53
vi CHAPTER PAGE
Region-of-Origin Trait Distributions for the Skeletal Samples ... 55 Logistic Regression Analysis ...... 62 Mean Measure of Divergence ...... 64 Summary...... 66
V. Metric Results...... 69
Introduction ...... 69 Intraobserver Error ...... 69 Interobserver Error ...... 70 Sex Differences ...... 71 Morris (1986) Occlusal Polygon Method...... 74 Discriminant Function Analysis...... 77 Summary...... 85
VI. Discussion and Conclusion...... 88
Introduction ...... 88 Discussion...... 88 Conclusion...... 99
References Cited...... 102
Appendix
A. Adult Dentition: Nonmetrics...... 110
vii LIST OF TABLES
TABLE PAGE
1. Intraobserver Error Cohen’s Kappa Results...... 54
2. Interobserver Error Cohen’s Kappa Results...... 55
3. Sample Frequencies by Region-of-Origin...... 56
4. Pearson’s Chi-square and Fisher’s Exact Results of Traits Present (SWH and Mexican) ...... 58
5. Pearson’s Chi-Square and Fisher’s Exact Results of Traits Present (SWH and all UBC) ...... 60
6. Pearson’s Chi-Square and Fisher’s Exact Results of Traits Present (Mexican and all UBC) ...... 61
7. Logistic Regression Model Performance ...... 63
8. MMD Results for Biodistance among the Region-of-Origin Groups ...... 65
9. Paired t-test Results for Intraobserver Error...... 70
10. Paired t-test Results for Interobserver Error...... 71
11. Student’s t-test for Sex Differences in the Maxillary Dentition...... 72
12. Student’s t-test for Sex Differences in the Mandibular Dentition...... 73
13. Region-of-Origin Samples for Occlusal Polygon Assessment...... 75
14. Modified Morris (1986) Occlusal Polygon Results – Maxillary Dentition (in mm2) ...... 76
15. Modified Morris (1986) Occlusal Polygon Results – Mandibular Dentition (in mm2) ...... 76
viii TABLE PAGE
16. Mean Occlusal Polygon ANOVA Results with Tukey’s Correction – Maxillary Dentition...... 77
17. Mean Occlusal Polygon ANOVA Results with Tukey’s Correction – Mandibular Dentition...... 77
18. Maxillary Dentition Group Centroids per Function...... 81
19. Maxillary Dentition DFA Classification Results (in %) ...... 82
20. Mandibular Dentition Group Centroids per Function ...... 83
21. Mandibular Dentition DFA Classification Results (in %) ...... 84
22. Pooled Dentition Group Centroids per Function...... 85
23. Pooled DFA Classification Results (in %) ...... 86
ix LIST OF FIGURES
FIGURE PAGE
1. Map Highlighting the Locations of Reference and Study Collections Used in This Thesis ...... 4
2. An Example of Bilaterally Winged Central Maxillary Incisors ...... 31
3. Dental Cast Used to Score Labial Curvature...... 31
4. Dental Cast Used to Score Maxillary Central Incisor Shoveling ...... 32
5. Dental Cast Used to Score Maxillary Lateral Incisor and Canine Shoveling...... 32
6. Dental Cast Used to Score Mandibular Incisor Shoveling ...... 33
7. Dental Cast Used to Score Double Shoveling ...... 33
8. An Example of an Interruption Groove on a Right Lateral Maxillary Incisor...... 34
9. Dental Cast Used to Score Tuberculum Dentale ...... 35
10. Dental Cast Used to Score Canine Mesial Ridge ...... 35
11. Dental Cast Used to Score the Canine Distal Accessory Ridge in the Maxilla...... 36
12. Dental Cast Used to Score the Canine Distal Accessory Ridge in the Mandible ...... 36
13. Examples of Both Mesial and Distal Premolar Accessory Cusps Are Highlighted in the Maxilla ...... 37
14. Dental Cast Used to Score Maxillary Premolar Accessory Ridge...... 37
15. Dental Cast Used to Score the Metacone...... 38
x FIGURE PAGE
16. Dental Cast Used to Score the Hypocone...... 38
17. Dental Cast Used to Score Cusp 5 in the Maxilla...... 39
18. Dental Cast to Score Carabelli’s Cusp...... 39
19. Dental Cast Used to Score the Parastyle...... 40
20. Dental Cast Used to Score Premolar Cusp Variation in the First Mandibular Premolar...... 41
21. Dental Cast Used to Score Premolar Cusp Variation in the Second Mandibular Premolar ...... 41
22. Dental Cast Used to Score the Anterior Fovea...... 42
23. Dental Cast Used to Score the Deflecting Wrinkle ...... 42
24. Dental Cast Used to Score Mid Trigonid Crest ...... 43
25. Dental Cast Used to Score the Protostylid...... 43
26. Dental Cast Used to Score Cusp 5 in the Mandible...... 44
27. Dental Cast Used to Score Cusp 6...... 44
28. Dental Cast Used to Score Cusp 7...... 45
29. Prevalence of Non-Metric Dental Traits for Each Reference and Sample Grouping (in %) ...... 57
30. Multidimensional Scale of MMD Results ...... 67
31. Left Maxillary Molar with Occlusal Polygon and Oblique Triangles Highlighted...... 74
32. Maxillary Dentition Plot for the Four Regions-of-Origin ...... 82
33. Mandibular Dentition Plot for the Four Regions-of-Origins...... 84
34. Pooled Dentition Plots for the Four Regions-of-Origins...... 86
xi ABSTRACT
NON-METRIC AND METRIC DENTAL ASSESSMENT OF ANCESTRY
IN CONTEMPORARY MEXICAN INDIVIDUALS: AN AID TO
IDENTIFYING UNDOCUMENTED BORDER CROSSERS
by
Rebecca George
Master of Arts in Anthropology
California State University, Chico
Spring 2015
Immigration across the U.S.-Mexico border has been an issue since the
Mexican-American War. While U.S. immigration policies have fluctuated over time to reflect the labor needs within the country, more recent policies have strengthened the border against crossings. This increased protection along the U.S.-Mexico border, particularly since the early 1990s, has forced undocumented migrants to find less restricted, but more dangerous points of entry into the U.S., resulting in increased death rates for these immigrants. As the death toll climbs for undocumented border crossers
(UBC), medical examiners’ offices face the task of improving identification methods necessary to repatriate them. A majority of these individuals are of Hispanic ancestry, indicating that they are from Mexico, Latin America, Central America, and South
xii America. Until recently, forensic research was less expansive on Hispanic individuals in comparison to that for individuals of European and African American ancestry.
As research increases on distinctive skeletal features of Hispanic populations, there remain some unexplored methods that could aid in identification, such as aspects of dental anthropology. This thesis explores the utility of two such dental anthropology methods, dental non-metric traits and the occlusal polygon, for UBC identification purposes. Adult dentition from six areas within the American Southwest and Mexico were scored and measured for these methods, respectively. These samples consist of known Southwest Hispanic and Mexican individuals from four locations and unknown
UBC individuals from two locations.
The results indicate that both the non-metric dental traits and the occlusal polygon show some differences between the Southwest Hispanic and Mexican samples.
The dental non-metric traits, in particular, show promise for being able to classify UBC based on known samples. These results also demonstrate that Hispanic as a classificatory term does not account for the amount of variation observed among the groups in this study, and calls into question the validity of this term to describe populations from
Mexico, Central America, and Latin America.
xiii
CHAPTER I
INTRODUCTION
Studying human variation has been one of the building blocks of physical anthropological research since the 18th century. While the field has come a long way from classifying groups of people based on skin color, such as Coon’s work, the foundation of physical anthropology is still rooted in examining and understanding human biological differences. A much greater understanding of how human populations differ, though, has come through research within the subfields of physical anthropology, which include, but are not limited to, anthropological genetics, human biology, bioarchaeology, and forensic anthropology.
Morphological differences are still very much a part of physical anthropology studies, particularly in bioarchaeology and forensic anthropology. Skeletal morphology provides information for the biological profile of sex, ancestry, age, and stature that is critical for many physical anthropological research questions. This information is particularly important for modern populations, which fall under the purview of forensic anthropology. The biological profile aids in providing an identification for an individual.
One of the current research topics within forensic anthropology where this is predominant involves the identification of undocumented border crossers at the U.S.-
Mexico border. Undocumented border crossers (UBC) are defined as any “foreign-born non-U.S.-citizens actively involved in crossing the border without proper authorization
1 2 from the United States government” (Martinez et al. 2013:9). As this is the definition adopted by the Pima County Office of the Medical Examiner (PCOME), the office that investigates the highest number of UBC deaths every year, this is the definition used within this project to refer to UBC. The increase in UBC deaths in the past 25 years has placed a corresponding pressure on forensic anthropologists to improve their identification methods so that the decedents can be identified and repatriated to their families. Many of these methodological improvements have focused on using cranial morphology and postcranial metrics (Birkby et al. 2008; Spradley et al. 2008; Hurst
2012; Tise et al. 2013; Hefner 2015), as well as personal effects associated with the decedents (Birkby et al. 2008), to differentiate UBC individuals from Southwest Hispanic individuals.
The dentition of UBC has only been examined in a handful of studies.
Primarily, it has been examined in cultural contexts for classifying a decedent as UBC
(Birkby et al. 2008). For example, decorative dental work and generally poor, untreated dental conditions are understood to signify an individual from a low socioeconomic status, such as UBC and only two dental non-metric traits are typically utilized to help characterize UBC individuals skeletally (Birkby et al. 2008).
Dental morphology is highly genetically controlled. Non-metric dental studies have been used in modern contexts to distinguish between populations (Edgar 2013) and metric dental assessments have been utilized in paleoanthropological studies of biodistance. As there is genetic evidence for differences among groups classified under the Hispanic population umbrella (i.e., Green et al. 2000; Bertoni et al. 2003; Campos-
3
Sánchez et al. 2006), dental morphology research could be useful to aid with the current
UBC identification methodology.
To examine the utility of dentition for UBC identification, it is examined in this thesis in modern Mexicans, the primary region-of-origin group that are found as deceased UBC. Contemporary Mexican individuals from Central Mexico are compared to Southwest Hispanic samples from Albuquerque, New Mexico and Phoenix, Arizona.
Both samples are then compared to unidentified UBC decedents housed in San Marcos,
Texas, and Tucson, Arizona. It is noted that the designation of “Southwest Hispanic” within this thesis is derived from literature such as Birkby et al. (2008) that utilizes regional terminology to classify Hispanic Americans and sources like Edgar (2013) that discuss dental differences between New Mexican Hispanic and South Florida Hispanics.
Figure 1 shows a map of the locations where the skeletal collections used are housed.
Additional information about each collection is included in Chapter III of this thesis.
Research Hypothesis Overview
Dental non-metric traits and the occlusal polygon will be used in this project to examine the biological relationships among contemporary Mexicans, Southwest
Hispanics, and UBC. The research hypothesis for the project is: There will be enough biological variation in the dentition to differentiate between Mexican and Southwest
Hispanic groups. Dental non-metric traits and metric assessment, the occlusal polygon, will be the methodologies employed to test this hypothesis. If there is enough biological variation between the Mexican and Southwest Hispanic samples, this approach may be utilized to assign a potential region-of-origin to unknown individuals, such as UBC.
4
Figure 1. Map highlighting the locations of reference and study collections used in this thesis. (Google Maps)
The basis for this research hypothesis comes from the genetic evidence of groups classified under the Hispanic umbrella (Green et al. 2000; Bertoni et al. 2003;
Campos-Sánchez et al. 2006). Given the different levels of admixture in Southwest
Hispanic groups and Mexican groups, there should be differences between their
5 dentitions. The Southwest Hispanic samples have higher levels of European ancestry than
Native American ancestry, while the Mexican samples fit genetic models of trihybrid ancestry. These differences in admixture should result in variations in dental non-metric trait prevalence. If the UBC samples originated from either of these groups, the dental non-metric traits should be similar, reflecting the region-of-origin of these unknown samples. Additionally, the genetic differences indicate that the occlusal polygon may work to differentiate between the Mexican and Southwest Hispanic samples within this thesis. While the method has only been used in a couple modern biodistance studies, it cannot be ruled out for potentially being useful for distinguishing closely related groups from one another. Similarly to the dental non-metric traits, differences in the size of the occlusal polygon between the two reference groups could lead to assisting with UBC region-of-origin classification.
One of the underlying goals of this research is to explore whether or not the term Hispanic is a suitable ancestral classification for forensic purposes, especially as it relates to UBC. If dental differences are found between the Mexican and Southwest
Hispanic samples, this research could provide important information to that topic. If dental differences are not found between the two reference groups, though, that would also provide some insight to the discussion of how best to classify groups of mixed
Native American, European, and African ancestry. Support for, or a lack of it, for the research hypothesis could also add to the on-going dialogue within the forensic community on which directions are best to pursue to improve the identification methodologies for deceased UBC in the U.S. and as to the need for population-specific formulae, as suggested in sources such as Spradley et al. (2008).
6
Organization of Thesis
Chapter II discusses the background for this thesis. The reasons why undocumented migrants cross into the U.S. are presented, along with why the death rates have become so high in the past 25 years. The necessity for forensic anthropologists to assist with the UBC identification efforts is also discussed. The genetic evidence for differences in Hispanic populations is presented to demonstrate the need for continual improvement of methodology to identify UBC. The remainder of the chapter is devoted to discussing how dentition can play a role in the identification process.
Chapter III presents the materials and methods utilized in this thesis. The skeletal and cast collections used for data collection are discussed. The data collection methods are described in detail, following Turner et al.’s (1991) methodology. The occlusal polygon measurements are also outlined. The statistical methods employed to analyze the data are discussed in the remainder of the chapter.
Chapter IV discusses the non-metric results for this project and chapter V presents the occlusal polygon results. Both chapters outline the statistical results of each test and examines the biodistance results for the reference and study samples used in this project.
Chapter VI is a detailed discussion of the results of this thesis. The results are presented in the framework of the research hypotheses. The implications of these results for the larger process of identifying UBC will also be discussed. The conclusion of this chapter and overall thesis will discuss future avenues of research to expand the understanding of biological differences among Hispanic populations.
CHAPTER II
BACKGROUND
Introduction
This chapter will describe the background information for this project. The first portion of the chapter will outline some history of when and why undocumented border crossers (UBC) move across the U.S.-Mexico border and when forensic anthropologists began to play a role in the identification efforts. Non-metric cranial traits, craniometric data, and metric postcranial traits are often the focus of forensic anthropology research being used to identify UBC remains and will be discussed in this chapter. Additionally, the cultural associations of and the genetic makeup of Hispanic ancestry will be explored. The second portion of this chapter will discuss how dental anthropology can play a role in the identification process. Both dental non-metric studies and occlusal polygon studies will be included in this discussion to demonstrate their potential application for the identification of UBC remains.
Undocumented Border Crossers
History of Mexican Immigration into the U.S.
Illegal immigration at the U.S.-Mexico border is something that makes news headlines frequently in the United States. A brief search on any major news website makes it clear that immigration reform is a popular topic in both current political
7 8 situations and for presidential campaigns. This raises the question as to when these issues with immigration across the U.S.’s southern border began.
Mexico’s political instability as a nation after gaining its independence in
1821 led to restlessness along its northern border. Immigration issues were present from the beginning, with U.S. immigrants living in the Mexico territory of Texas refusing to follow Mexican law. By the end of 1845, Texas had seceded from Mexico and joined the
U.S. This helped to trigger the Mexican-American War, which lasted from 1846 to 1848.
The war ended with the signing of the Treaty of Guadalupe Hidalgo (Crawford 1999).
This treaty gave the U.S. approximately half of Mexico’s territory, which includes many of the current western and southwestern states (Martinez 2000).
The Treaty of Guadalupe Hidalgo in 1848 was followed by the Gadsden
Purchase in 1853. What was an arbitrary border became more official as U.S. commerce, including railroads, grew across the American Southwest. Mexican nationals crossed the border to work for higher wages (Overmeyer-Velázquez 2011). Sonoran miners were the major Mexican settlers when gold was discovered in California in 1848, but were forced out because of tax law in the early 1850s (Mora-Torres 2011). Mexican immigrants found work on Texas ranches starting in the early 1850s that came with higher wages and protection that was not afforded when working on Mexican ranches. By the late 1870s and early 1880s, Mexico was paying the U.S. to extradite illegal immigrants, which would close off Texas as an employment option (Mora-Torres 2011).
The end of the nineteenth century and the beginning of the twentieth century found Mexico trying to use its natives to build its economy. Native Mexicans, though, were heading into the U.S. in higher numbers than before. They were easily finding jobs
9 in the railroad, agriculture, and mining economies of the U.S. (Mora-Torres 2011). The first bracero program was established in the U.S for legal, temporary Mexican labor from
1917 to 1921. The Great Depression, however, resulted in hundreds of thousands of
Mexican laborers returning to Mexico to be with their families and to find work between
1929 and 1940 (Overmeyer-Velázquez 2011).
The second bracero program began shortly after the U.S. entered World War
II. From 1942 to 1964, Mexican migrants were once again welcomed to the U.S. to work and support the American economy. By the middle to late 1960s, however, legal Mexican workers had difficulties finding work in the U.S. due to an increase in illegal laborers filling in employment gaps. Mexico experienced an economic boom from the 1940s through the 1970s, but still suffered from a lack of native workers (Overmeyer-Velázquez
2011). The tensions led to the U.S. severely restricting Mexican immigration from the mid-1960s through the late 1970s.
Illegal immigration to the U.S. continued to increase after Mexico suffered an economic collapse in 1982. Despite efforts, such as a wall at the border in El Paso, Texas, the numbers of Mexican immigrants continued to rise and they began to settle in various areas across the U.S. (Overmeyer-Velázquez 2011). This led to the passing of the
Immigration Reform and Control Act (IRCA) of 1986, which increased the number of
Border Patrol officers and provided legalized status for undocumented migrants that were in the country since at least 1982. Additionally, the IRCA allowed for sanctions against any employer that hired illegal migrants (Chávez 2011; “Immigration Reform and
Control Act of 1986”). Despite its intentions of restricting illegal immigration, more migrants entered the country seeking work after the IRCA of 1986 was passed.
10
In the 1990s, the U.S. started strongly enforcing the border with Mexico. The federal government’s increased militarization of common crossing points during
Operation Hold-the-Line in 1993 and Operation Gatekeeper in 1994 (San Diego-Tijuana border) forced migrants to cross through more dangerous entry sites. These dangerous sites include those through the Sonoran Desert in Arizona and through Texas (Caminero-
Santangelo 2010; Chávez 2011; Martinez et al. 2013).
The increased border protection of the 1990s had many affects along the U.S.-
Mexico border. Trade and once friendly relations in border cities were also strained due to the Border Patrol’s efforts. Nogales, for example, is a split city; one half of it lies in
Arizona and the other lies in the Mexican state of Sonora. A large fence was erected at the international border in 1996 to prevent migrants from crossing illegally into the U.S.
(McGuire 2013). It was an eye-sore for both halves of the city and a hazard for migrants trying to find employment on the U.S. side. Tourism and services that thrived in both halves of Nogales before the fence suffered immensely afterwards from the prohibition of interaction between the Sonorans and Arizonans (McGuire 2013). All the fence succeeded in doing in Nogales was preventing day travel between the halves of the community and making it that much more dangerous to cross from Mexico to the U.S.
Reasons Behind Illegal Immigration
Operation Gatekeeper, as part of the 1990s border reform, increased the militarization of the Tijuana-San Diego border. As Chávez (2011) discussed, the increased border protection in Tijuana led to migrants with Border Crosser Cards (BCC) to get creative about their intentions in the U.S. The BCC were only to be used for tourism, not employment, though many individuals learned from the mistakes of others
11 on how to use them for illegal employment, according to Chávez’s (2011) paper. Tijuana, for some of these reasons, was no longer the primary entry point for illegal workers as the more dangerous, less strictly patrolled areas in the Sonoran Desert increased in popularity
(Chávez 2011).
The dangers associated with crossing were not unknown phenomena for undocumented migrants. A study by DeLuca et al. (2010) examined the types of information available to individuals, particularly males, before they attempted to cross the U.S.-Mexico border. While all of the men interviewed for the study were aware of at least some of the potential dangers, including the high temperatures, risks of Border
Patrol captures, and thieves, they were motivated by a need to help their families
(DeLuca et al. 2010). Some of the interviewees turned themselves in to Border Patrol officials before making it to the U.S. due to the stress of the journey. Despite this, a few stated they would be willing to cross again. The men that fell into this group wanted to earn as much money in the U.S. as possible and return home; none of them seemed to have long term plans to reside in the U.S. (DeLuca et al. 2010). This study helped to highlight the motivations behind the increase in border crossers. A need to support their families is one of the largest driving forces for men to risk their lives by crossing through the desert from Mexico to the U.S. While this is only one study, it demonstrated that increased border militarization does not deter migrants from attempting to cross the border.
12
Role of Forensic Anthropology in the Identification of UBC
The increase in crossings through more dangerous entry points has, unfortunately, led to an increase in migrant deaths (Sapkota et al. 2006; Anderson and
Parks 2008; Martinez et al. 2013). These deaths necessitated forensic investigation and began a dialogue on UBC identification methods that is still ongoing. One of the most common causes of death in the early post-Gatekeeper days was hyperthermia, or heat- related illnesses (Anderson 2008; Anderson and Parks 2008; Hinkes 2008; Ruttan et al.
2013). As the Sonoran Desert was, and continues to be, one of the primary entry points for undocumented migrants, the remains of the decedents often rapidly decompose
(Anderson 2008). This reduced the effectiveness of forensic pathology examinations and increased the necessity of forensic anthropology examinations.
Anthropologists, particularly those within the forensic or cultural subdisciplines, have become the forerunners for identifying UBC remains. As the number of deceased UBC continues to rise, this is becoming an increasingly important area of research. The Pima County Office of the Medical Examiner (PCOME), the office that investigates the largest number of UBC deaths annually, recorded 2,238 migrant deaths between 1990 and 2012 (Martinez et al. 2013). The rate of UBC deaths per 100,000
Border Patrol apprehensions doubled from 2009 to 2011 alone (Martinez et al. 2013).
Martinez et al. (2013) also report that approximately one-third of UBC individuals remain unidentified. Recent research attempting to rectify this situation focuses on examining the personal effects associated with the deceased and developing non-metric trait lists and metric formulae to classify individuals as UBC through an understanding of
13 the populations from which they originate. Of the UBC that have been identified, the majority are from various regions in Mexico; understanding cultural and skeletal variation within Mexico, therefore, is a major key to identifying UBC.
As described by Birkby et al. (2008) and Martinez et al. (2013), personal effects that can be associated with a set of UBC remains can be critically important for narrowing a region-of-origin for the decedents. Personal effects include objects such as voter registration cards, birth certificates, and foreign currency are just some of the identifying material remains associated with UBC individuals. Additionally, having saint cards, like the Virgin of Guadalupe, foreign phone numbers, poor dental work, and even select clothing brands help to distinguish these remains as UBC. An additional factor that is considered for classifying remains as belonging to UBC is the geographic location where the remains were found. Birkby et al. (2008) and Martinez et al. (2013) note that this methodology may overestimate the number of UBC deaths because it assigns an estimated nationality, but they report that this is preferable to underreporting the number of UBC deaths.
The work that has been done so far on Hispanic populations has been on non- metric analyses of the crania and metric analyses of the postcrania. The PCOME utilizes a series of non-metric skeletal traits to help classify individual cases as UBC (Birkby et al. 2008). A study by Hurst (2012) examined the effectiveness of these traits by using them to distinguish between Hispanics and American whites and blacks. She utilized a variety of cranial morphoscopic traits to determine which would be the best suite to use for this ancestral distinction (Hurst 2012: 860). She found that the majority of the morphoscopic cranial traits used at the PCOME are useful in not only differentiating one
14 ancestral group from another, but are especially prevalent in Hispanic individuals (Hurst
2012: 863-864).
Additional non-metric work by Hefner (2015) compared the use of seven cranial traits to differentiate among American blacks and whites and Hispanic UBC from the PCOME. He utilized discriminant function analysis (DFA) as a simplistic method to separate these groups. The functions performed best using three, five, or seven traits when only discriminating between American blacks and whites. To achieve the highest results among the three groups, all seven cranial traits must be utilized in the analysis
(Hefner 2015).
Osteometric work by Spradley et al. (2008) and Tise et al. (2013) discuss the postcranial assessment of Hispanic ancestry and sex. These studies utilize the postcrania as the male cranium has a tendency to misclassify as female based on reference samples from other populations (Spradley et al. 2008). Spradley et al. (2008) note the difficulty with assessing several aspects of the biological profile for Hispanic individuals, particularly UBC. One of the main findings described in this study was the necessity for population specific formulae for stature, as different Hispanic populations, such as
Mexicans and Puerto Ricans, have proportionality differences in limb lengths (Spradley et al. 2008). Both Spradley et al. (2008) and Tise et al. (2013) highlight the major sampling issue associated with UBC; most UBC are males, but a lack of data in the FDB often leads to misclassification of males as females. Tise et al. (2013) found that the humerus and radius were the most accurate postcranial bones to assess sex after the innominates. Sexual dimorphism, however, was minimal, though this may have been due to small sample sizes (Tise et al. 2013).
15
Hughes et al. (2013) utilized craniometric data from modern Mexican groups to determine if any differences existed and if they corresponded with genetic information on admixture from the postcolonial era. This was a long-term study that utilized identified, male, border crosser crania from the PCOME (Hughes et al. 2013). The genetic information the authors were looking to corroborate with the craniometric data was that Amerindian admixture increased moving north to south in Mexico, while
European admixture increased moving from south to north. The samples represented a geographic spread across Mexico, and the ancestries aligned fairly well with the expected patterns from genetic information. In trihybrid ancestry modeling, Amerindian populations were predominant, with European being the second most influential ancestry, and African ancestry being the least present in admixture; African ancestry was noted to be distributed similarly to the Amerindian ancestry in that it follows an increasing pattern moving from north to south (Hughes et al. 2013). This study shows that skeletal data can be utilized to trace migration and ancestry patterns within closely related populations.
Finally, another form of identification that is beginning to be incorporated into
UBC research is stable isotope analysis. Studies such as Juarez (2008), Bartelink et al.
(2015), and Chesson et al. (2015) demonstrate that a region-of-origin can be discerned for an individual based on a scientific understanding of the environment in which he or she lived. Hair, bone, and tooth enamel and dentin samples are the most common sources of this information. The stable isotopes of oxygen, strontium, and hydrogen that can be measured in different tissue samples are the most useful for determining a region-of- origin (Katzenberg 2008; Ehleringer et al. 2008). By collecting local tap water samples, such as seen in Ehleringer et al. (2008), models are established for the hydrogen and
16 oxygen ratios for an area. Samples from individuals of unknown origin compared against these models helps to narrow down possible locations (Ehleringer et al. 2008; Chesson et al. 2015). Juarez’s (2008) study utilized strontium to create a predictive model for regions of Mexico. In conjunction with skeletal indicators of region-of-origin, stable isotope analysis can contribute greatly to identifying UBC remains.
Ancestry and Identity
As skeletal remains demonstrate, not all “Hispanic” individuals look alike
(Spradley et al. 2008). This issue can also be seen culturally, as well, though. The U.S.
Census serves as an example of the difficulty in culturally defining ancestry groups, let alone specific populations. The 2010 U.S. Census used “Hispanic” interchangeably with
“Latino” and defined Hispanic as individuals of “Mexican, Puerto Rican, Cuban,” (Ennis
2011:4) or another country in Central and South America, as well as providing a category for other Spanish descent (Ennis et al. 2011). Based on the discussion of question phrasing in Ennis et al. (2011), the categories within “Hispanic” were not exclusive and at least two categories, such as heritage and country of birth, had to be combined in order to assign an individual Hispanic “ethnicity” (Ennis 2011:1).
The ways in which individuals of Hispanic ancestry define themselves is important, too, because it reflects cultural and social classifications. It is also possible that cultural differences are representative of biological differences that could be utilized to separate Hispanic populations from one another. A study by Emeka and Vallejo (2011) highlights the complexity of self-identified Hispanic ancestry. Their article utilized data from the 2006 American Community Survey to discuss Hispanic identity versus Spanish or Latin American ancestry in terms of perceived assimilation within U.S. culture.
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Inherent problems with surveys and self-identification are discussed, though the results of the survey are still are still insightful regarding the ancestry of Hispanic individuals in the
U.S. Many of respondents identified as being of Mexican or of Mexican-American ancestry. Frequently, the respondents that chose Mexican-American as their ancestry also chose Hispanic as their identifier (Emeka and Vallejo 2011). Individual responses that claimed Latin American ancestry were then grouped together to discern any patterns that
Latin American ancestry might have had on whether they identified as Hispanic or not.
The more mixed respondents’ ancestries were, such as claiming white, black, or Asian ancestries in addition to Latin American ancestry, the more likely they were to respond with a non-Hispanic identifier. The authors also found that there was likely an undercount of people of Spanish or Latin American ancestry because of how survey questions were phrased. Additionally, they found support for their hypothesis that assimilation pressures are in place within groups claiming ancestry outside of Latin America, causing individuals from these groups to not always identify as being Hispanic (Emeka and
Vallejo 2011).
Genetic Evidence of Admixture
The complexity of the meaning of “Hispanic,” along with the number of unidentified UBC remains just at the PCOME, and the discussion in Spradley et al.
(2008) demonstrate that there is a necessity to seek out population-specific indicators of region-of-origin. There is genetic evidence from American Hispanics and Mexicans that supports this research direction within forensic anthropology, as the admixture within these groups allows for genotypic differences to be made apparent.
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Hispanic origin is typically thought of as trihybrid as most Hispanic individuals have some combination of Native American, European, and African ancestry.
Genetic studies, like that of Bertoni et al. (2003), use gene flow modeling to determine the proportion of admixture, while other studies, such as Campos-Sánchez et al. (2006), examine mitochondrial DNA (mtDNA) and Y-chromosome data to examine the biological distance of one specific Hispanic population to another.
Bertoni et al. (2003) examined admixture within Hispanic populations from the eastern, western, southwestern, and southeastern United States. As the authors state, the trihybrid model works for most of the samples within these geographical groupings, with the exception of southwestern, southeastern, and Pennsylvania (a sample within the eastern U.S. grouping). These samples are dihybrid, with the southwest lacking an
African component and the other two lacking a Native American component (Bertoni et al. 2003).
Campos-Sánchez et al. (2006) found that Y-chromosome markers were similar among Mexicans, Costa Ricans, and Southwest Americans. The American
Southwest and Mexican populations shared certain alleles, and the Mexicans and Costa
Ricans share other alleles. The mtDNA among the three populations also show some overlap, which the authors related to shared migration histories. Campos-Sánchez et al.
(2006) indicated that these three populations were more strongly related on their maternal lineages than on their paternal lineages. They added Portugal to the mix of European ancestral contributors.
Additional genetic studies also examine aspects of Hispanic ancestry and these articles help to reveal where the variation within these populations originated.
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Green et al. (2000), for example, examined mtDNA haplotypes from two sites in north- central Mexico, shedding light on the genetic and historical background of ancestry patterns within Mexican populations as part of the trihybrid ancestry model. Of the
Native American individuals within this sample, the majority fell into one of four primary
Native American haplogroups (Green et al. 2000). The remainder of the identifiable samples from this study either represented European or African haplogroups. African ancestry was of particular interest to the authors of this study, who explained that the
African haplotypes in north-central Mexico were likely from African slaves that were brought into Mexico when there was a decline in Native American slave labor.
Collins-Schramm et al. (2004) examined allele frequencies among likely ancestral, or parent, groups for northern California Mexican Americans to see how useful admixture mapping could be for this population. European and Amerindian parental populations were discerned using selective ancestry identification markers, though the authors noted that their results indicated that more genetic markers should be included to tease out the specific ancestral populations, as opposed to the larger groupings that they currently provide. The ancestral identification markers were able to distinguish between the European and Amerindian samples, which demonstrates that admixture mapping would be successful in this population of Mexican Americans. More than half of the ancestry could be attributed to European genes, while the rest was Amerindian, though attributed to different groups.
Silva-Zolezzi et al.’s (2009) research had its basis in genomic medicine for the
Mexican population, but demonstrated its usefulness for supporting anthropological research by highlighting genetic differences among subpopulations within Mexico. The
20 genetic differences among subpopulations are attributed to varying levels of European and Amerindian genetics, like the results seen in Collins-Schramm et al. (2004). Major differences were found between Mexican mestizos and other Mexican subpopulations.
The authors conclude that haplotype mapping for genomic purposes would be helpful in recognizing the differences among Mexican subpopulations.
Bryc et al. (2015) utilized data from 23andMe, a personal genetics company, to examine ancestry patterns within African, European, and Latino populations within the
U.S. Ancestry and ethnicity surveys were used in conjunction with the DNA samples from customers to map ancestral histories. For the Latino population within the U.S., it was found that, on average, European ancestry was the highest genetic contributor, followed by Native American and African ancestry, respectively (Bryc et al. 2015). There was also a relationship between self-reported ethnicity and admixture from parent populations. For example, U.S. Latinos that claimed Mexican or Central American ancestry have higher levels of Native American admixture; this is similar to the findings from Green et al. (2000) that found Native American ancestry to be the highest genetic contributor to Mexican ethnicity. Those identifying as black, Dominican, Puerto Rican have a higher level of African admixture. Similarly, Latinos that claim South American or white ancestry have higher levels of European ancestry in their DNA (Bryc et al.
2015).
The genetic differences among Hispanic populations show several things, but primarily that there are varying amounts of Native American (Amerindian), European, and African admixture. There is substantial genetic variation even within one country, as demonstrated by Collins-Schramm et al. (2004) and Silva-Zolezzi et al. (2009) with their
21 studies in Mexico and by Bryc et al.’s (2015) results from the U.S. Latino population.
Some of these genetic differences may be apparent when comparing skeletal remains from one Hispanic group to those of another, as demonstrated by Hughes et al. (2013).
The genetic and skeletal data provide evidence to expect that population-specific standards can be established in order to increase the identification rate for UBC remains.
Population Variation in the Human Dentition
As demonstrated by the genetic evidence, there are differences among
Hispanic groups. Studies like Spradley et al. (2008) have shown that population-specific formulae are required for estimating parameters of the biological profile in regards to the
UBC identification efforts. One method of skeletal analysis that has only been touched on in the UBC literature (Birkby et al. 2008 and Hurst 2012) is dental non-metric analyses.
The only dental non-metric traits examined at the PCOME, for example, are shoveled incisors and canines and enamel extensions. Metric dental studies have not been considered in the UBC literature.
Dental anthropology is a specialized sub-area within physical anthropology that utilizes the dentition for anthropological research (Scott and Turner 2008). As dental morphology, size, and even number are under high genetic control, teeth are a logical source of study for biological variation (Scott and Turner 1988). Of the many methodologies within dental anthropology, non-metric trait assessment will be one of the primary methods discussed for this project. Non-metric dental traits have been used for more than 100 years to distinguish populations from one another (Scott and Turner 2008), but it was not until 1991 that the standards used today were established. Turner et al.
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(1991) provided information on the non-metric traits in the permanent dentition, how to score them, and how to use the associated dental casts as scoring guides.
There are some non-metric trait studies that relate to Hispanic ancestry differentiation. Edgar (2013), for example, created regression formulae based on dental non-metric trait prevalence among American white, black, Southwest Hispanic, and
Southern Florida Hispanic samples. She found that dental non-metrics can differentiate among broad ancestral groups. Major differences between Hispanics and American whites and blacks are apparent, though Edgar (2013) noted the difficulty in distinguishing between Hispanic populations. The results of this article show some similarities to those of Spradley et al.’s (2008) results. When pooling multiple Hispanic populations together against larger ancestry groups, such as whites and blacks, it becomes difficult to separate the Hispanic populations from one another. Population-specific formulae would likely help to alleviate some of these issues, as suggested by Spradley et al. (2008).
Willermet and Edgar (2009) collected non-metric dental data from Southwest
Hispanics from Albuquerque to examine how well they fit the trihybrid ancestry model.
Native American ancestry is believed to be from the Pueblo Native Americans from
Mexico that were settled in the area before European colonization. Based on U.S. Census data, ancestral European and African American samples were chosen to compare with the
Albuquerque data, in addition to Native American samples from New Mexico and
Mexico (Willermet and Edgar 2009). Their results suggest that European ancestry is the largest contributor to the Albuquerque Hispanic population. Similarities to African
Americans are attributed to the similarities between Europeans and African Americans as this was not a significant population in Albuquerque’s history (Willermet and Edgar
23
2009). Based on the prevalence of Native American traits within the Albuquerque population, it is also believed that this admixture occurred early in Albuquerque history
(Willermet and Edgar 2009). The results of this study are important considerations for this project as the majority of the Southwest Hispanic sample utilized from the James K.
Economides Collection is the same as used in Willermet and Edgar (2009).
The other dental anthropological method utilized in this project is the occlusal polygon. This is a relatively experimental methodology for forensic anthropological purposes, as it has only been used in one known forensic study (Kenyhercz et al. 2014).
Kenyhercz et al. (2014) used an array of statistical methods, including Generalized
Procrustes Analysis, principal component analysis, discriminant function analysis, and
Student’s t-tests. They found that the first mandibular molar and the second maxillary molar were the best for group classification between American blacks and whites.
Morris’s (1986) original publication on the occlusal polygon employed it for a comparison of the maxillary first molar in African, Asian, Native American, and white populations. This metric dental assessment has since been primarily used in paleoanthropological studies. These studies usually combine geometric morphometrics with the occlusal polygon to compare dental morphologies among members of the genus
Homo. Bailey (2004), for example, compared the morphology of Neandertal maxillary first molars to that of early anatomically modern Homo sapiens, modern humans, and
Homo erectus. She found that Neandertals had less morphological similarities in their molars than the other groups combined. Bailey et al. (2008) used similar methodology to classify an unknown partial cranium as Neandertal. Gómez-Robles et al. (2012)
24 examined the maxillary second and third molars of a sample of European Pleistocene hominins to observe any transitions in morphology through time.
While this is only a sampling of the paleoanthropological available on this topic, it demonstrates that the occlusal polygon is not typically utilized for forensic purposes; this emphasizes the experimental nature of introducing this method to forensic anthropology. Any results from this project, though, will be informative of how well it can perform in differentiating among closely related populations.
Summary
This chapter opened with a review of the history of relations between the U.S. and Mexico over their shared border. Federal legislation and the increased militarization at the U.S.-Mexico border since the 1990s are among the more recent reasons behind the high numbers of deceased UBC found in the U.S. Specifically, fences and Border Patrol agents have forced migrant routes from places like Tijuana to more dangerous entry points in Arizona and Texas. The increased dangers associated with crossing through these points include dehydration, heat-related illnesses, robbery, and being captured by the Border Patrol. Often times these dangers are known before migrants, usually males, attempt to cross the border, but their need to provide for their families motivates them to head to the U.S., despite the risks. Social interactions in split border cities, like Nogales, have ceased to exist, as well, because of the fences and border militarization.
Forensic anthropologists became involved in the UBC deaths due to the advanced states of decomposition in which the remains are often found. At least one- third, if not more, of the deceased UBC in the PCOME remain unidentified. Research on
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Hispanic morphology has increased in attempts to provide identifications for the deceased beyond classifying them as UBC. The methods used include cranial non-metric traits, craniometrics, postcranial metric measurements, DNA, and stable isotope analysis.
It is difficult to define Hispanic groups, both culturally and skeletally. Population-specific standards are needed, which should shed light on defining different subgroups within the
Hispanic umbrella.
There is promise for this research direction as studies like Hughes et al. (2013) find skeletal indicators that correlate with genetic evidence. The genetic data for Hispanic groups demonstrates that there are different levels of trihybrid admixture in populations that can be used to distinguish their regions-of-origin. As dental morphology is under strong genetic control, these differences in genetic admixture rates provide a basis for examining dental non-metric traits and metric dental measurements in Hispanic groups.
Previous studies on Hispanic dental non-metrics demonstrated that it might be difficult to separate populations from one another, though, despite the genetic differences, such as
Edgar (2013). Similar complications may be seen in the occlusal polygon measurements as they have not been extensively tested for forensic purposes in closely related populations, even though it is an effective method for paleoanthropological studies.
The materials and methods utilized to examine the dental non-metric traits and occlusal polygon measurements for this project are outlined and discussed in the next chapter. Some of the methodologies highlighted in the background will be elaborated on as applicable to the current research at hand.
CHAPTER III
MATERIALS AND METHODS
Introduction
This chapter outlines the materials and methods utilized in this project. Data were gathered from four reference collections and two sample collections for this study.
The reference collections include Southwest Hispanic samples from both the James K.
Economides Collection at the University of New Mexico and the Maricopa County
Office of the Medical Examiner, and the Mexican samples are from the Instituto de
Investigaciones Antropológicas and the Laboratorio de Antropología Física, both on the campus of the Universidad Nacional Autónoma de México in Mexico City, Mexico. The non-metric dental traits are outlined in detail below as to how they were scored according to the Turner et al. (1991) methodology. The metric, or occlusal polygon, measurements are also outlined. The materials utilized to collect these data are also discussed.
The statistical methods employed to determine the utility of region-of-origin estimations will be reviewed in this chapter. A variety of statistical methods are utilized to analyze the data in this project to explore the relationships among the region-of-origin samples under study.
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Skeletal and Cast Collections
Reference Samples
The first reference sample is from the James K. Economides Orthodontic
Collection housed at the Maxwell Museum of Anthropology at the University of New
Mexico in Albuquerque. This is a collection of orthodontic casts representing various stages of treatment and corresponding patient records from nearly 5,600 individuals.
While the collection contains individuals from many ancestral backgrounds, only those classified as Hispanic (Edgar et al. 2011) were included in data collection. Data were gathered from a total of 71 individuals from the James K. Economides Orthodontic
Collection.
The second reference collection is the Zimapán cemetery collection from
Hidalgo, Mexico. It is housed at the Instituto de Investigaciones Antropológicas (IAA) at the Universidad Nacional Autónoma de México (UNAM) and contains individuals buried from 1800 to 2004. It is unknown if individuals included in this thesis cover this period.
Burial dates, if affiliated with individual remains, were not shared with the author when the information was requested. The collection was excavated from the Santiago Apóstal church in Zimapán, Hidalgo, when the church was in need of repairs; the collection originally consisted of nearly 900 adults and children (Taboada 2009: 86-87; Figueroa-
Soto 2012). A total of 46 individuals from the Zimapán collection were scored for dental non-metric traits and the occlusal polygon.
The third collection is housed at the Laboratorio de Antropología Física in the
Department of Medicine at UNAM (UNAM collection). This collection consists of individuals from Mexico City that were donated from hospitals, funeral homes, and
28 forensic cases. New additions to the collection began in 1993, with systematic organization and skeletonization of donations occurring in the past decade. At the time of data collection, there were nearly 300 donated remains. Of these, data were gathered from 44 individuals. The second and third reference collections were pooled to create the
Mexican sample utilized in this study. It is important to note that these samples only represent central Mexico.
The fourth collection is from the Maricopa County Office of the Medical
Examiner (MCOME). Three individuals were included in data analyses. These individuals were estimated to be of Hispanic ancestry based on their biological profiles, but have not yet been positively identified. Based on the cultural estimations of UBC, such as the personal effects outlined by Birkby et al. (2008), these individuals were not believed to fall into this category (Anderson 2008:12). Additionally, one individual from the Forensic Anthropology Center at Texas State’s (FACTS) donated skeletal collection, housed at the Osteological Research and Processing Laboratory, was included for analyses. These four individuals were added to the Southwest Hispanic sample.
Sample Collections
Two sample collections were utilized in this research. The first was a subset of thirteen individuals excavated from the Sacred Heart Burial Park in Falfurrias, Texas, housed at FACTS. These are UBCs that were buried in a variety of grave conditions.
Excavations were carried out over the past two summers by teams from Baylor
University and the University of Indianapolis. The remains have been primarily taken to
Baylor University and Texas State University for skeletal analyses, DNA extraction, and
29 storage until they can be repatriated to their families once their identities have been confirmed.
The second sample collection is from the PCOME. Data were gathered from
20 individuals at this location. The PCOME is the primary location where UBC remains are housed until they are identified, as the Tucson Sector in Arizona is the major illegal entry point for migrants crossing the U.S.-Mexico border (Martinez et al. 2013:11). In a recent presentation, Dr. Bruce Anderson (2015) stated that more than 800 migrants remain unidentified at the PCOME. While it is likely that, based on previous UBC data, the majority of these UBCs are Mexican nationals (Martinez et al. 2013:14), difficulties with establishing family profiles in the National Missing and Unidentified Persons
System (NamUS) hinders the identification process for these individuals. Both of the
UBC samples are considered groups in this study based only on the locations in which they are housed, as they represent unidentified individuals at the time of data collection.
Data Collection Methods
Data were gathered between June and July of 2014, with an additional trip to the University of New Mexico in September 2014. For inclusion in this study, an individual needed to have a minimum of 50 percent scoreable dentition. At least half of the adult dentition (n = 16) had to be present and had to be scoreable for at least one non- metric trait. As the goal of this project is to study variation within closely related groups, this criterion aimed to maximize the amount of data available. Additionally, as the majority of UBCs are adults (Martinez et al. 2013), subadults were not included in either the non-metric or the metric analyses. The potential problem of not generating large
30 enough sample sizes for statistical comparison was also a consideration in choosing to only examine adult dentitions for this study (Scott and Turner 2000).
A one-week training period was undertaken with Dr. Heather Edgar at the
Laboratory of Human Osteology on the campus of the University of New Mexico. This helped provide familiarity with the non-metric traits and how to score them, including her suggested edits to the Turner et al. (1991) methodology, known as the Arizona State
University Dental Anthropology System (ASUDAS). The dental non-metric traits were scored utilizing this methodology in conjunction with the dental casts for each trait, which were borrowed from the Human Identification Laboratory on the California State
University, Chico campus. Data were recorded on individual sheets (Appendix A). The data sheets were adapted from Scherer (2004).
The dental casts correspond with many of the traits used in the ASUDAS. The casts show the range of scoring options for a given trait through tooth casts that represent the typical morphology for a particular score.
The non-metric dental traits scored in this study include most of the traits as described in Turner et al. (1991). The traits described here correspond to the order they are in for the data sheets (Appendix A). Tooth wear scores range from 0 to 4, with 0 showing no wear, 0.5 showing some wear facets, 1 having some dentin exposure, 2 having worn down cusps, 3 showing exposed pulp, and 4 representing a functional root stump. If a tooth cannot be scored, there are available notations for antemortem tooth loss, congenital loss, impacted tooth, postmortem loss, or unerupted tooth. All teeth are scored for wear. Winging refers to rotation in the maxillary central incisors (Figure 2).
The scores range from 1 to 4, with 1, 2, and 4 referring to rotational variations in the
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Figure 2. An example of bilaterally winged central maxillary incisors.
central incisors and 3 meaning the teeth exhibit no rotation. This trait can only be scored, however, if the maxillary central incisors are in occlusion; loose teeth cannot be scored.
Labial curvature is also scored in the maxillary central incisors (Figure 3). It is scored based on the curvature of the occlusal surface of the teeth. Scoring ranges from 0 to 4, with 0 lacking curvature and 4 showing very pronounced convexity. In this study, a score of 2 or above would be present.
Figure 3. Dental cast used to score labial curvature.
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Shoveling is a major trait in the ASUDAS that grades the presence and strength of lingual marginal ridges. It is scored in the maxillary central and lateral incisors, maxillary canines, and the mandibular central and lateral incisors (Figures 4-6).
The scores range from 0 to 7, with 0 indicating absence of the trait and 7 indicating “barrel-shaped,” meaning that the entire crown is shoveled. A score of 2 or above was considered present in the maxillary dentition, while a score of 1 or above was
Figure 4. Dental cast used to score maxillary central incisor shoveling.
Figure 5. Dental cast used to score maxillary lateral incisor and canine shoveling.
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Figure 6. Dental cast used to score mandibular incisor shoveling.
Figure 7. Dental cast used to score double shoveling.
considered present in the mandibular dentition this study. Double shoveling is scored in the maxillary and mandibular central and lateral incisors for this project (Figure 7). This trait is the presence of labial marginal ridges. The scores range from 0 to 6, with 0 indicating absence of the trait and 6 indicating full expression of it.
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The interruption groove is a trait in the maxillary central and lateral incisors
(Figure 8). It is a grove that crosses the cingulum and usually continues down the root. It was scored in this project as 0 to 4, with 0 indicating absence of the trait, 1 is a groove on the medial side of the tooth, 2 is a groove on the distal side, 3 is a groove on both the medial and distal sides, and 4 is a single groove in the middle of the cingulum.
Figure 8. An example of an interruption groove on a right lateral maxillary incisor.
The tuberculum dentale ranges from a ridge to a full cusp, or tubercle on the maxillary central and lateral incisors and canines (Figure 9). The scoring ranges from 0 to
6, with 0 indicating absence of the trait and 6 indicating a full, separate cusp. A score of 1 or above was considered present in this study. The canine mesial ridge is scored in the maxillary canines (Figure 10). The size of the mesiolingual ridge is scored from 0 to 3, with 0 indicating absence of the trait and 3 indicating a large mesiolingual ridge. The canine distal accessory ridge can be found in the maxillary and mandibular canines between the distolingual marginal ridge and tooth apex (Figures 11-12). The scores range
35
Figure 9. Dental cast used to score tuberculum dentale.
Figure 10. Dental cast used to score canine mesial ridge.
from 0 to 5, with 0 indicating absence of the trait and 5 indicating a pronounced distal accessory ridge. Scores of 2 or above were considered present in this study.
The presence of premolar distal and mesial accessory cusps can be scored in the maxillary premolars (Figure 13). These cusps may be found at either the mesial or distal ends of the sagittal grooves. They are scored on a 0 to 3 scale, with 0 indicating trait absence, 1 is a mesial cusp, 2 is a distal cusp, and 3 indicating both cusps are present.
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Figure 11. Dental cast used to score the canine distal accessory ridge in the maxilla.
Figure 12. Dental cast used to score the canine distal accessory ridge in the mandible.
The maxillary premolar accessory ridge (MxPAR) is a ridge, or ridges, on the occlusal surface of the premolars that can extend down to premolar sulcus (Figure 14). The ridges can be on either the mesial or distal side, or both. Scores range from 0 to 4, with 0 indicating absence, and 4 indicating a ridge that extends to the sulcus. The presence of
37
Figure 13. Examples of both mesial and distal premolar accessory cusps are highlighted in the maxilla.
Figure 14. Dental cast used to score maxillary premolar accessory ridge.
the distosagittal ridge on the first maxillary premolar is scored as presence or absence.
The ridge extends from the tooth apex of the buccal cusp to the distal occlusal border of the sagittal sulcus.
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The metacone is the distobuccal cusp of the maxillary molars (Figure 15). It is scored based on size from 0 to 6 for this project, with 0 indicating absence of the cusp and 6 is a full-sized expression of the cusp. A score of 5 or above was considered present for this study. The hypocone is the distolingual cusp of the maxillary molars (Figure 16).
It is scored using the same range as the metacone. A score of 5 or above was considered present in this study for the first molar, and a score of 3 or above was considered present in the second molars. The metaconule is the fifth, distal cusp on the maxillary molars
Figure 15. Dental cast used to score the metacone.
Figure 16. Dental cast used to score the hypocone.
39
(Figure 17). It is scored based on its size, as observed with the metacone and hypocone.
A score of 1 or above was considered present in this study.
Figure 17. Dental cast used to score cusp 5 in the maxilla.
Carabelli’s cusp is scored on the lingual surface of the mesiolingual cusp in the maxillary molars (Figure 18). It can be expressed anything from a groove or a pit to a
Y-shaped depression to a full cusp. The scores range from 0 to 7, with 0 indicating the absence of expression and 7 is a full cusp. A score of 2 or above was considered present in this study. A parastyle is an additional cusp on the mesiobuccal cusp of the maxillary molars (Figure 19). Its expression ranges from a pit to a full cusp, with scores from 0 to
Figure 18. Dental cast to score Carabelli’s cusp.
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Figure 19. Dental cast used to score the parastyle.
6, where 0 indicates the absence of expression and 6 is a full cusp. A score of 1 or above was considered present for this study.
Enamel extensions are scored on both the maxillary and mandibular molars for this project, and include any extension of the enamel in the direction of the tooth apex. The scores range from 0 to 3, with 0 indicating no extension and 3 indicating an extension in excess of 4mm. The root and radical numbers are scored in all maxillary and mandibular teeth. A reduced, or peg, tooth is scored in the maxillary lateral incisors and third molars. Scores range from 0 to 2, with 0 indicating normal tooth morphology, 1 indicating a reduced size, and 2 indicating fully peg shaped. The presence or absence of an odontome is scored as either present or absent in both the maxillary and mandibular premolars. An odontome is a spiky projection of the enamel and dentin on the occlusal surface. Congenital absence is scored as either present or absent in the maxillary lateral incisors, maxillary second premolars, maxillary third molars, mandibular central incisors, mandibular second premolars, and mandibular third molars.
Premolar cusp variation is scored in the mandibular premolars (Figures 20-
21). This is to look at lingual cusp variation. Scores range from 0 to 9, with 0 indicating one lingual cusp and 9 indicating three lingual cusps. Variations of the number of cusps,
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Figure 20. Dental cast used to score premolar cusp variation in the first mandibular premolar.
Figure 21. Dental cast used to score premolar cusp variation in the second mandibular premolar.
such as the side and size of the number of cusps are accounted for within the scoring range. The anterior fovea is scored in the mandibular first molar (Figure 22). It is a groove in the anterior occlusal surface. The scoring ranges from 0 to 4, with 0 indicating absence of the trait and 4 indicating full expression. A score of 2 or above was considered present in this study. Groove pattern of the mandibular molars are scored from 1 to 3, with 1 indicating a Y-shape, 2 indicating a + shape, and 3 indicating an X shape.
42
Figure 22. Dental cast used to score the anterior fovea.
The cusp number of the mandibular molars is scored from 4 to 6 based on the total cusps present. The deflecting wrinkle is a variation of the medial ridge on the mesiolingual cusp of the first mandibular molar (Figure 23). Variation ranges from a straight cusp to an L-shaped cusp; the scores are 0 to 3, with 0 indicating absence and 3 indicating the L-shaped cusp. A score of 1 or above was considered present in this study.
The mid trigonoid crest is a ridge connecting the mesiolingual and mesiobuccal cusps of the mandibular molars (Figure 24). It is scored as either present or absent.
Figure 23. Dental cast used to score the deflecting wrinkle.
The protostylid is an accessory cusp on the mesiobuccal cusp of the mandibular molars (Figure 25). It ranges from a pit or groove to a full cusp. The scoring
43
Figure 24. Dental cast used to score mid trigonid crest.
Figure 25. Dental cast used to score the protostylid.
ranges from 0 to 7, with 0 indicating absence and 7 indicating a full cusp. A score of 1 or above would be considered present in this study. Cusp 5 in the mandibular molars is scored based on size from 0 to 5, with 0 indicating absence of the cusp and 5 indicating a full cusp comparable in size to the other cusps (Figure 26). A score of 4 or above was considered present for the first molars, while a score of 1 or above was considered present in the second molars. Cusp 6 in the mandibular molars is scored based on comparisons to cusp 5 (Figure 27). The scoring ranges from 0 to 5, with 0 indicating absence and 5 indicating that cusp 6 is equal in size to cusp 5. A score of 1 or above was
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Figure 26. Dental cast used to score cusp 5 in the mandible.
Figure 27. Dental cast used to score cusp 6.
considered present in this study. Cusp 7 in the mandibular molars occurs between the mesiolingual and distolingual cusps (Figure 28). It ranges in scoring from 0 to 4, with 0 indicating absence and 4 indicating a full cusp. A score of 1 or above was considered present in this study. An additional trait scored in the mandibular first premolars called
Tomes’s root, which is two separate roots. The scoring gradient ranges from 0 to 5, with
0 being one normally developed root and 5 being two separate roots.
The dental metrics used in this study is the occlusal polygon method. This method was employed at the suggestion of Dr. Edgar. Following the methodology of
45
Figure 28. Dental cast used to score cusp 7.
Morris (1986), the cusp distances were measured on each of the first maxillary and mandibular molars. Figure 3 in the metric results chapter outlines these measurements.
Mitutoyo ABSOLUTE Digimatic dental calipers, with an accuracy within 0.02mm, were utilized to measure the cusp distances and the data were recorded using an accompanying foot pedal into Microsoft Excel. The equipment for the occlusal polygon measurements were borrowed from the Laboratory of Human Osteology at the University of New
Mexico.
Statistical Methods
The majority of the statistics utilized in this project were processed using
SPSS (v. 22). All tests were run using an alpha value of 0.05. As antimeres of the dentition were both scored for the non-metric traits, the individual count method was employed to decide which trait expression would be counted. This method states that the antimere with the highest expression of a trait will be considered in analysis (Edgar
2002:77). The theoretical basis of this method lies in the understanding that the expression of non-metric traits is genetically controlled. Utilizing the individuals count
46 method also necessitates that data be dichotomized for analysis. Individuals with a given trait score below the break point for the trait were coded as 0, while a given trait score at or above the breakpoint were coded as 1. Missing data was coded as a 2.
For the dental non-metric data, Cohen’s kappa was first used to assess intraobserver error between the author’s observations and interobserver error between the author’s and Dr. Edgar’s observations. Cohen’s kappa is a non-parametric statistic that estimates observer agreement for categorical data (Landis and Koch 1977:160). The results for the intraobserver error determined which non-metric traits would be included for further analysis; only traits with kappa values greater than 0.41 were included as a moderate agreement is considered a minimum standard for acceptance per Landis and
Koch (1977).
Frequency data for the reference and sample groups were recorded. Pearson’s chi-square and Fisher’s exact tests were utilized to compare non-metric trait prevalence between the reference and sample groups. Pearson’s chi-square test is a non-parametric test that examined the relationship between two independent, categorical variables based on their frequency counts (Field 2013:871). Fisher’s exact test is the adjustment for small samples that violate the Pearson’s chi-square assumption of having frequency counts less than five (Field 2013:875). Phi values were also reported with Pearson’s chi-square results to show the strength of the relationship between categorical variables. It ranges from 0 to 1, with 0 representing a weak relationship and 1 representing a strong relationship between variables (Field 2013:725).
Logistic regression analysis was also applied to the non-metric data. Logistic regression analysis is a multiple regression test used for categorical variables, as opposed
47 to scalar variables (Field 2013). This type of regression is appropriate for this study as logistic regression does not assume data linearity and can predict group membership using dichotomous data (Field 2013; Edgar 2013). The methods outlined in Edgar (2013) for closely related groups stipulate that groups must differ by at least 25 percent for a given trait for that trait to be included in the logistic regression. Models were derived based on the Southwest Hispanic and Mexican samples before being tested on the UBC samples.
The Mean Measure of Divergence (MMD) was run using the publically- available statistical software package R, including the AnthropMMD package. MMD was created by C.A.B. Smith to examine the biodistance between populations based on non- metric data (Sjøvold 1977). While it has been criticized for being adjusted since its original from (see discussion in de Souza and Houghton 1977), the AnthropMMD package within R is resistant to any deviations and has been specially constructed for anthropological analyses. This statistic is critical to this project as it is one of the most widely used measures of intergroup differences for dental non-metric studies (Irish 2009).
As the object of this study is to determine if any differences exist among the reference and sample collections, MMD is the primary statistic employed to do so for the dental non-metric traits.
The metric data analyses began with utilizing paired samples t-tests to assess intraobserver error between the author’s observations and interobserver error between the author’s and another CSU, Chico graduate student’s observations. Student’s t-tests were employed to assess any sex differences within the samples. Levene’s test for the equality of variances was used in conjunction with the Student’s t-tests; this statistic examines
48 whether the variances between groups are equal or not (Field 2013:878). Frequency data for each region-of-origin group were determined based on the results of the Student’s t- test and Levene’s tests.
As noted earlier, the occlusal polygon was chosen as the metric assessment at the advice of Dr. Edgar. This method had previously only been utilized once in biodistance estimates (Morris 1986) and once in a forensic context (Kenyhercz et al.
2014). It has typically been used as part of paleoanthropological geometric morphometric projects to predict group membership in members of the genus Homo. Its use here is investigational to see if the occlusal polygon can be used in biodistance studies involving closely related populations.
To estimate the utility of the occlusal polygon, two methods of assessment were examined: a modified version of Morris’ (1986) occlusal polygon and the discriminant function analyses. Morris (1986) had divided the occlusal polygon into two obtuse triangles and used the angle and hypotenuse measurements to determine the area of the polygon. The area of any triangle, however, can be determined using the more basic formula of A = (1/2)(b)(h); this was applied for this project. The area of each of the two triangles in the occlusal polygon were calculated and added together by hand and recorded in a Microsoft Excel spreadsheet. The mean area scores for each region-of- origin were compared through ANOVA. This was the most applicable method of comparison for these groups as more than two means were being compared in a linear model (Field 2013:430).
Following the suggestion of Field (2013:654), MANOVA tests were run prior to discriminant function analysis. As there are four length and width measurements for
49 each molar in each region-of-origin group, the number of variables permits MANOVA to be run. MANOVA compares the matrices within the selected data to determine their similarities (Field 2013:638-640).
There are a few statistics that SPSS includes with MANOVA to help interpret the results: Pillai’s trace, Hotelling’s T2, Wilk’s lambda, and Roy’s largest root. These tests are important because they provide the significance of the relationship between the variables. Pillai’s trace represents the “proportion of explained variance” within discriminant functions (Field 2013:640). Hotelling’s T2 is comparable to the F-ratio within ANOVA analyses in that it is the sum of variance of the variables (Field
2013:641). Wilk’s lambda represents unexplained variance for each of the variables within discriminant functions (Field 2013:641). Roy’s largest root is the amount of unexplained variance within the first discriminant function only; Field (2013:642) notes that this is the most powerful of the MANOVA statistics because it represents the greatest amount of between-group differences. These four statistics are included because, based on the type of relationship among the variables, one may be more important than the other. Sample size and distribution factor into choosing which statistic to report the results from.
Discriminant function analysis (DFA) was utilized after running a series of
MANOVA tests to see how well region-of-origin groups could be predicted based on the occlusal polygon measurements (Field 2013:654). Additionally, DFA is a good way to see which variables differ from one another, which is not made clear in the MANOVA results. The output from DFA describes how much variation can be captured by the statistical models, as well as providing visual representations of how the models classify
50 groups based on the given data. The DFA results helped to determine how well the occlusal polygon works in the closely related region-of-origin groups discussed within this project. DFA is a commonplace statistic in Hispanic ancestry assessment studies, such as Spradley et al. (2008) and Hefner (2015). This project is working along similar lines of classification as these studies, which is trying to predict group membership based on certain variable measures. For this reason, DFA seems to be a logical statistic to incorporate.
Summary
This project utilized four reference samples of both Southwest Hispanic and
Mexican origins and two UBC study samples. Only individuals with at least 50 percent scoreable adult dentition were included in the study. The dental non-metric traits used were collected based on the methodology presented in Turner et al. (1991) and suggested edits from Dr. Edgar; the ASUDAS dental plaques were also used as guides for the data collection process. The occlusal polygon measurements were taken according to the methodology presented in Morris (1986). A variety of statistical applications were applied to the non-metric and occlusal polygon data. These statistical tests included
Cohen’s kappa for interobserver and intraobserver error, Pearson’s chi-square tests, logistic regression analysis, MMD, paired and Student’s t-tests, and DFA. The alpha level for all tests was set at 0.05 and SPSS (v. 22) was utilized for analysis, except where otherwise noted.
The dental non-metric analyses results are presented in the next chapter. The goal was to identify traits that are uniquely present in the reference and study collections
51 in hopes of creating a trait list that might be beneficial in the UBC identification process.
The importance of these trait differences would be expressed using the MMD analysis in
R, which presented the biodistance among the samples based on the dental non-metric traits.
CHAPTER IV
NON-METRIC RESULTS
Introduction
This chapter presents the non-metric results for this project. The results for intraobserver and interobserver error will be discussed first. Cohen’s kappa is used to explore the error rates. Next, frequency data of the regions-of-origin within the reference and study groupings will be presented. Pearson’s chi-square and Fisher’s exact test results will be used to test for associations between the reference and sample collection groups. Traits that are significantly more prevalent within a group will provide the most meaningful information for predicting region-of-origin. The logistic regression results between groups will then be discussed. These results will demonstrate if any traits or combination of traits can be used to predict a region-of-origin group. Finally, the mean measure of divergence (MMD) results will be presented to demonstrate the biological distance between the region-of-origin groups. Lower numbers will indicate greater similarity between the groups while larger numbers will indicate greater distance between the groups (Irish 2010).
Intraobserver Error
To determine the precision when comparing one observer’s data collection performance, Cohen’s kappa was used for intraobserver error tests. Thirty individuals from the James K. Economides Orthodontic Collection were scored twice by the author 52 53 and compared for agreement between the observations. The non-metric dental traits for comparison were selected based on traits found critical for ancestry estimation in Edgar
(2013:S5) and additional traits that could be important for region-of-origin distinction within this project. Table 1 shows that most of the non-metric trait comparisons had a
Cohen’s kappa value of at least 0.41, which is the minimum value for a moderate agreement between observers (Landis and Koch 1977:165). Traits that were close to the minimum Cohen’s kappa value of 0.41 were included for further non-metric analysis within this project.
The only exception to this is the protostylid on the mandibular second molar
(M2 protostylid). While it is not prevalent in the Southwest Hispanic sample utilized to run the intraobserver tests (n = 2), it is a prevalent trait within the other groups for this project, as will be seen in the Pearson’s chi-square and logistic regression results sections. Some possible reasons other than its absence in the Southwest Hispanic samples are presented in the discussion chapter of this thesis. This highlights one of the problems with sample selection for a study. Other traits that did not yield kappa results were reviewed for their prevalence within the other groups, as well; these traits (tuberculum dentale. M2 cusp 5, and M1 parastyle) were found to be as minimally present within the larger project sample as they were for this intraobserver error sample.
Interobserver Error
To evaluate interobserver error in this study, the scores of 25 individuals from the James K. Economides Orthodontic Collection were compared with data from the same individuals collected by Dr. Heather Edgar. As can be seen in Table 2, only some of
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Table 1. Intraobserver error Cohen’s Kappa results.
Non-Metric Trait Kappa Result Labial Curve 0.455* I1 Shoveling 0.384*** I2 Shoveling 0.495* I1 TD N/A** I2TD N/A** UC TD N/A** UC Mesial Ridge 0.433* M1 Metacone -0.14 M2 Metacone 0.235 M1 Hypocone 0.52 M2 Hypocone 1.00* M1 Cusp 5 0.630* M2 Cusp 5 N/A** M1 Carabelli 0.917* M1 Parastyle N/A** I1 Shoveling 0.222 I2 Shoveling 0.396*** LC Distal Ridge 0.883* Anterior Fovea 0.400*** Deflecting Wrinkle 0.462* M1 Protostylid 0.417* M2 Protostylid (see above discussion) M1 Cusp 5 0.655* M2 Cusp 5 0.762* M1 Cusp 6 0.835* M2 Cusp 6 1.00* M1 Cusp 7 0.837* M2 Cusp 7 N/A**
*Minimum of “moderate” agreement found **Small sample prevalence prohibits kappa result ***Included based on proximity to 0.41/trait importance
the non-metric traits compared had a Cohen’s kappa value of 0.41 or above. This indicates, in general, a lack of consistency between observers for this study. This could also serve as an indicator of bias that exists within the Turner et al. (1991) method of scoring non-metric dental traits.
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Table 2. Interobserver error Cohen’s Kappa results.
Non-Metric Trait Kappa Result Labial Curve 0.400 I1 Shoveling 0.195 I2 Shoveling 0.076 I1 TD 0.039 I2TD -0.207 UC TD N/A** UC Distal Ridge N/A** M1 Metacone 0.778* M2 Metacone 0.324 M1 Hypocone 0.591* M2 Hypocone 0.500* M1 Cusp 5 0.046 M2 Cusp 5 0.146 M1 Carabelli 0.714* M1 Parastyle 0.330 I1 Shoveling 0.091 I2 Shoveling 0.060 LC Distal Ridge 0.262 Deflecting Wrinkle 0.444* M1 Protostylid 0.463* M2 Protostylid 0.274 M1 Cusp 5 0.429* M2 Cusp 5 0.673* M1 Cusp 7 0.474* M2 Cusp 7 0.412*
*Minimum of “moderate” agreement found **Small sample prevalence prohibits kappa result
Region-of-Origin Trait Distributions for the Skeletal Samples
Skeletal Sample Distribution
There are more Mexican individuals (n = 90) than Southwest Hispanic individuals (n = 75) included in the non-metric dental traits sample, as shown in Table 3.
There are fewer undocumented border crossers (UBC) (n = 33) represented; 20 of these individuals are from the Pima County Office of the Medical Examiner (PCOME) and 13
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Table 3. Sample frequencies by region-of-origin.
Region-of-Origin Number % of Total Southwest Hispanic 75 37.9 Mexican 90 45.5 UBC-PCOME 20 10.1 UBC-FACTS 13 6.6
are from the Sacred Heart Burial Park in Falfurrias, Texas, that are currently housed at the Forensic Anthropology Center at Texas State University (FACTS). The size of the
UBC samples was smaller as these are the study samples and the Southwest Hispanic and
Mexican samples are the reference samples for the project. Since it is believed that the individuals in the PCOME samples are primarily Mexican in origin (Martinez et al.
2013:14-15) and it is currently unknown where most of the individuals in the FACTS samples originated, they are separated for some analyses and pooled for others based on these smaller sample sizes.
It is also noted that since the origin of these UBC individuals was unknown at the time of data collection, they have been separated at times throughout this project based only on the location of where data was collected. This separation does not indicate a definitive region-of-origin for these individuals. When the UBC samples from both locations are pooled, it is only to compare the prevalence of traits in these individuals against the reference samples. Ideally, both the PCOME and FACTS samples would be separated for all analyses, but sample sizes do not permit that for certain statistical analyses.
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Non-Metric Dental Trait Distribution
It is important to understand the region-of-origin sample distribution for the study as a whole before examining the relationships between each group. As not all individuals within a group were scoreable for or have every non-metric dental trait, the sample size fluctuated for certain analyses. The total number and percent of the individuals for each trait comparison were included in the Pearson’s chi-square test results to maintain a perspective on how prevalent each trait was within the current samples.
The non-metric trait prevalence in each group is presented in Figure 29. This bar graph shows the percentage of each region-of-origin group that has a given trait.
These percentages were determined after individuals with missing data for each trait were removed from consideration.
Figure 29. Prevalence of non-metric dental traits for each reference and sample grouping (in %).
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Pearson’s chi-square and Fisher’s exact tests were utilized to determine if there were any significant differences in the non-metric trait prevalence between each region. The first comparison was between the Southwest Hispanic samples and the
Mexican samples. Table 4 presents the results of this comparison by trait. The number of
Table 4. Pearson’s Chi-square and Fisher’s Exact results of traits present (SWH and Mexican).
% of Group w/ Highest Non-Metric Trait n χ2 Phi P Value Groups Prevalence Labial Curve 36/109 33% 0.037 0.018 0.847 SWH I1 Shoveling 78/104 75% 4.313 0.204 0.038* SWH I2 Shoveling 69/107 64.50% 14.514 0.368 ≤0.001* Mexican Upper C Distal Ridge 73/91 80.20% 0.914 0.100 0.392 SWH 103/14 M1 Hypocone 69.10% 4.885 0.181 0.027* SWH 9 2 M Hypocone 89/126 70.60% 0.532 0.065 0.466 Mexican M1 Cusp 5 25/149 16.80% 0.001 0.003 0.972 Mexican M1 Carabelli’s Cusp 66/137 48.20% 6.132 0.212 0.013* SWH
I2 Shoveling 59/107 55.10% 3.777 0.188 0.052 Mexican Lower C Distal Ridge 35/74 47.30% 0.028 0.061 0.599 SWH
M1 Anterior Fovea 41/88 46.60% 0.541 0.078 0.462 SWH
M1 Deflecting Wrinkle 52/95 54.70% 0.090 0.031 0.765 Mexican
M1 Protostylid 89/128 69.50% 2.788 0.148 0.095 Mexican
M2 Protostylid 48/110 43.60% 30.202 0.524 ≤0.001* Mexican 120/14 M Cusp 5 84.50% 1.936 0.117 0.164 Mexican 1 2
M2 Cusp 5 40/129 31.00% 0.000 0.002 0.983 Mexican
M1 Cusp 6 16/140 11.40% 0.004 0.005 0.952 N/A
M2 Cusp 6 6/127 4.70% (F. exact) 0.113 0.234 SWH
M1 Cusp 7 37/148 25% 0.000 0.000 1.000 Mexican
*Significant at p ≤ 0.05
scoreable individuals within each group are listed, as well as the total percentage of individuals within the two regions-of-origin that number represents. Five of the dental traits differed significantly (p ≤ 0.05) between the two groups: I1 shoveling, I2 shoveling,
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1 1 1 M hypocone, M Carabelli’s cusp, and M2 protostylid. Three of these traits (I shoveling,
M1 hypocone, and M1 Carabelli’s cusp) were more prevalent in the Southwest Hispanic
2 sample, while the other two traits (I shoveling and M2 protostylid) were more prevalent in the Mexican sample. The phi values for this comparison ranged from weak to moderate, however, as indicated by the highest value being 0.524 for M2 protostylid.
These results demonstrate that there are some differences between the
Southwest Hispanic and Mexican samples, though the genetic similarities in their ancestries is likely a factor for there not being greater differences in trait prevalence. One trait, M1 cusp 6, for example, had nearly identical presence in both groups; there was also only a small number of individuals from each group considered scorable for this trait, which factors into this interpretation, as well.
The second comparison was between the Southwest Hispanic samples and all
UBC samples (Table 5). Due to the small size of the study samples, UBC individuals from both the PCOME and FACTS collections were pooled for the Pearson’s chi-square and Fisher’s exact tests. Four of the non-metric traits were significantly different (p ≤
1 0.05) between the two groups: M hypocone, lower canine distal ridge, M2 protostylid, and M1 cusp 7. Three of these traits (lower canine distal ridge, M2 protostylid, and M1 cusp 7) were more prevalent in the UBC samples than in the Southwest Hispanic samples. As in the first Pearson’s chi-square test, M1 hypocone was more prevalent in the
Southwest Hispanic sample. Also similar to the first round of comparisons were the phi values; the highest phi value for the comparison between the Southwest Hispanic and
UBC samples was 0.378 and was once again for M2 protostylid. Once again, ancestral
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Table 5. Pearson’s Chi-Square and Fisher’s Exact results of traits present (SWH and all UBC).
Group w/ % of Non-Metric Trait n χ2 Phi P Value Highest Groups Prevalence Labial Curve 26/76 34.20% 0.990 0.114 0.434 UBC I1 Shoveling 52/75 69.30% 0.426 0.075 0.743 UBC I2 Shoveling 44/81 54.30% 2.631 0.180 0.105 UBC Upper C Distal Ridge 65/80 81.30% (F. exact) 0.080 0.485 SWH M1 Hypocone 73/103 70.90% 5.524 0.232 0.019* SWH 2 M Hypocone 60/86 69.80% 0.450 0.072 0.502 UBC M1 Cusp 5 15/103 14.60% (F. exact) 0.091 0.544 SWH M1 Carabelli’s Cusp 55/99 55.60% 0.939 0.097 0.332 SWH
I2 Shoveling 41/78 52.60% 3.279 0.205 0.070 UBC Lower C Distal Ridge 41/72 56.90% 5.860 0.285 0.015* UBC
M1 Anterior Fovea 34/72 47.20% 0.507 0.084 0.477 SWH M Deflecting 1 35/63 55.60% 0.419 0.082 0.517 UBC Wrinkle
M1 Protostylid 54/81 66.70% 1.943 0.155 0.163 UBC
M2 Protostylid 19/72 26.40% 10.311 0.378 0.001* UBC
M1 Cusp 5 79/95 83.20% (F. exact) 0.132 0.343 UBC
M2 Cusp 5 24/80 30.00% 0.069 0.029 0.792 SWH
M1 Cusp 6 11/92 12.00% (F. exact) 0.019 1.000 SWH
M2 Cusp 6 6/78 7.70% (F. exact) 0.008 1.000 UBC
M1 Cusp 7 32/98 32.70% 7.228 0.272 0.007* UBC
* Significant at p ≤ 0.05
similarities are likely factoring into why there are not greater differences in trait prevalence between the Southwest Hispanic and UBC samples.
The third Pearson’s chi-square and Fisher’s exact tests compared the Mexican samples to the pooled UBC samples (Table 6). Only two of the traits significantly differ
(p ≤ 0.05) between these groups: lower canine distal ridge and M1 cusp 7. Both of these traits were more prevalent in the UBC samples, just as they were in the second Pearson’s
2 chi-square analysis. There were three traits (upper canine distal ridge, M hypocone, M1
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Table 6. Pearson’s Chi-Square and Fisher’s Exact results of traits present (Mexican and all UBC).
Group w/ % of Non-Metric Trait n χ2 Phi P Value Highest Groups Prevalence Labial Curve 18/49 36.70% (F. exact) 0.122 0.443 UBC I1 Shoveling 46/55 83.60% (F. exact) 0.101 0.428 Mexican I2 Shoveling 53/66 80.30% (F. exact) 0.171 0.190 Mexican Upper C Distal Ridge 32/43 74.40% (F. exact) 0.010 1.000 N/A M1 Hypocone 64/108 59.30% 0.352 0.057 0.553 Mexican 2 M Hypocone 75/102 73.50% 0.010 0.010 0.920 N/A M1 Cusp 5 16/108 14.80% (F. exact) 0.092 0.550 Mexican M1 Carabelli’s Cusp 41/100 41.00% 1.014 0.101 0.314 UBC
I2 Shoveling 46/69 66.70% 0.141 0.045 0.707 UBC Lower C Distal Ridge 22/36 61.10% 6.116 0.412 0.013* UBC
M1 Anterior Fovea 25/60 41.70% 0.008 0.012 0.928 N/A M Deflecting 1 37/64 57.80% 0.192 0.055 0.661 UBC Wrinkle
M1 Protostylid 71/93 76.30% 0.062 0.026 0.803 UBC
M2 Protostylid 51/80 63.70% 1.593 0.141 0.207 Mexican
M1 Cusp 5 85/95 89.50% (F. exact) 0.042 1.000 UBC
M2 Cusp 5 30/99 30.30% 0.084 0.029 0.772 Mexican
M1 Cusp 6 11/94 11.70% (F. exact) 0.024 1.000 UBC
M2 Cusp 6 4/99 4.00% (F. exact) 0.117 0.264 UBC
M1 Cusp 7 33/102 32.40% 7.365 0.269 0.007* UBC
* Significant at p ≤ 0.05
anterior fovea) that were equally present in both the Mexican and UBC samples and had more than 40 percent of the samples included in analysis. The phi values are similarly weak to moderate, as in the previous analyses, with the highest value being a 0.412 for the lower canine distal ridge.
Knowing that there is similar ancestry among the reference samples, it is not surprising that the differences between those two groups were only weak to moderate, at best. Given that the results were similar when the reference samples were compared to
62 the sample collections, this indicates there are some similarities in ancestry within the sample collections, as well. There are some differences, though, in non-metric dental trait prevalence in the three region-of-origin groups. For example, the M2 protostylid was more prevalent in the Mexican samples overall, but was also prevalent in the UBC samples when they were compared to the Southwest Hispanic samples. This reflects that the M2 protostylid is nearly absent from the Southwest Hispanic samples. Additionally, the lower canine distal ridge and M1 cusp 7 were significantly prevalent in the UBC samples when they were compared to both of the other region-of-origin groups. The comparisons through the Pearson’s chi-square analyses helped to create a trait list of what could be prevalent in each group.
Logistic Regression Analysis
As Pearson’s chi-square and Fisher’s exact tests are not as robust as methods that collectively factor in multiple ordinal variables, logistic regression was also employed to examine non-metric trait prevalence among the reference samples.
Following the methods described in Edgar (2013: S4-S5) for closely related groups, non- metric dental traits were chosen to include in this analysis. There were only five traits that meet the 25 percent difference threshold between the reference sample groups: M2
2 protostylid, lower canine distal ridge, upper canine distal ridge, M2 cusp 6, and M cusp 5.
The traits are listed in order of greatest distance to least difference between the Southwest
Hispanic and Mexican samples.
There were only 29 individuals from both reference sample groups that had all five of these traits scored; 23 were Southwest Hispanic and six were Mexican. Five
63 models were created to see how well the data fit. The first model was only for M2 protostylid. Each successive model built upon that and included the next trait until the fifth model factored in all five traits. Table 7 shows how well the models performed in terms of correctly classifying individuals from the reference samples to the correct region-of-origin, the chi-square value (which is the improvement upon the previous model here), and the significance of that value.
Table 7. Logistic regression model performance.
Model Classification % χ2 P Value Traits Included
1 79.3 4.201 0.040* M2 Protostylid
2 75.9 0.348 0.555 M2 Protostylid, LC Distal Ridge
3 75.9 2.592 0.107 M2 Protostylid, LC Distal Ridge, UC Distal Ridge
4 86.2 3.706 0.054 M2 Protostylid, LC Distal Ridge, UC Distal Ridge, M2 cusp 6
5 89.7 2.346 0.126 M2 Protostylid, LC Distal Ridge, UC Distal Ridge, 2 M2 cusp 6, M cusp 5
*Significant at p ≤0.05
As Model 1 had the highest chi-square and the only significant p value at
0.040, it was tested to see how well it fit the data as a whole. Model 4 was tested, as well, because it had the next highest chi-square value and was significant at the α = 0.05 level, but it did not fit the data well.
The test of Model 1 included 110 Southwest Hispanic and Mexican individuals that had the trait, M2 protostylid. It correctly classified Southwest Hispanic individuals 84.3 percent of the time, yet only classified Mexican individuals correctly
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67.8 percent of the time. While it only correctly classified individuals 75.5 percent of the time overall, this trait is still a good indicator of region-of-origin. The Wald statistic (z =
26.059) and the odds ratio (exp(B) = 11.316) are both high, which show that the trait in this model is a strong predictor for region-of-origin (Field 2013: 766-767). Also, this is supported by three chi-square tests (χ2 = 32.243, p ≤ 0.001).
Even though this result is statistically significant, it is not realistic. One non- metric dental trait is not enough to separate regions-of-origin. Additionally, when the
UBC samples were tested against this model, the sample size was too small for the model to run. Since the UBC individuals for this project are a realistic sampling of decedents from the U.S.-Mexico border, this demonstrates that logistic regression does not fit this particular set of data well. Also, this chapter has already highlighted some of the issues with M2 protostylid in this project. While it could just be that it could not be run in the intraobserver error tests because it is not very prevalent in Southwest Hispanics, it is a trait that warrants further investigation to its true presence in these region-of-origin populations.
Mean Measure of Divergence
As the Freeman and Tukey correction factor for small sample sizes could be applied within R, the UBC samples were separated for MMD analysis. For the purpose of this analysis, A = Southwest Hispanic, B = Mexican, C = UBC-PCOME, and D = UBC-
FACTS. The R output provides the number of individuals within each group that were scoreable for each trait and then the frequency of those individuals that had the trait.
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Next, the MMD matrix is given, followed by which of these matrices were significantly different from each other, and finally the individual MMD for each trait.
One of the assumptions of MMD is that there is no correlation between traits
(i.e. Irish 1997:458; Edgar 2002:68; Edgar 2009:62). Traits that do not discriminate between populations pose a potential risk of skewing the results. Given the small sample sizes for many traits within this project, however, it was decided to keep all traits already included in the Pearson’s chi-square and Fisher’s exact analyses for the MMD statistical analysis. One of the benefits to keeping as many traits included as possible is that divergence is less likely to be exaggerated among these groups, especially considering their close genetic associations (Irish 2010:383). If the sample sizes were larger for this project, standard protocol or eliminating non-discriminatory traits would have been followed.
The MMD matrix results for this project are presented in Table 8. The upper triangle of output are the MMD results as biological distances and the standard deviations
Table 8. MMD results for biodistance among the region-of-origin groups.
A (SW Hispanic) B (Mexican) C (UBC-PCOME) D (UBC-FACTS) A (SW Hispanic) 0 0.106* 0.072 0.103* B (Mexican) 0.012 0 -0.018 0.068 C (UBC-PCOME) 0.042 0.043 0 0.031 D (UBC-FACTS) 0.043 0.045 0.074 0
*Significant at p ≤ 0.05
of these distances are in the lower triangle (Santos 2014:3). As noted in Irish (2010:378), the smaller the MMD distance value is, the less distance exists between two groups.
66
Significant differences in biological distance were observed between the Mexican and
Southwest Hispanic samples (0.106), as well as between the Southwest Hispanic and
UBC-FACTS samples (0.103). The distance between the Southwest Hispanic and UBC-
PCOME (0.072) and Mexican and UBC-FACTS (0.068) samples, respectively, approach significance. The distance between the UBC-PCOME and UBC-FACTS (0.031) samples is non-significant; this is likely due to the small sample size for these populations.
Finally, the distance between the Mexican and UBC-PCOME samples is also non- significant. Based on discussions from Edgar (2002:83) and Irish (2010:380), there is no evidence of divergence between these two samples. These results, in general, support the assertion that the UBC-PCOME samples are likely Mexican in origin. These results could also help indicate that the UBC-FACTS samples are most likely not of Mexican origin.
Figure 30 presents the multidimensional scaling of the MMD results. As indicated by the output, the Southwest Hispanic (A) samples are not as closely related to the other three region-of-origin group samples are to each other.
Summary
The results of the interobserver error Cohen’s kappa highlighted potential issues with comparing these results to outside researchers, as well as the difficulties with scoring non-metric dental traits on casts. The intraobserver error Cohen’s kappa results found more similarities within the core project data, despite the limitations associated with utilizing dental casts.
The Pearson’s chi-square and Fisher’s exact tests revealed some patterns, despite a wide range of individuals included in trait analyses for each set. The UBC
67
MDS performed on MMDs
UBC-FACTS
D
UBC- SWH PCOME C A MDS_Axis_2
Mexican
B
MDS_Axis_1
Figure 30. Multidimensional scale of MMD results.
samples had higher prevalence rates of a lower canine distal ridge and M1 cusp 7. The
Southwest Hispanic samples had higher presence of M1 hypocone, M1 Carabelli’s cusp,
1 2 and I shoveling. Mexican samples had a higher prevalence rate of I shoveling; the M2 protostylid was present in both the UBC and Mexican samples when compared to the
Southwest Hispanic samples.
The logistic regression analyses demonstrated, like the Pearson’s chi-squares, that the presence of M2 protostylid can differentiate between Southwest Hispanic and
Mexican individuals, but is not realistic on its own accord. Finally, the MMD showed that the Mexican and two UBC samples are more closely related to each other than they are to the Southwest Hispanic samples.
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The next chapter will present the metric results from the region-of-origin groups. Aspects of the occlusal polygon will be examined through various statistical methods, including MANOVA, DFA, and a modified form of Morris’s (1986) method for assessing the area of the polygon. This dental metric measurement has only been used in two other modern biodistance estimations and is therefore not as methodologically grounded for a forensic application as it would be for paleoanthropological assessments.
Given the genetic similarities among the groups included in this project, the results for the occlusal polygon could provide interesting insight to how this method works in closely related modern populations.
CHAPTER V
METRIC RESULTS
Introduction
This chapter will discuss the metric results for this project. Intraobserver error results and interobserver error results for the occlusal polygon will be presented first.
Paired samples t-tests are used to determine the error rates. Any potential sex differences in the dentition are examined through Student’s t-tests. The results of these tests will determine if the sexes can be pooled for further analyses within this chapter. A modified version of Morris’s (1986) method will be assessed next. The area of the occlusal polygon for the maxillary and mandibular first molars will be calculated for this method.
A discussion of the discriminant function analysis (DFA) for this project follows the occlusal polygon area assessment. This includes a discussion on the statistics within
MANOVA that are calculated before DFA is run. The results of the DFA examinations are outlined to show how well aspects of the occlusal polygon predict group membership.
This study uses a modified Morris (1986) method and DFA to determine if there is a better method of assessing the occlusal polygon differences for region-of-origin groups.
Intraobserver Error
The intraobserver error sample, as with the non-metric traits, examined consistency within the measurements that would be included in additional analyses.
69 70
Twenty-five individuals from the James K. Economides Orthodontic Collection were scored twice, in succession, for all 16 measures of the occlusal polygon. Paired samples t- tests were utilized to examine any potential differences between the two observations. As seen in Table 9, none of the observations were significantly different (p ≥ 0.05); two of the occlusal measurements, ULCA (p = 0.058) and URAB (p = 0.055), however, did approach significance. All aspects of the occlusal polygon were considered in the next phase of analysis.
Table 9. Paired t-test results for intraobserver error.
P Measurement Mean t d.f Value ULAB 0.016 1.462 23 0.157 ULBD 0.023 1.274 23 0.215 ULDC 0.020 1.180 23 0.250 ULCA -0.023 -1.997 23 0.058 URAB 0.016 2.025 23 0.055 URBD 0.018 1.751 23 0.093 URDC 0.070 1.711 23 0.100 URCA 0.019 1.458 23 0.158 LLAB 0.014 1.056 24 0.302 LLBD 0.022 1.410 24 0.171 LLDC 0.029 1.687 24 0.105 LLCA 0.009 0.675 24 0.506 LRAB -0.002 -0.228 23 0.822 LRBD 0.012 0.729 23 0.473 LRDC 0.019 1.447 23 0.161 LRCA -0.010 -0.766 23 0.451
Interobserver Error
A small subsection of individuals from the James K. Economides Orthodontic
Collection were resampled by CSU, Chico graduate student Heather MacInnes. This also served to test the dental calipers for accuracy within the study. The right maxillary first
71 molar from ten individuals were remeasured. Utilizing paired samples t-tests for each of the occlusal polygon measurements, it was demonstrated that there were no significant differences (p ≥ 0.05) between observers (Table 10). This also shows that the dental calipers utilized were consistent enough between observers to yield similar results.
Table 10. Paired t-test results for interobserver error.
P Measurement Mean t d.f Value URAB 0.416 1.557 9 0.154 URBD -0.541 -1.736 9 0.117 URDC 0.109 0.519 9 0.616 URCA -0.080 -0.271 9 0.793
Sex Differences
Student’s t-tests were conducted on samples of both the maxillary and mandibular dentitions of 15 known males and 15 known females from the James K.
Economides Orthodontic Collection. It was necessary to determine if the sexes could be pooled for analyses as sex information was not available for many of the individuals included in this project. In a situation where the sex differences were statistically significant, only individuals with known sex could be included; this would be the least beneficial option as it would drastically reduce the overall sample size for each region-of- origin. The Student’s t-tests were run in hopes of avoiding this situation.
As seen in Table 11, the Levene’s Test for Equality of Variance was significant for measurement URAB in the maxillary dentition, indicating that there were unequal variances (p = 0.016) between the sexes. The left first maxillary molar had no
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Table 11. Student’s t-test for sex differences in the maxillary dentition.
Levene's Test Measurement F P Value t d.f P Value Eq. variances assumed -0.985 28 0.333 ULAB Eq. variances not 1.594 0.217 -0.985 26.068 0.334 assumed Eq. variances assumed -1.037 28 0.309 ULBD Eq. variances not 0.039 0.844 -1.037 27.246 0.309 assumed Eq. variances assumed -1.175 28 0.250 ULDC Eq. variances not 3.467 0.073 -1.175 22.317 0.252 assumed Eq. variances assumed -0.465 28 0.645 ULCA Eq. variances not 3.220 0.084 -0.465 22.213 0.646 assumed Eq. variances assumed 0.014 28 0.989 URAB Eq. variances not 6.513 0.016* 0.014 21.772 0.989 assumed Eq. variances assumed 1.213 28 0.235 URBD Eq. variances not 0.571 0.478 1.213 27.165 0.236 assumed Eq. variances assumed -1.006 28 0.323 URDC Eq. variances not 0.208 0.652 -1.006 27.884 0.323 assumed Eq. variances assumed 0.617 28 0.542 URCA Eq. variances not 0.000 0.989 0.617 27.962 0.542 assumed
*Significant at p ≤ 0.05
significant differences between the sexes for any of its measurements and was retained for further analyses.
In the mandibular dentition (Table 12), none of the Levene’s tests were significant, indicating equal variances in the sample; however, two of the left molar measurements, LLAB (p = 0.007) and LLDC (p = 0.047) were significantly different for the Student’s t-test. None of the right molar measurements were significant for the
Student’s t-test, even though one of them (LRBD) approaches significance (p = 0.053).
The right mandibular molar was chosen for further analyses. Since there was at least one
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Table 12. Student’s t-test for sex differences in the mandibular dentition.
Levene's Test Measurement F P Value t d.f P Value Eq. variances assumed -2.912 28 0.007* LLAB Eq. variances not 2.939 0.097 -2.912 23.613 0.008* assumed Eq. variances assumed -1.850 28 0.075 LLBD Eq. variances not 1.582 0.219 -1.850 23.403 0.077 assumed Eq. variances assumed -2.081 28 0.047* LLDC Eq. variances not 2.919 0.099 -2.081 22.514 0.049* assumed Eq. variances assumed -0.010 28 0.992 LLCA Eq. variances not 0.148 0.703 -0.010 27.864 0.992 assumed Eq. variances assumed -1.062 28 0.297 LRAB Eq. variances not 0.672 0.419 -1.062 27.58 0.297 assumed Eq. variances assumed -2.017 28 0.053 LRBD Eq. variances not 0.172 0.682 -2.017 27.365 0.054 assumed Eq. variances assumed -1.468 28 0.153 LRDC Eq. variances not 0.746 0.395 -1.468 27.46 0.153 assumed Eq. variances assumed -1.554 28 0.131 LRCA Eq. variances not 1.254 0.272 -1.554 22.345 0.131 assumed
*Significant at p ≤ 0.05
first molar in each dental arcade with no statistically significant differences for sex, it was assumed that the sexes can be pooled for the occlusal polygon analyses within the remainder of this project.
Given that information, the sample distribution for the occlusal polygon measurements was determined. There were 72 Southwest Hispanic individuals, 79
Mexican individuals, 20 UBC individuals from the PCOME collection, and 12 UBC individuals from the FACTS collection. These data will undergo two methods of analysis, similar to Kenyhercz et al. (2014), to see if there is a best combination of factors to
74 estimate ancestry: Morris’s (1986) method and discriminant function analysis. As with the dental non-metric traits, it is noted that since the origin of the UBC individuals was unknown at the time of data collection, they have been separated for analyses based only on the location of where data was collected. This separation does not indicate a definitive region-of-origin for these individuals.
Morris (1986) Occlusal Polygon Method
Morris’s (1986) method for assessing the occlusal polygon utilizes angular measurements, along with length and width comparisons. He treated the occlusal surface of the first molar as an “irregular” (Morris 1986:333) polygon that can be divided into two oblique triangles (1986:334). This is demonstrated in Figure 31, where the triangles
Figure 31. Left maxillary molar with occlusal polygon and oblique triangles highlighted.
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ABD and DCA can be seen by highlighting the cusps and placing a dashed line through the occlusal surface on a left maxillary molar.
Morris (1986) also compared the mean lengths (AC and BD) and widths (BA and DC) for each population in his research. For this project, the mean lengths and widths were compared for the reference and study collections, as well as the total average areas for the polygon. One of the caveats of this method is that all four sides of the occlusal polygon must be measured. This reduced the number of individuals from each of the region-of-origin included for this analysis. Table 13 shows the number of individuals for each group per dental arcade. There were a total of 154 individuals with complete maxillary left first molar measurements and 144 individuals with complete mandibular right first molar measurements.
Table 13. Region-of-origin samples for occlusal polygon assessment.
Region-of-Origin Maxilla Mandible Southwest Hispanic 68 68 Mexican 60 55 UBC-PCOME 16 10 UBC-FACTS 10 11
The area of each triangle was calculated using the basic formula Area =
(1/2)(b)(h), where (b) is the base of the triangle and (h) is the height. The area for the two triangles within the occlusal polygon were added together to record the total area for each first molar and the mean for each reference and study group was determined. Table 14 presents these results for the maxillary dentition by region-of-origin group, as well as the mean lengths (ULCA and ULBD) and widths (ULAB and ULDC) for each group. Table
15 presents the mean occlusal polygon area for the mandibular dentition, along with the
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Table 14. Modified Morris (1986) occlusal polygon results – maxillary dentition (in mm2).
Southwest Hispanic Mexican UBC-PCOME UBC-FACTS Mean Occ. Polygon Area 24.6 23.0 25.1 24.4 Mean ULAB 5.7 5.9 5.7 5.5 Mean ULBD 4.2 3.9 4.2 4.4 Mean ULDC 5.8 5.6 6.0 5.8 Mean ULCA 4.3 4.0 4.1 4.1
Table 15. Modified Morris (1986) occlusal polygon results – mandibular dentition (in mm2).
Southwest Hispanic Mexican UBC-PCOME UBC-FACTS Mean Occ. Polygon Area 23.1 20.5 20.0 17.9 Mean LRAB 4.7 4.5 4.5 4.2 Mean LRBD 5.2 4.5 4.5 4.4 Mean LRDC 5.4 5.5 5.4 5.1 Mean LRCA 3.8 3.6 3.8 3.4
mean lengths (LRCA and LRBD) and widths (LRAB and LRDC) for each region-of- origin group.
As can be seen from the above tables, the average occlusal polygon areas are fairly close together. ANOVA tests with Tukey’s correction were run to determine if there were any significant differences between the area means. While there were no significant (p ≥ 0.05) differences in the maxillary dentition (Table 16), there were two significant differences in the mandibular dentition (Table 17). Similar to the output for
77
Table 16. Mean Occlusal polygon ANOVA results with tukey’s correction – maxillary dentition.
UBC- UBC- Southwest Hispanic Mexican PCOME FACTS Southwest Hispanic 0 0.340 0.985 1.000 Mexican 0.926 0 0.498 0.865 UBC-PCOME 1.452 1.470 0 0.988 UBC-FACTS 1.770 1.785 2.107 0
Table 17. Mean occlusal polygon ANOVA results with tukey’s correction – mandibular dentition.
UBC- UBC- Southwest Hispanic Mexican PCOME FACTS Southwest Hispanic 0 0.022* 0.254 0.007* Mexican 0.879 0 0.991 0.365 UBC-PCOME 1.642 1.667 0 0.748 UBC-FACTS 1.576 1.602 2.120 0
*Significant at p ≤ 0.05
MMD seen in the previous chapter, the upper triangle for the ANOVA results contains the p values, while the lower triangle contains the standard error.
Discriminant Function Analysis
Based on suggestions provided in Field (2013:654), MANOVA tests were run before the discriminant function analyses. The maxillary and mandibular dentitions were separated for these analyses. The initial round of MANOVA testing examined any
78 differences among the length and width measurements between Southwest Hispanic and
Mexican individuals.
The maxillary dentition was found to differ significantly (p ≤ 0.001) between
Southwest Hispanic and Mexican individuals for Pillai’s trace (V = 0.176). Since the sample sizes are relatively equal and the covariance matrices are nearly homogenous,
Pillai’s trace should be viewed as a robust test for this MANOVA (Field 2013:644, 648).
This suggests that the multivariate statistics indicate a significant difference between
Southwest Hispanics and Mexicans when comparing the maxillary dentition. When examining the univariate output provided by MANOVA, only one significant difference was observed in length measurement ULCA (p = 0.009). One length is not enough realistically to differentiate between region-of-origin groups, though. Field (2013:650) attributed differences between multivariate and univariate statistics to the enhanced ability of multivariate methods to distinguish between groups than univariate methods.
The results for the mandibular dentition are significant for Pillai’s trace (V =
0.243, p ≤ 0.001) between Southwest Hispanics and Mexicans. This statistic was utilized as the sample sizes are nearly equal and the covariance matrices are relatively homogenous. Unlike in the maxillary dentition, Levene’s test was significant (p = 0.004) for the LRDC width measurement; the variances are not comparable between groups at that given measure based on this result. Similar to the maxillary dentition, however, only one length measurement, LRBD (p ≤ 0.001) was significant.
The second round of MANOVA analysis included all four region-of-origin groups. Roy’s largest root (Θ = 0.245) was significant at p ≤ 0.001. This statistic was
79 used because the covariance matrices are homogenous, but the sample sizes are unequal.
The univariate output yielded no significant results for any of the lengths or widths.
The mandibular dentition output demonstrated some differences that were not seen in the maxillary dentition. Roy’s largest root (Θ = 0.345) was significant at p ≤
0.001. This statistic was chosen due to the homogeneity of the covariance matrices and lack of equality in the sample sizes. Levene’s test was significant (p = 0.022) for the
LRDC width measurement, indicating unequal variances among the samples at this width. The univariate results yielded two significant results that could differentiate among the four groups, one for the LRAB (p = 0.04) width measurement and one for the
LRBD (p ≤ 0.001) length measurement.
Given these results from the MANOVA tests, discriminant function analysis
(DFA) was utilized to assess where the group differences seen in MANOVA originated.
DFA was first run with just the Southwest Hispanic and Mexican samples; all four groups were then run as a final attempt at using the occlusal polygon to differentiate among the groups. Like the modified form of the Morris (1986) method, the four widths and lengths needed to be scoreable for an individual to be included in analysis.
When analyzing the maxillary dentition of Southwest Hispanics and
Mexicans, 23 of 151 individuals (15.2 percent) from the occlusal polygon dataset were excluded because of missing data. One discriminant function was defined and utilized in this analysis. This Wilks’ lambda value was significant (χ² = 24.076, df = 4, p ≤ 0.001), indicating that this function defines the occlusal polygon variation very well between the maxillary dentition of the Southwest Hispanic and Mexican samples. Length measurement ULCA was the most important aspect of the occlusal polygon for assessing
80 region-of-origin in this function, based on the structure matrix results. Approximately 67 percent of the individuals were classified correctly and 64.8 percent of the cases were correctly classified in cross-validation.
In the mandibular dentition, 28 of 151 individuals (18.5 percent) were excluded due to missing data. One discriminant function was defined and utilized in this analysis. This Wilks’ lambda value was significant (χ² = 33.098, df = 4, p ≤ 0.001), indicating that this function defines the occlusal polygon variation very well between the mandibular dentition of the Southwest Hispanic and Mexican samples. Length measurement LRBD was the most important aspect of the occlusal polygon for assessing region-of-origin in this function, based on the structure matrix results. About 69 percent of the individuals were classified correctly and 68.3 percent of the cases were correctly classified in cross-validation.
Combining the maxillary and mandibular dentitions for the Southwest
Hispanic and Mexican samples resulted in higher classification rates. Forty-six of 151 individuals (30.5 percent) individuals were omitted due to missing data. The Wilks’ lambda value for the one discriminant function was significant (χ² = 37.053, df = 8, p ≤
0.001). Length measurement LRBD was the most important aspect of the occlusal polygon for assessing region-of-origin in this function, based on the structure matrix results. Using the pooled dentition samples, 74.3 percent of cases were correctly classified, with 71.4 percent correctly classifying in cross-validation.
The analyses for all four groups were executed next. For the maxillary dentition, 29 of 183 individuals (15.8 percent) from the occlusal polygon dataset were missing variables critical for analysis. Three discriminant functions were defined and
81 utilized in the DFA for the maxillary dentition that included all groups. The first function accounted for 83.7 percent of the variance, with an eigenvalue of 0.245. The second function accounted for 12.3 percent of the variance and had an eigenvalue of 0.036, while the third accounted for 4 percent of the variance, with an eigenvalue of 0.012. The three functions together yielded the only significant Wilks’ lambda value (χ² = 39.599, df = 12, p ≤ 0.001). The four length and width measurements were nearly equally important in predicting region-of-origin, based on the structure matrix results. Group centroids for each of the three functions are presented in Table 18. Figure 32 presents the plot of these centroids from functions 1 and 2. As shown in the plot, groups are clustered closely together. Only 42.9 percent of the individuals were classified correctly and 38.3 percent were correctly classified in cross-validation. This DFA had the most success with the
Mexican samples. Table 19 shows the classification rates for the DFA.
Table 18. Maxillary dentition group centroids per function.
Function Region-of-Origin 1 2 3 Southwest Hispanic 0.302 0.174 0.013 Mexican -0.588 -0.045 -0.025 UBC-PCOME 0.384 -0.352 0.225 UBC-FACTS 0.859 -0.351 -0.297
For the mandibular dentition, 39 of 183 individuals (21.3 percent) were eliminated due to missing data. Three discriminant functions were defined and utilized in this DFA. The first function accounted for 88.1 percent of the variance, with an eigenvalue of 0.345. The second function accounted for 9.1 percent of the variance and had an eigenvalue of 0.036, while the third function only accounted for 2.8 percent of the
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Figure 32. Maxillary dentition plot for the four regions-of-origin.
Table 19. Maxillary dentition DFA classification results (in %).
Predicted Group Membership Southwest Mexican UBC- UBC- Region-of-Origin Hispanic (%) (%) PCOME (%) FACTS (%) Southwest Hispanic 26.5 27.9 17.6 27.9 Mexican 20.0 60.0 11.7 8.3 UBC-PCOME 25.0 12.5 43.8 18.8 UBC-FACTS 0.0 30.0 20.0 50.0
83 variance, with an eigenvalue of 0.011. The three functions together yielded the only significant Wilks’ lambda value (χ² = 47.639, df = 12, p ≤ 0.001). LRBD was the most important factor of the four lengths and widths in assessing region-of-origin, based on the structure matrix results. Group centroids for each region-of-origin for of the three functions are presented in Table 20. Figure 33 presents the plot of these centroids from
Table 20. Mandibular dentition group centroids per function.
Function Region-of-Origin 1 2 3 Southwest Hispanic 0.613 0.001 0.000 Mexican -0.550 0.131 -0.049 UBC-PCOME -0.550 -0.066 0.365 UBC-FACTS -0.535 -0.604 -0.089
functions 1 and 2. As can be seen, the Mexican, UBC-PCOME, and UBC-FACTS groups are clustered more closely to each other than they are to the Southwest Hispanic group, especially along function 1, which represents the most variation of the three functions.
Only 45.1 percent of the individuals were correctly classified in this DFA and 41.0 percent were correctly classified in cross-validation. Southwest Hispanics and the UBC-
FACTS samples had the highest classification rates for these DFA (Table 21).
Pooling the dentitions does result in higher classification rates for the four groups. Sixty-one of 183 individuals (33.3 percent) of the cases were omitted from the analysis based on missing data. Of the three discriminant functions defined for this DFA, the first accounts for 67.7 percent of the variation, with an eigenvalue of 0.483. The second function accounts for 29.0 percent of the variation, with an eigenvalue of 0.207, and the third accounts for 3.3 percent of the variation, with an eigenvalue of 0.023. A
84
Figure 33. Mandibular dentition plot for the four regions-of-origins.
Table 21. Mandibular dentition DFA classification results (in %).
Predicted Group Membership Region-of-Origin Southwest Hispanic Mexican UBC-PCOME UBC-FACTS Southwest Hispanic 52.9 17.6 8.8 20.6 Mexican 23.6 34.5 29.1 12.7 UBC-PCOME 20.0 10.0 40.0 30.0 UBC-FACTS 9.1 18.2 18.2 54.5
combination of the three discriminant functions yielded a significant Wilks’ lambda value
(χ² = 69.542, df = 24, p ≤ 0.001). Additionally, the second and third functions together also yield a significant lambda result (χ² = 24.252, df = 14, p = 0.043). LRBD was the
85 most important factor of the four lengths and widths in assessing region-of-origin, based on the structure matrix results. Group centroids for each region-of-origin for of the three functions are presented in Table 22. Figure 34 presents the plot of these centroids from functions 1 and 2. As can be seen, the Mexican, UBC-PCOME, and UBC-FACTS groups are clustered more closely to each other than they are to the Southwest Hispanic group.
Only 56.6 percent of the individuals were correctly classified in this DFA and 45.1 percent were correctly classified in cross-validation. Southwest Hispanics sample had the highest classification rates for these DFA (Table 23).
Table 22. Pooled dentition group centroids per function.
Function Region-of-Origin 1 2 3 Southwest Hispanic 0.643 0.061 0.007 Mexican -0.613 -0.466 -0.044 UBC-PCOME -0.962 0.561 0.490 UBC-FACTS -0.924 1.190 -0.286
Summary
The interobserver and intraobserver error results for the occlusal polygon measurements yielded no significant results, indicating that this method is repeatable.
There were sex differences between males and females for the right maxillary first molar and left mandibular molar, eliminating them from further analyses. The modified Morris
(1986) methods demonstrated no significant differences in the mean occlusal polygon areas for the region-of-origin groups in the maxillary dentition, but did discover two in the maxillary dentition between Southwest Hispanic individuals and Mexicans and
Southwest Hispanic individuals and the UBC-FACTS individuals. The mixed results
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Figure 34. Pooled dentition plots for the four regions-of-origins.
Table 23. Pooled DFA classification results (in %).
Predicted Group Membership Region-of-Origin Southwest Hispanic Mexican UBC-PCOME UBC-FACTS Southwest Hispanic 62.5 20.3 12.5 4.7 Mexican 19.5 51.2 19.5 9.8 UBC-PCOME 25.0 12.5 50.0 12.5 UBC-FACTS 11.1 33.3 11.1 44.4
from the MANOVA and discriminant function analyses demonstrate a general lack of consistency within the occlusal polygon measurements. Classification rates, though, did improve when the maxillary and mandibular dentitions were pooled. It is likely that these
87 results highlight issues within the methodology for these regions-of-origins, not the method itself.
The next chapter will discuss the results from both the non-metric and occlusal polygon measurements, as well as the conclusion to this project. This discussion will be framed by the methodologies and understanding of human variation that were laid out in the background chapter for this study. Future directions for this research will also be presented in the next chapter.
CHAPTER VI
DISCUSSION AND CONCLUSION
Introduction
This chapter discusses the implications of the dental non-metric and occlusal polygon results, as well as provides concluding remarks and directions for future research. An overall summary of how well each statistical method performed for both the non-metric and metric assessments will be presented first. The results will then be discussed within the framework of the research hypotheses. Genetic and cultural evidence will be utilized as support for this discussion. The study limitations will be presented next. These include issues with the collections used in this project and with methods employed.
The chapter closes with the thesis conclusion. A summary of the thesis will be provided, highlighting the implications of the results. Future research directions will also be discussed. Adjusting for the study limitations and including additional comparative samples will be chief among these.
Discussion
Dental Non-Metric Results
Non-metric dental traits, as described by Turner et al. (1991), are one of the more well-established methodologies for using the adult human dentition to distinguish
88 89 among ancestral populations (Scott and Turner 2008). They were utilized in this study to determine if they could differentiate between Mexican and Southwest Hispanic samples, and then if UBC could be classified to a reference group based on these non-metric traits.
The intraobserver error Cohen’s kappa analysis for this project found that 19 of the 28 traits selected for region-of-origin estimation were eligible for inclusion in the study. These 19 traits, as presented in Table 1, had Cohen’s kappa values of 0.41 or greater, except where noted, indicating that there was at least a moderate agreement between the two observations. The interobserver error results from the Cohen’s kappa analysis only found ten of the 28 traits (Table 2) to have at least a moderate agreement between the observers. The limitations of the study will expand upon issues with interobserver error, as will the directions for future research.
Based on the results of the Pearson’s chi-square and Fisher’s exact tests, there are several traits that may aid in distinguishing between Mexican and Southwest Hispanic dentitions, as well as the UBC samples included in these analyses (Tables 4-6). M1 hypocone was significantly more prevalent in the Southwest Hispanic samples when compared to the Mexican and UBC samples. I1 shoveling and M1 Carabelli’s cusp were also significantly more prevalent in the Southwest Hispanic samples, but only when compared to the Mexican samples. The M2 protostylid is present in both the Mexican and
UBC samples and nearly absent in the Southwest Hispanic sample, though it is more prevalent in the Mexican samples. The lower canine distal ridge and M1 cusp 7 were only significantly present in the UBC samples when they were compared to both the
Southwest Hispanic and Mexican samples. Despite the genetic similarities among these groups, the Pearson’s chi-square and Fisher’s exact tests performed well in differentiating
90 among the samples. These tests demonstrated that dental non-metric trait lists may be useful in the UBC identification process.
Logistic regression analysis was included in this study because it was utilized by Edgar (2013) in her publication on the forensic potential for non-metric dental traits in assessing Hispanic identity. The logistic regression analysis used in this project was not as successful as the Pearson’s chi-square and Fisher’s exact tests, but did highlight some of the issues that Edgar (2013) found with trying to distinguish among Hispanic groups.
Only one of the models in this project fit the data well (Table 7). As this model utilized only the M2 protostylid trait, it was not surprising that it discriminated between Southwest
Hispanics and Mexicans well. Overall, this logistic regression model correctly categorized individuals 75.5 percent of the time. The M2 protostylid, based on the
Pearson’s chi-square and logistic regression results, is an important trait to consider in region-of-origin assessment among groups currently classified as Hispanic. While one trait is not enough to classify a region-of-origin on its own, it could be considered in conjunction with current non-metric skeletal traits, such as those described in Birkby et al. (2008).
MMD was utilized in this study to look at the biological distance among the
Southwest Hispanic, Mexican, and UBC samples based on the suite of non-metric dental traits employed in the above analyses. MMD analyses are a common method used to examine the distance among populations based on dental non-metric traits. The results of this project indicated that the trait prevalence significantly differed between the Mexican and Southwest Hispanic samples, as well as between the Southwest Hispanic and UBC-
FACTS samples (Table 8). Results also indicated that there was no divergence between
91 the Mexican and UBC-PCOME samples. The differences between the Southwest
Hispanic and UBC-PCOME samples approach significance, as do the results between the
Mexican and UBC-FACTS samples.
The MMD differences between the Southwest Hispanic and Mexican samples align with the Pearson’s chi-square and Fisher’s exact test results between these two groups. The differences between both the Southwest Hispanic and Mexican samples and the UBC-FACTS samples indicate that it is unlikely the UBC-FACTS individuals are members of either region-of-origin group. This implies that, based on the non-metric traits, the samples from Sacred Heart Burial Park in Falfurrias, Texas, are likely not of
Mexican or Southwest Hispanic origins. These results align with those from Spradley
(2014). Her cranial analysis of individuals from The Sacred Heart Burial Park in comparison to modern Mexican and Guatemalan samples, as well as unidentified samples from the PCOME, demonstrated that it is unlikely the UBC buried in Falfurrias are of
Mexican or Guatemalan descent (Spradley 2014). These MMD results provide some insight to the Pearson’s chi-square and Fisher’s exact test results that only indicate a few trait differences among these groups (Figure 3). Additionally, the results comparing the
UBC-PCOME samples to the Southwest Hispanic and Mexican samples, respectively, demonstrate that it is unlikely the UBC-PCOME samples are of Southwest Hispanic origin and are more likely to be of Mexican descent. This is to be expected, considering that roughly 86 percent of identified migrant remains brought through the PCOME are of
Mexican descent. It does need to be noted again, however, that the Mexican samples are from Central Mexico and do not represent all the variation across Mexico.
92
The dental non-metric results indicate that region-of-origin is discernible from the dentition. While additional regions, especially within Mexico, need to be included in further analyses, these results are a step in the right direction.
Dental Metric Results
While the occlusal polygon is still a newer methodology within forensic anthropological research, it has been well-established in paleoanthropological studies that examine members of the genus Homo. Given the success within that subfield, it was expected that this technique may aid in distinguishing the Southwest Hispanic samples from the Mexican samples. It was hoped that the UBC samples might be classified using the reference samples from this study.
The intraobserver error paired samples t-test yielded no significant results between observations (Table 9). This indicated that all aspects of the occlusal polygon could be included in metric analyses based on the consistency of observations alone. The interobserver error paired samples t-test was on a subsection of the intraobserver error samples. The results from this test also yielded no significant differences between the two observers (Table 10), which also demonstrated the reliability of the dental calipers utilized for this study.
Sex differences were also assessed before continuing on with the occlusal polygon analyses. Student’s t-tests were run on both the maxillary and mandibular dentition to compare any similarities or differences between males and females (Tables
11-12). Based on the results of Levene’s Test for Equality of Variances and the Student’s t-tests, the maxillary left first molar and mandibular right first molar were chosen for additional analyses due to the lack of sexual dimorphism in these teeth. By removing sex
93 as a limiting factor on sample size, this allowed for as many individuals as possible to be included in the analyses. This helped to provide a better understanding of the occlusal polygon variation within the study samples.
The two major methodologies utilized to examine the occlusal polygon were the modified Morris (1986) method and DFA. The results of these methods are discussed to determine if one is preferable to the other, similar to the style that Kenyhercz et al.
(2014) used in their study. Like what was seen in Kenyhercz et al. (2014), the statistical methodology discussion is important to understanding how the occlusal polygon can best be applied in forensic anthropological research.
The mean areas of the occlusal polygon were calculated, along with the mean lengths and widths as part of the modified Morris (1986) method (Tables 14-15).
Utilizing ANOVA, the mean occlusal polygon areas for each of the reference and study samples were analyzed to determine if there were any significant differences among them. No significant differences were found among the region-of-origin group means in the maxillary dentition (Table 16). Two significant differences were found, though, in the mandibular dentition (Table 17). Differences in the occlusal polygon means were found between the Mexican and Southwest Hispanic samples and between the Southwest
Hispanic and UBC-FACTS samples. These results align with what was seen in the non- metric trait results; Southwest Hispanic and Mexican dentitions are discernible from each other and the UBC-FACTS samples are likely not of Southwest Hispanic origin.
The sample sizes and lack of testing of this method in forensic science, however, are complicating factors for making large scale conclusions just on the mean occlusal polygon areas. DFA is a more robust method of examining where differences
94 within the occlusal polygon measurements are among the reference and study sample groups. The suggested MANOVA tests, which were run before the DFA, revealed significant results between the length and width measurements of the maxillary dentition between the Southwest Hispanic and Mexican samples. When the univariate output of
MANOVA was reviewed, only one length (ULCA) measurement had a significant differences between the groups. In the mandibular dentition, Levene’s test found unequal variances at one width (LRDC) measurement and the univariate output noted a significant difference between the groups at one length (LRBD) measurement.
The second set of MANOVA analyses included all four region-of-origin groups. Significant differences were found in the length and width of the multivariate output of the maxillary dentition among the four groups, though no significant length or width measurements in the univariate output. In the mandibular dentition, significant differences were found between the reference and study sample groups. Levene’s test was significant at one length (LRDC) measurement and the univariate output found two significant differences, one width (LRAB) and one length (LRBD) measurement, between the groups. These results suggest that there are enough differences among the region-of-origin groups to distinguish them based on the occlusal polygon measurements.
The DFA were divided into maxillary, mandibular, and pooled dentitions for both Southwest Hispanic and Mexican differentiation and discrimination among all four region-of-origin groups. Using the maxillary dentition between Southwest Hispanics and
Mexicans correctly classified 67.2 percent of individuals and correctly classified 64.8 percent of the cases in cross-validation. The mandibular dentition correctly classified
69.1 percent of the individuals and 68.3 percent of cases in cross-validation. Pooling the
95 dentitions resulted in higher classification rates. The classification rate was 74.3 percent and 71.4 percent in cross-validation. While these are only moderate classification rates, they are still better than chance, which would be 50 percent for this comparison.
Classification rates were lower when analyzing the four region-of-origin groups together. The maxillary dentition had a 42.9 percent classification rate and only
38.3 percent in cross-validation. The mandibular dentition had a 45.1 percent classification rate and a 41.0 percent classification rate in cross-validation. Pooling the dentitions for all four groups resulted in a classification rate of 56.6 percent and only 45.1 percent in cross-validation. These classification rates are low, even though they are better than chance, which would be 25 percent for the four group comparison.
Given the results of the DFA and modified Morris (1986) method, both are useful in differentiating among the regions-of-origin for the occlusal polygon. The DFA, though, provides classification rates that are useful in forensic investigations where known error rates are necessary for identification methods. Additional research is needed on the occlusal polygon if it is to become integrated within forensic methodology.
Research Hypothesis Framework
The research hypothesis for this study is: There will be enough biological variation in the dentition to differentiate between Mexican and Southwest Hispanic groups. To determine how well the results support the hypothesis, they are reviewed here.
In the non-metric dental traits, the Pearson’s chi-square, Fisher’s exact tests, and the logistic regression analysis results help to support this hypothesis as the M2 protostylid is a prevalent trait in the Mexican samples in this study. Additionally, the MMD results demonstrate that the Mexican samples are significantly different from the Southwest
96
Hispanic samples. One dental non-metric trait, however, is not enough to distinguish one region-of-origin group from another on its own. It could be integrated within the current methodology, outlined in Birkby et al. (2008), to help classify individuals as UBC, though. The identification process Birkby et al. (2008) describes is based on population- level methodologies and UBC individuals are compared against these traits to estimate whether they fit the criteria for UBC.
In the metric results, ANOVA results from the modified Morris (1986) method supported the hypothesis in the right mandibular first molar. The DFA results provided moderate classifications rates when discriminating between the Southwest
Hispanic and Mexican samples, and they performed equally as well in cross-validation.
When the two UBC samples were factored in, classification rates did drop for the DFA.
These methods only partially supported the research hypothesis.
As noted in the background information, genetic admixture helps to explain why differences are observed in the dental non-metric traits and occlusal polygon measurements in this study. For example, Campos-Sánchez et al. (2006) demonstrated that both genetic overlaps and distinct differences exist among samples from the
American Southwest, Mexico, and Costa Rica. Bertoni et al. (2003) and Bryc et al.
(2015) found a wide variety of genetic variation among U.S. Latino groups alone. Green et al. (2000) showed the range of genetic variation within Mexico. Bryc et al. (2015) also demonstrated that genetic evidence aligned with people’s preferred ethnic classifications among U.S. Latinos. Furthermore, as Emeka and Vallejo (2011) discuss, U.S. Latinos only tend to choose to be labeled as Hispanic if they only have Mexican or Mexican-
97
American ancestry. Also, studies like Spradley et al. (2008) and Hughes et al. (2013) found skeletal differences both among and within Hispanic groups, respectively.
The results from this thesis can also add to the dialogue on the appropriateness of the term Hispanic for use in forensic purposes. There are some significant differences between the Mexican and Southwest Hispanic samples and the Southwest Hispanic and
UBC-FACTS samples. These results would support the assertion from Spradley et al.
(2008) that stated the need for population-specific formulae for groups classified under the Hispanic umbrella. Additionally, within Willermet and Edgar’s (2009) on whether
New Mexico Hispanics fit the trihybrid model of Hispanic ancestry, they provide a discussion on the use of the term Hispanic. While there was only one group within their study, the discussion is no less applicable to Hispanic population as a whole. This particular quote from their discussion sums up the need for region-of-origin specific terminology: “Simply aggregating Hispanics into one group would lose sight of local and regional populational composition, and ultimately be ineffective for understanding the biological structure of Hispanic populations. An anthropological perspective, combining information from historical, cultural, biological sources is the key to understanding the biological structure of Hispanic populations.” Spradley (2014) also indicated that her results from comparing individuals from the Sacred Heart Burial Park in Texas to
Mexican, Guatemalan, and unidentified individuals from the PCOME support the ability of skeletal data to demonstrate that region-of-origin estimations are possible.
The genetic, and thereby, morphological variation that exists among groups classified as Hispanic warrants further research to define different regions-of-origin. The results from this study minimally suggest that there are morphologic differences in
98 individuals from the American Southwest and those from Central Mexico. They also suggest that UBC regions-of-origin can be classified using known reference samples.
Areas for future research to expand the understanding of region-of-origin variation are described later in this chapter.
Study Limitations
There were several major limitations to this study. One of the primary limitations was training time. While practice scoring for the dental non-metric traits had been started several months before data collection, there was only time for one week of training with Dr. Edgar. There is a big difference between being shown how to score the traits and just reading about how to score them. It is likely that the high interobserver error rates for the non-metric traits was due to the small amount of training time and familiarity with how to properly score the traits.
Another limitation for this study is that the James K. Economides Orthodontic
Collection are dental casts, not real teeth. The dental casts were mostly of a good quality, but they were thick in certain areas, which prevented observing trait expression at times.
Additionally, the casts represent various stages of orthodontic treatment. Braces and permanent retainers also occasionally prevented trait observation. It is likely that these impediments played a role in whether or not a trait was scored as present or absent. It was also difficult to learn how to score the non-metric traits on dental casts. The range of expression for certain traits, such as shoveling and double shoveling, were masked by the thickness of the casting material at times. It is possible that these traits were underscored for the Southwest Hispanic sample in this study.
99
A third major limitation of this project are the occlusal polygon measurements. As previously discussed, this method has only been used in one other forensic setting and an additional biodistance study. Beyond that, the occlusal polygon is one of the dental metrics for paleoanthropological research on members of the genus
Homo. These are very different types of comparison as the groups in this study are closely related and the paleoanthropological studies are among different species and subspecies. There are no known datasets for Hispanic groups that the present data can be compared to for validation. Until the occlusal polygon is researched further in modern, forensically-applicable populations, the results presented in this study should be considered preliminary.
Conclusion
Summary
This thesis sought to find unique aspects of the Mexican dentition that would be useful in the UBC identification process. The goal was to find dental non-metric traits, and possibly metric measurements, that could be integrated in medical examiner’s offices, such as the PCOME, to narrow the potential region-of-origin for a deceased migrant when used in conjunction with existing methodology. Through a variety of statistical applications for both the non-metric traits and occlusal polygon measurements, it was found that Mexican dentition could be distinguished from that of Southwest
Hispanics. While the occlusal polygon is not the best methodology for this separation, the dental non-metric traits are quite useful.
100
Despite the limitations of this project, it demonstrated that there is potential to use the dentition to discriminate among regions-of-origin. Interpretations about where
UBC individuals originate are the prime example of this. These data also help to support the genetic information describing the amount of variation among groups. This thesis is a small step towards improving the identification methods for deceased UBC.
Future Research
One of the keys for further understanding the dental variability within the groups that have been classified as UBC is to consider the limitations from this study.
Continuing to practice and study the dental non-metric traits on actual teeth will be crucial to reducing the interobserver and intraobserver error rates. Also, more applicable metric measurements, the mesiodistal and buccolingual measurements and cervical measurements, will be employed in future research. Studies such as Pilloud et al. (2014) demonstrate that these measurements are preferable for forensic dental anthropology research.
Despite the issues with the occlusal polygon, it would be interesting to compare areas of the occlusal polygon from this study to other studies to observe how
Mexican and Southwest Hispanic samples relate to black and whites. Additionally, looking at GIS points that track the location of UBC remains in the Sonoran Desert, as the PCOME already utilizes, could be an interesting addition to this research. It might be possible to track migration routes per region-of-origin to help with identifying individual sets of UBC remains.
Another aspect of improving this research will be to add additional regions of study. There are other areas outside of Central Mexico where UBC have come from, as
101 well as locations in Guatemala, Honduras, and El Salvador (Martinez et al. 2013).
Modern individuals from these regions should be included in future research to understand the range of variation observed in UBC samples. Comparing the range of modern Latino groups to outside samples, such as modern American black and white data, will help to test the reliability of standards developed on these groups.
Beyond studying modern groups though, there needs to be an understanding of where the dental non-metric traits originated in ancestral populations. As described, it is known that the differences among Hispanic groups come from varying levels of Native
American, European, and African ancestry. The varied ancestral histories of these groups indicate which specific populations were genetic contributors. This information can be used to decide on where to conduct bioarchaeological research on dental and cranial morphology in the future. Prehistoric groups from Mexico, Central America, and Latin
America, for instance, can be included to create a more comprehensive picture of where modern morphometric cranial and dental traits originate. Studies like Haydenblit (1996) demonstrate that this type of bioarchaeological research is possible. While this would still provide broad regions-of-origin, it could add insightful information to the UBC identification process.
As the number of UBC deaths continues to rise, identification methodologies have to evolve to meet the current situation. While small, this thesis provides insight on how the dentition of known reference samples can narrow the region-of-origin for UBC individuals. Given the genetic nature of dental morphology, it is possible that this type of research could be pivotal to providing identifications for unknown, deceased migrants.
REFERENCES CITED
REFERENCES CITED
Anderson, Bruce E. 2008 Identifying the Dead: Methods Utilized by the Pima County (Arizona) Office of the Medical Examiner for Undocumented Border Crossers: 2001–2006. Journal of Forensic Sciences 53(1): 8-15.
Anderson, Bruce E. 2015 The Identification Process Involving American Citizen (AMCIT) and Undocumented Border Crossers (UBCs): The Pima County Office of the Medical Examiner (PCOME) Experience. Paper presented at the 67th annual meeting of the American Academy of Forensic Sciences, Orlando, Florida, February 16-21, 2015.
Anderson, Bruce E. and Bruce O. Parks 2008 Symposium on Border Crossing Deaths: Introduction. Journal of Forensic Sciences 53(1): 6-7.
Bailey, Shara E. 2004 A Morphometric Analysis of Maxillary Molar Crowns of Middle-Late Pleistocene Hominins. Journal of Human Evolution 47: 183-198.
Bailey, Shara, Michelle Glantz, Timothy D. Weaver, and Bence Viola 2008 The Affinity of the Dental Remains from Obi-Rakhmat Grotto, Uzbekistan. Journal of Human Evolution 55: 238-248.
Bartelink, Eric J., Lesley A. Chesson, Brett J. Tipple, and Gregory E. Berg 2015 Recent Applications of Stable Isotope Forensics for Tracking Region-of- Origin and Residence Patterns of Unidentified Individuals. Paper presented at the 67th annual meeting of the American Academy of Forensic Sciences, Orlando, Florida, February 16-21, 2015.
Bertoni, Bernardo, Bruce Budowle, Mónica Sans, Sara A. Barton, and Ranajit Chakraborty 2003 Admixture in Hispanics: Distribution of Ancestral Population Contributions in the Continental United States. Human Biology 75(1): 1-11.
Birkby, Walter H., Todd W. Fenton, and Bruce E. Anderson 2008 Identifying Southwest Hispanics Using Nonmetric Traits and the Cultural Profile. Journal of Forensic Science 53(1): 29-33.
103 104
Bryc, Katarzyna, Eric Y. Durand, J. Michael Macpherson, David Reich, and Joanna L. Mountain 2015 The Genetic Ancestry of African Americans, Latinos, and European Americans Across the United States. The American Journal of Human Genetics 96: 37-53.
Caminero-Santangelo, Marta 2010 The Lost Ones: Post-Gatekeeper Border Fictions and the Construction of Cultural Trauma. Latino Studies 8: 304-327.
Campos-Sánchez, Rebeca, Ramiro Barrantes, Sandra Silva, Michael Escamilla, Alfonso Ontiveros, Humberto Nicolini, Ricardo Mendoza, Rodrigo Munoz, and Henriette Raventos 2006 Genetic Structure Analysis of Three Hispanic Populations from Costa Rica, Mexico, and the Southwestern United States Using Y-Chromosome STR Markers and mtDNA Sequences. Human Biology 78(5): 551-563.
Chávez, Sergio 2011 Navigating the US-Mexico Border: The Crossing Strategies of Undocumented Workers in Tijuana, Mexico. Ethnic and Racial Studies 34(8): 1320-1337.
Chesson, Lesley A., Brett J. Tipple, James Ehleringer, and Eric J. Bartelink 2015 Forensic Applications of Isotope Landscapes (“Isoscapes”): A Tool for Predicting Region-of-Origin in Forensic Anthropology Cases. Paper presented at the 67th annual meeting of the American Academy of Forensic Sciences, Orlando, Florida, February 16-21, 2015.
Collins-Schramm, Heather E., Bill Chima, Takanobu Morii, Kimberly Wah, Yolanda Figueroa, Lindsey A. Criswell, Robert L. Hanson, William C. Knowler, Gabriel Silva, John W. Belmont, Michael F. Seldin 2004 Mexican American Ancestry-Informative Markers: Examination of Population Structure and Marker Characteristics in European Americans, Mexican Americans, Amerindians and Asians. Human Genetics 114: 263-271.
Crawford, Mark, ed. 1999 Encyclopedia of the Mexican-American War. Santa Barbara: ABC-CLIO, Inc.
DeLuca, Lawrence A., Marylyn M. McEwen, and Samuel M. Keim 2010 United States-Mexico Border Crossing: Experiences and Risk Perceptions of Undocumented Male Immigrants. Journal of Immigrant and Minority Health 12: 113-123. de Souza, Peter and Philip Houghton 1977 The Mean Measure of Divergence and the Use of Non-Metric Data in the Estimation of Biological Distances. Journal of Archaeological Science 4: 163-169.
105
Edgar, Heather J.H. 2002 Biological Distance and the African American Dentition. Ph.D. Dissertation, Department of Anthropology, The Ohio State University.
2009 Biohistorical Approaches to “Race” in the United States: Biological Distances Among African Americans, European Americans, and Their Ancestors. American Journal of Physical Anthropology 139: 58-67.
2013 Estimation of Ancestry Using Dental Morphological Characteristics. Journal of Forensic Sciences 58(S1): S3-S8.
Edgar, Heather, J.H., Shamsi Daneshvari, Edward F. Harris, and Philp J. Kroth 2011 Inter-Observer Agreement on Subjects’ Race and Race-Informative Characteristics. PLoS ONE 6(8): 1-8.
Ehleringer, James R., Gabriel J. Bowen, Lesley A. Chesson, Adam G. West, David W. Podlesak, and Thure E. Cerling 2008 Hydrogen and Oxygen Isotope Ratios in Human Hair are Related to Geography. Proceedings of the National Academy of Sciences of the United States of America 105(8): 2788-2793.
Emeka, Amon and Jody Agius Vallejo 2011 Non-Hispanics with Latin American Ancestry: Assimilation, Race, and Identity Among Latin American Descendants in the US. Social Science Research 40: 1547-1563.
Ennis, Sharon R., Merarys Rios-Vargas, and Nora G. Albert 2011 The Hispanic Population: 2010. 2010 Census Briefs. U.S. Department of Commerce: Economics and Statistics Administration, U.S. Census Bureau.
Field, Andy 2013 Discovering Statistics Using IBM SPSS Statistics. London: Sage Publications Ltd.
Figueroa-Soto, Cristina 2012 Sexual Dimorphism and Socioeconomic Status: Comparison Between Migrant and Non-Migrant Populations. M.A. Thesis, Department of Anthropology, Texas State University-San Marcos.
Gómez-Robles, Aida, José María Bermúdez de Castro, María Martinón-Torres, Layre Prado-Simón, and Juan Luis Arsuaga 2012 A Geometric Morphometric Analysis of Hominin Upper Second and Third Molars, with Particular Emphasis on European Pleistocene Populations. Journal of Human Evolution 63: 512-526.
106
Green, Lance D., James N. Derr, and Alec Knight 2000 mtDNA Affinities of the Peoples of North-Central Mexico. American Journal of Human Genetics 66: 989-998.
Haydenblit, Rebeca 1996 Dental Variation Among Four Prehispanic Mexican Populations. American Journal of Physical Anthropology 100: 225-246.
Hefner, Joseph T. 2015 Cranial Morphoscopic Traits and the Assessment of American Black, American White, and Hispanic Ancestry. In Biological Affinity in Forensic Identification of Human Skeletal Remains: Beyond Black and White. Gregory E. Berg and Sabrina C, Ta’ala, eds. Pp. 27-41. Boca Raton: CRC Press.
Hinkes, Madeleine J. 2008 Migrant Deaths Along the California-Mexico Border: An Anthropological Perspective. Journal of Forensic Science 53(1): 16-20.
Hughes, Cris E., Meredith L. Tise, Lindsay H. Trammell, and Bruce E. Anderson 2013 Cranial Morphological Variation Among Contemporary Mexicans: Regional Trends, Ancestral Affinities, and Genetic Comparisons. American Journal of Physical Anthropology 151: 506-517.
Hurst, Carolyn V. 2012 Morphoscopic Trait Expressions Used to Identify Southwest Hispanics. Journal of Forensic Sciences 57(4): 859-865.
Immigration Reform and Control Act of 1986 1986 U.S. Citizenship and Immigration Services, accessed March 11, 2015, http://www.uscis.gov/tools/glossary/immigration-reform-and-control-act-1986- irca.
Irish, Joel D. 1997 Characteristic High- and Low-Frequency Dental Traits in Sub-Saharan African Populations. American Journal of Physical Anthropology 102: 455-467. 2010 The Mean Measure of Divergence: Its Utility in Model-Free and Model- Bound Analyses Relative to the Mahalanobis D2 Distance for Nonmetric Traits. American Journal of Human Biology 22: 378-395.
Juarez, Chelsey A. 2008 Strontium and Geolocation, the Pathway to Identification for Deceased Undocumented Mexican-Border Crossers: A Preliminary Report. Journal of Forensic Sciences 53(1): 46-49.
107
Katzenberg, M. Anne 2008 Stable Isotope Analysis: A Tool for Studying Past Diet, Demography, and Life History. In Biological Anthropology of the Human Skeleton. M. Anne Katzenberg and Shelley R. Saunders, eds. Pp. 413-441. Hoboken: John Wiley & Sons, Inc.
Kenyhercz, Michael W., Alexandra R. Klales, and William E. Kenyhercz 2014 Molar Size and Shape in the Estimation of Biological Ancestry: A Comparison of Relative Cusp Location Using Geometric Morphometrics and Interlandmark Distances. American Journal of Physical Anthropology 153: 269- 279.
Landis, Richard J. and Gary G. Koch 1977 The Measure of Observer Agreement for Categorical Data. Biometrics 33(1): 159-174.
Martinez, Daniel E., Robin C. Reineke, Raquel Rubio-Goldsmith, Bruce E. Anderson, Gregory L. Hess, and Bruce O. Parks 2013 A Continued Humanitarian Crisis at the Border: Undocumented Border Crosser Deaths Recorded by the Pima County Office of the Medical Examiner, 1990-2012. Tucson: Binational Migration Institute, University of Arizona.
Martinez, George A. 2000 Dispute Resolution and the Treaty of Guadalupe Hidalgo: Parallels and Possible Lessons for Dispute Resolution under NAFTA. Bilingual Review 25(1): 39-61.
McGuire, Randall H. 2013 Steel Walls and Picket Fences: Rematerializing the U.S.-Mexican Border in Ambos Nogales. American Anthropologist 115(3): 466-480.
Mora-Torres, Juan 2011 “Los de casa se van, los de fuera vienen:” The First Mexican Immigrants, 1848-1900. In Beyond La Frontera: The History of Mexico-U.S. Migration. Mark Overmeyer-Velázquez, ed. Pp. 3-27. New York: Oxford University Press, Inc.
Morris, Donald H 1986 Maxillary Molar Occlusal Polygons in Five Human Samples. American Journal of Physical Anthropology 70: 333-338.
Overmeyer-Velázquez, Mark 2011 Histories and Historiographies of Greater Mexico. In Beyond La Frontera: The History of Mexico-U.S. Migration. Mark Overmeyer-Velázquez, ed. Pp. xix- xlv. New York: Oxford University Press, Inc.
108
Pilloud, Marin A, Joseph T. Hefner, Tsunehiko Hanihara, and Atsuko Hayashi 2014 The Use of Tooth Crown Measurements in the Assessment of Ancestry. Journal of Forensic Sciences 59(6): 1493-1501.
Ruttan, Tim, Uwe Stolz, Sara Jackson-Vance, Bruce Parks, and Samuel M. Keim 2013 Validation of a Temperature Prediction Model for Heat Deaths in Undocumented Border Crossers. Journal of Immigrant and Minority Health 15: 407-414.
Santos, Frederic 2014 A GUI for Mean Measure of Divergence. Package description.
Sapkota, Sanjeeb, Harold Kohl III, Julie Gilchrist, Jay McAulifee, Bruce Parks, Bob England, Tim Flood, Mack Sewell, Dennis Perrotta, Miguel Escobedo, Corrine E. Stern, David Zane, and Kurt B. Nolte 2006 Unauthorized Border Crossings and Migrant Deaths: Arizona, New Mexico, and El Paso, Texas, 2002-2003. American Journal of Public Health 96(7): 1282- 1287.
Scherer, Andrew Kenneth 2004 Dental Analysis of Classic Period Population Variability in the Maya Area. Ph.D. Dissertation, Department of Anthropology, Texas A&M University.
Scott, G. Richard and Christy G. Turner II 1988 Dental Anthropology. Annual Review of Anthropology 17: 99-126. 2000 The Anthropology of Modern Human Teeth: Dental Morphology and Its Variation in Recent Human Populations. Cambridge: Cambridge University Press. 2008 History of Dental Anthropology. In Technique and Application in Dental Anthropology. Joel D. Irish and Greg C. Nelson, eds. Pp 10-34. Cambridge: Cambridge University Press.
Silva-Zolezzi, Irma, Alfredo Hidalgo-Miranda, Jesus Estrada-Gil, Juan Carlos Fernandez- Lopez, Laura Uribe-Figueroa, Alejandra Contreras, Eros Balam-Ortiz, Laura del Bosque- Plata, David Velazquez-Fernandez, Cesar Lara, Rodrigo Goya, Enrique Hernandez- Lemus, Carlos Davila, Eduardo Barrientos, Santiago March, Gerardo Jimenez-Sanchez and Eric S. Lander 2009 Analysis of Genomic Diversity in Mexican Mestizo Populations to Develop Genomic Medicine in Mexico. Proceedings of the National Academy of Sciences of the United States of America 106(21): 8611-8616.
Sjøvold, Torstein 1977 Non-Metrical Divergence Between Skeletal Populations: The Theoretical Foundation and Biological Importance of C.A.B. Smith’s Mean Measure of Divergence. OSSA 4(S1): xii-133.
109
Spradley, M. Katherine 2014 Toward Estimating Geographic Origin of Migrant Remains along the United States- Mexico Border. Annals of Anthropological Practice 38(1): 101-110.
Spradley, M. Katherine, Richard L. Jantz, Alan Robinson, Fredy Peccerelli 2008 Demographic Change and Forensic Identification: Problems in Metric Identification of Hispanic Skeletons. Journal of Forensic Sciences 53 (1): 21-28.
Taboada, María Teresa Menéndez 2009 Minería y Enfermadad en Zimapán, Hidalgo: Estudio Patológico de la Colección Esquelética del Panteón de Santiago Apóstal [Mining and Sickness in Zimapán, Hidalgo: A Pathological Study of the Skeletal Collection from the Santiago Apóstal Church]. M.A. thesis, Instituto de Investigaciones Antropológicas, Universidad Nacional Autónoma de México.
Tise, Meredith L., M. Katherine Spradley, and Bruce E. Anderson 2013 Postcranial Sex Estimation of Individuals Considered Hispanic. Journal of Forensic Sciences 58 (S1): S9-S14.
Turner, Christy G. II, Christian R. Nichol, and G. Richard Scott 1991 Scoring Procedures for Key Morphological Traits of the Permanent Dentition: The Arizona State University Dental Anthropology System. In Advances in Dental Anthropology. Marc A. Kelley and Clark Spencer Larsen, eds. Pp. 13-31. New York: Wiley-Liss, Inc.
Willermet, C.M., and H.J.H. Edgar 2009 Dental Morphology and Ancestry in Albuquerque, New Mexico Hispanics. Journal of Comparative Human Biology 60: 207-224.
APPENDIX A
Adult Dentition: Nonmetrics Site Name/Site Number Acc./Burial No. ______Age Sex: M M? F F? ? Date ______
Maxilla I1R I1L I2R I2L CR CL P1R P1L P2R P2L M1R M1L M2R M2L M3R M3L
Status/Wear
Winging
Labial Curve Palatine Torus:______Shoveling Supernumerary Teeth: Double Shoveling
Interrupt. Groove
I & C Tub. Dent.
C Mesial. Ridge
C. Dist Acc. Ridge
P M. & D. Cusps
MxPAR (mes/dis)
Metacone Uto-Aztecan M
Hypocone H
Cusp 5 5
Carabelli’s Cusp C
C2 Parastyle P
Enamel Extension X
Root Number R
Radical Number r
Peg(<7)/reduce p
Odontome
Cong. Absence c New Variants/Comments: Cong. Absence – 2 = antemortem loss; 3 = postmortem loss
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Site Name/Site Number Acc./Burial No. ___
Mandible I1L I1R I2L I2R CL CR P1L P1R P2L P2R M1L M1R M2L M2R M3L M3R
Status/Wear
Shoveling
Double Shoveling
C. Dist Acc. Ridge
P. Ling. Cusps
Anterior Fovea A Mandibular Torus:______Groove Pattern G Rocker Jaw: ______Molar Cusp No. C Supernumerary Teeth: ______Deflecting Wrinkle D
Mid Trigonid Crest T
Protostylid P
Cusp 5 5
Cusp 6 6
Cusp 7 Tomes’ Root 7
Enamel Extension X
Root Number R
Radical Number r
Odontome t
Cong. Absence c
New Variants/Comments: Cong. Absence – 2 = antemortem loss; 3 = postmortem loss
112