Fluctuating Dental Asymmetry As an Indicator of Stress in Prehistoric Native Americans of the Ohio River Valley Dissertation

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FLUCTUATING DENTAL ASYMMETRY AS AN INDICATOR OF STRESS IN PREHISTORIC NATIVE AMERICANS OF THE OHIO RIVER VALLEY

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree

Doctor of Philosophy in the Graduate School of The Ohio State University

Christopher K. Barrett, M.A.

* * * * *

The Ohio State University 2005

Dissertation Committee: Approved by Professor Paul W. Sciullli, Adviser

Associate Professor Douglas Crews ______Associate Professor Kristen Gremillion Adviser Graduate Program In Anthropology Assistant Professor Debbie Guatelli-Steinberg

ABSTRACT

Fluctuating asymmetry is characterized by random deviations from perfect symmetry in bilaterally symmetrical structures. Elevated levels of fluctuating asymmetry indicate developmental instability usually associated with environmental stress. The utility of fluctuating dental asymmetry as an indicator of developmental instability and stress was tested using human adult teeth from prehistoric and modern populations from the Ohio River valley area; eight Late Archaic, two Protohistoric and one modern dental sample. Fluctuating asymmetry was estimated from thirty-six buccolingual measurements and compared to an index value for linear enamel hypoplasia (LEH). Some researchers have claimed that the transition to agriculture and maize diets caused a decline in health evident in the increased frequency of skeletal and dental stress indicators in the Late Prehistoric. Contrary to expectations, fluctuating asymmetry was different in only one of the measurements between periods, even though LEH varied significantly. The presence of strong leptokurtosis in the Protohistoric sample prevented many of the measurements from being used. The absence of other factors that

ii explain leptokurtosis suggests that individuals in the Protohistoric were heterogeneous for the expression of fluctuating asymmetry. In addition, fluctuating asymmetry correlated positively with the LEH index in two measurements from the Protohistoric. Since fluctuating asymmetry cannot be estimated for non-normal distributions, the presence of leptokurtosis may be a better indicator of developmental instability than fluctuating asymmetry alone. This is supported by data from the Late

Prehistoric site of Pearson Village. Despite similar levels of measurement error and fluctuating asymmetry, measurement distributions at Pearson

Village remained relatively normal. This suggests that populations in the

Protohistoric were under greater stress than other Late Prehistoric populations and that maize agriculture alone does not account for these differences.

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Dedicated to my grandparents: William F. and Lucy S. Bean

ACKNOWLEDGMENTS

I wish to thank my adviser, Dr. Paul W. Sciulli for his encouragement, patience, and his willingness to provide feedback on matters of style, content and statistical analysis.

I would also like to thank Martha Otto of the Ohio Historical

Society and Dr. Robert Riordan of Wright State University for allowing me access to their skeletal collections.

I am grateful to my mother, Deborah S. Bean, for encouraging my scientific leanings from an early age.

I would also like to thank my good friends Craig Demel, Marcus

Williford, Sara Williford and David Yonek for helping me to remember what is really important in life and reminding me not to take myself too seriously.

Above all I would like to thank my wife, Elizabeth Barrett. Without her support, both emotional and financial, this dissertation might never have been written.

VITA

December 4th, 1970………………Born – Columbus, OH

1995 …………………………………B.A. Anthropology, Ohio State University

1998 …………………………………M.A. Anthropology, Ohio State University

1999 – present …………………….Adjunct Professor Columbus State Community College Columbus, OH

1999 – present …………………….Graduate Teaching Associate / Lecturer Ohio State University Columbus, OH

2002 – present …………………….Instructor Wright State University Dayton, OH

2004 – present …………………….Adjunct Professor Pontifical Josephenum Columbus, OH

PUBLICATIONS

Research Publications

1. Barrett, Christopher K, Cavallari, Wendy A, and Sciulli, Paul W. “Determination of Sex from the Talus in Prehistoric Native Americans” Collegium Anthropologicum, 25(1):13-19, (2001)

2. Sciulli, Paul W., Barrett, Christopher K., and Proctor, Rick. “The Proctor Rock Shelter (46NI84) Burial: Possible Carcinoma or Myotic Infection” West Virginia Archaeologist 45(1):35-42, (1993)

FIELDS OF STUDY

Major Field: Anthropology

Specialization: Osteology Forensic Anthropology Human Biology

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TABLE OF CONTENTS

Page Abstract…………………………………………………………………………. ii Dedication………………………………………………………………………. iv Acknowledgments…………………………………………………………….. v Vita………………………………………………………………………………. vi List of Tables…………………………………………………………………… x List of Figures…………………………………………………………………. xii

Chapters:

1. Introduction……………………………………………………….. 1 Problem Statement………………………………………….. 6 Research Question…………………………………………… 7 Methods………………………………………………………… 9 Limitations…………………………………………………….. 11

2. Literature Review Developmental Stability, Stress, and Fluctuating Asymmetry…………………………………………………….. 12 Statistical Analysis of Fluctuating Asymmetry………… 21 Studies of Fluctuating Asymmetry………………….……. 29 Dental Development………………………………………….. 39 Interpretation of Health from Archaeological Samples…………………………………………………………. 46 Summary……………………………………………………….. 69

3. Methods…………………………………………………………….. 72 Sample Composition…………………………………………. 72 Data Collection………………………………………………… 77 Analysis…………………………………………………………. 79 Summary……………………………………………………….. 85

4. Results…………………………………………………………….…. 87 Descriptive Statistics……………………………………….…. 88 Assessment of Outliers…………………………………….…. 91 Homogeneity of Error Variances………………………….… 94 Test for Size Dependence………………………………….…. 95

viii Test for Normality……………………………………………… 96 Adjustment for Leptokurtosis………..……………………… 101 Fluctuating Asymmetry……………………………………… 104 Linear Enamel Hypoplasia………………………………….. 107 Summary……………………………………………………….. 110

5. Discussion and Conclusions……………………………………. 112 Problem Statement……………………………………………. 113 Review of Methods…………………………………………….. 115 Summary of Results………………………………………….. 117 Discussion of Results………………………………………… 119 Conclusion……………………………………………………… 133

Bibliography……………………………………………………………………. 135

Appendix A: Critical Kurtosis Values…………………………………….. 149

Appendix B: Descriptive Statistics………………………………………… 151

Appendix C: Results of Analysis of Variance…………………………… 160

LIST OF TABLES

Table Page

1. Relationship of Variance to Asymmetry………………………….. 23

2. Indexes of Asymmetry………………………………………………… 24

3. Composition of Sample………………………………………….……. 73

4. Average Number of Observations by Period and Sex………….. 88

5. Descriptive Statistics for BL Measurements by Period…..……. 89

6. Descriptive Statistics for LEH Index………………………………. 91

7. Outliers by Period……………………………………………………… 92

8. Observations Rejected by Grubb’s Test…………………………… 93

9. Results of Levene’s Test of Equality of Variances: Significant Measurements…………………………………………… 95

10. Trait-size Dependence: Significant Correlations Coefficients... 96

11. Kurtosis values for 1st and 2nd Measurements with Outliers Removed…………………………………………………………………. 98

12. Number of Measurements Exceeding Critical Values for Leptokurtosis…………………………………………………………… 99

13. Significant Results of Paired T-Test of Left and Right 1st Measurements………………………………………………………….. 100

14. Measurements Rejected Due to Leptokurtosis with with Significance Level After Log Transformation……………… 102

15. Directionally Asymmetrical Measurements……………………… 105

2 16. Average Si Values…………………………………………………….. 106

2 17. F-Test of Fluctuating Asymmetry (Si ) Between Periods………. 107

18. Analysis of Variance for LEH Index……………………………….. 108

19. Significant Correlations Between LEH Index and FA2 with Sexes Combined……………………………………………………….. 108

20. Significant Correlations Between LEH Index and FA2 by Sex………………………………………………………………………… 109

21. Critical Values of Kurtosis Test Statistic From Normality in the Direction of Platykurtosis and Leptokurtosis……………… 150

22. Descriptive Statistics: Late Archaic Males………………………. 152

23. Descriptive Statistics: Late Archaic Females……………………. 153

24. Descriptive Statistics: Late Archaic Unknown Sex…………….. 154

25. Descriptive Statistics: Protohistoric Males………………………. 155

26. Descriptive Statistics: Protohistoric Females…………………… 156

27. Descriptive Statistics: Protohistoric Unknown Sex……………. 157

28. Descriptive Statistics: Modern Unknown Sex…………………… 158

29. Average Differences Between Sexes and Periods………………. 159

30. Analysis of Variance, Late Archaic, Sexes Combined (Posterior Maxillary)………………………………………………….. 161

31. Analysis of Variance, Late Archaic, Sexes Combined (Anterior Maxillary)………………………………………………….. 162

32. Analysis of Variance, Late Archaic, Sexes Combined (Mandibular)……………………………………………………………. 163

33. Analysis of Variance, Protohistoric, Sexes Combined…………. 164

34. Analysis of Variance, Modern, Sexes Combined………………… 165

LIST OF FIGURES

Figure Page

1. The Odontogenic Homeobox Code…………………………………. 41

2. Model for the Interpretation of Skeletal Stress Indicators………………………………………………………………… 47

3. Location of Samples…………………………………………………... 76

4. Distribution of L-R for Protohistoric Maxillary Canine……….. 97

5. Distribution of Log-Transformed L-R for Protohistoric Maxillary Canine………………………………………………………. 103

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CHAPTER 1

INTRODUCTION

The purpose of this study was to evaluate differences in two stress indicators for populations from the Late Archaic, Protohistoric, and modern periods in the Ohio River Valley area. The Late Archaic (ca.

6000-3000 BP) is a period of population growth while the Protohistoric

(ca. 500-250 BP) is a period of population decline that precedes direct contact with Europeans. It is during the Late Archaic that previously mobile groups became semi-sedentary, and during which the first domesticates appear in the archaeological record. The Late Prehistoric period (ca. 1000-500 BP) is a period of subsistence change and settlement nucleation just prior to the Protohistoric. It is generally accepted that populations in the Late Prehistoric and Protohistoric periods were under greater social and environmental stress than populations from the Late Archaic (Goodman et al., 1984; Perzigian et al.,

1984; Pollack and Henderson, 2000).

1 The applicability of many metric and non-metric indicators of growth disruption to the interpretation of stress from archaeological contexts has been criticized by Wood et al. (1992) and other supporters of the Osteological Paradox. These criticisms question the generally accepted view that prehistoric populations adopting maize agriculture were less healthy than earlier, non-agricultural groups in the same area.

In particular, Wood et al. (1992) have criticized the interpretation of linear enamel hypoplasia and short stature as indicative of declining health and increased stress. Poor health, according to the supporters of this view, is more than just the presence or absence of infectious lesions; whether a person is healthy or unhealthy has a cultural dimension that cannot be addressed by skeletal or dental remains alone.

Stress may be defined in several ways. Goodman (1984) defined it as a physiological disruption causing organisms to become unable to buffer physical and/or cultural stress resulting in the disruption of metabolic processes. Emlen et al. (2003) have defined stress as any stimulus that produces an energy requiring response. This response disrupts other metabolic pathways creating inefficiencies and delays between the production of signaling molecules and their metabolic response. Both definitions imply disruption to normal function. These disruptions are often manifested in morphological and histological changes to bone and teeth.

2 Traditionally, bioarchaeologists have relied upon three distinct taxa of stress indicators: general cumulative stress, general episodic stress and stress associated with a specific disease (Goodman, 1984:15).

General cumulative stress is usually represented by short stature and changes in the degree or pattern of sexual dimorphism. It is often associated with nutritional and/or environmental stressors, however the exact cause may remain unknown. General episodic stress indicators are dental and skeletal lesions associated with growth disruption. These include linear enamel hypoplasia (LEH), local hypoplasia of the primary canine (LHPC), hypocalcification, fluctuating dental asymmetry and

Harris lines. Stress indicators for specific diseases include such skeletal lesions as cribra orbitalia and porotic hyperostosis associated with anemia or iron deficiency. Examples of dental indicators include

Hutchinson's incisor and mulberry molars that are indicative of syphilis

(Hillson, 1996).

Interpreting the type, frequency and degree of stress from skeletal and dental remains is problematic. Poor preservation and sampling error hinder straightforward assessment of stress and make conclusions about prehistoric health open to multiple and sometimes contradictory interpretations. Wood et al. (1992) have cited three conceptual problems that confound interpreting health from the frequency of stress indicators

3 in archaeological populations: demographic non-stationarity, selective mortality and hidden heterogeneity. Together, they are referred to as the

Osteological Paradox.

Hidden heterogeneity is especially difficult to address. It implies that individuals exhibit differential susceptibility to stress and that the most susceptible individuals may not survive long enough for skeletal lesions to develop. This means that a skeleton free of lesions may either be free of disease, or that the individual died before skeletal changes could occur. Likewise, individuals with lesions may have been exceptionally healthy (to survive long enough for lesions to form) or unhealthy (allowing lesions to form in the first place). Similar arguments have been made about the use of stature, Harris Lines and LEH.

One promising area of research into stress that has been pursued extensively by geneticists and biologists is fluctuating asymmetry.

According to Van Valen (1962), fluctuating asymmetry, “results from the inability of organisms to develop along precisely determined paths.” (Van

Valen, 1962:126). Morphological structures on the right and left sides of bilaterally symmetrical organisms are controlled by the same genes

(Palmer and Strobeck, 1992:59). In the absence of other modifiers the right and left sides of a bilaterally symmetrical organism should be identical. This is seldom the case. Many genetic and environmental factors introduce developmental noise that interferes with growth and

4 development. Deviations from true symmetry have been suggested to represent a measurement of developmental instability and are an easily quantified measurement of stress (Van Valen, 1962; Palmer and Strobeck

2003a, 1992, 1986; Klingenberg, 2003).

The use of fluctuating asymmetry to infer stress has a number of advantages over some of the methods traditionally used in bioarchaeology. Since organisms tend to develop along well organized, highly canalized pathways the relative degree of fluctuating asymmetry should remain low and be resistant to short-term stress. Research into the heritability of fluctuating asymmetry has shown virtually no heritable component; heritabilities are less than h2=0.03 (Polak and Starmer,

2001; Woods, 1999). The lack of additive genetic factors that contribute to fluctuating asymmetry means that measurements of fluctuating asymmetry should represent the effects of environment and measurement error only. In the absence of other forms of asymmetry

(directional asymmetry or antisymmetry) fluctuating asymmetry should be a reliable indicator of stress. While many dental and skeletal indicators may be interpreted in more than one way the same cannot be said for fluctuating asymmetry. Increased levels of fluctuating asymmetry represent decreased developmental stability and therefore increased stress on the organism.

Problem Statement

Paleopathology at the Origins of Agriculture (Cohen and Armelagos,

1984) was a landmark publication in bioarchaeology. Contrary to the widely held belief that agriculture led to improvements in health, the many authors who contributed to Paleopathology at the Origins of

Agriculture argue that health actually declined. Many of the examples in this work are based on skeletal populations from North America (Indian

Knoll, Dickson Mounds, etc.). Increases in hypoplastic defects coincident with the adoption of maize agriculture have also been noted by Sciulli

(1997) and Sciulli and Oberly (2002). Health in these studies is not defined; poor or declining health is taken to mean an increase in the frequency of stress indicators, infectious lesions, or declining stature and sexual dimorphism. Good health, in this sense, is simply the absence of these indicators.

Wood et al. (1992) have suggested that what researchers think is a reduction in overall health as evidenced by increasing frequencies of dental caries, skeletal lesions, LEH and shorter statures could actually be evidence of improved health; that the shift to cultivation made available weaning gruels which reduced the spacing of births increasing fertility. The increased frequency of skeletal lesions and short stature can, according to Wood et al., be explained by selective mortality and

6 sampling problems. They suggest that a shift to sedentary agriculture does not inevitably lead to a decline in health and that in some areas it might lead to a marked improvement (Wood et al. 1992:367). Health in the sense of physical, mental and social well-being cannot be determined from skeletal populations, according to Wood et al., since increased stress indicators alone do not necessarily indicate impairment of vital function, nor do they inevitably lead to a decline in well-being.

Research Question

The current research seeks to address the following question: do changes in Late Archaic and Protohistoric subsistence in the Ohio River

Valley area significantly affect the expression of stress as evidenced by fluctuating asymmetry and linear enamel hypoplasia? Based upon the preceding arguments, several hypotheses are considered and tested.

Hypothesis 1: If the dentition deviates significantly from left-right symmetry then it will be due to fluctuating asymmetry and not other forms of asymmetry (directional asymmetry or antisymmetry) or measurement error. Fluctuating asymmetry will therefore represent environmental perturbation of development.

hypoplasia. Hypoplastic defects will be more sensitive to stress and occur more frequently while elevated levels of dental asymmetry will occur only during periods of serious, acute stress.

Hypothesis 3: If levels of stress differ significantly among Late

Archaic, Protohistoric, and modern samples then measures of fluctuating asymmetry and the frequency of linear enamel hypoplasia should also differ. Fluctuating asymmetry is expected to be highest in the

Protohistoric because individuals were subjected to higher stress than in the modern sample or the Late Archaic. While Late Archaic populations were under less stress than the Protohistoric they were under greater stress than modern groups and will therefore have levels of fluctuating asymmetry somewhere between these two populations.

The current research is relevant to both human biologists and bioarchaeologists alike. Fluctuating asymmetry has been applied infrequently to prehistoric populations and when it has it was usually without the methodological and analytical precautions necessary to insure valid conclusions. The current research tests these methods and assumptions. It also tests whether cumulative and episodic stress indicators differed significantly among three temporally separate populations from the Ohio River Valley. These results will be of particular interest to bioarchaeologists studying the transition from

8 nomadic to sedentary life ways and human biologists investigating responses to stress in modern human groups. The application of a morphological parameter that represents developmental stability may also be of interest to those looking for a way of evaluating the benefits of health care and food supplementation programs in developing countries.

Methods

The basis of the study is dental measurements taken from three periods in the Ohio River Valley: the Late Archaic, the Protohistoric and a modern sample. Eight skeletal populations represent the Late Archaic from Ohio dating to the Terminal Late Archaic period, approximately

3000 years BP. The Protohistoric is represented by two populations: one from Ohio the other from West Virginia. Both date to approximately 350-

400 years BP. The modern sample is a series of dental casts made during the 1950's and 1960's by a dentist in Dayton, OH.

A measurement of the buccolingual diameter and a count of LEH lines were taken for each tooth of the permanent dentition present.

Buccolingual measurements were taken with a pair of digital calipers.

Measurements were taken three times for each tooth, with the second and third measurements taken at least a month after the first. All dental measurements were taken by the author. LEH was scored for each tooth during the first round of measurements and was aided by a handheld magnifying glass and desk mounted magnifying lamp. An LEH index

9 that summarized total lines divided by the number of teeth present was computed to facilitate comparison of individuals. Measurements were taken without prior knowledge of the individuals’ sex, age or health status.

Following the guidelines of Palmer and Strobeck (1986; 2003a,b), scatter plots of data were visually inspected for outliers and the data tested for size dependence. Directional asymmetry was evaluated by looking for a consistent side bias. The presence or absence of antisymmetry was determined by examining the kurtosis of each sample and comparing it to a table of critical values (Palmer and Strobeck,

2003a:305). Measurement distributions that exhibited significant platykurtosis or leptokurtosis were excluded from analysis.

Fluctuating asymmetry was estimated by the use of a two-way mixed model ANOVA. Unlike other statistical tests, the two-way mixed model ANOVA is the only test that permits estimates of fluctuating asymmetry where measurement error has been factored out (Palmer and

Strobeck, 2003a:300). The significance of differences in fluctuating asymmetry and error variance between groups was evaluated using a

Levene's test. The association between fluctuating asymmetry and linear enamel hypoplasia was tested with a correlation coefficient of estimated fluctuating asymmetry versus LEH Index.

Limitations

There are a number of reasons why the current study must lack generalizability. First, sample sizes are relatively small, with the Late

Archaic sample being less than 100 individuals. Small sample sizes decrease the statistical power of tests like an ANOVA and increase the probability that deviations from symmetry may be the result of chance.

Second, the degree of difference between antimeric teeth (the corresponding tooth on the opposite side of the dentition) is usually measured in fractions of a millimeter. With such small margins it is possible for measurement error to be a larger portion of variance than fluctuating asymmetry, thus preventing its detection.

It must also be remembered that the two prehistoric groups under study, while living in the same geographic area, were separated by approximately 2,000 years of biological and cultural change. It is possible that other genetic factors or metabolic adaptations made these populations especially resistant or prone to the expression of fluctuating asymmetry in the dentition. For these reasons, caution should be taken when applying the conclusions of this study to other population groups.

CHAPTER 2

LITERATURE REVIEW

A large body of literature on developmental stability, asymmetry, dental development and the interpretation of stress from dental and skeletal elements provide a background for the present study. This chapter will examine these topics from both a theoretical and empirical perspective.

Developmental Stability, Stress and Fluctuating Asymmetry

Organisms develop along highly conserved growth trajectories that tend to resist environmental stresses. The ability to resist or buffer against developmental disruption is called developmental stability

(Nijhout and Davidowitz, 2003:4). Developmental instability (also called developmental noise) refers to processes that exceed the buffering capacity of an organism to accommodate one or more of these stresses

(Nijhout and Davidowitz, 2003:4). In the case of bilaterally symmetrical organisms, developmental instability can give rise to deviations from perfect symmetry (Palmer and Strobeck, 1992:59; Van Valen, 1962:126).

12 Canalization is a process closely associated with developmental stability. Canalized structures develop toward a target phenotype by a combination of genetic and environmental processes (Nijhout and

Davidowitz, 2003:4). Genetic canalization reduces allelic effects brought on by mutation and recombination. Environmental canalization, on the other hand, is a process that leads to a structure becoming less sensitive to stress (Nijhout and Davidowitz, 2003:4). The rate of growth for highly canalized structures may fluctuate over short periods of time; over the long run the majority of individuals in a population tend to achieve similar phenotypic outcomes.

The precise role of natural selection in the evolution of canalized growth systems is still largely unresolved although a number of studies have attempted to address the issue. King (1961) found that inbreeding plants tends to reduce vigor, and that the variance for a randomly breeding population is lower than that of the inbred (King, 1961:349).

King suggests that a reduction in the number of redundant alleles in the heterozygote makes it less susceptible to developmental noise than the homozygote and thus more stable. In contrast, the genomic coadaptation hypothesis (Dobzhansky, 1950, 1970) predicts that genomes evolve toward stability through genic balance and that homozygotes should exhibit greater fitness than heterozygotes in a stable environment.

Soule (1982) and Soule and Cuzin-Roudy (1982) investigated the association between homozygotes, heterozygotes and variation.

Homozygotes were often found at the tail ends of a distribution and heterozygotes near the mean with regards to the coefficient of variation

(CV). In a related study, heterozygosity was found to reduce developmental noise in morphologic traits associated with survival and reproduction (Soule and Cuzin-Roudy, 1982). Soule and Cuzin-Roudy suggest that the metabolic pathways of homozygotes may become congested by an accumulation of intermediate compounds at certain bottlenecks, thereby disrupting the functioning of other systems (Soule and Cuzin-Roudy, 1982:782).

Kobyliansky and Livshits (1986) tested the association between heterozygosity and morphological variability for bitrochanteric diameter, mesosternal chest circumference, stature, palm length and interocular diameter in a population of Ashkenazi Jews. They found a clear connection between the heterozygosity of blood group alleles and morphological variability, with heterozygotes exhibiting less variation.

This correlation did not hold true for all traits. For ridge-count asymmetry, heterozygotes had higher variability than homozygotes.

Kobyliansky and Livshits suggest two explanations for their observations.

First, measured heterozygosity could simply be a reflection of the general

14 heterozygosity of the genome. Second, stabilizing selection may produce co-adapted epigenotypes that favor greater genetic variety and dampens phenotypic variation (Kobyliansky and Livshits, 1986:262).

Clarke (1993) has argued against heterozygosity as a de facto basis for developmental stability. Many of the studies designed to investigate the relationship between developmental stability and heterozygosity fail to categorically state the level of heterozygosity and do not address other possible causes for instability (Clarke, 1993:17). At the same time, some studies have shown hybrids to be less stable than homozygotes from the same population (Clarke, 1993; Clarke and McKenzie, 1987; Hochwender and Fritz, 1999). Clarke concludes that there is little evidence to suggest that heterozygosity plays a significant role in the maintenance of developmental stability.

Not all traits exhibit the same level of developmental stability.

Several researchers (Soule and Cuzin-Roudy, 1982; Kimball et al. 1997;

Moller, 2002) have observed that ornamental traits (secondary sexual characteristics) such as coloration, length of plumage in birds, and stag beetle horn length exhibit greater variability than non-ornamental traits.

It has also been suggested that highly asymmetrical individuals may be of lower quality and less fertile than less variant and more symmetrical members of the same species.

15 Kimball (1997) looked at the association between trait variability and male quality in ornamental and non-ornamental traits. In all comparisons, ornamental traits showed greater variability. Kimball suggests that directional selection leads to reductions in developmental stability in ornamental traits through sexual selection. In non- ornamental traits, stabilizing selection should favor individuals near the mean who are of higher quality and less variation (Kimball, 1997:453).

Moller (2002) investigated the association between developmental instability and sexual selection in stag beetles from Chernobyl. In areas contaminated with radiation, beetles exhibited significantly higher fluctuating asymmetry than beetles in the control even though the overall size of secondary sexual characteristics was not different (Moller,

2002: 198). Moller found that mated vs. unmated beetles differed in their degree of fluctuating asymmetry but not size, indicating some evidence for selection against males with asymmetric horns (Moller,

2002:202). Similar findings were observed among the damselfly Ischnura elegans (Carchini et al. 2000). Males of this species with less fluctuating asymmetry had greater mating success. This correlation held for wing length but not variability in the number of setae on the legs, leading

Carchini et al. (2000) to conclude that the wing is under more selective control.

16 Among water boatmen (Callicorixa vulnerata) no association between sex and increased fluctuating asymmetry was found (Nosil and

Reichman, 2001). Survival rate, however, was associated with nutritional condition and fluctuating asymmetry was negatively associated with nutritional condition. Nosil and Reichman (Nosil and

Reichman, 2001:718) suggest that increased fluctuating asymmetry leads to reduced feeding ability that in turn reduces nutritional reserves.

The reduction in nutritional reserves leads directly to a reduction in fitness.

These studies suggest that stabilizing selection reduces developmental noise. Secondary sexual characteristics, on the other hand, exhibit greater variability and may be strongly influenced by directional selection. There is also some evidence that more fit individuals, especially males, exhibit less developmental noise than their less fit conspecifics. Measurement of developmental stability should, therefore, be made from morphological and not secondary sex traits.

Bilateral traits may deviate from perfect symmetry in three ways

(Van Valen, 1962). Directional asymmetry is the normally greater development of a structure or structures on one side or the other (Van

Valen, 1962:125). Antisymmetry results in greater development of a characteristic on one particular side, but the side in question is variable.

Finally, fluctuating asymmetry results from the inability of an organism

17 to develop along precisely determined paths, resulting in a binomial (or normal) distribution of differences between sides (Van Valen, 1962:126).

One, two or all three forms of asymmetry may influence concordance between antimeres.

Many researchers have investigated the causes of deviations from perfect symmetry. It has largely been concluded that both directional asymmetry and antisymmetry are genetic in nature (Roff and Reale,

2004; Graham et al, 1998; Palmer and Strobeck, 1997; Van Valen 1962).

Fluctuating asymmetry, on the other hand, has been shown to reflect stochastic deviations from perfect symmetry due to environmental and/or epigenetic stress.

The exact causes of stochastic variation in right-left symmetry are still not completely understood, although a number of candidate theories have been proposed. Nijhout and Davidowitz (2003) have suggested three possible causes. First, near threshold values of certain metabolic components might translate into a “coin-flip” between two alternate chemical pathways (Nijhout and Davidowitz, 2003:10). Second, if transcription factors are produced at very low concentrations they may be unevenly distributed, resulting in differential development of otherwise symmetrical traits (Nijhout and Davidowitz, 2003:11). Finally,

18 random variation may result from chaotic processes; small variations in the initial conditions of a structure may lead to greater differences later in development (Nijhout and Davidowitz, 2003:11).

Klingenberg (2003) also cites activation problems at the molecular level as a likely cause of developmental noise. There may be delays in the transportation of molecules between the cytoplasm and the nucleus introducing random delays in the transcription of proteins. This would create variations in the concentration of proteins and an oscillation between production and effect. According to Klingenberg, this condition would favor cellular mechanisms that reduce these oscillations. Heat- shock proteins that prevent other proteins from becoming denatured may be one example of a cellular mechanism providing developmental stability (Kilngenberg, 2003:30).

Emlen, et al. (2003) have proposed a basic model for developmental noise based upon perturbation of molecular signals. Under this model, developmental systems evolve toward energetically adaptive levels of organization and are effectively in homeostasis (Emlen, et al. 2003:53).

Stress forces these systems to become less efficient which requires more energy and produces more excess molecular motion (heat). Heat disrupts molecular interactions giving rise to asymmetries in structures otherwise destined to be symmetrical (Emlen, et al. 2003:56). This model is supported by the observation of many researchers that heterozygotes

19 exhibit greater vigor and less asymmetry (King, 1961; Soule and Cuzin-

Roudy, 1982; Kobyliansky and Livshits, 1986). Since heterozygotes express different alleles, they are less likely to deviate enough from the mean trait value to produce significant asymmetry.

Controversy remains as to whether fluctuating asymmetry may have an additive genetic component. Moller and Thornhill (1997) in a meta- analysis of studies on developmental stability concluded that the overall mean effect size of heritability was h2=0.27. Palmer and Strobeck (1997) have questioned these results. They attribute previous findings of a significant genetic component for fluctuating asymmetry to small sample sizes and lack of statistical rigor.

Woods et al. (1999) tested the heritability of additive genetic variance in Drosophila melanogaster and found no evidence for additive genetic variance (Woods, et al. 1999:498). These results are supported by several studies. Klingenberg and Nishout (1999) simulated a six-locus trait with randomly fluctuating right and left side asymmetries. They found no evidence for a gene-based control of fluctuating asymmetry.

Polak and Starmer (2001) investigated heritability of positional and non- positional fluctuating asymmetry (PFA) in Drosophila fellini. While significant heritability in PFA was detected, heritability of fluctuating asymmetry was found to be only 0.007 to 0.024 (Polak and Starmer,

2001:507). These conclusions refute Moller and Thornhill's assertion

20 that fluctuating asymmetry may have a large additive genetic component.

It is now generally accepted that the additive genetic component of fluctuating asymmetry, if anything, is less than 0.03.

Statistical Analysis of Fluctuating Asymmetry

A number of theoretical and methodological problems prevent the straightforward analysis of developmental stability by means of fluctuating asymmetry. This section will examine sample considerations, the selection of an index for fluctuating asymmetry, detection of fluctuating asymmetry, antisymmetry and directional asymmetry, and problems with measurement error. It will conclude with a review of human and non-human studies of fluctuating asymmetry.

Studies of fluctuating asymmetry that pre-date 1986 (Bader, 1965;

Garn, 1966; Bailit et al. 1970; Perzigian, 1977; DiBernardo, 1978; Sciulli et al., 1979) took relatively simple approaches to the detection and estimation of asymmetry. These studies typically employed correlation coefficients between right and left sided traits (Bader, 1965; Bailit et al.

1970; Garn, 1966; Perzigian, 1977; DiBernardo, 1978) or an analysis of variance (Sciulli et al. 1979). Many of these studies found significant associations between estimated levels of asymmetry and various stressors.

21 None of these studies, however, controlled for all of the factors that might confound the detection of fluctuating asymmetry. In only three of the six aforementioned studies was an attempt made to detect directional asymmetry (Garn, 1966; Perzigian, 1977; Sciulli et al., 1979). Of these studies only one tested for the presence of significant antisymmetry

(Sciulli et al., 1979); none of the studies estimated the amount of variance contributed by measurement error. These shortcomings, when combined with small sample sizes in some of the studies, lead to low statistical power and a general lack of confidence in the published results.

A. Richard Palmer and Curtis Strobeck have addressed these and other problems in a series of articles dating back to 1986 (Palmer and

Strobeck 1986; 1992; 1997; 2003a, 2003b). Palmer and Strobeck (1986) tested a series of indexes for fluctuating asymmetry against artificially generated samples with varying degrees of fluctuating asymmetry, directional asymmetry and antisymmetry. Their results show differences in the ability of various index values and statistical tests to differentiate between types of asymmetry and measurement error. The degree of asymmetry of a particular trait in an individual is a combination of directional asymmetry, genotype and measurement error. The remainder of the variance between sides is due to either antisymmetry or fluctuating asymmetry.

22 In this model (Table 1) the effects of fluctuating asymmetry and antisymmetry are indistinguishable from one another and measurement error will always be some fraction of the total observed variance (Palmer and Strobeck, 1986:405).

Single individual per genotype

Sides (S) MSs (S-1) 2 2 J 2 Directional " m +M(" i + #" ) S "1 asymmetry

2 2 2 Individuals (J) MSj (J-1) Size or shape " m +M(" i +S" j ) ! ! variation ! !

2 2 Remainder MSsj (S-1)(J-1) Nondirectional " m +M" i ! ! ! asymmetry (Antisymmetry I, II +FA/2) ! ! 2 Measurements MSm SJ(M-1) Measurement " m (M) error

Table 1: Relationship of Variance! to Asymmetry (modified from Palmer and Strobeck, 1986:405)

Palmer and Strobeck have made a number of suggestions on how these issues might be addressed (Palmer and Strobeck, 1986, 1992,

2003a, 2003b). Preferred traits for use in analyses of fluctuating asymmetry should have high repeatability, low plasticity, low vulnerability to changes due to wear, geometric independence and no predictable size dependence (Palmer and Strobeck, 2003a:285). The plasticity of bone due to nutritional deficiencies, disease and remodeling 23 make it inappropriate for use in studies of fluctuating asymmetry, as is most soft tissue. Teeth, on the other hand, are ideal for studies of fluctuating asymmetry since they are durable, not remodeled and have little predictable size dependence.

Measure of asymmetry for a given trait of individual i

2 2 FA10a: 0.798 " i where " i = (MSsj- MSm)/M

2 2 FA10b:! 0.798 " i ! where " i = FA10a, but data are analyzed as log transformed replicate measurements

! ! Table 2: Indexes of Asymmetry (from Palmer and Strobeck, 2003a:286)

Indices of asymmetry vary greatly in their sensitivity to deviations from normality (Table 2). FA1 and FA2 are less affected by skew or leptokurtosis than FA4a or FA6a. FA2, FA3, FA6a, FA7a, FA8a and

FA10b are dimensionless and and allow direct comparison of traits of different overall sizes (Palmer and Strobeck, 2003: 285). FA10a and

FA10b describe the average difference between sides after measurement error has been factored out.

The most frequently used index is FA2, [|R-L|/(R+L)/2]. This index minimizes variation due to size dependence effects (the greater variation of larger traits). Palmer and Strobeck recommend caution when applying this index, since arbitrary removal of trait size effects may obscure biologically relevant differences in fluctuating asymmetry

(Palmer and Strobeck, 2003a:288). All of these indices can be artificially inflated by the presence of antisymmetry or directional asymmetry.

Therefore, tests for normality (specifically, for the presence of platykurtosis) must precede the analysis of differences between groups

(Palmer and Strobeck, 2003a:286).

A serious problem with past studies of fluctuating asymmetry has been the inclusion of measurement error in estimates. Measurement error can either artificially inflate estimates of fluctuating asymmetry or completely obscure its detection (Palmer and Strobeck, 1994).

25 Measurement error among larger individuals or traits will be a smaller proportion of between-sides variation, thus making large individuals or traits appear less asymmetrical than smaller individuals or traits

(Palmer and Strobeck, 2003:289). Without its removal, a valid measure of fluctuating asymmetry cannot be made.

Testing whether fluctuating asymmetry varies significantly between samples is essentially testing whether the variances are homogeneous among groups (Palmer and Strobeck, 1986:408). While correlation coefficients measure the strength of a linear relationship, and right and left sided traits should, by definition, be very linear, this method has two major drawbacks. First, correlation coefficients rely on both mean and standard deviation for their calculation and are therefore sensitive to outliers (Moore and McCabe, 1999:129). Second, there is no way to discriminate between variation caused by fluctuating asymmetry or by antisymmetry and/or measurement error. These problems may cause over or under estimation of fluctuating asymmetry between samples.

Palmer and Strobeck (1986) have shown that a two-way mixed model ANOVA offers the best ability to distinguish differences in fluctuating asymmetry between samples. The ANOVA tests the null hypothesis that the variances between samples are equal. A two-way mixed model ANOVA (sides = fixed, individuals = random) has several advantages over other statistical tests (Palmer and Strobeck, 2003a:300).

26 First, it tests for the significance of directional asymmetry. Second, it allows an estimate of repeatability to be computed. Third, and most important, it is the only test that provides an estimate of fluctuating asymmetry with measurement error factored out.

There are also several disadvantages to the ANOVA. The ANOVA is sensitive to deviations from normality (such as antisymmetry). Likewise, when variances are small (as might be expected with studies of fluctuating asymmetry) large sample sizes are necessary to achieve acceptable statistical power. Even when variances are large, small sample sizes can make estimation of asymmetry problematic. A sample size of 40 would be necessary to detect a two-fold difference between variances only 75% of the time; sample sizes in excess of 120 would be necessary to achieve a 90% statistical power (Palmer and Strobeck,

1986:408).

Based upon these considerations, Palmer and Strobeck (2003b) have recommended the following methods for the analysis of fluctuating asymmetry:

1. All measurements should be taken by the same individual with 1st and 2nd measurements taken blindly and on separate days.

2. Data should be visually inspected for outliers. Outliers should be tested to see whether they are more deviant than by chance alone (for instance, by measurement error). If outliers are attributable to error they should be measured again (Palmer and Strobeck, 2003b: 7).

27 3. Data should be tested to determine if asymmetries are greater than measurement error. This can be tested with a two-way ANOVA of sides x individuals (once outliers have been removed).

4. Measurement error should be tested for heterogeneity among samples using a Levene's test (Palmer and Strobeck, 2003b:9). If heterogeneity is absent, no correction for measurement error among samples is necessary.

5. Size corrections (see index FA2, Table 2) should only be made if significant size effects are present, since this correction may inflate estimates of fluctuating asymmetry (Palmer and Strobeck, 2003b:10). This can be tested by examining scatter plots of trait asymmetry |R-L| vs. trait size [(R+L)/2] and by regression analysis. In the absence of size effects FA1 (mean |R-L|) or FA8 (mean |ln(R-L)| are more appropriate indexes.

6. Data should be tested for the presence or absence of both directional asymmetry and antisymmetry. Directional asymmetry can be tested with a t-test between sides. In cases where directional asymmetry is small (when directional asymmetry is 10-20% of the mean directional asymmetry) it is unlikely to confound estimates of developmental instability and can be ignored (Palmer and Strobeck, 2003a:301). For antisymmetry, skew and kurtosis values should be computed and compared to a set of critical values (see Appendix A). Bimodal or platykurtic distributions indicate the presence of antisymmetry. Under these conditions, a true value of fluctuating asymmetry cannot be computed.

7. In the absence of antisymmetry, once outliers and trait size dependence have been addressed, fluctuating asymmetry can be tested for significant differences between samples. A multi-way Levene's test provides a simple and robust test for FA differences among groups (Palmer and Strobeck, 2003b:12).

Van Dongen et al. (1999) have proposed an alternative method for the statistical analysis of fluctuating asymmetry using restricted maximum likelihood (REML) parameter estimation. This procedure yields exactly the same estimates of fluctuating asymmetry as the two-

28 way mixed model ANOVA, but is less cumbersome. It has several other advantages as well. First, it tests whether estimates of fluctuating asymmetry are significantly different from zero, something that the

Palmer and Strobeck method does not. Second, it models and tests heterogeneity for both fluctuating asymmetry and measurement error.

Third, the procedure tests for non-zero directional asymmetry. Finally,

REML parameter estimation allows unbiased estimation of individual levels of fluctuating asymmetry (Van Dongen, et al., 1999:100). REML may also provide greater statistical power when sample sizes are small by the inclusion of more than two replicated measurements (Van Dongen et al., 1999:97). Despite the advantages of REML, Palmer and Strobeck’s method was chosen for the current study because it was better documented.

Studies of Fluctuating Asymmetry

Fluctuating asymmetry has been used in both human and non- human studies to investigate the causal link between stress and developmental instability. The following is a review of the recent research into this topic.

The effects of chemical, genetic and environmental stressors on fluctuating asymmetry have been investigated for a number of plants and animals. Clarke and McKenzie (1987) investigated the resistance of the

Australian blowfly (Lucilia uprina) to diazinon, an organophosphate and

29 pesticide. They found that phenotypes of the blowfly resistant to diazinon exhibited higher levels of fluctuating asymmetry than non- resistant phenotypes, but that after several generations levels of fluctuating asymmetry returned to normal. Clarke and McKenzie suggest that new traits disrupt canalization, but that over time natural selection will fine-tune new adaptations returning the organism to optimized canalization (Clarke and McKenzie, 1987:346).

Leamy et al. (1998) investigated the effects of methooxychlor

(MXC), a component of DDT, on the development of mouse mandibles.

Increasing levels of MXC led to increased fluctuating asymmetry in the mandibular corpus and teeth. In the first molar, rising levels of MXC eventually produced directional asymmetry. These effects were limited to the mandibular corpus and not the mandibular ramus. The results of

Leamy et al. (1998:70) show that fluctuating asymmetry increases with stress and that it may not affect all parts of an organism equally.

Eriksen et al. (2003) studied the effects of prenatal stress and fluctuating asymmetry in chickens. Stress was simulated by injecting corticosterone, a hormone associated with elevated stress levels in hens, into an experimental group of eggs. While no differences in hatching weight were found, the group exposed to corticosternone exhibited smaller average trait sizes and greater levels of fluctuating asymmetry than the control group (Eriksen, et al., 2003:693). As with mice

30 mandibles and MXC, increased fluctuating asymmetry was not found in all of the traits under study. Eriksen et al. attributed the differential expression of fluctuating asymmetry to the fact that both traits were small, making measurement error a greater proportion of total asymmetry (Eriksen, et al., 2003:696).

Environmental and genetic stressors also can induce elevated levels of fluctuating asymmetry. Hochwender and Fritz (1999) tested two competing hypotheses about developmental stability by examining the effects of stress on willow hybrids (Hochwender and Fritz, 1999:409).

They found that hybrids exhibited greater fluctuating asymmetry than parental species, and that F2 hybrids were more asymmetrical than F1 hybrids (Hochwender and Fritz, 1999:412). They did not, however, find any association between water and pathogen stress and fluctuating asymmetry, suggesting that the hybrids may be insensitive to these stressors (Hochwender and Fritz, 1999:414). The observations suggest that developmental stability is the result of a genic balance established over evolutionary time. These results support the genomic coadaptation hypothesis.

Woods et al. (1999) studied the relationship between multiple environmental stresses and fluctuating asymmetry in Drosophila melanogaster. They found no evidence for a genetic component of additive genetic variance (Woods, et al., 1999:498). As with the

31 aforementioned studies, fluctuating asymmetry increased in only one of the five traits under study. This led Woods et al. to the following conclusions. First, not all traits are reliable indicators of stress. Second, the selective mortality of highly asymmetrical individuals may bias a sample. Finally, changes in fluctuating asymmetry may be stress specific. They suggest limiting the study of fluctuating asymmetry to genetically conserved features (Woods et al., 1999:502). These results do not support the use of fluctuating asymmetry as a reliable indicator of stress in all circumstances.

Fluctuating asymmetry has also been studied extensively in the soft tissue, skeleton and the dentition of human populations. Livshits et al. (1988) measured fluctuating asymmetry for eight morphometric traits

(upper limbs: biepicondylar breadth, bistyloid breadth, hand breadth. lower limbs: bicondylar breadth, bimalleolar breadth, foot breadth in addition to ear breadth and ear length) in pre-term, term infants, and their parents in Israel. Both fluctuating asymmetry and the coefficient of variation were higher in pre-term infants and their parents than in full- term infants. Similarly, both fluctuating asymmetry and the coefficient of variation decreased with gestational age (Livshits, et al., 1988:797).

These findings imply a relationship between fluctuating asymmetry and developmental stability in infants. Livshits et al. suggest that progress in health care has reduced the intensity of stabilizing selection, possibly

32 leading to a breakdown of developmental homeostasis (Livshits et al.,

1988:794). They caution that for these conclusions to be valid the environmental contribution to fluctuating asymmetry must first be understood (Livshits et al., 1988:804).

Dittmar (1998) studied the fluctuating asymmetry of ridge counts in Andean Indians and compared them with several other populations.

Dermatoglyphs are ideal for studies of fluctuating asymmetry, since they can be easily recorded and are not altered by the postnatal environment

(Dittmar, 1998:378). No significant differences between males and females were found, refuting the assertion that males may be less canalized than females. Only two ridge counts exhibited significant fluctuating asymmetry in females and none in males (Dittmar,

1998:380). Between populations, the data suggest a cline of decreasing asymmetry and diversity values from Europe through Africa (Dittmar,

1998:386). The lack of significance reported by Dittmar when using ridge counts is supported by an earlier study by Jantz (1975) and suggests that digital ridge counts may be too variable and thus inappropriate for analysis of asymmetry.

Flinn et al. (1999) compared the growth and fluctuating asymmetry of stepchildren and non-stepchildren in a rural village on the east coast of Dominica. Prior research suggested that stepchildren were under greater stress, as evidenced by elevated levels of cortisol (Flinn, et al.,

33 1999:466). Contrary to expectations, observed fluctuating asymmetry was actually lower in stepchildren than non-stepchildren (Flinn, et al.,

1999:475). They suggest that the results may be associated with other factors, such as inbreeding, duration of breastfeeding, illness and the pace of growth (Flinn et al., 1999:477). The presence of antisymmetry

(0.025%) in the sample may also have influenced these estimates (Flinn et al., 1999: 474).

A study of the Hadza of Tanzania found significant levels of asymmetry for measurements of the ear, elbow width, wrist width, finger lengths, ankle width and foot width (Gray and Marlowe, 2002). In comparison to a sample of US college males and females, Hadza men had significantly higher levels of fluctuating asymmetry (Gray and Marlowe,

2002:497). Hadza females also exhibited higher levels of fluctuating asymmetry than Hadza males, while in the US sample no difference between the sexes was detected. In addition, fluctuating asymmetry was significantly correlated with age among the Hadza, but not with age among the US sample. This lack of an association between age and fluctuating asymmetry in the US may be due to the limited age range of the sample (Gray and Marlowe, 2002:498). Gray and Marlowe propose that greater physical stress, especially among Hadza females, contributes to the higher fluctuating asymmetry among this group.

34 The human dentition has been used extensively in studies of fluctuating asymmetry. Townsend and Brown (1980) studied asymmetry in dental casts from several hundred Australian Aborigines. Levels of fluctuating asymmetry were then compared to an earlier study by Bailit et al. (1970) in which dental dimensions from four populations

(Tristanites, Nasioi, Kwaio and Boston Children) were analyzed.

Townsend and Brown found no evidence for a genetic component of fluctuating asymmetry. Two of their conclusions were significant. First, asymmetry increased from the mesial to more distal teeth, suggesting differential levels of developmental instability during odontogenesis

(Townsend and Brown, 1980:661-673). Second, males tended to have more asymmetry than females, and Aboriginal females tended to have less asymmetry than Kwaio females, although there was no significant difference between Aboriginal males and Kwaio males (Townsend and

Brown, 1980:664). These results are similar to those of Gray and

Marlowe (2002) and the Hadza.

Noss et al. (1983) tested several interrelated hypotheses about fluctuating asymmetry using the molar dimensions of Pima Indians. No significant differences were found in the expression of fluctuating asymmetry between the sexes; nor was evidence found for a relationship between molar size and morphological asymmetry (Noss et al., 1983:439).

Evidence was found for greater variation in the second molar compared

35 to the first molar (Noss et al., 1983:439). This supports Townsend and

Brown (1980) and suggests that more distal, later-developing teeth are exposed to greater environmental perturbation.

Julius Kieser has studied fluctuating asymmetry in a number of populations. Kieser et al. (1986) looked at antimeric differences in the teeth of 202 Leguna Indians in Paraguay. As with other studies, they found no difference in asymmetry between the sexes (Kieser, et al.,

1986:495). First molars and canines were found to be the least asymmetric, while the third molar exhibited the greatest amount of asymmetry. Likewise, older individuals had more asymmetry than younger individuals. Kieser et al. suggest that older individuals may have had less access to modern medicine during their tooth-forming years (Kieser, et al., 1986:495). Leguna Indians displayed similar levels of asymmetry to the Ticuna Indians of Columbia, but significantly higher levels than those from industrialized populations (Kieser et al.,

1986:496). These findings are similar to those of Bailit (1970) and

Townsend and Brown (1980).

Kieser (Kieser et al., 1997; Kieser and Groenveld, 1994) investigated the link between tobacco smoke and developmental stability in children. No evidence was found for a connection between smoking and increased dental asymmetry in a sample of children from upper middle class families (Kieser and Groenveld, 1994). A connection was

36 found between obesity and increased dental asymmetry. Children of obese women, whether the woman smoked or not, had significantly higher levels of fluctuating asymmetry (Kieser, et al., 1997:135). In addition, Kieser et al. determined that the canine was the least asymmetrical and most canalized tooth (Kieser, et al., 1997:135). These findings support previous studies that demonstrate the lack of an organism-wide response to stress (Woods, et al., 1990; Dittmar, 1998;

Eriksen et al., 2003).

Sciulli (2003) estimated fluctuating asymmetry for a sample of Late

Archaic and Late Prehistoric individuals from the Ohio River Valley.

These estimates were compared with the duration of time spent in soft tissue and the coefficient of variation for each sample. Both the Late

Archaic and Late Prehistoric samples exhibited directional asymmetry and significant fluctuating asymmetry, although there was no significant difference between the populations (Sciulli, 2003:39). These results suggest that stress levels in the Ohio River Valley were not increased significantly with the introduction of maize agriculture.

Recently, Corrucini et al. (2005) investigated the relationship between enamel hypoplasia and asymmetry in a large sample of

Australian twins. They found no differences between males and females or between monozygotic or dizygotic twins for either hypoplasia or asymmetry (Corrcucini et al., 2005:179). They also failed to find any

37 relationship between the occurrence of hypoplasia and increased fluctuating asymmetry, although hypoplastic individuals exhibited greater variability for asymmetry measures. Corrucini et al. suggest that their approach will be more profitable in prehistoric samples with more infection and poorer nutrition (Corrucini et al., 2005:180).

In summary, fluctuating asymmetry is the result of small growth disruptions during ontogeny and is a measure of developmental noise.

While it has great potential as a marker of individual or population wide stress, any analysis of fluctuating asymmetry must proceed with caution.

Many early attempts at quantifying fluctuating asymmetry failed to differentiate measurement error, antisymmetry or directional asymmetry.

A number of studies also failed to show consistent cause and effect relationships between specific stressors and increasing levels of fluctuating asymmetry. Teeth are the most promising medium for studying fluctuating asymmetry since they are resistant to wear and do not remodel. Previous studies of fluctuating asymmetry using teeth have, however, produced mixed results. The steady improvement in methods and an appreciation for the many factors that may confound analysis suggest that fluctuating asymmetry is still an appropriate tool for assessing developmental stability.

38 Dental Development

Dental development is among the most conserved evolutionary processes in human ontogeny and is under strict genetic control (Scott and Turner, 1997; Thesleff, 2000). Severe genetic or environmental stressors are necessary to cause deviation from target phenotypes.

Teeth, therefore, are an excellent medium for the study of developmental stability and stress. This section will review the molecular and histological development of the human dentition and the biological basis for crown asymmetry and enamel defects.

Odontogenesis is controlled by an interaction between mesenchymal and epithelial tissues (Thesleff and Sharpe, 1997:114).

Initiation of dental growth begins six weeks after conception when mesenchymal cells proliferate into arch-shaped zones in the developing mandible and maxilla (Hillson, 1996:118). Epithelium then grows into the two zones producing the primary epithelial band that divides into two lobes: the vestibular and dental lamina. The dental lamina will eventually become the deciduous teeth. Enamel organs for the permanent dentition begin to form around the sixteenth week after fertilization with the latest of them appearing only after birth (Hillson,

1996:118).

39 Until recently, little was known about how the various tooth types were determined. Several theories have been proposed to explain how the four classes of teeth develop. Dahlberg suggested an elaboration of

Butler’s field theory in which more distally located teeth experience lower concentrations of chemical morphogens than mesial teeth resulting in a loss of precise developmental control (Thesleff and Sharpe, 1997; Scott and Turner, 1997:102). This theory predicts that distal teeth should be more variable in size, shape and cusp pattern and has been supported by a number of studies (Bailit et al., 1970; Potter and Nance, 1976;

Perzigian, 1977; Townsend and Brown, 1980; Kieser, 1986).

Sharpe (Thesleff and Sharpe, 1997; McCollum and Sharpe, 2001;

Ohazama and Sharpe, 2004) has shown that tooth type is determined by the interaction of signaling molecules and homeobox factors present in the mesenchymal and epithelial tissues of the developing mandible and maxilla. Homeobox-containing genes Alx-3, Msx-1 and 2, Barx1 and Dlx

1,2 are present in the dental mesenchyme and provide the spatial information necessary to determine tooth type (see Figure 1). In experiments with knockout mice, mice lacking Dlx1 or Dlx2 failed to develop maxillary molars while all other teeth were the same (Ohazama and Sharpe, 2004:513).

Figure 1: The Odontogenic Homeobox Code (A) in Mus musculus (B) and in humans (C). (from McCollum and Sharpe, 2001:483)

Signaling molecules of the fibroblast growth factor (FGF) and bone morphogenetic protein (BMP) families induce expression of these homeobox genes. FGF8 and BMP4 both induce expression of Msx1 and

Dlx1 in the mesenchyme. For the transcription factor Pax9, FGF8 induces expression while BMP4 antagonizes the signal. This is necessary for the tooth to develop beyond the bud stage (McCollum and Sharper,

2001:483). The expression of exclusively distal homeobox-containing genes by inhibition of epithelial BMP-4 using Noggin, a BMP2/4 41 antagonist, causes formation of multi-cusped teeth in place of incisors

(McCollum and Sharpe, 2001:484). These observations show that the type and location of teeth is controlled by specific genes and refutes the premise of Dahlberg’s modified field theory (Thesleff and Sharpe, 1997).

Signaling molecules from the BMP, FGF, Hh and Wnt families are also critical for the determination of tooth shape. Three distinct stages of development follow the regional determination of the teeth: the bud stage, the cap stage and the bell stage. The formation of a tooth bud signals the start of crown morphogenesis (Jernvall and Thesleff,

2000:21). During the bud stage the enamel organ bud develops a hollow filled with mesenchyme called the dental papilla while the mesenchyme forms a bag like structure called the dental follicle (Hillson, 1996:118).

The papilla will eventually form the dentine of the tooth, while the follicle will form the cementum. BMP4 most likely induces the transition from the bud to cap stage (Jernvall and Thesleff, 2000:23).

During the cap stage, the tooth bud folds at its tip surrounding the mesenchymal dental papilla. The site at the tip of the tooth bud where the folding starts marks the formation of the primary enamel knot

(Jernvall and Thesleff, 2000:23). 24 hours after formation, the enamel knot is apoptotically removed in response to BMP4. Shortly after, a number of secondary enamel knots appear where the cusps will later form (McCollum and Sharpe, 2001:486). Molecules from the Shh family

42 and BMP4 may inhibit the FGF4 signal, creating spaces between the cusps. During the bell stage the hollow becomes deeper. The ultimate shape of the crown results from the rapid cell proliferation and folding of the epithelium that occurs during both the cap and bell stage (Jernvall and Thesleff, 2000:23).

Several factors may interfere with the aforementioned process.

Kronmiller (1995) showed that exogenous sources of epithelial growth factor (EGF) altered the pattern of dental lamina in mice (Kronmiller,

1995:142). Kronmiller et al. (Kronmiller et al., 1992, 1994) have shown that the presence of retinoids may interfere with these molecular signals.

Kronmiller et al. (1994) showed that exposure to retinal produced much the same effect as EGF (Kronmiller et al., 1994:843). The effects of retinoids appear to produce a dose-response effect, at least up to a point

(Kronmiller, 1992:135). Lu, Jin and Tipoe (Lu, et al., 2000:136) have shown that another retinoid, retinoic acid, interferes with BMP2,4 and 5, and blocks a positive signal for BMP4 which allows cell division to continue unimpeded. The under or overabundance of retinoids in the environment, then, may strongly influence the ultimate size and shape of the tooth.

Epithelial cells in the bell stage eventually differentiate into ameloblasts that produce enamel, while mesenchymal cells differentiate into odonotblasts that produce dentin. The process of odontoblastic

43 differentiation appears to be independent of crown shape (Lisi, et al.,

2003). Enamel and dentin formation begins at the border between the ameloblasts and odnotoblasts with the odnotoblasts forming the dentin matrix (Goodman and Rose, 1991:280). Ameloblasts secrete the enamel proteins amelogenin and enamelin that form the enamel matrix (Scott and Turner, 1997:78). In the early stages of enamel formation the matrix contains 20% protein, 5-10% calcium salts and a high water content

(Nikiforuk and Fraser, 1981:891). Ameloblasts eventually change function, causing the enamel matrix to lose protein and water, becoming

97% calcified salt (Goodman and Rose, 1991:281).

Disturbance of amelogenesis can only occur prior to mineralization during enamel matrix formation (Nikiforuk and Fraser, 1981:891). Three types of enamel defects can occur: hypoplasias, opacities and discolorations. Opacities are caused by a disruption of mineralization at the maturation stage (Hillson, 1996:165). Discolorations are caused by deposits of pigments due to metabolic disorders (Hillson, 1996:165).

Hypoplastic defects occur along the same lateral bands as perikymata

(ridges in the enamel) and represent disruption to matrix secretion

(Hillson, 1996:165). This disruption is caused by ameloblastic cell death

(Larsen, 1987:365).

44 Hypoplasias refer to a variety of defects in enamel thickness resulting from disruptions in enamel formation. Three causes of hypolasia have been identified (Goodman and Rose, 1991:281).

Hereditary anomalies producing hypoplasia include hereditary rickets and hypoparathyroidism (Nikiforuk and Fraser, 1981:888). Localized trauma caused by injury or teething behaviors in infants and toddlers may also cause hypoplasia. Skinner and Hung (1989) found significant numbers of hypoplasias caused by minor physical trauma associated with mouthing behaviors six months after birth. The thin bones of the mandible and maxilla provide little protection for the developing crowns and especially the canine (Skinner and Hung, 1989). These findings may be associated with reductions in breastfeeding.

Numerous systemic stressors are associated with the occurrence of hypoplasia, including low socioeconomic status (Goodman and Rose,

1991:283), vitamin A, C and D deficiencies (Nikiforuk and Fraser, 1981;

Hillson, 1996; Lukacs, et al., 1998:577) and infectious diseases such as measles and smallpox (Hillson, 1996:165). A number of anthropological studies have suggested a causal link between dietary changes and the frequency of linear enamel hypoplasia in prehistoric populations worldwide. Systemic metabolic stress has often been cited as a primary cause of enamel hypoplasia in prehistoric populations since individuals

45 with hereditary defects and local trauma are rare (Goodman and Rose,

1991:281; Lukacs, et al., 1998:577). These findings will be addressed in the next section.

In summary, the development of the human dentition is a highly conserved process with low trait heritability. Few hereditary or environmental factors can alter the trajectory of dental growth and development. Factors that can influence the size and shape of teeth occur during the cap or bell stage and likely interfere with signals between mesenchymal and epithelial tissues. Disruptions to enamel formation are well documented and include a number of nutritional, environmental and genetic causes. These findings suggest that hypoplastic defects will be more sensitive to periodic stress while dental asymmetry will only occur during serious, acute periods of stress during crown formation.

Interpretation of Health From Archaeological Samples

The World Health Organization defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (WHO, 1946:100). Health status may be determined for living populations using standard growth curves, tests of immunocompetency, fertility, mortality and nutritional status. The absence of direct indicators of these parameters from the archaeological record makes interpretation of health in prehistoric populations difficult.

46 Wood et al. (1992) have asserted that these problems make contradictory interpretations of prehistoric health equivocal, and suggest that studies documenting a decline in health with the advent of maize agriculture may just as likely indicate increases in health.

This section will review the skeletal and dental indicators that correspond to stress and review the application of these stress indicators to archaeological populations from Eastern North America and the Ohio

Valley. A review of the Osteological Paradox and its relevance to the current study will also be discussed.

Figure 2: Model for Interpretation of Skeletal Stress Indicators (from Goodman, et al. 1984:14)

Goodman et al. (1984) have modeled health as a system of resources, stressors and cultural buffers/stressors that mediate the expression of skeletal and dental responses (see Figure 2). In this model, cultural systems operate as both a buffering system and as a producer of new stresses and constraints (Goodman and Martin, 2002:17).

Agriculture can be seen as a cultural buffer, providing resistance to

47 malnutrition. Since agriculture leads to increases in population density it also functions as a stress. The impact of stress on the population may include decreased health, decreased work capacity, decreased reproductive capacity, and socio-cultural disruption (Goodman and

Martin, 2002:17).

Stress indicators found on bone and teeth are limited in their ability to provide specific details about the time, duration, and the type of stress that produced them. Generally, these indicators represent either cumulative or episodic stress (Goodman, 1984:15). Skeletal or dental lesions associated with a specific stress are rare (Goodman and Martin,

2002).

Stature has long been used as an indicator of health status, nutrition and growth. In general, taller individuals tend to be healthier than shorter individuals (Meadows and Jantz, 1999; Crooks, 1999).

Factors influencing stature include disease status, genetics, nutritional status (Goodman and Martin, 2002:19), socioeconomic status (Crooks,

1999) and sex (Meadows and Jantz, 1999; Crooks, 1999). The release of catabolic hormones during periods of stress that inhibit the anabolic process of growth has been advanced as the proximate cause of this phenomenon (Goodman, et al. 1984:18).

48 Estimation of stature in sub-adults is usually inferred from the long bones. Small sample sizes, the frequent absence of the epiphyses and the inability to sex sub-adults make inferences from these remains problematic (Goodman and Martin, 2002:21). While adult skeletons are more numerous in archaeological samples and allow more accurate determination of sex, they suffer from several drawbacks. Adult skeletal elements represent the endpoint of a multi-decade process that may include periods of stress followed by catch-up growth. Likewise, the most stressed members of a population may never reach adulthood, biasing the sample (Goodman and Martin, 2002:22). Since stress that produces smaller statures due to a specific cause or limited resources looks the same, stature should be looked at as a cumulative, non-specific indicator of stress.

Reduced sexual dimorphism has been noted as a consequence of poor diets. Stini (1975) notes that in nutritionally stressed populations the skeleton grows more slowly, especially in males, and that epiphyseal fusion may not occur until the third decade of life (Stini, 1975:29). A number of researchers have suggested that males are more susceptible to environmental fluctuations. Since females have a greater capacity to store nutrients than males, this susceptibility will be manifested in a greater reduction in the skeletal dimensions of males than females

(Larsen, 1987:353).

49 Deborah Crooks in a study of high-poverty children in Eastern

Kentucky found a marked difference between height and weight in boys and girls (Crooks, 1999). Boys from poor families tended to be well above the reference means for weight, while girls tended to be above the reference means for height (Crooks, 1999:135). When conditions improved, both sexes increased in stature, with boys more so than girls.

This suggests a faster biological response to environmental improvement in males (Crooks, 1999:140).

Meadows and Jantz (1999) examined the length of long bones in males and females who died between 1800 and 1970. While they found no significant differences between “black” and “white” populations, they did find significant differences between males and females. “White” males exhibited size and shape changes for every bone while females showed none (Meadow and Jantz, 1999:63). Meadow and Jantz suggest two explanations for these observations. First, males may be more sensitive to environmental changes. Second, as the prenatal period and the first three years of life have improved over the last two centuries, males have benefited more than females (Meadow and Jantz, 1999:66).

These observations strongly support the use of sexual dimorphism as an indictor of dietary adequacy and suggest that long-term changes in sexual dimorphism are concordant with stress. Application of sexual dimorphism to archaeological samples is discussed in the next section.

50 Harris Lines and skeletal pathology represent two types of general episodic stress indicators. Harris lines are transverse areas of increased opacity (visible on an x-ray) spanning the medullary cavity of tubular bones or tracing the outlines of flat bones and indicate stress by deprivation of a relatively short duration (Larsen, 1987:362; Webb,

1995:114). Harris lines are produced when growth is arrested and followed by a period of recovery (Goodman, 1984:22). Defects identical to

Harris Lines have been produced in domesticated pigs through protein calorie malnutrition and are found at high frequencies (>70%) in children suffering from Kwashiorkor (Webb, 1995:115). The majority of Harris

Lines form in the first five years of life, peaking between two and three years of age.

Several factors prevent Harris lines from being a more useful indicator of stress. First, they often disappear through the process of bone remodeling (Larsen, 1987:363). Boys produce more Harris Lines than girls but have greater rates of resorption, possibly due to increased physical activity (Webb, 1995:115). Second, there is some indication that

Harris lines are too sensitive to stress; they may reach high frequencies when other indicators of stress are absent or they may be negatively correlated with other pathologies (Goodman et al., 1984; Webb,

1995:116). Harris Lines are generally considered useful only when used in conjunction with other stress indicators.

51 Skeletal lesions caused by nutritional deficiency or infection may represent a primary form of stress, or be the consequence of reduced immunocomeptency due to poor nutrition (Larsen, 1987:383; Shell-

Duncan, 1997). Porotic hypersotosis and cribra orbitalia are patches of porous bone that develop when the trabecular portion of flat bones expands, exposing the underlying trabeculum (Ortner and Putschar,

1981:257). These lesions may be caused by hemolytic anemia or by iron deficiency; in the New World the occurrence of porotic hyperostosis is correlated with dietary staples low in iron (Goodman and Martin,

2002:27). Vitamin deficiencies (A, C and D) may also cause specific changes to the skeleton. While these changes provide important information about the diet they represent months or years of accumulative effects. Like stature, these lesions may be incapable of addressing questions about specific periods of stress.

Infectious lesions are categorized according to how they affect the bone. Periostitis lesions are patches of remodeled bone on the cortical surface, usually representing a reaction to pathologic changes on the underlying bone (Ortner and Putschar, 1981:129). Osteomyelitis is infection of the medullary cavity with the presence of cloacae (“pus holes”) in dry bone and results in extensive remodeling (Ortner and

Putschar, 1981:106). These lesions can be distinguished from trauma induced periosteal reactions since they are more widely distributed and

52 are often destructive. The most common causes of skeletal lesions are staphylococcus and streptococcus, the effects of which are indistinguishable (Ortner and Putschar, 1981:106). Fungi, various cancers and some metabolic disorders may also produce nonspecific lesions. Determining the specific causes of bone lesions has been most successful for tuberculosis and treponemeal diseases (Goodman and

Martin, 2002:35).

Impairment of the immune system due to poor nutrition has been documented extensively and suggests a synergistic relationship between infection and nutrition; undernutrition lowers resistance and infection interferes with nutrition (Larsen, 1987:383). Iron deficiency may lower disease resistance making other skeletal lesions more common

(Goodman and Martin, 2002). Shell-Duncan (1997) looked at nutritional sufficiency and cellular immunosuppression in a group of sixty-two

Turkana children. Mild to moderate malnutrition was a strong predictor of anergia (lack of an immune response). These results support the hypothesis that nutritional stress and infection are independently associated with reduced immunocompetency (Shell-Duncan, 1997:387).

Two general problems prevent determination of specific stress agents from the skeleton. First, changes due to nutritional stress, infection, or the interaction between these factors are difficult if not impossible to distinguish. Second, some infections produce skeletal

53 lesions, but most short-term infections do not (Goodman and Martin,

2002:32). This means that a significant portion of stress due to infectious disease is probably not reflected in the archaeological record.

Linear enamel hypoplasia (LEH) represents interruptions and resumed growth in enamel formation and is characterized by single grooves or pits located on the tooth crown. Unlike many of the aforementioned stress indicators, LEH represents specific periods of stress during enamel formation (Webb, 1995:105; Larsen, 1987;

Goodman et al. 1984). Since tooth enamel is not remodeled, LEH provides a permanent indicator of stress, from twelve weeks prior to birth in the deciduous teeth and from birth to seven years in the permanent teeth (Goodman and Martin, 2002:23). Various methods exist for determining the precise age at which specific hypoplasias occur

(Goodman et al., 1980; Blakely and Armelagos, 1985; Sciulli, 1992).

Local hypoplasia of the primary canine (LHPC), a pitting of the labial surface of the deciduous canine, has also been associated with stress

(Lukacs and Walimbe, 1998).

The occurrence and timing of LEH has been studied in many prehistoric populations. In general, the majority of LEH occurs between the ages of three and five (Goodman and Rose, 1991; Webb, 1995:110).

This has been linked to dietary changes and stress associated with weaning (Blakely and Armelagos, 1985; Webb, 1995). Increased LHPC

54 has been reported for prehistoric populations in India (Lukacs and

Walimbe, 1998). Increased frequencies of LEH have also been observed for the Late Classic Maya (Storey, 1997) and in Native American maize agriculturalists (Goodman et al., 1980, 1984; Blakely and Armelagos,

1985; Larsen, 1987; Sciulli and Oberly, 2002). As a diagnostic tool LEH suffers from its inability to identify the specific causes of stress.

Likewise, hypoplasias may be underrepresented in individuals or populations with high tooth wear.

Identifying and quantifying stress in archaeological populations remains difficult, even after decades of advancement in bioarchaeology and paleopathology. Most methods suffer from either a lack of permanency or the susceptibility to multiple and sometimes contradictory analyses. Two indicators appear promising for the analysis of stress in skeletal populations. Sexual dimorphism represents the accumulative affects of stressors while LEH represents shorter, acute periods of non-specific stress. Used together, they may circumvent some of the problems that have hampered previous studies.

The publication of Paleopathology at the Origins of Agriculture

(Cohen and Armelagos, 1984) represents a watershed in bioarchaeology.

For the first time information about the changes in stress indicators associated with the transition to settled agriculture were brought together in a single volume.

55 Several conclusions are significant. First, the transition to agriculture from nomadic or semi-sedentary groups caused significant changes in the expression of skeletal and dental stress indicators worldwide.

Second, these changes appear to represent a reduction in overall health rather than an improvement. Especially in North America, where the transition to tropical domesticates such as maize, beans and squash was more recent, populations may have been far worse off than during earlier periods. These conclusions suggest that while agriculture may have increased fertility it also resulted in populations living with greater amounts of disease.

Major subsistence changes in the Ohio River Valley took place in the Late Archaic and Late Prehistoric periods. During the Late Archaic

(ca. 6000-3000 BP), modern ecological conditions were becoming established in the Ohio Valley that lead to population growth and expansion (Brose, 1979). In general, the Late Archaic is a period of

“settling down” during which formerly mobile hunters and gatherers began to occupy habitation sites for multiple seasons but not year round

(Sciulli and Oberly, 2002:441). This is indicated by the presence of extensive cemeteries, although actual habitation sites are rare.

Increased sedentism leads to several important cultural changes during the Late Archaic. Between 3950-2950 BP native plants were first brought under domestication, including Cucurbita (gourds), Iva annua

56 (sumpweed), Helianthus annus (sunflower) and Chenopodium berlandieri

(goosefoot) (Smith, 1989). Price (1985) analyzed strontium levels at three

Late Archaic sites including the Price Site (Wisconsin), and the Williams and DuPont sites in Ohio. The most pronounced differences between strontium levels in humans and local deer were found at the Williams site, suggesting heavy reliance on meat and fish (Price, 1985:457). Price concludes that while cultivation was important, plants probably accounted for only 20% of the total diet.

The Late Archaic is also the beginning of the mortuary elaboration that reaches its apex among the Adena and Hopewell (Strothers, 1979).

Small sub-populations eventually formed regional identities characterized by the mortuary complexes of Old Copper, Red Ochre,

Glacial Kame and Poverty Point (Stothers, 1979:14). Brose (1979) has suggested that Late Archaic cemeteries represent territorial ownership.

In Ohio, the Glacial Kame people inhabit thirty-seven counties from

Cleveland to Cincinnati. Glacial Kame is similar to but distinctive from

Red Ochre; no caches of flints objects are found and grave goods are uncommon (Converse, 1980:17). Shell gorgets found in some burials are similar to those found in Tennessee and Kentucky, and the presence of conch shell indicates trade from the Gulf of Mexico (Converse, 1980:27;

Sciulli, 1984). The lack of significant differences in mortuary features suggests a socially egalitarian population (Sciulli, et al. 1993)

Biologically, Late Archaic populations in Ohio appear to be members of a larger, regional population. Sciulli (1984) has shown that differences in size and frequency of discreet traits between the Late

Archaic sites of Stratton-Wallace, Muzzy Lake and Clifford Williams are not significant. The lack of physical variation between groups has also been demonstrated for the Boose, Duff and Kirian-Treglia sites (Sciulli and Pacheco, 1991). Populations of the Late Archaic appear to have been distributed in a continuous fashion across the environment with genetic and geographic distance being correlated (Tatarek and Sciulli,

2000:368). Demographically, sub-adults and the very old are underrepresented among Late Archaic cemetery populations. This may indicate that habitation sites near cemeteries were occupied for only half of the year; the young, old and sick may have died during the winter at smaller hunting camps (Sciulli and Oberly, 2002:446).

The generally good health of populations in the Late Archaic is indicated by the low incidence of stress indicators. Stature in the Late

Archaic is high, with males averaging 168.6 cm (5’ 6”) and females 154.5 cm (5’ 1”) (Sciulli and Oberly, 2002:453). Differences between Late

Archaic populations in stature may be due to some villages being located on lower calcium soils (Sciulli and Oberly, 2002:455). Sexual dimorphism in the Late Archaic is also higher than in the Woodland and

58 Prehistoric populations that follow (Barrett et al., 2001; Sciulli and

Oberly, 2002:453). While an association was found between porotic hyperostosis, LEH, infectious lesions and short individuals, the overall frequency of these indicators in the populations under study was relatively low, with none of the infectious lesions scored as severe and the incidence of LEH and porotic hyperostosis both under 20% (Sciulli and Oberly, 2002:471).

The Late Prehistoric from 1000-500 BP represents a period of biological and cultural change. It includes the Protohistoric, the period during which Native Americans of the Ohio River Valley first came into contact with Europeans. With the exception of the Little Ice Age between

500 to 350 BP, conditions were favorable for agriculture. After 950 BP summers grew warmer and moister and winters were dry which increased the growing season (Brose, 2000:98). A larger range of environments was being exploited during the Late Prehistoric, including large river valleys and rolling uplands (Nass and Hart, 2000:127).

Growing populations are indicated by the increase in house sizes from the Middle Prehistoric to Late Prehistoric, and by extensive cemeteries

(Pollack and Henderson, 2000:205). At Protohistoric sites such as

Buffalo in West Virginia over five hundred separate internments were excavated (Hanson, 1975). Occupation of village sites appears to have been year round and may have resembled the Miami-Potawatomi style of

habitation, with able bodied members of the population dispersing to hunting camps in the late winter and early spring (Pollack and

Henderson, 2000:207).

Most of the oily, starchy seed crops that characterize the Eastern

Agricultural Complex of the Late Archaic are abandoned by the Late

Prehistoric in favor of higher-yield domesticates, supplemented by wild fruits and nuts (Pollack and Henderson, 2000:197). Corn, beans and squash come to dominate the diet (Brose, 2000; Pollack and Henderson,

2000;smith, 1989; Vickery et al., 2000). Increased consumption of maize is indicated by a jump in C13/C12 ratios; this also suggests that plants contributed up to 50% of the carbon in the diet (Sciulli and Oberly,

2002:447). While maize is found as early as 1750 BP in some parts of

North America, it is not until after 1150 BP that it becomes a staple crop

(Smith, 1989:1570). Smith (1989) has suggested that maize may have originally been used as a ceremonial crop, and not until the development of eight-row and flint varieties does it become practical to do maize agriculture on a large-scale. Faunal species include cervids (deer and elk), turkey, and raccoon (Vickery et al., 2000; Pittner 2000). White tailed deer and elk are the most numerous faunal species consumed, accounting for as much as 80% of the diet at some sites (Vickery et al.,

2000:292).

The Late Prehistoric is represented in the middle and upper Ohio

Valley by five cultural traditions; Sandusky in the northwest,

Monongahela and Belmont in the East, Whittlesey in the northeast and

Fort Ancient in the south and southwest (Sciulli and Oberly, 2002:443).

Evidence exists for contact between these populations from the inclusion of Whittlesey tradition ceramics in Fort Ancient excavations and from the presence of non-local goods (Pollack and Henderson, 2000:210;

Strothers, 2000:75). Contact with Mississippian groups is indicated by large quantities of marine shell and by Mississippian iconography in the form of shell gorgets (Pollack and Henderson, 2000:209).

Fort Ancient represents both Late Prehistoric and Protohistoric populations in Ohio, West Virginia and Kentucky from 1150-250 BP

(Drooker, 2002:235). It is characterized by large, palisaded villages such as SunWatch in Ohio, Hardin Village in Kentucky, and Buffalo in West

Virginia. Pollack and Henderson (2000) have suggested that the socio- political organization of Fort Ancient is that of a “Big Man Collectivity”.

Leaders in Late Fort Ancient (550-200 BP) may have exercised more power over their villages than in the Middle Fort Ancient, but unlike

Mississippian elites were unable to extend this influence to households outside the local area (Pollack and Henderson, 2000:212). Pollack and

Henderson (2000) have used the terms middle-range tribal and

61 transegalitarian to describe this pattern of localized political influence.

Unlike populations in the Late Archaic, Late Prehistoric populations in the Ohio Valley demonstrate marked differences in metric and non-metric features (Sciulli and Oberly, 2002:444; Tatarek and

Sciulli, 2000). The amount of variation for these characteristics in the

Late Prehistoric is almost twice that of the Late Archaic (Tatarek and

Sciulli, 2000:368). Major differences are between populations from the northwest and the southeast, suggesting that Late Prehistoric populations in the Ohio Valley shared a Late Woodland ancestor but were thereafter more isolated (Sciulli and Oberly, 2002:444).

While these populations are not homogeneous with regards to stress indicators, research suggests that the health of populations in the

Ohio Valley declined during the Late Prehistoric (Sciulli and Oberly,

2002). Average stature for males is 162.9 cm (5’ 4”) and females are

152.4 cm (5’), a reduction of several inches from the Late Archaic (Sciulli and Oberly, 2002:453). Likewise, sexual dimorphism is only 6.4%, a reduction of 2% from the Late Archaic. Data suggest that the decline in sexual dimorphism was primarily due to a reduction in the size of males

(Barrett et al., 2001).

The most striking difference between Late Archaic and Late

Prehistoric stress indicators is in the frequency of skeletal lesions, dental caries and LEH. Porotic hyperostosis and cribra orbitalia in some

62 populations are three times that of the Late Archaic, and the frequency of systemic skeletal infection, which had been below 5% in the Late

Archaic, reaches approximately 20% in several groups (Sciulli and

Oberly, 2002:471). The frequency of LEH is also quite high for Late

Prehistoric populations. At Pearson Village and SunWatch Village the frequency of LEH of the upper canine for females is 57.9% and 27.8% respectively; a significant increase from the 13.3% reported for the Late

Archaic. The heavy use of maize also led to an increase in dental caries and tooth loss. Antemortem tooth loss, which prior to the Late

Prehistoric had been 6-9%, reaches 17.5% during this period (Sciulli and

Oberly, 2002:447). While these frequencies are high by modern standards, populations in the Ohio River Valley area (with the exception of SunWatch Village) are all above the median quality-adjusted life years

(QALY) compared to other prehistoric populations in the Western

Hemisphere (Sciulli and Oberly, 2002:477). QALY, as it has been adapted to skeletal and dental remains by Steckel et al. (2002), measures not only the frequency of lesions but also their duration, providing insight into how free of disease an individual might have been compared to how long they lived. The high QALY ranking reported by Sciulli and

Oberly (2002) may be due to the large stature and low incidence of LEH in Ohio River Valley populations.

Findings similar to these have been reported for other Late Archaic and Late Prehistoric populations in Eastern North America.

Paleopathology at the Origins of Agriculture contains several examples.

Cook (1984) looked at stature, sexual dimorphism, enamel defects, pathologies in Middle Archaic through Late Prehistoric populations of the

Lower Illinois Valley. No significant time trends were noted for males, although females showed a slight tendency to get taller in later samples

(Cook, 1984:241). Sexual dimorphism is reduced through time as females get bigger and males remain unchanged. These changes in sexual dimorphism may be due to more favorable resource distribution within communities during the Mississippian (Cook, 1984:243). High numbers of caries and enamel defects were reported for the Late-Late

Woodland with a reduction in the Mississippian (Cook, 1984:253). Low frequencies of pathology and no instances of cribra orbitalia were reported for the Late Archaic, however significant increases are evident in the Mississippian sample. Cook concludes that the transition to maize agriculture (which occurred in the Late Woodland) initially caused worsening health in childhood but was subsequently accommodated by biological and/or cultural buffers in later periods (Cook, 1984:261).

Cassidy (1984) looked at skeletal and dietary evidence from

Archaic, Adena and Fort Ancient sites in Kentucky and culturally related

64 areas of southeastern Ohio and western Tennessee. A comparison of several health indicators was made for Indian Knoll (Archaic) and Hardin

Village (Fort Ancient). While bone infections for all ages were approximately equal, a marked increase in the frequency of bone infections in children and in periosteal reactions was noted for Hardin

Village (Cassidy, 1984:321). Significant increases in the prevalence of

LEH also were found. LEH of the deciduous incisor and canine were absent from Indian Knoll, but reached a frequency of 9.1% and 12.1% respectively at Hardin Village. Only one health indicator, Harris lines, was higher at Indian Knoll then at Hardin Village (Cassidy, 1984:331).

These same changes in health are not evident between Late Archaic and

Adena populations, leading Cassidy to conclude that while the initial domestication of plants in the Eastern Agricultural Complex was adaptive, the transition to maize agriculture was less successful

(Cassidy, 1984:338).

Perzigian et al. (1984) compared the health of one Late Archaic and two Fort Ancient populations based upon several skeletal and dental stress indicators. Individuals from the Late Archaic Dupont site exhibit less robusticity and sexual dimorphism than the Fort Ancient sites of

Turpin and State Line, although these results may be due to the small sample size for the Late Archaic (Perzigian et al., 1984:351). As with

Indian Knoll, percentages of individuals with Harris lines dropped in Fort

65 Ancient to less than 30%, suggesting reduced seasonality and a more consistent food supply (Perzigian et al., 1984:353). The frequency of LEH is equal between the Late Archaic and Middle Woodland, with frequencies of approximately 20% (Perzigian et al., 1984:354). The frequency of LEH in Fort Ancient, however, rises dramatically and is almost three times (60.3%) that of the Late Archaic. Frequencies of dental caries, periosteal reactions also increase in Fort Ancient populations. Perzigian et al. conclude that while increases in nutrition and health can be demonstrated for the Middle Woodland over the Late

Archaic, this trend cannot be shown for Fort Ancient (Perzigian et al.,

1984:361).

Goodman et al. (1984) studied the health effects of economic and cultural change at Dickson Mounds, a site in Illinois that spans the Late

Woodland through Middle Mississippian periods (950-1300 AD).

Goodman et al. identify three Mississippian trends at Dickson mounds: increased population and sedentism, intensification of maize agriculture, and intensification of trade (Goodman et al., 1984:272). While sexual dimorphism and the frequency of Harris lines were invariable among the three groups at Dickson Mounds, eight other indicators of stress showed marked increases in stress or severity. Mean numbers of hypoplasias rise from 0.90 in the Late Woodland to 1.61 in the Middle Mississipian.

Similar increases are noted for the frequency of porotic hypersostosis

(13.6% in the Late Woodland to 51.5% in the Middle Mississipian), and infectious lesions (31% to 67%).

Wood et al. (1992) published “The Osteological Paradox” questioning many of the aforementioned conclusions. Three conceptual problems were presented as hindering direct analysis of health from skeletal populations. Demographic non-stationarity refers to the departure of a population from a stationary state caused by immigration, age specific mortality and/or fertility. Life expectancies, then, would be better measures of fertility than mortality (Wood et al., 1992:344).

Selective mortality refers to the fact that only individuals of a particular age group in a skeletal sample are those that died; since they had to have died from something, the frequency of lesions in any age group should overestimate the frequency in a living population. Finally, hidden heterogeneity in risks is the differential susceptibility of individuals to disease and death. Some individuals may be frail, succumbing quickly to a disease and leaving no skeletal markers. These individuals would nevertheless appear healthy when assessing health from the presence or absence of skeletal lesions.

Wood et al. (1992) have also directly criticized the conclusions of

Paleopathology at the Origins of Agriculture, asserting that the increase in

67 stress indicators represents healthier individuals living longer and the effects of increased fertility that coincided with maize agriculture. They present a hypothetical model to illustrate this point. Three populations, one that has low stress and no skeletal lesions, one with moderate stress and many lesions and one with high stress and few lesions are compared. Based upon the number of skeletal lesions, both the low stress and high stress groups would be indistinguishable (Wood et al.,

1992:355). Wood et al. conclude that lesion frequency alone is a poor indicator of health and makes any conclusion open to multiple and sometimes contradictory interpretations.

Alan Goodman (1993) has responded to these criticisms.

According to Goodman, the selective nature of skeletal samples has long been recognized, and is true for some, but not all stress indicators. The key to avoiding the Osteological Paradox is the use of multiple stress indicators (Goodman, 1993:281). LEH is a permanent record of stress that can be compared to other indicators of health such as stature and skeletal lesions. Mortality, too, can yield insights into the association between lesion prevalence and stress. Low and high stress groups may exhibit low frequencies of lesions but their respective patterns of mortality will differ dramatically. Goodman concludes that while single sources of data may be open to multiple interpretations, using multiple types of data greatly reduce this likelihood (Goodman, 1993:285).

While the criticisms of Wood et al. are compelling, they suggest no practical ways to validate their conclusions. The link between low stature, LEH, skeletal lesions, and stress has been demonstrated in many living populations (Crooks, 1999; Goodman and Rose, 1991;

Meadows and Jantz, 1999; Larson, 1987; Shell-Duncan, 1997). To date these observations have yet to be invalidated by a single example of a living population under stress that doesn’t exhibit some increase in the frequency of dental and skeletal indicators of stress. At the same time most definitions of health (including the definition used by the WHO) refer not just to an absence of pathology but also to some measure of well being. Neither the contributing authors of Paleopathology at the

Origins of Agriculture, nor Wood et al. have proposed a definition of health that is testable with skeletal or dental evidence.

Summary

Developmental stability is a measure of an organism’s ability to resist environmental and genetic disruption (developmental instability).

In general, organisms evolve toward greater genetic integration which reduces developmental noise increasing the symmetry of bilaterally symmetrical features. Deviations from perfect symmetry include directional asymmetry, antisymmetry and fluctuating asymmetry.

Fluctuating asymmetry is a reliable indicator of developmental instability

69 when samples are large and other factors, such as measurement error, are taken into account. The precise cause of fluctuating asymmetry is still not completely understood, but appears to be the result of random disturbances at the molecular level. Fluctuating asymmetry in human teeth has a small additive genetic component and is due primarily to environmental stress. These deviations from perfect symmetry may be explained by molecular perturbation during crown formation.

A number of studies have investigated fluctuating asymmetry in plants and animals. In general, increased stress is associated with greater fluctuating asymmetry. Not all traits, however, are equally sensitive to stress. A number of human studies have failed to demonstrate a relationship between stress and fluctuating asymmetry for such physical traits as dermatoglyphs, dental traits and various cranial and postcranial measurements. Recent studies of fluctuating dental asymmetry in populations of prehistoric Native Americans and

Australians indicate little significant difference between males and females or between time periods.

Interpretation of health from skeletal populations is problematic; many stress indicators are inappropriate for the task or yield results that are contradictory. Based on stature, the frequency of linear enamel hypoplasia, sexual dimorphism and the frequency of infectious skeletal lesions, Eastern North American populations from the Late Archaic

70 appear healthier than populations from the Late Prehistoric. This decline in health has often been blamed on poor nutrition and increased group size associated with the transition to maize agriculture. Wood et al. suggest that increasing frequencies of skeletal and dental lesions may be interpreted in two equally valid but contradictory ways; presence of lesions may indicate robust health and differential survival, or poor health and increased mortality. Neither side, however, has operationally defined health in a way that would allow it to be evaluated with skeletal or dental samples. Fluctuating dental asymmetry may provide a way around some of these shortcomings.

CHAPTER 3

METHODS

This chapter reviews the methods used in carrying out this study.

Smaller than expected sample sizes, wear on dental elements and preservation issues prevented several of the original goals from being accomplished.

Sample Composition

Eight Late Archaic and two Protohistoric sites provided the prehistoric samples. Ninety-two plaster dental casts represent the modern sample made between 1947 and 1967 by a Dayton, Ohio dentist.

Sample sizes are represented in Table 3.

Late Archaic sites are consistent with Glacial Kame mortuary practices. They are situated in sand and gravel end moraines throughout western Ohio (see Figure 3). These cemetery sites include no information regarding habitation, which is unavailable for this period. At

Stratton-Wallace, Clifford Williams, Boose, and Duff red ochre is associated with burials (Dunlap, 1960; Sciulli, et al. 1984; Sciulli et al.

1993). The majority of burials from the Late Archaic sites are flexed. At

72 Stratton-Wallace, Muzzy Lake, Clifford Williams and Lakeview Heights

Farm a significant portion of the assemblage is from ossuary pits making precise determination of age and sex impossible. Grave goods include copper beads, birdstones, gorgets, shell beads, dear bone awls and a bear’s tooth. Lack of differentiation among burials from the same site suggests a socially egalitarian population (Sciulli et al., 1993).

Dates Males Females Undetermined Total

Late Archaic

Boose 2960-3105 BP1 3 5 2 10 Duff 2950 +/- 100 BP2 11 11 5 27 Kirian- Treglia (33AL39) 2850 BP2 5 3 0 8 Muzzy Lake / Urbana Kame 19 19 (33CH10; 33CH25) Clifford Williams (33LO25) 5 5 Davis (33FR38) 3150 +/- 120 BP2 4 4 Stratton-Wallace 10 10 Lakeview Hts Farm (33CL54) 9 9 Total Late Archaic 19 19 54 92

Protohistoric

Buffalo (46PU31) 270 +/- 120 BP3 43 62 33 138 Norma Grantham (33LA139) ca. 300 BP 20 22 6 48 Total Protohistoric 63 84 39 186

Modern

Dayton Dental Casts 1947-1967 AD 92 92

Table 3: Composition of Sample

1 Sciulli and Tatarek, 2000:365 2 Sciulli and Oberly, 2002 3 Hanson, 1975

73 The Buffalo and Norma Grantham sites provide the Protohistoric samples. The Buffalo site is a 17th century village located on the east bank of the Kanawha River, 15 miles upstream from its juncture with the

Ohio River (Hanson, 1975). Over five hundred burials, the majority of which were dug into the floors of houses, are represented (Hanson,

1975:23). Extended and flexed burials make up 77.2% of the internments at Buffalo, with the remainder being either re-deposited or partially flexed. Only 22.3% of burials had any grave goods; 26 burials included shell gorgets or pendants, 51 burials had marine shell beads and small pendants in necklaces. One burial included a glass bead, indicating some access to European derived trade goods (Hanson,

1975:31). Ceramics found at the Buffalo site include Madisonville Plain and Madisonville-Fox Farm and place it within the Clover Complex of

Fort Ancient (Hanson, 1975:93).

Norma Grantham is a mortuary site located east of Cleveland at the outlet of the Grand River near the Whittlesey tradition Fairport

Harbor Site. It dates to the same time period as Buffalo, approximately

300 BP (Bush, 1984; Strothers, 2000:76). Grantham has been assigned to the Eastwall complex of Whittlesey (Brose, 2000:108). The presence of

European derived trade goods at other sites in the region which date from approximately the same time period (350-450 BP) suggest that the

Whittlesey tradition should be assigned to the Protohistoric time period

74 (Strothers, 2000:76). As Brose (2001b) points out, however, no

European derived trade goods have ever been found among Whittlesey tradition sites. This suggests that Whittlesey settlements were abandoned just prior to European contact.

The modern sample consisted of ninety-two dental casts from orthodontic sub-adult patients made between 1947 and 1967. Neither age, sex, ancestry, residency or socioeconomic status are available for this sample. According to U.S. Census data from 1950 and 1960, the county composition of Montgomery County, Ohio was predominantly white (1950=87%, 1960=88%) with blacks making up approximately ten percent of the population (1950=9.3%, 1960=11.6%) (Geostat Center,

2004). Native Americans and individuals of other races made up less than 5% of the total population during this period, while women outnumber men in both periods. Still, the male/female ratio was largely unchanged from 1950 to 1960 (1950: males=48%, females=52%; 1960: males=48.7%, females =51.3%). It is assumed that since all individuals in this sample were being fitted for braces they represent families with incomes above the poverty level.

Figure 3: Location of samples from which data have been collected. Late Archaic (squares): (1) Clifford Williams, (2) Muzzey Lake, (3) Davis, (4) Duff, (5) Boose, (6) Kirian Treglia, (7) Stratton-Wallace, (8) Lakeview Hts. Farm. Late Prehistoric (circles): (1) Buffalo, (2) Norma Grantham (modified from Tatarek and Sciulli, 2000)

76 Data Collection

Data were collected between October 2002 and November 2004 at the Ohio State University Department of Anthropology, the Wright State

University Archaeology Lab and at the Ohio Historical Society. Most of the prehistoric teeth show extensive wear, both on the crown and on the mesial and distal surfaces. This prevented crown height and mesiodistal crown diameters from being recorded. In general the Late Archaic sample exhibited extensive occlusal wear with few dental caries, while the Protohistoric sample had less occlusal wear but larger numbers of caries. The soft consistency of dental plaster made measurement difficult and may have inflated measurement error in the modern sample.

Linear enamel hypoplasia was assessed first without magnification and then with a 5X hand lens and a 10X lighted magnifying glass. LEH was recorded as the number of visible lines for each tooth of the adult dentition. Teeth that were not suitable for measurement of buccolingual diameter were not assessed for LEH. LEH was recorded during the first round of measurements only. A LEH index value was calculated for each individual using the following formula:

LEH Index = Observed Number of X # Teeth Present LEH lines 32

Crown dimensions were recorded using Mitutoyo Digital Calipers, model

CD-6”CS. According to the manufacturer, these calipers are accurate to

77 .01 mm. This is a ten-fold improvement in precision when compared to older, analog calipers; it was hoped that greater precision would aid in the detection of the small component of variance attributable to fluctuating asymmetry. To allow estimates of measurement error, all measurements were taken three times. The second and third measurements were always taken during the same session and at least thirty days after the first set. The second and third measurements were recorded on the opposite side of the data sheet to prevent the original results from introducing bias during data collection and entry. Calipers were closed and reset to zero between each set of measurements.

Efforts were made to take measurements according to the guidelines described in Hillson (1996:71). Since buccolingual crown diameter is usually taken at right angles to the plane in which the mesiodistal measurement is taken, and since this measurement could not be reliably assessed, the orientation of the calipers had to be estimated. Rather than rely upon an estimate that may introduce considerable error under repeated trials, the buccolingual diameter was measured as the maximum distance between the labial/buccal and lingual sides of the crown. For the first molar an anterior and posterior dimension was recorded, taken as the maximum buccolingual diameter that transects the two most mesial and distal cusps. This was done to allow the 1st molar to be recorded even when a portion of the crown was

78 missing and maximize the number of teeth included in the study.

Second and third molars were often occluded by the mandibular ramus; measurements were recorded as the maximum buccolingual diameter with the calipers held at a 45-degree angle to the occlusal surface.

Teeth that were cracked, chipped, showed labial/buccal or lingual wear or could not be accurately typed or sided were excluded from measurement. Broken teeth were glued together when possible. In some cases crowns were worn well past the cingulum; these teeth were also excluded from data collection. A number of teeth from the modern sample could not be recorded because the casting included the gumline, which obscured the cingulum. The modern sample is therefore overrepresented by premolars and molars, and underrepresented by anterior teeth. Only teeth with both the right and left elements present and meeting the aforementioned requirements were included in the final analysis. These precautions, while necessary for ensuring accurate collection of data, reduced the sample size significantly.

Analysis

Data were analyzed according to the guidelines suggested by

Palmer and Strobeck (see Chapter 2). All statistical analyses were performed using either SPSS version 11 for Macintosh OSX or Microsoft

79 Excel version 11.1 for Macintosh OSX. Since it was not known whether asymmetry or measurement error would be similar between different teeth, each tooth position was analyzed separately.

Data were visually inspected for outliers using scatter plots of first measurements versus an average of replicated measurements. A correlation coefficient (r2) was calculated, and the residuals from a linear regression of measurements was plotted and visually inspected. To determine if any of the outliers were more deviant than expected due to chance a Grubb’s test was used (Palmer and Strobeck, 2003b:v.4).

Grubb’s statistic (tG) is the deviation of the observed value (Xi) from the sample mean as a proportion of the sample standard deviation (SD):

tG = ( x i " x)/SD

These values were then compared! against a table of critical values (Rohlf and Sokal, 1995). Since the primary and replicated measurements are independent it is possible that one or more of the outliers might be judged significant by chance alone (Rice, 1989:223). A Bonferroni correction for multiple tests was used to compensate for this possibility and reduce Type I errors. The Bonferroni correction guarantees that the probability of any false rejection among all comparisons is no greater

than 0.05 by increasing the critical value (Moore and McCabe,

1998:770). Observations significantly deviant at the 0.05 level were then replaced with one of the other replicated measurements.

Several tests were performed to determine if systematic deviations from expected symmetries were present in the samples. For samples where sex was known, homogeneity of variances between sexes was tested using an analysis of variance. In the absence of significant differences, males and females were pooled to increase sample size. Trait size asymmetry was assessed by correlation and scatter plots of trait asymmetry |R-L| vs. trait size [(R+L)/2] (Palmer and Strobeck,

2003b:v.10). Systematically greater asymmetry in larger traits was corrected by using index FA2 (see Table 2).

Histograms of the absolute differences between sides were inspected to determine if measurements were normally distributed. Both distributions with outliers and with outliers excluded were analyzed.

Side bias (directional asymmetry) was tested by visual inspection of right versus left scatter plots, by a one-sample t-test comparing the mean left- right differences to zero, and by scrutinizing the sides component of the analysis of variance (Palmer and Strobeck, 2003b:v.11). In the absence of directional asymmetry right and left differences should be normally distributed with a mean of zero.

To test for the presence of antisymmetry, kurtosis was computed using SPSS for each sample and compared to a set of critical values (see

Appendix A). SPSS uses the following formula to compute kurtosis:

(n +1)n y 4 3(n "1)2 kurtosis = " - 4 ( n #1)(n # 2)(n # 3)s ( n " 2)(n " 3)

! This formula provides! an unbiased estimate of kurtosis and is preferred when attempting to predict the kurtosis of a population from a subsample (Palmer and Strobeck, 2003a:302). Significant skewness was tested by comparing the skew statistic to an estimate of the standard

6n(n -1) errors of skewness, (Sokal and Rohlf, 1995). Values (n - 2)(n +1)(n + 3) that exceeded twice the standard error of skewness were considered significant (Brown,! 1997). Skewness was computed by SPSS using the following formula:

n"y 3 skew = 3 ( n #1)(n # 2)s

82 As noted in Chapter 2, the presence of either directional asymmetry or antisymmetry makes a precise estimate of fluctuating asymmetry impossible. A natural log transformation of data was used to eliminate or significantly reduce Leptokurtosis in the sample.

A two-way analysis of variance (sides = fixed, burials = random) was used with replicated measurements to determine if measurement error contributed significantly to between-sides variation. Trait size dependence was eliminated by correcting for size with the following:

Size corrected measure = original measure / (L+R/2)

This correction also eliminated variance between individuals, leaving only variance caused by directional asymmetry, antisymmetry, measurement error and fluctuating asymmetry. The remainder variance component of the ANOVA was compared to the measurement error to determine if measurement error contributed significantly to variance. When between- sides variation does not exceed error variation, further analysis of fluctuating asymmetry is not warranted (Palmer and Strobeck,

2003b:v.9). A Levene’s test of error heterogeneity was used to determine if measurement error was comparable between samples.

83 An estimate of fluctuating asymmetry was computed for measurement distributions that were normally distributed, in which error variance did not exceed the remainder, and for which error between

2 groups was homogeneous. Fluctuating asymmetry (Si ) was estimated using the difference in variance between the mean square of the remainder (MSsj) and error (MSerr) divided by the number of replications

(M) from the analysis of variance:

2 Si = (MSsj - MSerr) / M

Estimates of fluctuating asymmetry were compared between samples using an F-test with the degrees of freedom adjusted according to the Satterthwaite formula for degrees of freedom (Palmer and

Strobeck, 1986:408; Sciulli, 2003:36). The correction was made as follows:

2 2 2 dfs = (MSsj – MSm) /((MSsj) / J-1) + ((MSm) / SJ)

The Satterthwaite formula is an appropriate correction to the degrees of freedom when degrees of freedom are small; this correction reduces the degrees of freedom for the sample. Values for each sex and period were calculated separately.

84 To determine if fluctuating asymmetry was associated with other stress indicators, estimates of fluctuating asymmetry were compared to frequencies of LEH in the Late Archaic and Protohistoric samples. The

LEH index was analyzed using an analysis of variance with period and sex as fixed factors to determine if these factors differed significantly.

The LEH index was then compared to the size-corrected FA index (FA2) on a tooth-by-tooth basis by computing the correlation coefficient between these two variables. Comparisons were made between periods and sexes using measurements that were normally distributed.

Summary

Dental elements from the Late Archaic, Protohistoric and a set of modern dental casts are the basis of the current study. Buccolingual diameter and the number of linear enamel hypoplasias were recorded for each tooth of the permanent dentition that were free of chips, cracks and wear. Following the guidelines of Palmer and Strobeck (2003a,b) data were inspected for outliers, differences between right and left measurements tested for trait size asymmetry, directional asymmetry and antisymmetry. Fluctuating asymmetry and measurement error were estimated using a two-way mixed model analysis of variance of each measurement with sides fixed and burial (individuals) random.

Fluctuating asymmetry was estimated as the difference between the mean square of interaction and error. The index value of fluctuating

85 asymmetry for each individual was then compared to an LEH index using a correlation coefficient. The next chapter reports the results of these analyses.

CHAPTER 4

RESULTS

The study reported here examined the relationship between two dental indicators of stress in three time periods: the Late Archaic (LA),

Protohistoric (PH) and modern (MO). Descriptive statistics are presented, followed by an analysis of outliers from the data. Data were tested for the presence of antisymmetry by evaluating the kurtosis in each distribution. Directional asymmetry was tested using a t-test and

ANOVA. Data were tested for size dependence by examining correlation coefficients of left versus right measurements. Estimates of fluctuating asymmetry and measurement error were derived from a two-way ANOVA

(sides = fixed, burials = random). A Levene’s test was used to assess whether error variances were equal between groups. Finally, the relationship between fluctuating asymmetry and linear enamel hypoplasia was assessed using correlation coefficients between a size- corrected index for fluctuating asymmetry and the LEH index value.

87 Descriptive Statistics

Descriptive statistics for buccolingual measurements are presented in Table 5 and Appendix B. Results (Table 5) are for each tooth with sexes and sides combined. The results in Appendix B were generated separately for males, females, and individuals of unknown sex and are divided according to side and period. Descriptive statistics were generated prior to removal of outliers or data transformations.

Sample sizes on a per tooth basis were small, especially when grouped by sex and period (see Table 4). Many individuals lacked antimeric teeth, which excluded these observations from the final data set. The young age of the individuals in the modern sample prevented collection of data from M3. Overall, the average number of observations per sex and by period was approximately twenty-three (22.9).

Late Archaic Protohistoric Modern

Males Females Unknown Males Females Unknown Unknown

Mean 8.2 11.6 21.4 31.8 40.8 17.8 29.03

SD 2.7 2.4 4.2 4.2 8.9 5.3 32.26

Min 3 8 15 15 21 5 0

Max 15 16 30 30 54 25 81

Table 4: Average number of observations by period and sex

Late Archaic Protohistoric Modern N X SD N X SD N X SD

UM3 67 11.12 .86 104 10.52 .72 0 UM2 101 12.13 .59 211 11.61 .77 4 11.29 .18 ! ! ! UM1A 73 12.07 .55 211 11.81 .54 152 11.54 .54 UM1P 74 11.25 .64 209 11.05 .58 147 10.79 .59 UP4 78 9.69 .59 190 9.65 .58 126 9.51 .53 UP3 85 9.92 .62 215 9.65 .53 160 9.36 .49 UC 90 8.74 .63 192 8.45 .60 10 8.36 .32 UI2 80 6.65 .54 167 6.59 .49 2 6.94 .06 UI1 77 7.31 .44 156 7.20 .44 2 7.17 .07

LI1 65 5.74 .32 165 5.66 .36 6 6.34 .60 LI2 75 6.24 .32 205 6.09 .36 3 6.77 .05 LC 82 7.85 .64 205 7.77 .53 2 7.48 .17 LP3 85 8.33 .52 223 8.09 .46 95 7.90 .55 LP4 105 8.62 .57 216 8.49 .43 86 8.51 .50 LM1A 85 11.02 .55 164 10.78 .44 128 10.38 .53 LM1P 78 11.10 .55 157 10.89 .44 126 10.31 .50 LM2 84 10.90 .50 147 10.51 .56 2 10.90 .35 LM3 73 10.82 .84 111 10.35 .69 0

Table 5: Descriptive Statistics for BL Measurements by Period.

Differences in size between periods were much as expected.

Average size with sides and sexes combined was greatest for the Late

Archaic and smallest for the modern sample, with the Protohistoric between the two. Several teeth from the modern sample were found to be

89 larger than their Protohistoric counterparts (UI2, LI1, LI2 and LM2), although this may be due to small sample sizes. The tooth exhibiting the greatest variation (as measured by standard deviation) was the third molar. Average absolute differences were greater between the Late

Archaic and Protohistoric (0.33 mm) than between the Protohistoric to modern sample (.02 mm).

Table 29 in Appendix B summarizes the differences in average size between subgroups. In general, average buccolingual diameter is larger in males than in females in both Late Archaic and Protohistoric samples with several exceptions. Maxillary I2 is larger in Late Archaic females than males. The left maxillary I2 is also larger in Protohistoric females than males, as is I1 and the right maxillary P4. Average buccolingual length for mandibular teeth was larger in males than females for both the

Late Archaic and Protohistoric. The greatest differences in size between males and females for the Late Archaic were observed in the maxillary

M1, while in the Protohistoric the greatest differences were found in maxillary M2. Average differences between males and females for the Late Archaic was 0.37 mm, more than double that for the Protohistoric (0.16 mm).

Table 6 summarizes the descriptive statistics for the LEH index.

Several of these observations are noteworthy. The average LEH index for males and females changes from the Late Archaic to Protohistoric. While

90 males exhibit an LEH index more than double that of females in the Late

Archaic this situation is reversed in the Protohistoric. Individuals for which sex was not known show little difference in average LEH index between any of the three periods.

Males Females Unknown Sex

N Mean SD N Mean SD N Mean SD

LA 19 5.05 7.49 19 1.64 3.38 54 2.04 3.67

PH 84 1.61 2.98 63 3.56 6.02 39 2.19 4.55

MO 92 2.05 .88

LEH Index = observed LEH lines X (# teeth present / 32)

Table 6: Descriptive statistics for LEH index

Assessment of Outliers

Data were inspected visually for outliers using scatter plots of 1st vs. 2nd measurements generated with SPSS. Outliers were marked on each scatter plot and then referenced back to the original data set. In all, 137 outliers were identified for the thirty-six measurements under study. Table 7 summarizes the percentages of outliers by period. For the Late Archaic the number of outliers found was almost exactly proportional to the percentage of individuals in the data. For the

91 Protohistoric the number of outliers was slightly higher than expected while for the modern sample it was lower. None of the periods appeared to contain significantly more outliers than any other period.

Outliers Individuals N % N % Late Archaic 33 24.6 92 24.8 Protohistoric 79 58.9 186 50.2 Modern 22 16.4 92 24.8 Total 134 370

Table 7: Outliers by period

Outliers were evaluated using a Grubb’s Test (Rohlf and Sokal,

1995:179) and compared to a set of critical values. Grubb’s test provides an unbiased method for evaluating outliers based upon how deviant a particular observation is compared to the mean and standard deviation of the sample. Out of 137 possible outliers, only six were rejected by the

Grubb’s test as being significantly more deviant than by chance (see

Table 8).

Measurement st nd Tooth Period Site Burial 1 2 3rd Critical tg Value L UM3 PH Grantham G22B1 9.43* 10.32 10.25 2.607 -2.88

L UM1P PH Buffalo D11B10 11.88 12.57* 12.27 3.009 3.19

L UP4 PH Grantham G61B1 10.17 11.65* 11.65 2.939 3.72

L UP4 MO Dayton 2002 9.59 6.54* 9.59 3.411 -5.51

L UC LA Boose 7C 6.58* 7.59 7.57 2.963 -3.32

R LI2 PH Buffalo E10B79 7.09* 6.75 6.80 2.747 2.72

Table 8: Observations rejected by Grubb’s Test

Replacement of the deviant measurements by 3rd measurements brought the Grubb’s statistic (tg) below the critical value for 5 out of 6 measurements, the exception being burial G61B1 from the Grantham site. In all cases the discrepancy between 1st and 2nd measurements for the outliers was large and suggests that data may have been transcribed from the instrument incorrectly.

Data were also tested for aberrant individuals (individuals who may deviate significantly due to injury or other stochastic events).

Scatter plots of right versus left 1st measurements were generated using

SPSS and outliers were marked. In all, 52 outliers were identified and evaluated using Grubb’s test. Only two of these outliers, maxillary P4 from Duff site burial D11B10 and maxillary C from Boose site burial 7C, exceeded the critical value. Both burials had also been identified as

93 possible outliers in the first set of scatter plots. For Boose 7C the outlier was corrected by replacing the deviant measurement with the 3rd measurement. Buffalo D11B10, however, appears to be either a result of measurement error or a truly aberrant individual (tg = 4.98, critical value

= 2.939).

Four burials, Duff 31, Buffalo E10B54 and D11B25B and

Grantham G63B1 accounted for 18 outliers. These measurements were sequential, and suggest that the instrument was bumped or reset without the researchers knowledge. The measurements for each of the four burials were corrected by replacement of the deviant measurement with the 3rd measurement.

Homogeneity of Error Variances

Since an analysis of variance assumes approximately equal variances between samples a Levene’s test for the equality of error variances was applied to the data. Levene’s statistic was calculated for the absolute differences between 1st and 2nd measurements with period and sex as factors using SPSS. Measurements that were found to contain significant differences in variance between groups (Table 9). A sequential Bonferroni procedure was applied to these results (.05/36 =

.00138) after which none of the measurements were found to be significant. Variances among samples are therefore sufficiently equal to permit an analysis of variance.

Measure F df1 df2 Significance (numerator) (denominator) UM2 3.626 6 142 .002 UM1A 3.351 6 210 .004 UM1P 2.140 6 206 .050 LM1A 2.654 6 179 .017 LM1P 2.422 6 170 .028 LP4 2.651 6 193 .017

Table 9: Results of Levene’s test of equality of variances: significant measurements

Test for Size Dependence

Trait size dependence was evaluated by visual inspection of scatter plots of 1st measurements with the x-axis representing the average between right and left observations (L+R/2) and the y-axis the absolute difference between left and right observations (|L-R|). No discernable patterns were found on the scatter plots. A second test was conducted using correlation coefficients between the two aforementioned variables as recommended by Palmer and Strobeck (2003a). No significant correlations were found when periods and sexes were combined. When data were analyzed separately by period and sex several measurements exhibited significant trait size-dependence, although three of these correlations (UM1A and LM1A in Late Archaic females and UM2 from the modern sample) were rejected due to small sample size. Table 10

95 summarizes the remaining correlations that were significant. Only one of these correlations was significant above the .05 level. All six of the measurements are from the Protohistoric sample and are evenly divided between males, females and burials of unknown sex. The strongest correlation was for mandibular M3 in Protohistoric females (r = .607).

Measure Period Sex N Pearson r Significance UI2 PH M 22 .461 .05

LP3 PH M 43 .362 .05

LM3 PH F 17 .607 .01

LM1P PH F 15 .545 .05

UP4 PH U 17 .562 .05

LP4 PH U 15 .631 .05

Table 10: Trait-size dependence: significant correlation coefficients.

Tests of Normality

Data were tested for the presence of directional asymmetry and antisymmetry by examining the distribution of each measurement.

Histograms of the differences between left and right 1st measurements were visually inspected and suggested that the many of the distributions were leptokurtic (see Figure 4 below).

96 The presence of antisymmetry was tested by the analysis of kurtosis for each measurement, as shown in Table 10. Kurtosis estimates were compared to a set of critical values from Palmer and

Strobeck (2003a) at the .01 level (see Appendix A). Table 11 summarizes the results of this test.

2 Std. Dev = .04 Me an = .004 0 N = 73.00 ------.0 .0 .0 .0 .1 .1 .1 .0 .0 .0 .0 1 3 6 8 1 3 1 8 6 3 1 3 8 3 8 3 8 2 7 2 7 2

MAXC.1

Figure 4: Distribution of L-R for Protohistoric Maxillary Canine

Kurtosis Meas. Arch Tooth LA PH MO UM3 0.90 0.69 UM2 1.09 10.46**

UM1A 3.27** 23.67** -0.24 y r UM1P -0.45 3.24** 2.16** a l l

i UP4 23.09** 20.53** 5.05** x

a UP3 2.07 10.46** 13.90** M UC 0.87 5.58** -2.63*** UI2 5.94** 2.27**

s UI1 -0.86 -0.11 r i LM3 F 3.03** 9.21** LM2 0.46 0.74

r LM1A 3.27** 23.67** -0.24 a l LM1P u 1.78* 11.72** 2.67** b i LP4 2.60* 4.13** 19.52** d

n LP3 a 1.96* 21.14** 9.76**

M LC 0.66 16.39** LI2 0.11 7.76** LI1 -0.83 4.98** UM3 -0.25 1.91* UM2 0.55 7.77**

UM1A 5.51** 9.64** -0.65 y r UM1P -0.25 2.24** 1.53* a l l

i UP4 0.18 15.64** -0.14 x

a UP3 0.16 4.26** 0.01 M UC 0.18 46.37** 3.16**

UI2 5.09** 12.75** d

n UI1 -0.86 -0.11

c LM3 e 2.07 12.60** S LM2 1.98* 0.63

r LM1A -0.59 2.98** 2.97** a l

u LM1P -0.75 5.69** 2.13** b i LP4 -0.55 1.67* 12.60** d

n LP3 a 0.27 3.40** 12.79**

M LC 0.18 46.37** 3.16** LI2 -0.47 5.88** LI1 0.70 1.65* * Significant at .05 ** Significant at .01 *** Insufficient sample size

Table 11: Kurtosis values for 1st and 2nd Measurements with outliers removed.

Only one of the measurement distributions was found to contain significant platykurtosis: the 1st measurement of maxillary C from the modern sample (kurtosis = -2.633 critical value = -2.395 at .01). The same measurement was not platykurtic for the 2nd measurement replication. The small number of observations for this measurement

(N=5) may explain the discrepancy.

Outliers included Outliers removed

Late Archaic 8 7

Protohistoric 29 28

Modern 9 12

Table 12: Number of measurements exceeding critical values for leptokurtosis (1st and 2nd measurements combined).

Leptokurtosis was significant for a number of measurements in all three periods and is summarized in Table 12. Significant skewness was also observed in all measurement distributions. As Table 12 shows, the removal of outliers from the sample decreased the number of measurement distributions exhibiting significant leptokurtosis.

Subsequent analyses were conducted using data without outliers.

99 Directional asymmetry was evaluated using SPSS with a paired samples t-test of left and right 1st measurements. A pooled test of all data and separate tests of each subgroup by period and sex were performed. Table 13 summarizes those measurements with p-values of less than .10.

Measure Period Sex t df Critical Value Significance (2-tailed) LM1P PH M 1.941 26 2.479 .063

LI1 PH M -1.719 24 2.492 .098

UM1A PH F 2.469 22 2.508 .022

LM2 PH F -1.975 14 2.624 .068

LM1A PH F -2.495 17 2.567 .023

LM1P PH F -1.839 14 2.624 .087

LC PH F -2.517 33 2.457 .017

LM1A PH U -2.915 17 2.567 .010

LI1 PH U 2.454 13 2.650 .029

UM1A MO U -2.941 75 2.374 .004

LP4 MO U -1.971 41 2.423 .056

Table 13: Significant results of paired t-test of left and right 1st measurements

No measurement was significantly side-biased when sexes and periods were combined. When the data were divided into subgroups based on period and sex, several significant results were found. The

100 majority of measurements approaching or exceeding the t-test critical value were molars; 8 out of 13 from the first molar, 4 from maxillary M1 and 4 from mandibular M1. Both of the significant results from the Late

Archaic were rejected due to small sample size (N < 5). Nine of the

Protohistoric measurements were found to be near critical values at a .01 level. Of these measurements, 8 out of 9 were from mandibular teeth, six of which were molars. Only one of these measurements, mandibular

M1A, exceeded the critical value at the .01 level. The most significant results came from maxillary M1A in the modern sample. This is considered especially significant since the number of observations was large (N=76). Missing data and small sample sizes prevented testing of

M3 and anterior teeth (I1, I2, C) in the modern sample.

Adjustment for Leptokurtosis

An attempt was made to eliminate or at least reduce the amount of leptokurtosis in the sample by transforming the data. Ideally, estimates of fluctuating asymmetry based upon variance should be made on untransformed data, since any non-linear transformation may eliminate significant variance. A significant portion of data were, however, untestable in their raw state. Two transformations of the data were considered, a size correction (|L-R|/ [L+R/2]) and differences between natural log transformed left and right values [ ln(l) – ln(r) ]. Tests of both methods yielded almost identical results, with kurtosis values for the log-

101 transformed data being slightly higher than the size corrected data. Size correction of data also eliminated significant skewness from the sample.

Measure LA PH MO UM3 NA UM2 .01 UM1A .01 UM1P UP4 .01 .01 .01 UP3 .01 .01 UC .01 UI2 UI1 LM3 .01 .01 NA LM2 LM1A .01 LM1P LP4 .01 .01 LP3 .01 .01 LC .01 LI2 LI1 .01 Total Rejected 2 11 4

Table 14: Measurements rejected due to leptokurtosis with significance level after log transformation.

After log transformation a number of measurements were still significantly leptokurtotic at the .01 level in all three periods (Table 14).

Figure 5 shows one such distribution for the maxillary canine. The Late

102 Archaic and modern samples had similar levels of leptokurtosis while only seven of the measurements for the Protohistoric lacked significant leptokurtosis. While this is a significant reduction in non-normality over the untransformed data it eliminates all but six measurements from comparison between periods.

Std. Dev = .06 Me an = -.001 0 N = 73.00 ------. 0 .0 .1 .4 .3 .3 .2 .2 .1 .1 .0 .0 2 7 2 2 7 2 7 2 7 2 7 2 5 5 5 5 5 5 5 5 5 5 5 5

MAXC.1

Figure 5: Distribution of log-transformed L-R for Protohistoric Maxillary Canine

Leptokurtosis can be caused by data outliers, the presence of antisymmetry, variation in measurement error, trait-size dependence or two or more samples with different levels of fluctuating asymmetry being 103 pooled together (Palmer and Strobeck, 2003a:303). The results of the

Levene’s test and the absence of data outliers, antisymmetry and trait- size dependence suggest that the remaining leptokurtic distributions are the result of underlying differences between individuals in the population.

Fluctuating Asymmetry

Fluctuating asymmetry was estimated using those measurements from each period with sufficiently normal distributions to permit analysis. Prior to the analysis of fluctuating asymmetry, data were tested using a one-way ANOVA to determine if significant differences between males and females were present. Significant differences between sexes were found in both the Late Archaic and Protohistoric sample; once a size correction was applied to the raw measurements, eliminating size- dependant effects, differences between males and females for both periods were insignificant. This correction also eliminated significant skewness. Sexes were then combined to increase sample size.

Appendix C summarizes the results of a two-way ANOVA (sides = fixed, burials = random) with sexes combined. Fluctuating asymmetry is

2 represented by Si , which represents the difference in variance between the remainder (MSsj) and error (MSerr) divided by the number of replications (M). Measurement error is reported both as the percentage

104

of interaction, and as the square root of MSerr (ME2). ME2 represents the average error per measurement in millimeters (Palmer and Strobeck,

2003a:296).

Results were assessed for the presence of directional asymmetry by examining the sides variance component of each ANOVA table. A number of measurements were found initially to contain significant amounts of directional asymmetry. Table 15 summarizes these measurements. None of these measurements, however, were significant once a sequential Bonferroni correction was applied (.01/23 = .0004).

Tooth Period F Sig UM3 LA 9.653 .001 UM3 PH 5.965 .025 UM1P PH 11.441 .001 LM2 PH 8.483 .005 UM1A MO 14.234 p < .001 LM1A MO 5.674 .02

Table 15: Directionally asymmetrical measurements

Descriptive statistics of fluctuating asymmetry are summarized in

Table 16. Differences in the amount of fluctuating asymmetry between periods were small. Fluctuating asymmetry was higher in males than

2 females for both the Late Archaic and Protohistoric. Si for burials of

105 unknown sex were equidistant between male and female values for the

Late Archaic which suggests that this group is composed of relatively equal numbers of males and females for each time period. In all samples variance caused by the interaction of sides and burials exceeded measurement error variance for all measurements.

Estimates of fluctuating asymmetry were evaluated for significance

2 between groups with an F-Test (ratio of Si values) between the Late

Archaic and Protohistoric, Late Archaic and modern sample, and between the Protohistoric and modern sample. Degrees of freedom were corrected with the Satterthwaite formula (Palmer and Strobeck,

1986:408). Table 17 summarizes the results of this analysis.

Period Male Female Unknown Combined Late Archaic 5.59 2.11 3.84 4.63 Protohistoric 5.73 2.82 2.53 4.70 Modern ------3.33 3.33 Average 5.66 2.46 3.23

2 Table 16: Average Si values

Only one of the comparisons between periods was found to be significant at the .01 level. Palmer and Strobeck (2003a) have suggested repeating the analysis of variance using sub-groupings of data. Once the data were divided by sex, however, sample sizes were too small to

106

facilitate analysis. No significant differences were observed between the

Late Archaic and modern sample, or between the Protohistoric and modern sample.

Period Tooth df F Critical Value* UM1P 22,55 2.10 2.20 UI2 30,53 2.19 2.03 LA vs. PH UI1 26,51 1.55 2.03 LM2 28,44 1.08 2.20 LM1P 21,40 2.27 2.37 LI2 25,70 2.78** 2.12

LA vs. MO UM1P 22,62 0.94 2.20 LM1P 21,51 0.94 2.20

MO vs. PH UM1P 62,55 0.45 1.84 LM1P 51,40 0.41 2.06 * Critical values at .01 level (Rohlf and Sokal, 1995) ** Significant at .01 level

2 Table 17: F-Test of Fluctuating Asymmetry (Si ) between periods

Linear Enamel Hypoplasia

Several tests were conducted to determine if the LEH index differed significantly between samples and if LEH was positively associated with fluctuating asymmetry. Table 18 summarizes the results of an analysis of variance for the LEH index (period, sex = fixed).

107

Source SSQ Df MSQ F Significance Period 182.516 2 91.258 5.666 .004 Sex 54.171 2 27.086 1.682 .187 Period * Sex 228.647 2 114.323 7.099 .001 Error 5846.207 363 16.105

Table 18: Analysis of variance for LEH index

Period and the interaction between period and sex were highly significant while sex by itself was not a significant factor. Error

(remainder) contributed the least to variance between groups.

Comparing the LEH index against an index of fluctuating asymmetry tested the association between linear enamel hypoplasia and fluctuating asymmetry. The FA2 index was selected because it eliminates trait-size dependence. Table 19 summarizes the significant correlation coefficients between these variables with sexes combined.

Table 20 summarizes the same analysis grouped by sex.

Tooth Period Pearson r Significance N UP3 PH .256 .018 85 LM2 PH .288 .035 54 LC PH .246 .026 82 UP4 MO .382 .002* 63 LM1P MO .273 .033 61

* Significant at .01 Table 19: Significant correlations; LEH index and FA2, sexes combined.

108

Tooth Period Sex Pearson r Significance N LM2 PH M .598 .001* 26 UI1 PH F .449 .041 21 UM3 PH U -.918 .004* 7

* Significant at .01

Table 20: Significant correlations between LEH index and FA2, by sex

With the sexes combined only one of the correlations is significant

(UP4). Surprisingly, this measurement was from the modern sample.

UP4 was still significant after a sequential Bonferroni correction was applied (.01/5 = .002).

When the data were divided by sex and by period several correlations became significant (Table 20). All of the significant correlations between LEH index and fluctuating asymmetry were from the Protohistoric sample. The strongest correlation was LM2 for

Protohistoric females followed by UM3 among Protohistoric burials of unknown sex. The significance of UM3 is questionable, however, due to the small sample size. A number of other strong correlations (r > |.50|) were found in all of the groups studied, however none of these correlations were significant at greater than a .10 level. Following a sequential Bonferroni correction, only LM2 in Protohistoric males remained significant (.01/3 = .0033).

109 Summary

A number of differences were found between periods and sexes for buccolingual diameter and for the LEH index. Outliers caused by measurement error or aberrant individuals were identified and corrected by substituting 3rd measurements. Data were tested for the presence of trait size dependence, directional asymmetry and antisymmetry. While antisymmetry was not significant, many of the measurement distributions were highly leptokurtic and skewed. An attempt was made to correct for leptokurtosis by log-transforming the data. Even after this correction a large number of measurements were still leptokurtic, especially in the Protohistoric sample. Measurement distributions exhibiting significant leptokurtosis at the .01 level were excluded from further analysis. Trait-size dependence was significant for a number of measurements but was accommodated by the use of a size-corrected index. Directional asymmetry was present in several measurements from each period but was not significant after a sequential Bonferroni correction.

An analysis of variance was used to assess the portion of variance in the sample caused by directional asymmetry, fluctuating asymmetry and error. No significant differences in the amount of fluctuating asymmetry were found when the sexes were pooled or when data were analyzed by sex. A Levene’s test showed no significant differences in

110 error variances between samples. UM3, however, approached significance in Late Archaic and Protohistoric males. The LEH index was found to differ significantly between samples; interaction between period and sex was especially significant. Three estimates of fluctuating asymmetry from the Protohistoric were significantly correlated with the

LEH index. Only one of these correlations remained significant after a sequential Bonferroni correction.

111

CHAPTER 5

DISCUSSION AND CONCLUSIONS

The Late Archaic is a period of population growth and cultural change in the Ohio River Valley while the Protohistoric is a period of settlement nucleation and population decline. It has been argued that changes associated with maize agriculture caused a decline in health during the Late Prehistoric (which includes the Protohistoric), evident from the increased frequency of stress indicators on the skeleton and teeth when compared to earlier populations. Data on two dental stress indicators, linear enamel hypoplasia and fluctuating dental asymmetry was collected and compared between Late Archaic, Protohistoric and modern dental samples. Fluctuating dental asymmetry has been suggested as a better measurement of developmental stability than stature and skeletal lesions. This chapter restates the research problem, reviews the methods used, and discusses the implications of the results.

112 Problem Statement

Paleopathology at the Origins of Agriculture (1984) was a landmark publication in bioarchaeology. Contrary to the widely held belief that agriculture led to improvements in health, the many authors who contributed to Paleopathology at the Origins of Agriculture argue that health actually declined. Many of the examples in this work are based on skeletal populations from North America (Indian Knoll, Dickson

Mounds, etc.). Increases in hypoplastic defects that coincide with maize agriculture have also been noted by Sciulli (1978) and Larsen (1978).

Wood et al. (1992) have suggested that what researchers think is a reduction in overall health as evidenced by the increasing frequencies of dental carries, skeletal lesions, LEH and shorter statures could actually be evidence of improved health; that the shift to cultivation made available weaning gruels which reduced the spacing of births increasing fertility. The increased frequency of skeletal lesions and short stature can, according to Wood et al., be explained by selective mortality and sampling problems. They suggest that a shift to sedentary agriculture does not inevitably lead to a decline in health and that in some areas it might lead to a marked improvement (Wood et al. 1992:367).

Three interrelated hypotheses regarding fluctuating asymmetry, linear enamel hypoplasia and their expression in Late Archaic,

Protohistoric and modern populations were considered and tested.

113 Hypothesis 1: If the dentition deviates significantly from left-right symmetry then it will be due to fluctuating asymmetry and not other forms of asymmetry (directional asymmetry or antisymmetry) or measurement error. Fluctuating asymmetry will therefore represent environmental perturbation of development.

Hypothesis 2: If fluctuating dental asymmetry is related to elevated levels of stress during development then it will be positively associated with a general, episodic form of stress such as linear enamel hypoplasia. Hypoplastic defects will be more sensitive to stress and occur more frequently while elevated levels of dental asymmetry will occur only during periods of serious, acute stress.

Hypothesis 3: If levels of stress differ significantly among Late

114 Review of Methods

Data on buccolingual length was collected from the permanent teeth using a Mitutoyo Digimatic Caliper (Model No. CD-6”CS). Wear on the occlusal, mesial and distal surfaces of the Late Archaic and

Protohistoric samples prevented data on crown height and mesiodistal length from being collected. Teeth that were cracked or chipped on the labial/buccal or lingual crown surfaces were excluded. Measurements were taken three times, with at least four weeks between 1st and 2nd measurements. Linear enamel hypoplasia was scored for each tooth as the number of lines present. LEH was first assessed with the naked eye and then with the help of 10X magnifying lamp. All data were collected by the author.

Eight cemetery populations from Ohio represent the Terminal Late

Archaic, dating to approximately 3000 BP. Two large cemetery populations, one from northern Ohio and the other from West Virginia represent the Protohistoric. Both Protohistoric populations date to approximately 350-400 BP. Data for the modern component of the study was recorded from dental casts of orthodontic patients in Dayton, OH made between 1947 and 1967.

A variety of statistical methods were used to evaluate differences in the buccolingual dimension and LEH index between sexes and periods.

Prior to these analyses scatter plots of 1st and 2nd observations for each

115 measurement were visually inspected to identify outliers. Scatter plots of left and right observations were inspected to identify aberrant individuals. Outliers were subjected to a Grubb’s test to ascertain whether or not these observations were more deviant than by chance alone. Few of the 189 observations identified as outliers were significant.

In almost all cases the deviant observations found to be significant were corrected by replacement with the 3rd measurement.

Each measurement distribution was tested for antisymmetry, directional asymmetry and trait size dependence, the presence of which can make estimation of fluctuating asymmetry impossible.

Antisymmetry was evaluated by comparing kurtosis values against a table of critical values. Directional asymmetry was evaluated by both a paired-samples t-test and by examination of the sides variance component of a two-way ANOVA. Trait size dependence was tested using correlation coefficients between the average of left and right observations and the absolute difference between left and right sides. Data were transformed using a size correction (|L-R|/[L+R/2]) to reduce leptokurtosis and eliminate trait size dependence and skewness.

Measurement distributions that deviated significantly from a normal distribution after size correction were eliminated from subsequent analysis.

116 Fluctuating asymmetry was estimated using a two-way analysis of variance (sides=fixed, burials=random). Measurement error was evaluated by comparing the error component of the ANOVA to the interaction between sides and burials. A Levene’s test of error heterogeneity was used to test whether measurement error varied significantly between groups. Since variance between burials was eliminated by the size correction (SSQ, MSQ = 0), the portion of variance

2 caused by fluctuating asymmetry (Si ) was estimated by subtracting the error from the remainder and dividing it by the number of measurement replications ((MSsj-MSerr)/3). Fluctuating asymmetry between periods and sexes was tested for significance with an F-test, degrees of freedom corrected according to the Satterthwaite formula.

Linear enamel hypoplasia was evaluated with an analysis of variance (period, sex = fixed). The association between linear enamel hypoplasia and fluctuating asymmetry was tested by the correlation coefficient between the LEH index and the FA2 index (Palmer and

Strobeck, 2003).

Summary of Results

The results of the aforementioned tests were mixed. A number of issues prevented the preceding analysis from being more revealing. All measurements were initially collected for the purpose of generating descriptive statistics, however small sample sizes, especially after

117 elimination of observations that lacked antimeres, made testing of some measurement distributions impossible. Many measurements, especially those from the Protohistoric were highly leptokurtic even after log transformation and had to be excluded from analysis. Samples used to estimate fluctuating asymmetry and the association between fluctuating asymmetry and LEH were less than 10% of initial projections. The following is a summary of results relating to the three hypotheses addressed by the study.

Hypothesis 1: None of the measurements were found to be platykurtic, thus eliminating antisymmetry as an explanation for variation. Directional asymmetry was found to be insignificant by t-test; subsequent examination of the sides component of the analysis of variance revealed significant side-bias in several measurements.

Fluctuating asymmetry was a greater portion of variance than measurement error in all measurements. This hypothesis was supported in most, but not all, of the measurements used in the study.

Hypothesis 2: The LEH index was positively associated with fluctuating asymmetry in seven measurements, five for the Protohistoric and two for the modern sample. Of these measurements, three were significant at P < .01: mandibular M2 in Protohistoric males, maxillary

M3 in Protohistoric burials of unknown sex and maxillary P4 in the modern sample. This hypothesis is supported.

118 Hypothesis 3: Descriptive statistics for the LEH index suggest that the frequency of LEH for males and females in the Late Archaic and

Protohistoric was different. An analysis of variance supports these observations; the interaction between period and sex is the largest source of variance. This is due to a reversal in the frequency of LEH between the sexes from the Protohistoric to the Late Archaic and the low frequency in the modern sample. Several measurements were sufficiently normal to permit comparison of fluctuating asymmetry between periods using an F-test. Only one of these results was significant, however. Although LEH is significantly lower in the modern sample, there is no evidence that fluctuating asymmetry varies greatly between periods. These findings refute the original hypothesis.

Discussion of the results

Contrary to expectations, fluctuating dental asymmetry did not perform well as a measure of developmental instability. Fluctuating dental asymmetry was chosen for the current study because it provides a permanent record of growth instability during the earliest part of development, is not overly sensitive to stress (like Harris lines) and, unlike other stress indicators, is not obscured by remodeling of bone. As the study demonstrated, these advantages may easily be eliminated due to the requirements of the statistical methods used in estimating fluctuating asymmetry.

119 Small sample sizes significantly impacted the results of the study.

Even though a large number of Late Archaic individuals were included in the study this number was significantly reduced because of missing teeth. While sample size was adequate for the Protohistoric many of the measurement distributions were leptokurtic; since an analysis of variance has been shown to be the only means of differentiating fluctuating asymmetry from measurement error, and since the ANOVA requires relatively normal distributions of data, this eliminated more than 75% of the data from the Protohistoric and approximately 50% of the data overall. Sample sizes were increased by combining sexes, but were still approximately 1/3 of original projections. Statistical power for the two-way ANOVA was calculated using PiFace v1.61 by Russ Lenth

(2004). Powers were between 12% and 90%, with both the highest and lowest scores coming from the modern sample. Average statistical power of all tests was 59% and 62% in the Late Archaic and Protohistoric respectively.

Measurement error, which was initially believed to be a major obstacle to estimating fluctuating asymmetry, was lower than expected.

The square root of the mean square for error (ME2) was used to estimate the amount of measurement error in millimeters. This ranged from a low of .039 mm in Late Archaic maxillary I1 to a high of .285 mm in Late

Archaic maxillary C. Average measurement error for all teeth and

120 periods was .105 mm. The percentage of measurement error in comparison to the interaction variance for each distribution was

2 estimated as the mean square of error divided by Si . Average percentage of measurement error for the Late Archaic, Protohistoric and modern samples was 25.3%, 25.6% and 25% of the interaction variance respectively. The average median percentage of measurement error across all three periods was approximately 18%. These results are comparable to those of Sciulli (2003), who found measurement error to be between 5.2 and 39.2% for the Late Archaic and between 2.7 and

12.1% for the Protohistoric. The fact that fluctuating asymmetry accounted for more than three times the measurement error in the sample supports the use of fluctuating dental asymmetry for studies of this type.

Fluctuating asymmetry and directional asymmetry were present in teeth from all three periods. The expression of fluctuating asymmetry was not uniform, however. Later developing teeth, especially the molars and canines exhibited greater asymmetry than earlier developing teeth such as the central incisor and first molar. The only exception to this was the lateral incisor. These results are consistent with those of other researchers (Bailit, 1970; Noss, 1983; Sciulli, 2003; Townsend and

Brown, 1980). In general the third molars were the most asymmetrical teeth, followed by the lateral incisor, canine and premolars in the

121 maxillary dentition. In all of the samples the first molar ranked last or second to last in total asymmetry. Despite the expectation that fluctuating asymmetry would differ between periods little evidence was found to support this premise. Average fluctuating asymmetry was higher among males than females and highest in the Protohistoric sample (see Table 16, Chapter 4). When these differences were tested, however, only one of them was found to be significant between periods.

These findings suggest several things about the pattern of dental development in these populations. First, at least with regards to the dentition, fluctuating asymmetry is a relatively small portion of the total variance between sides. This suggests that dental development is well buffered against stress. Second, teeth like the first molar that develop in utero are better protected from perturbation that later developing teeth, such as the third molar, lateral incisor or canine. Since crown shape results from folding of the epithelium during the cap and bell stage of odontogenesis it is the stability of this event that most influences the expression of fluctuating asymmetry. This event is well protected from environmental disturbance in the 1st molar and central incisor, and less so in the other teeth. The lateral incisor and canine appear especially vulnerable to disruption. Crown development of the lateral incisor occurs between 9 months after birth and 4-5 years of age. This is the period during which weaning usually takes place and is typically a period

122 of high stress, although why this stress would not also affect the central incisor remains unclear (Hillson, 1996:123). Increased levels of fluctuating asymmetry in the canine may be due to its comparatively long period of crown development.

The frequency of linear enamel hypoplasia also differs between sexes and periods. Descriptive statistics for the LEH index show that males have higher frequencies of LEH than females in the Late Archaic, and lower frequencies than females in the Protohistoric. The strong significance of the interaction between period and sex in an ANOVA of

LEH index values suggests that these differences are not due to chance alone. These results may indicate a change in the early lives of males and females during the Protohistoric. The drop in LEH for males between the Late Archaic and Protohistoric may be due to a reduction in stress, or it may be a consequence of selective mortality; males may have been less likely to survive events that caused LEH during the

Protohistoric than during the Late Archaic. This situation may have been reversed for females. Meadow and Jantz (1999) and Deborah

Crooks (1999) have shown that males and females respond differently to nutritional changes. In both studies, males were more sensitive to changes in diet than females, who were better able to accommodate stress. The increased availability of carbohydrates associated with maize agriculture may have benefited the overall health of males more than

123 females, as indicated by the decline of LEH in Protohistoric males. Sub- groupings of sexed individuals for each period, however, were too small to determine if differences in fluctuating asymmetry between sexes and periods was significant.

A positive correlation was expected between fluctuating asymmetry and linear enamel hypolasia. In fact, only five measurements show any association when sexes were combined; the strongest of these correlations was for maxillary P4 in modern burials. While three measurements from the Protohistoric were initially found to correlate significantly with LEH these results were non-significant after a sequential Bonferroni correction. After this correction, only maxillary P4 from the modern sample remained significant.

When samples were grouped by sex several correlations were found to be significant. One of the strongest correlations was for maxillary M3 in the Protohistoric (r = -.918), although the small size of the sample makes this questionable. A negative correlation was unexpected, but not unprecedented. Corrucini et al. (2005) report a significant ( p < .0005 ) negative correlation between hypoplasia and asymmetry in the maxillary canine. Corrucini et al. have explained negative correlations as evidence for the lack of association between fluctuating asymmetry and stress. In this case, individuals who survived long enough for their third molars to erupt may have been healthier than other individuals in the population

124 and less likely to develop hypoplasia. The most significant correlation between fluctuating asymmetry and LEH was for mandibular M2 in

Protohistoric males. No significant correlations between fluctuating asymmetry and LEH were found in the Late Archaic or modern samples.

The finding that none of the teeth from either the Late Archaic or modern sample show a significant correlation between LEH and fluctuating asymmetry supports the notion that Protohistoric populations were under increased stress compared to earlier or more recent populations living in the Ohio River valley. Since hypoplasias develop after crown shape formation the presence of LEH in conjunction with fluctuating asymmetry indicates at least two and possibly multiple episodes of stress in the Protohistoric. Protohistoric populations are also shorter and less sexually dimorphic than populations from the Late

Archaic. The association between all four of these stress indicators may mean that the lives of Protohistoric individuals were punctuated with multiple periods of stress throughout growth and development. Late

Archaic populations may have experienced a period of stress early in development that produced some linear enamel hypoplasia but this was followed by a period of recovery.

An unexpected outcome of the study was the presence of multiple, strongly non-normal distributions in the Protohistoric sample. Both the

Late Archaic and modern samples have only a few measurements that

125 are leptokurtic. The Protohistoric sample, however, is almost completely leptokurtic. In the absence of trait size dependency, directional asymmetry or statistical outliers, variance is caused by either antisymmetry or measurement error. Under these circumstances fluctuating asymmetry should distribute normally around a mean of zero. The failure to find significant antisymmetry in the samples and the relative homogeneity of error variances means that the fluctuating asymmetry in the sample is truly non-normal and suggests that some as yet unidentified process is responsible.

Palmer and Strobeck (2003a) have cautioned the use of leptokurtosis by itself as an indicator of developmental instability. The presence of other stress indicators and their correlation with fluctuating asymmetry in the current study, however, supports this interpretation.

Working with the Late Prehistoric population of Pearson Village, Sciulli

(2003) found no difference in fluctuating asymmetry between it and Late

Archaic populations from the same area. Estimates of measurement error and directional asymmetry at Pearson Village were also similar to the levels reported here. Pearson Village was also relatively normal in its distribution, even without log transformation. Despite these similarities,

Protohistoric distributions of data in the current study remained leptokurtic even after log transformation. Additional support for the current interpretation comes from Gangestad and Thornhill (1999). In a

126 study of simulated populations in which fluctuating asymmetry was a significant portion of variance, distributions were strongly leptokurtic.

This analysis was extended to several previously published studies of real data. As with the simulated populations, leptokurtosis was present in many of the samples. Gangestad and Thornhill conclude that population distributions in which individuals are heterogeneous in susceptibility to developmental noise not only are, but should be leptokurtic.

The conclusion reached here, that Protohistoric populations were subjected to greater stress which increased developmental instability

(developmental noise) has several important implications. First, it indicates that adaptations to the physical and/or social environment at the time of contact were not sufficient to buffer populations from developmental perturbation. It has been argued that reduced stature and sexual dimorphism are adaptations to the new social and nutritional realities of this period. The fact that populations were no longer able to develop along precisely controlled paths seems to question this interpretation. Populations under selection for shorter, less sexually dimorphic individuals might still be expected to develop normally, albeit not reaching their full growth potential. This is demonstrated by Pearson

127 Village. While Pearson Village individuals are similar in stature and degree of sexual dimorphism to the Grantham and Buffalo sites, little evidence exists for a dramatic change in developmental stability.

Further support for these conclusions is provided by the fluctuating asymmetry estimates from the Late Archaic and modern samples used in the study. No significant differences were found in the degree of asymmetry between periods. This is, however, a misleading result. Since only relatively normal distributions of measurements could be used to estimate fluctuating asymmetry it is quite possible that measurements that truly reflect developmental instability were excluded from analysis. If significant asymmetry is only present in leptokurtic measurements then what is actually being reported is not developmental instability due to stress, but “background” developmental noise that may be part of the growth process for all organisms and in all morphological traits. If this is the case we should expect similar levels of fluctuating asymmetry in all normally distributed measurements in all populations.

This might explain the lack of sensitivity reported by other researchers between fluctuating asymmetry and some forms of stress.

Does fluctuating dental asymmetry provide a straightforward interpretation of health, eliminating the paradoxical nature of interpretations made from skeletal lesions? The answer to this question is far less clear. The presence of fluctuating asymmetry by itself is not

128 necessarily indicative of a loss of developmental stability. When distributions are leptokurtic and other causes of leptokurtosis have been eliminated the interpretation is less ambiguous. It appears from the present research, however, that levels of stress above and beyond those that produce short stature, low sexual dimorphism and increases in linear enamel hypoplasia are necessary to produce changes in degree of fluctuating asymmetry. In conjunction with other stress indicators the interpretation of health using estimates of fluctuating asymmetry as one of the components is anything but paradoxical. To claim otherwise requires demonstrating that the loss of developmental control is somehow beneficial or that asymmetry is not associated with fitness.

More than refuting the Osteologcial Paradox, the present study introduces its own paradox; fluctuating asymmetry can be estimated only for normal distributions, which by their very nature may contain insignificant levels of fluctuating asymmetry.

The current study supports the use of fluctuating asymmetry as an indicator of developmental instability, but not as a measurement, per se.

It is possible that the degree of leptokurtosis may prove a better estimate of developmental instability than the indices proposed by Palmer and

Strobeck, but this has yet to be shown. It may be that a stress threshold must be reached before significant changes to symmetry take place. This might explain why Pearson Village and the samples studied here differ.

129 If this is true then something other than maize agriculture accounts for the differences between these populations. Cultivation of maize as the primary food crop may not have been as nutritious as the semi- sedentary, hunting and gathering diet of the Late Archaic, but it alone is not responsible for the decline in health associated with sites in the

Protohistoric. More recent populations of Native American maize agriculturalists from the same area whose stature and degree of sexual dimorphism increased dramatically following contact with Europeans further support this (Sciulli and Oberly, 2002).

What, then, is responsible for the decline in health during the

Protohistoric? Two explanations seem to present themselves. Population climbed steadily throughout the Late Prehistoric as a result of the increased availability of carbohydrates due to a maize diet. There is some evidence that the cooler temperatures associated with the hypsithermal ( “Little Ice Age“ ) between 1450 and 1600 AD forced populations to abandon upland sites, creating competition for more sheltered agricultural land in the river valleys (Carskadden, 2000).

Competition for limited resources may have resulted in an overall decline in nutrition, which up until this point had been adequate to support these populations.

130 Another possibility is that health declined as a result of European contact. It is possible that competition for European derived trade goods as prestige items created new cultural stresses; resources may have become less evenly distributed as food is traded rather than redistributed among community members. This seems unlikely, however, since no evidence of European derived trade goods have been found at Grantham and only a single glass bead has been found at Buffalo. Likewise, the social composition of both Buffalo and Norma Grantham appear less strongly ranked and more transegalitarian than other Late Prehistoric populations.

A more plausible scenario is that these populations were already under stress and were subsequently exposed to infectious disease carried along existing trade routes, such as the trade-axis for European goods suggested by Strothers (2000). Multiple graves from the Hardin Village and Augusta sites in Kentucky may represent some of the first victims of infectious disease brought on by contact (Pollack and Henderson, 2000).

Sciulli and Oberly (2002) have suggested that Late Prehistoric populations may never have completely adapted to maize agriculture in the Ohio River Valley. If this is true then Protohistoric populations may have been particularly susceptible to infectious disease because of an immune system already compromised by poor nutrition.

131 There are several directions for future research on the topics addressed in this study. Additional research into the causes of fluctuating asymmetry is still needed. It remains unclear whether fluctuating dental asymmetry increases steadily with stress or becomes significant only when a threshold has been reached. It is also unclear whether there exist particular critical periods, such as at or around weaning, when crown dimensions might be particularly susceptible to disturbance. These issues are best addressed by controlled animal studies, or by the analysis of human samples from periods of high stress where the type and quantity of stressor is known. With regards to bioarchaeological applications it is probably unrealistic to think that fluctuating asymmetry can be fully understood in prehistoric samples when it is not completely understood in living populations.

Before these questions can be addressed, however, the statistical methods for the detection and analysis of fluctuating asymmetry need to be improved. Presently, non-normal data must be rejected from analysis.

At best this might simply reduce sample sizes. At worst it may completely eliminate samples that contain individuals who exhibit different levels of fluctuating asymmetry. Van Dongen et al. (1999) have suggested using mixture analysis for identifying the presence of directional asymmetry, antisymmetry and fluctuating asymmetry in samples containing all three. While this method is cumbersome and

132 does not provide a means for quantifying the proportions of each type of asymmetry, it is an improvement over current methods that simply reject distributions containing significant amounts of playtkurtosis or leptokurtosis. Results of the present study also suggest using the amount of leptokurtosis present in a sample, measured as kurtosis, in place of fluctuating asymmetry estimates based on variance.

Conclusion

Despite expectations to the contrary, fluctuating asymmetry was not found to differ significantly between periods or sexes. Fluctuating asymmetry is significantly associated with linear enamel hypoplasia only in the Protohistoric samples used in the study. Significant levels of fluctuating asymmetry may have been present in a number of measurements, but due to leptokurtosis in many of the measurement distributions was not quantifiable. Protohistoric populations in the Ohio

River Valley were under increasing stress due to population density and competition for resources. While Late Prehistoric populations were shorter and less sexually dimorphic than populations from the Late

Archaic, little evidence exists to suggest that developmental stability was negatively affected by maize agriculture. It is not until the Protohistoric when populations are in contact with European groups that developmental instability increases significantly. The frequency of linear enamel hypoplasia shows no correlation with fluctuating asymmetry for

133 the Late Archaic or modern samples used in the study. Several correlations are significant for the Protohistoric sample, however, which suggests that the capacity of these populations to buffer stress culturally or physiologically had been exceeded.

134

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148

APPENDIX A

CRITICAL KURTOSIS VALUES

149

Table 21: Critical Values of the Kurtosis Test Statistic From Normality in the Direction of Platykurtosis and Leptokurtosis (from Palmer and Strobeck, 2003a:305)

150

APPENDIX B

DESCRIPTIVE STATISTICS

151

Tooth N Minimum Maximum Mean Std. Deviation UM3 6 11.38 12.69 11.9000 .45418 UM2 7 11.07 12.91 12.2186 .56925 UM1A 5 12.22 13.12 12.5760 .35090

UM1P 5 11.31 12.09 11.7780 .30120 t f UP4 e 6 9.11 10.43 10.0150 .47010 L UP3 6 9.35 10.38 9.9800 .36916 UC 7 7.50 10.06 9.0171 .89833

UI2 8 5.55 7.39 6.5188 .59082 a l l UI1 i 10 7.08 8.45 7.5870 .40893 x a UI1 10 6.88 8.47 7.5630 .43765

M UI2 7 5.33 7.21 6.3800 .64789 UC 7 7.42 9.79 8.9900 .82573

t UP3 7 9.90 10.59 10.2257 .26400 h

g UP4 5 9.19 10.51 9.9820 .53457

Ri UM1A 3 12.24 12.91 12.5700 .33511 UM1P 3 10.91 11.86 11.4367 .48336 UM2 8 11.64 12.80 12.2400 .44973 UM3 7 10.02 12.01 11.5043 .70192 LM3 11 8.98 12.22 10.8509 .94002 LM2 8 10.43 12.13 11.1613 .57267 LM1A 8 10.73 12.04 11.5575 .48266

LM1P 8 10.78 12.06 11.3675 .46619 t f LP4 e 14 8.10 9.46 8.9136 .36397 L LP3 8 7.88 9.27 8.6888 .48743 LC 12 7.13 9.11 8.2775 .64517

e LI2 l 10 5.87 6.84 6.3550 .30519 b i LI1 9 5.41 6.01 5.7867 .20087 d n LI1 8 5.49 6.00 5.8175 .17069 a LI2 M 10 5.97 7.06 6.4480 .37139 LC 11 7.30 9.02 8.1936 .53625

t LP3 12 8.02 9.17 8.5983 .38264 h

g LP4 15 8.07 9.40 8.8673 .37061

Ri LM1A 8 10.81 11.99 11.3837 .45738 LM1P 9 10.92 12.09 11.5367 .41473 LM2 6 10.60 11.63 11.1083 .49491 LM3 10 9.19 12.51 10.9370 1.01149

Table 22: Descriptive Statistics: Late Archaic Males

152

Tooth N Minimum Maximum Mean Std. Deviation UM3 9 9.73 13.31 11.1756 1.05896 UM2 15 10.40 13.78 11.7207 .82115 UM1A 9 11.20 13.11 11.9567 .54571

UM1P 10 10.29 11.88 10.8940 .50731 t f UP4 e 13 8.81 10.75 9.7608 .58607 L UP3 13 8.16 11.43 9.9362 .78424 UC 16 7.98 10.33 8.7044 .62701

UI2 15 5.24 7.97 6.7800 .71234 a l l UI1 i 14 6.46 8.08 7.2464 .42878 x a UI1 13 6.68 8.05 7.2485 .33570

M UI2 16 5.13 8.26 6.7131 .67974 UC 15 7.75 10.48 8.6113 .67303

t UP3 14 8.42 11.42 9.7943 .75035 h

g UP4 12 8.90 10.92 9.7975 .60908

Ri UM1A 10 11.37 13.10 11.8640 .50879 UM1P 9 10.24 11.28 10.7967 .37202 UM2 15 11.03 13.66 11.8613 .71846 UM3 9 10.13 13.30 11.0489 1.05827 LM3 12 9.72 13.29 10.7950 .90960 LM2 10 10.17 11.93 10.8270 .63603 LM1A 9 10.34 11.71 10.7656 .39906

LM1P 9 10.25 11.48 10.8367 .36712 t f LP4 e 13 6.97 9.97 8.4477 .79957 L LP3 11 7.19 9.56 8.3182 .61475 LC 12 6.75 9.36 7.7167 .72836

e LI2 l 9 5.83 6.70 6.1067 .30627 b i LI1 9 5.24 6.41 5.7689 .41390 d n LI1 9 5.27 6.23 5.7733 .31619 a LI2 M 8 5.77 6.57 6.0663 .29223 LC 12 6.78 9.13 7.6400 .67273

t LP3 12 7.26 9.58 8.2158 .65662 h

g LP4 14 7.32 10.00 8.4757 .78194

Ri LM1A 10 10.29 11.89 10.9260 .51097 LM1P 9 10.33 11.79 10.9311 .46326 LM2 11 9.65 11.89 10.7545 .70110 LM3 13 9.45 12.20 10.7485 .75277

Table 23: Descriptive Statistics: Late Archaic Females

153

Tooth N Minimum Maximum Mean Std. Deviation UM3 15 10.79 13.12 11.5673 .65601 UM2 28 11.27 13.26 12.1354 .51778 UM1A 23 10.72 13.40 12.1087 .58886

UM1P 23 9.65 12.90 11.2743 .72681 t f UP4 e 20 8.77 10.45 9.5410 .51686 L UP3 23 8.14 11.26 9.8613 .66476 UC 23 6.58 9.70 8.6696 .63241

UI2 15 5.70 7.20 6.5380 .49778 a l

l UI1 i 15 5.98 7.67 7.1200 .43630 x

a UI1 16 6.18 7.85 7.1675 .44535

M UI2 22 5.65 7.70 6.6541 .44675 UC 23 7.46 9.87 8.6613 .61689

t UP3 24 8.86 11.14 9.8683 .59593 h

g UP4 24 7.94 10.55 9.5525 .66046

Ri UM1A 26 10.83 13.26 12.0392 .57349 UM1P 25 9.64 12.90 11.1736 .73078 UM2 30 10.80 13.19 12.1033 .58250 UM3 16 10.53 13.07 11.5988 .78413 LM3 17 9.10 11.73 10.6547 .75820 LM2 23 10.12 11.61 10.8361 .40339 LM1A 27 9.55 12.15 10.9311 .56163

LM1P 25 9.69 12.08 11.0348 .58085 t f LP4 e 27 7.18 9.42 8.5670 .47248 L LP3 23 7.29 9.08 8.2170 .47599 LC 19 6.70 8.82 7.7589 .59644

e LI2 l 20 5.61 7.07 6.2635 .31074 b i LI1 16 4.97 6.38 5.6900 .35646 d

n LI1 17 4.92 6.53 5.7371 .38616 a LI2 M 17 5.62 6.63 6.2212 .27418 LC 19 6.86 8.34 7.7326 .43614

t LP3 20 7.28 9.04 8.2850 .49409 h

g LP4 26 7.45 9.52 8.4623 .51672

Ri LM1A 24 9.64 12.03 10.9271 .53268 LM1P 20 9.80 12.52 10.9980 .61412 LM2 24 10.01 11.43 10.8213 .37966 LM3 15 9.64 11.37 10.4840 .50402

Table 24: Descriptive Statistics: Late Archaic Unknown Sex

154

Tooth N Minimum Maximum Mean Std. Deviation UM3 13 10.08 11.64 10.7469 .44222 UM2 36 10.17 13.26 11.7628 .72829 UM1A 35 10.91 13.33 11.9320 .56205

UM1P 33 10.08 12.27 11.0773 .56415 t f UP4 e 38 8.53 10.66 9.6366 .56626 L UP3 42 8.67 11.17 9.7340 .62828 UC 35 7.68 9.73 8.5280 .54218

UI2 30 5.64 7.80 6.5143 .50478 a l l UI1 i 31 5.84 8.23 7.1213 .49811 x a UI1 25 5.79 7.94 7.0560 .50859

M UI2 33 5.50 7.98 6.7003 .61190 UC 40 7.26 9.76 8.5487 .55830

t UP3 42 8.15 11.15 9.6429 .62998 h

g UP4 34 8.50 10.48 9.6079 .53154

Ri UM1A 33 10.98 13.40 11.9285 .60619 UM1P 31 10.18 12.47 11.1119 .67694 UM2 33 10.02 13.52 11.7700 .75050 UM3 12 10.15 12.24 10.9725 .66816 LM3 21 9.64 12.47 10.5148 .72822 LM2 26 9.39 11.36 10.5477 .47977 LM1A 25 10.07 11.94 10.8128 .47040

LM1P 25 10.03 11.75 10.8988 .47873 t f LP4 e 38 7.62 10.10 8.5205 .49142 L LP3 42 7.26 9.58 8.1386 .51077 LC 37 6.55 8.97 7.8781 .53064

e LI2 l 35 5.38 7.34 6.1546 .40143 b i LI1 32 5.18 6.47 5.7306 .33733 d n LI1 29 5.12 6.52 5.7341 .38788 a LI2 M 39 5.59 7.40 6.1495 .38424 LC 42 6.97 9.00 7.9712 .49000

t LP3 41 7.24 9.44 8.1466 .48943 h

g LP4 37 7.75 9.26 8.4389 .41606

Ri LM1A 30 10.00 11.91 10.8647 .43934 LM1P 26 10.11 11.95 10.8131 .45824 LM2 20 9.55 11.66 10.5995 .51576 LM3 23 9.64 11.31 10.3283 .46378

Table 25: Descriptive Statistics: Protohistoric Males

155

Tooth N Minimum Maximum Mean Std. Deviation UM3 30 8.98 11.80 10.4917 .71043 UM2 46 10.03 12.83 11.3680 .61162 UM1A 48 10.50 12.99 11.7565 .51246

UM1P 47 9.68 12.02 10.9162 .52832 t f UP4 e 41 8.40 11.08 9.5876 .57844 L UP3 44 8.54 10.76 9.5995 .47742 UC 42 6.88 9.73 8.3579 .57425

UI2 31 5.81 7.56 6.5419 .44220 a l l UI1 i 30 6.36 8.06 7.1433 .44209 x a UI1 37 6.43 8.09 7.1616 .45076

M UI2 37 5.74 7.47 6.5654 .43584 UC 44 6.82 9.39 8.3511 .59397

t UP3 47 8.78 10.78 9.6428 .40035 h

g UP4 36 8.98 10.93 9.6664 .47405

Ri UM1A 52 10.56 12.87 11.6531 .55907 UM1P 49 9.78 11.91 10.8376 .56613 UM2 54 9.87 13.26 11.3874 .71154 UM3 25 9.05 12.35 10.7928 .90188 LM3 28 9.04 11.41 10.1918 .69680 LM2 35 9.42 11.73 10.3634 .56808 LM1A 34 9.68 11.43 10.6050 .46344

LM1P 34 9.87 11.56 10.7874 .43745 t f LP4 e 50 7.67 9.38 8.4176 .42386 L LP3 53 7.21 9.45 7.9698 .46948 LC 46 6.31 8.58 7.5441 .53990

e LI2 l 48 5.13 6.89 5.9815 .34246 b i LI1 39 4.97 6.50 5.5867 .35865 d n LI1 35 4.88 6.68 5.5583 .38476 a LI2 M 49 5.45 6.99 5.9982 .32722 LC 51 6.77 8.65 7.5822 .46523

t LP3 53 7.33 9.20 8.0047 .44628 h

g LP4 51 7.53 9.48 8.4371 .45501

Ri LM1A 35 9.83 11.54 10.7557 .41238 LM1P 35 9.95 11.46 10.8051 .42540 LM2 32 9.22 11.32 10.4075 .54680 LM3 21 9.01 11.91 10.0743 .69414

Table 26: Descriptive Statistics: Protohistoric Females

156

Tooth N Minimum Maximum Mean Std. Deviation UM3 5 9.97 11.37 10.8800 .55942 UM2 23 10.44 12.89 11.6470 .69998 UM1A 25 10.70 12.74 11.7732 .45676

UM1P 25 10.28 12.07 11.0692 .47338 t f UP4 e 22 8.56 10.59 9.6391 .54237 L UP3 21 9.07 10.29 9.6976 .36066 UC 18 7.50 9.46 8.4322 .52435

UI2 19 5.78 7.44 6.6332 .42126 a l

l UI1 i 18 6.57 8.03 7.2900 .40916 x

a UI1 15 6.59 8.00 7.2173 .36515

M UI2 17 5.83 8.51 6.7635 .64947 UC 16 7.56 10.45 8.6575 .84030

t UP3 21 8.13 10.49 9.6433 .60280 h

g UP4 22 8.68 12.33 9.7664 .78125

Ri UM1A 23 10.21 12.51 11.7235 .47920 UM1P 24 9.42 12.00 10.9596 .57523 UM2 21 10.13 13.41 11.5095 .74640 UM3 5 9.92 12.07 11.2480 .82672 LM3 9 9.59 11.91 10.7278 .91623 LM2 14 9.31 11.42 10.5157 .56798 LM1A 22 9.93 11.27 10.6895 .35903

LM1P 23 9.88 11.45 10.7313 .39753 t f LP4 e 20 8.11 9.17 8.6505 .30025 L LP3 18 7.48 8.65 8.1672 .40568 LC 19 6.91 8.91 7.8063 .48869

e LI2 l 15 5.67 6.76 6.0487 .29127 b i LI1 17 5.15 6.14 5.6735 .29464 d

n LI1 14 5.15 5.96 5.5629 .24069 a LI2 M 16 5.67 7.09 6.1181 .36220 LC 11 6.98 9.07 7.9718 .57477

t LP3 18 7.68 8.66 8.1967 .34057 h

g LP4 19 8.15 9.38 8.6311 .33436

Ri LM1A 20 10.16 11.65 10.8820 .42779 LM1P 20 9.69 11.65 10.6940 .49098 LM2 19 9.53 11.38 10.5258 .51560 LM3 6 10.00 11.75 10.7750 .71899

Table 27: Descriptive Statistics: Protohistoric Unknown Sex

157

Tooth N Minimum Maximum Mean Std. Deviation UM3 0 UM2 2 11.43 11.73 11.5800 .21213 UM1A 76 10.32 12.87 11.5167 .56862

UM1P 73 9.60 12.83 10.8601 .58210 t f UP4 e 63 8.04 10.49 9.4570 .53432 L UP3 79 7.54 10.46 9.3508 .49062 UC 5 7.80 8.88 8.4260 .40568

UI2 1 6.99 6.99 6.9900 . a l

l UI1 i 1 7.20 7.20 7.2000 . x

a UI1 1 7.20 7.20 7.2000 .

M UI2 1 6.92 6.92 6.9200 . UC 5 8.08 8.77 8.4120 .29525

t UP3 81 7.64 10.46 9.3286 .50636 h

g UP4 63 7.99 10.73 9.4962 .54981

Ri UM1A 76 10.42 12.93 11.5929 .51520 UM1P 72 9.47 12.69 10.8814 .58125 UM2 2 11.15 11.30 11.2250 .10607 UM3 0 LM3 0 LM2 1 10.87 10.87 10.8700 . LM1A 64 9.30 11.97 10.4323 .59079

LM1P 63 9.14 11.75 10.3884 .55785 t f LP4 e 43 7.75 9.57 8.4840 .48669 L LP3 46 6.69 9.03 7.8952 .53361 LC 1 7.41 7.41 7.4100 .

e LI2 l 1 6.73 6.73 6.7300 . b i LI1 3 5.38 6.71 6.2567 .75936 d

n LI1 3 5.58 6.80 6.3667 .68245 a LI2 M 2 6.84 6.87 6.8550 .02121 LC 1 7.59 7.59 7.5900 .

t LP3 48 6.51 9.63 7.9433 .57330 h

g LP4 43 7.59 9.82 8.6130 .53586

Ri LM1A 63 9.10 11.99 10.4897 .57134 LM1P 61 9.12 11.48 10.3892 .49381 LM2 1 11.17 11.17 11.1700 . LM3 0

Table 28: Descriptive Statistics: Modern Sample Unknown Sex

158

Avg Avg Sex Diffs, Sex Diffs, LA Pooled PH Pooled MO Pooled LA - PH - Tooth LA PH Avg Avg Avg PH MO UM3 0.72 0.26 11.54 10.62 0.92 UM2 0.50 0.39 11.97 11.57 11.58 0.40 -0.01 UM1A 0.62 0.18 12.27 11.84 11.52 0.42 0.33 UM1P

0.88 0.16 11.34 11.00 10.86 0.34 0.14 t f UP4 e 0.25 0.05 9.89 9.61 9.46 0.28 0.16 L UP3 0.04 0.13 9.96 9.67 9.35 0.29 0.32 UC 0.31 0.17 8.86 8.44 8.43 0.42 0.02 UI2 -0.26 -0.03 6.65 6.53 6.99 0.12 -0.46 UI1 0.34 -0.02 7.42 7.13 7.20 0.28 -0.07 UI1 0.31 -0.11 7.41 7.11 7.20 0.30 -0.09 UI2 -0.33 0.13 6.55 6.63 6.92 -0.09 -0.29 UC 0.38 0.20 8.80 8.45 8.41 0.35 0.04

UP3

t 0.43 0.00 10.01 9.64 9.33 0.37 0.31 h

g UP4 i 0.18 -0.06 9.89 9.64 9.50 0.25 0.14 R UM1A 0.71 0.28 12.22 11.79 11.59 0.43 0.20 UM1P 0.64 0.27 11.12 10.97 10.88 0.14 0.09 UM2 0.38 0.38 12.05 11.58 11.23 0.47 0.35 UM3 0.46 0.18 11.28 10.88 0.39 LM3 0.06 0.32 10.82 10.35 0.47 LM2 0.33 0.18 10.99 10.46 10.87 0.54 -0.41 LM1A 0.79 0.21 11.16 10.71 10.43 0.45 0.28 LM1P

0.53 0.11 11.10 10.84 10.39 0.26 0.45 t f LP4 e 0.47 0.10 8.68 8.47 8.48 0.21 -0.01 L LP3 0.37 0.17 8.50 8.05 7.90 0.45 0.16 LC 0.56 0.33 8.00 7.71 7.41 0.29 0.30 LI2 0.25 0.17 6.23 6.07 6.73 0.16 -0.66 LI1 0.02 0.14 5.78 5.66 6.26 0.12 -0.60 LI1 0.04 0.18 5.80 5.65 6.37 0.15 -0.72 LI2 0.38 0.15 6.26 6.07 6.86 0.18 -0.78 LC 0.55 0.39 7.92 7.78 7.59 0.14 0.19

LP3

t 0.38 0.14 8.41 8.08 7.94 0.33 0.13 h

g LP4 i 0.39 0.00 8.67 8.44 8.61 0.23 -0.18 R LM1A 0.46 0.11 11.15 10.81 10.49 0.34 0.32 LM1P 0.61 0.01 11.23 10.81 10.39 0.42 0.42 LM2 0.35 0.19 10.93 10.50 11.17 0.43 -0.67 LM3 0.19 0.25 10.84 10.20 0.64 Mean 0.37 0.16 9.49 9.16 9.01 0.33 -0.02 SD 0.26 0.13 2.03 1.92 1.76 0.17 0.37

Table 29: Average Differences Between Sexes and Periods

159

APPENDIX C

RESULTS OF ANALYSIS OF VARIANCE

160 2 * Tooth Source df SSQ MSQ F Sig Si dfs % Err ME2 UM3 Side 1 0.104 0.104 9.652 0.01 Burial 11 0 0 0 1 S*B 11 0.119 1.08E-02 151.865 0 35.8 9.87 0.02 0.104 Error 48 3.42E-03 7.13E-05

UM2 Side 1 8.34E-04 8.34E-04 1.765 0.191 Burial 43 0 0 0 1 S*B 43 2.03E-02 4.73E-04 17.849 0 1.49 37.43 0.18 0.083 Error 176 4.66E-03 2.65E-05

UM1A Side 1 1.00E-04 1.00E-04 0.321 0.576 Burial 29 0 0 0 1 S*B 29 9.05E-03 3.12E-04 27.866 0 1.00 26.03 0.11 0.058 Error 120 1.34E-03 1.12E-05

UM1P Side 1 1.13E-03 1.13E-03 2.772 0.107 Burial 28 0 0 0 1 S*B 28 1.14E-02 4.09E-04 9.809 0 1.22 21.77 0.34 0.119 Error 116 4.83E-03 4.17E-05

UP4 Side 1 2.93E-04 2.93E-04 0.403 0.53 Burial 32 0 0 0 1 S*B 32 2.33E-02 7.27E-04 15.237 0 2.26 27.06 0.21 0.142 Error 132 6.29E-03 4.77E-05

UP3 Side 1 1.10E-03 1.10E-03 1.373 0.249 Burial 35 0 0 0 1 S*B 35 2.79E-02 7.97E-04 60.543 0 2.61 32.89 0.05 0.100 Error 144 1.90E-03 1.32E-05 2 4 * = Si x 10

Table 30: Analysis of Variance, Late Archaic, sexes combined (Posterior Maxillary)

161 Tooth Source df SSQ MSQ F Sig Si2 * Sat. df % Err ME2 UC Side 1 7.17E-05 7.17E-05 0.056 0.815 Burial 35 0 0 0 1 S*B 35 4.51E-02 1.29E-03 25.05 4.12 31.34 0.12 0.285 Error 144 7.40E-03 5.14E-05

UI2 Side 1 1.870E-04 1.870E-04 .091 0.765 Burial 33 0 0 0 1 S*B 33 6.789E-02 2.057E-03 24.823 0 6.58 29.47 0.13 0.075 Error 136 1.127E-02 8.288E-05

UI1 Side 1 1.289E-05 1.289E-05 .023 0.880 Burial 30 0 0 0 1 S*B 30 1.674E-02 5.579E-04 20.978 0 1.77 26.30 0.15 0.059 Error 124 3.298E-03 2.659E-05 2 4 * = Si x 10

Table 31: Analysis of Variance, Late Archaic, sexes combined (Anterior Maxillary)

162

Tooth Source df SSQ MSQ F Sig Si2 * Sat. df % Err ME2 LM2 Side 1 7.24E-04 7.24E-04 1.664 0.206 Burial 33 0 0 0 1 S*B 33 1.44E-02 4.35E-04 17.393 0 1.37 28.43 0.18 0.099 Error 136 3.40E-03 2.50E-05

LM1P Side 1 1.19E-05 1.19E-05 0.024 0.879 Burial 28 0 0 0 1 S*B 28 1.40E-02 4.99E-04 8.175 0 1.46 20.80 0.42 0.103 Error 116 7.08E-03 6.10E-05

LP4 Side 1 6.95E-05 6.95E-05 0.074 0.787 Burial 42 0 0 0 1 S*B 42 3.96E-02 9.35E-04 40.9 0 3.04 38.98 0.08 0.062 Error 172 4.01E-03 2.33E-05

LP3 Side 1 2.69E-05 2.69E-05 0.046 0.832 Burial 34 0 0 0 1 S*B 34 2.00E-02 5.89E-04 22.618 0 1.88 30.15 0.14 0.066 Error 140 3.65E-03 2.60E-05

LC Side 1 6.77E-05 6.77E-05 0.052 0.821 Burial 31 0 0 0 1 S*B 31 4.05E-02 1.31E-03 35.811 0 4.23 28.35 0.09 0.073 Error 128 4.66E-03 3.64E-05

LI2 Side 1 6.40E-04 6.40E-04 0.861 0.361 Burial 31 0 0 0 1 S*B 31 2.30E-02 7.43E-04 18.186 0 2.34 26.79 0.17 0.052 Error 128 5.23E-03 4.09E-05

LI1 Side 1 9.43E-04 9.43E-04 1.062 0.312 Burial 27 0 0 0 1 S*B 27 2.40E-02 8.88E-04 45.747 0 2.89 24.88 0.07 0.039 Error 112 2.17E-03 1.94E-05 2 4 * = Si x 10

Table 32: Analysis of Variance, Late Archaic, sexes combined (Mandibular)

163

Tooth Source df SSQ MSQ F Sig Si2 * Sat. df % Err ME2 UM3 Side 1 3.21E-02 3.21E-02 5.965 0.025 Burial 19 0 0 0 1 S*B 19 0.102 5.38E-03 130.675 0 17.81 17.73 0.02 0.102 Error 80 3.30E-03 4.12E-05

UM1P Side 1 5.70E-03 5.70E-03 11.441 .001 Burial 72 0 0 0 1 S*B 72 3.60E-02 4.99E-04 8.201 0 1.46 54.74 0.42 0.139 Error 292 1.76E-02 6.09E-05

UI2 Side 1 4.56E-04 4.56E-04 .273 .603 Burial 57 0 0 0 1 S*B 57 9.52E-02 1.67E-03 45.402 0 5.43 53.27 0.08 0.091 Error 232 8.53E-03 3.68E-05

UI1 Side 1 3.58E-04 3.58E-04 0.512 0.477 Burial 57 0 0 0 1 S*B 57 3.98E-02 6.99E-04 23.657 0 2.23 51.36 0.13 0.060 Error 232 6.85E-03 2.95E-05

LM2 Side 1 5.38E-03 5.38E-03 8.483 0.005 Burial 52 0 0 0 1 S*B 52 3.30E-02 6.34E-04 13.393 0 1.95 43.67 0.24 0.096 Error 212 1.00E-02 4.73E-05

LM1P Side 1 9.44E-04 9.44E-04 2.279 0.137 Burial 51 0 0 0 1 S*B 51 2.11E-02 4.14E-04 9.235 0 1.23 39.76 0.36 0.127 Error 208 9.33E-03 4.49E-05 2 4 * = Si x 10

Table 33: Analysis of Variance, Protohistoric, sexes combined

164

Tooth Source df SSQ MSQ F Sig Si2 * Sat. df % Err ME2 UM2 Side 1 7.17E-04 7.17E-04 1.394 0.447 Burial 1 0 0 0 1 S*B 1 5.14E-04 5.14E-04 6.745 0.032 1.46 NA 0.52 0.157 Error 8 6.10E-04 7.63E-05

UM1A Side 1 7.52E-03 7.52E-03 14.234 0 Burial 75 0 0 0 1 S*B 75 3.96E-02 5.29E-04 24.847 0 1.69 68.16 0.13 0.075 Error 304 6.47E-03 2.13E-05

UM1P Side 1 1.01E-03 1.01E-03 .865 .355 Burial 70 0 0 0 1 S*B 70 8.19E-02 1.17E-03 19.564 0 3.70 62.13 0.16 0.128 Error 284 1.70E-02 5.98E-05

UC Side 1 2.80E-04 2.80E-04 0.178 0.695 Burial 4 0 0 0 1 S*B 4 6.30E-03 1.57E-03 18.509 0 4.96 2.68 0.17 0.115 Error 20 1.70E-03 8.51E-05

LM1A Side 1 7.88E-03 7.88E-03 5.674 0.02 Burial 62 0 0 0 1 S*B 62 8.61E-02 1.39E-03 18.336 0 4.38 54.53 0.17 0.129 Error 252 1.91E-02 7.58E-05

LM1P Side 1 8.25E-03 8.25E-03 6.720 .012 Burial 60 0 0 0 1 S*B 60 7.37E-02 1.29E-03 14.877 0 3.82 51.34 0.22 0.129 Error 244 2.02E-02 8.25E-05 2 4 * = Si x 10

Table 34: Analysis of Variance, Modern, sexes combined

165