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DENTAL HISTOLOGY AND AGE ESTIMATION AT DAMDAMA: AN INDIAN MESOLITHIC SITE Gwendolyn M. Robbins A MASTERS PAPER Presented to the Department of Anthropology And the Graduate School of the University of Oregon In partial fulfillment of the requirements for the degree of Master of Science December 2000 Approved by: Dr. John R. Lukacs
ii CURRICULUM VITA NAME OF THE AUTHOR: Gwendolyn Meredith Robbins PLACE OF BIRTH: Marietta, Georgia DATE OF BIRTH: 26 November 1972 GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: 1996­ 2000 University of Oregon 1993­ 1995 Lane Community College 1992­ 1993 University of Utah DEGREES AWARDED: Master of Science in Anthropology, 2000, University of Oregon Bachelor of Science in Anthropology, with departmental honors, 1998, University of Oregon AREAS OF SPECIAL INTEREST: Dental Anthropology, Human Osteology, Histology, Bioarchaeology, Biocultural Interactions, South Asia PROFESSIONAL EXPERIENCE: Jan ­ Feb 2000 Research Assistant: Nutritional Stress in Modern and Prehistoric Populations in Maharashtra, India. Principle investigators Dr. J.R. Lukacs and Dr. S. Walimbe July ­ August 1999 Assistant Director: U of O Archaeological Field School, Tutuila, American Samoa. Principle investigators Dr. W. Ayres and Joan Wozniak July ­ August 1998 Archaeological Field Assistant: U of O Archaeological Field
School, Tutuila, American Samoa. Principle investigator Dr. W. Ayres, Epi Suafo'a, and Joan Wozniak iii Spring 1998 Artifact Illustration: Carlon Village Project, Oregon State Museum of Anthropology. Principle investigator Dr. George Wingard Fall 1997 Archaeological Lab Technician: faunal analysis for the Carlon Village Project, Oregon State Museum of Anthropology. Principle investigator Dr. George Wingard August 1997 Archaeological Field Technician: CRM for Oregon Department of Transportation Project, Hines, Oregon State Museum of Anthropology. Principle investigators Dr. T. Connolly and Dr. D. Jenkins July 1997 Archaeological Field Technician: CRM for Oregon Department of Transportation Project, Salem, Oregon State Museum of Anthropology. Principle investigators Dr. T. Connolly and Dr. D. Jenkins 1997 U of O Archaeological Field School: Carlon Village Site, Eastern Oregon. Principle investigators Dr. M. Aikens and Dr. D Jenkins. 1996 Archaeological Lab Technician: Nunivak Island, Alaska Project, University of Oregon. Principle investigator Dennis Griffen FELLOWSHIPS AND HONORS: Summer 2000 Graduate Administrative Fellowship: Web Master for the Anthropology Department, University of Oregon. 1999­2000 Graduate Teaching Fellowship: Faculty Advisor for the General Science Department, University of Oregon. 1998­1999 Graduate Teaching Fellowship: Faculty Advisor for the General Science Department, University of Oregon. GRANTS: 2000­2001 Fulbright Scholarship: Paleodemography in India at the Rise of Agriculture, Deccan College, Pune, India. Spring 2000 Graduate Student Research Award: Dental Histological Section Preparation, Graduate School, University of Oregon ($400)
iv ACKNOWLEDGEMENTS Appreciation is extended to Dr. J.R. Lukacs of the University of Oregon for the opportunity to work on the material from Damdama, for continued support, and guidance throughout the project. Sincere thanks are also extended to the members of the Department of Ancient History, Culture and Archaeology at the University of Allahabad, India for granting permission for this project and for the sample collection. I thank Dr. Jeanne Selker of the Electron Microscopy Lab, University of Oregon for providing consultation and materials for preparing and sectioning the teeth. A special thank you is extended to Dr. Murray Marks of the University of Tennessee, Knoxville for sharing his expertise concerning preparation and sectioning. Sincere appreciation is extended to the National Geographic Society for funding parts of the training that went into the completion of this project. Thanks also to the University of Oregon Graduate School for partially funding a trip to the University of Knoxville, Tennessee and to the Department of Anthropology at the University of Oregon for providing funding for equipment necessary to the completion of this research. I wish to thank Barbara Robbins for providing funding and support to me for the duration of my academic studies. Finally, I thank my partner Michael Boyer for supporting and encouraging me in all of my endeavors.
v DEDICATION What is known about the ultrastructure, the surface characteristics, and the function of mineralized dental tissues is due to visionary researchers in the field of human biology who saw the potential of the electron microscope in examining the structure of mineralized tissues and in using those tissues for age estimation. Dr. Edward Reith was an anatomist and cell biologist who braved the hard world of mineralized tissues to study their development, structure and function. He was a pioneer in the study of developmental timing in enamel mineralization, the functional morphology of the dentine, and the ultrastructure of cementum. Dr. Reith was committed to these topics throughout his career. He authored many books and articles, inspired students through his teaching, and his memory energized colloquia and additional volumes of important research. Dr. Reith also happened to be the father of a good friend of mine, a member of my family. Marianne Reith M.S., R.N. encouraged me to pursue higher education and her undying commitment to science, to knowledge, and to healing continue to inspire me. This project is dedicated to the memories and the work of Marianne Reith and her father, Dr. Edward Reith.
vi TABLE OF CONTENTS INTRODUCTION 1 ESTIMATING AGE AT DEATH FOR PREHISTORIC HUMAN REMAINS 3 DAMDAMA: THE ARCHAEOLOGICAL CONTEXT 7 THE INDIAN MESOLITHIC BURIAL SITES ON THE GANGETIC PLAIN DAMDAMA 7 8 12 CEMENTUM ANNULATIONS AND MULTIVARIATE METHODS OF AGE ESTIMATION 16 THE ULTRASTRUCTURAL NATURE OF CEMENTUM ANNULATIONS CEMENTUM APPOSITION AND THE ESTIMATION OF AGE AT DEATH REVIEW OF THE LITERATURE ON AGE ESTIMATION SUMMARY 18 22 26 36 ROOT DENTINE TRANSLUCENCY AND SECONDARY DENTINE DEPOSITION IN AGE ESTIMATION 38 THE ULTRASTRUCTURAL NATURE OF ROOT DENTINE TRANSLUCENCY REVIEW OF THE LITERATURE ON AGE ESTIMATION FOR INTACT TEETH REVIEW OF THE LITERATURE ON AGE ESTIMATION FOR SECTIONED TEETH SUMMARY 38 40 42 44 MATERIALS AND METHODS 46 SAMPLE DERIVATION AND CHARACTERISTICS PROTOCOL 46 49 RESULTS 56 OBSERVER ERROR AND COMPARISONS WITHIN INDIVIDUALS AGE ESTIMATES FROM DENTAL HISTOLOGICAL METHODS DISCUSSION AND INTERPRETATION SUMMARY OF RESULTS AND CONCLUSIONS IMPLICATIONS FOR FUTURE RESEARCH 56 59 68 73 73 PALEODEMOGRAHIC PROFILE FOR DAMDAMA 76 TABLES 82 FIGURES 85 BIBLIOGRAPHY 100
vii LIST OF TABLES TABLE 1: RADIOCARBON DATES FOR INDIAN MESOLITHIC SITES TABLE 2: CLASS OF TOOTH AVAILABLE FOR ANALYSIS FROM DAMDAMA TABLE 3: DENTAL PATHOLOGICAL PROFILE FOR DAMDAMA SAMPLE TABLE 4: MACROSCOPIC METHODS FOR AGE ESTIMATION TABLE 5: MACROSCOPIC METHODS FOR SEX ESTIMATION TABLE 6: PROTOCOL FOR PREPARING AND SECTIONING TEETH TABLE 7: REGRESSION FORMULAS FOR AGE ESTIMATION (JOHANSON 1970) TABLE 8: REGRESSION FORMULAS FOR AGE ESTIMATION (MAPLES 1978) TABLE 9: REGRESSION FORMULAE FOR AGE ESTIMATION FROM AREA OF TRANSLUCENT DENTINE (LORENTSEN AND SOLHEIM 1989) TABLE 10: DENTAL EMERGENCE TIMING FOR CHILDREN IN CHANDIGARH, INDIA (YEARS) TABLE 11: MACROSCOPIC AGE AND SEX ESTIMATES FOR DAMDAMA SAMPLE TABLE 12: DESCRIPTIVE STATISTICS FOR CEMENTUM ANNULATION COUNTS TABLE 13: AGE ESTIMATES FOR INDIVIDUALS WITH MULTIPLE TEETH AVAILABLE TABLE 14: PAIRED T­TEST FOR SIGNIFICANT DIFFERENCES FOR INDIVIDUALS WITH MULTIPLE TEETH TABLE 15: MEAN ESTIMATES FOR AGE AT DEATH TABLE 16: PAIRED T­TEST FOR MEANS OF THE MACROSCOPIC AND HISTOLOGICAL METHODS TABLE 17: ANALYSIS OF VARIANCE (ANOVA) FOR METHODS TABLE 18: PEARSON CORRELATIONS AND P­VALUES FOR METHODS TABLE 19: EUCLIDEAN DISTANCE MATRIX TABLE 20: TEST FOR SIGNIFICANT DIFFERENCES IN INDIVIDUALS 16­29 YEARS OLD TABLE 21: TEST FOR SIGNIFICANT DIFFERENCES IN INDIVIDUALS 30­55 YEARS OLD TABLE 22: DESCRIPTIVE STATISTICS FOR AGE ESTIMATES FOR MALES AND FEMALES TABLE 23: AGE ESTIMATES FOR THE SKELETAL POPULATION FROM DAMDAMA TABLE 24: DAMDAMA, MAHADAHA, AND SARAI NAHAR RAI AGE DISTRIBUTION TABLE 25: STATIONARY POPULATION LIFE TABLE FOR DAMDAMA 7 47 49 81 82 83 52 53 54 55 56 57 57 58 60 61 62 63 64 64 65 67 75 77 79
viii LIST OF FIGURES FIGURE 1: MAP OF MESOLITHIC SITES ON THE GANGETIC PLAIN FIGURE 2: MEASUREMENTS DEVELOPED FOR MULTIVARIATE AGE ESTIMATION (KASHYAP AND RAO 1990) FIGURE 3: JOHANSON’S SCORING CRITERIA FIGURE 4: AREA OF ROOT TRANSLUCENCY (LORENTSEN AND SOLHEIM 1989) FIGURE 5: LENGTH OF ROOT TRANSLUCENCY (DRUSINI 1990) FIGURE 6: CEMENTUM ANNULATIONS FIGURE 7: COMPARISON OF MULTIPLE TEETH FROM THE SAME INDIVIDUAL FIGURE 8: RANGE OF AGE ESTIMATES PER METHOD AND PER INDIVIDUAL FIGURE 9: HISTOGRAMS FOR ESTIMATES FOR EACH METHOD FIGURE 10: HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL FIGURE 11: NORMAL QUANTILE PLOTS FOR METHODS FIGURE 12: SCATTERPLOTS FOR EACH METHOD FIGURE 13: DIFFERENCES BETWEEN MACROSCOPIC AND HISTOLOGICAL AGE ESTIMATES FIGURE 14: AGE PROFILE FOR DAMDAMA FIGURE 15: DISTRIBUTION OF AGE ESTIMATES FOR FEMALES AND MALES FIGURE 16: ADULT MORTALITY CURVE AND LIFE EXPECTANCY FOR DAMDAMA ADULTS 9 51 84 85 86 87 88 61 90 92 95 97 67 76 77 79
INTRODUCTION Bioarchaeological research must be built upon a foundation of accurate age and sex estimates. Unfortunately there are inherent biases which guarantee a certain amount of error in the assessment of these fundamental variables, certain biases that are seemingly unavoidable using any relative standard for age estimation. This research is an attempt to avoid some of the pitfalls of relative standards by using a method that has potential to be a chronometric measure of age in the human dentition. Cementum annulations may represent chronological age in a manner analogous to dendrochronology, however biologists have obtained mixed results in their attempts to understand the phenomenon of their accumulation and anthropologists have similarly obtained mixed results in their application. Whether or not the annulations represent strictly annual structures, they are highly correlated with age at death in modern samples (Condon et al. 1986). Here the biological basis for the annulations is explored and an attempt was made to use a count of acellular annulations in an archaeologically derived sample. Relative standards for age estimation using dentine translucency and various combinations of attrition, root resorption, and secondary dentine apposition were also used in this study. As histological research is a destructive process, it seemed important to test any other established histological aging methods that could be practically applied to this sample. As the sample was not of documented age, it seemed further that convergence (or lack of convergence) of age estimates from all the available methods would be necessary to fully test the relative accuracy of the cementum annulations. In the end, a combination of standards for macroscopic morphological changes, microscopic degenerative processes, and cementum annulations were compared with one another in an attempt to construct the estimates for age at death. Patterns within the sets of estimates were also considered in an attempt to discover methodological biases and explore questions about the relative usefulness of certain biological changes for age estimation.
Relative standards for age estimation, on a macroscopic or microscopic scale, are inherently flawed due to an interesting set of problems in human biology as well as anthropology. Some of the most important and most frequently discussed considerations include: 1.) the distinction between chronological and biological age, 2.) uniformitarian assumptions about aging through time and space, 3.) environmental and genetic differences between reference and sample populations, 4.) archaeological processes of preservation, diagenetic change, and recovery, and 5.) variance between dental and skeletal age determinations. The disparity between chronological and biological age has been the subject of research by both human biologists (Bittles and Collins 1986) and paleodemographers (Paine 1997). Bioarchaeologists use the term biological age in recognition of the discrepancy between an individual’s morphology and their chronological age, which is inaccessible for most skeletal populations. The discrepancy between chronological and biological age is a product of both measurable systemic reasons as well as significant idiosyncratic and seemingly random differences. There are three main sets of problems responsible for the differences between chronological and biological age: individual variation, pathological conditions, and methodological issues. The methods subject to the most individual variability are those developed for adults based on the process of senescence and consequent degenerative changes in the dentition and in the skeleton. Standards for dental attrition are among the most commonly used methods for estimating age at death for adults. There are multiple causes for inconsistencies in individual dentitions as well as between standard and reference populations including age, sex, diet, occlusal patterns, temporo­mandibular joint form, mineralization differences, bruxism, and cultural practices such as non­ dietary uses of teeth (Hillson 1996). If attrition is to be used to estimate age at death, one­quarter of one jaw would be the ideal minimum, careful consideration of context is warranted, and a population specific wear rate should be calibrated (Walker, Dean and Shapiro 1991). It is generally considered easier to determine the biological age of children because estimates are based on a sequence of interrelated developmental stages, which are more predictable than senescence. However variation in rates of development and
maturity caused by both genetic and environmental factors, continue to maintain the gap between chronological and biological age. Deprivation and disease add further to these difficulties. For example, even in situations where researchers are aware that a group of modern children are developing in conditions of severe environmental stress, with chronic mild to moderate protein­energy malnutrition and moderate to high levels of infectious disease, age may still be underestimated by one to three years (Lampl and Johnson 1996). The generalization (or averaging) of complex morphological changes in order to create reference standards introduces an inherent amount of inaccuracy in all relative methods of age estimation. This bias is due not only to the generalization itself, but also to observation error, scoring differences, level of experience using these discrete reference categories, and finally to the differences between the reference and the sample populations themselves. When age estimation methods are applied to archaeological populations, the number of biases are further compounded by uniformitarian assumptions about aging across time and space. Genetic and environmental differences between reference and sample populations lead to an uncertain level of error in the application of any method to an archaeological sample. Archaeological processes of diagenesis, preservation, and decomposition are primary considerations for age estimation and demographic inferences in archaeological populations. The elements of the skeleton that are present, recovered during excavation, and well preserved must primarily determine the method used for age estimation. Teeth are the most frequently represented and often the best­preserved elements in an archaeologically derived population but there can be significant differences between the estimates (and their standard errors) derived from dental remains versus estimates derived from skeletal material. In sub­adults, moderate to severe malnutrition can cause retardation of long bone lengths and consequently lower age estimates in comparison with estimates derived from dental development and eruption timing. The disparity is compounded by the fact that sex cannot yet be accurately determined for sub­adult remains. Female juveniles will give older estimates than males of equivalent age because their teeth tend to develop and erupt at younger ages. Skeletal growth patterns also differ for males and
females and these standards may tend to underage female sub­adults. Adult standards for age estimation will also vary between the dentition and the skeleton. Attrition, epiphyseal fusion, cranial suture closure, pelvic morphology, and degenerative conditions are all subject to different lurking variables. Despite these difficulties, some methods of age estimation are considered to be quite accurate, especially for juvenile remains. Some have claimed a margin of error of less than 2 weeks for estimates derived from long bone lengths for neonates (Fazekas and Kosa 1978; Scheuer 1980) though these estimates are obviously highly dependent on maternal and infant nutritional status and subject to questions about the comparability of reference and archaeological samples. Age estimates from tooth height (Liversedge 1993, 1998), or the numerous standards for dental mineralization and eruption timing, generally have a standard error of plus or minus three to six months under the age of five years. These techniques are useful because again teeth are often the most numerous and best­preserved elements in a skeletal collection, especially for juveniles whose small and gracile bones are frequently destroyed after burial. However dental development and eruption are still relative standards and thus suffer from the set of problems outline above. Bioarchaeologists commonly use a suite of methods, as determined by the elements preserved for each individual, in order to converge upon the most accurate estimate possible. As the chronological age at death is not known in an archaeologically derived population, accuracy does not here refer to the level of concordance between chronological and biological age. Here accuracy is defined only in the relative sense as the degree to which an estimate for biological age resembles estimates given by other methods and the degree to which they converge on a “true” biological age. Precision is here defined as the amount of error between observations made on different elements from a single individual, as well as the amount of error between observers, or whether the method can be precisely applied. In addition to applying as many methods as possible to ensure the greatest possible amount of accuracy in estimation, many researchers have looked at age related transformation on a different scale, generally with relatively successful results. Several methods of age estimation for juvenile dentitions have been developed based on
observations of microscopic age related phenomena. Boyde (1963, 1990) suggested that in juveniles, age at death could be calculated by counting the cross­striations in enamel from the neo­natal line to the completion of the crown and cross­referencing the brown straie between the first incisor and the lower first molar (cited in Hillson 1996). Bromage and Dean (1985) developed an aging method using a count of perikymata grooves on the enamel surface, to which Dean and Beynon (1991) added the count of cross­striations (cited in Hillson 1996). In adults, age has a positive (though sometimes weak) linear relationship with several microscopic degenerative variables including root dentine translucency and sclerosis, the formation of secondary dentine, the apposition of cementum at the root apex, cementum annulations, and periodontitis or gingival recession (Alt 1998). Gustafson (1950) devised a multivariate method of age estimation using six of these measures of histological change. His method combined scores from measurements of attrition, periodontitis, secondary dentine, cementum apposition (overall thickness), and root dentine translucency. He developed a four stage grading technique for each characteristic and calculated regression formulas for estimating age at death from the scores. Gustafson’s method relied on simple linear regression for each characteristic with all characters being given the same weight. He reported a 98% correlation between estimates from his technique and known age at death. He calculated the average error for his estimates to be plus or minus 3.63 years. Johanson (1970) tested Gustafson’s method on a larger, independent sample and he added two major refinements: intermediate grades for scoring each variable and multiple regression. Johanson found that the accuracy of age estimates improved when more than one tooth was available for analysis, but he concluded that there was no benefit to using more than five teeth per individual. Johanson’s formulas predicted age at death within five years 78.3% of the time, within ten years 95.7% of the time, and within fifteen years 97.8% of the time. The age of 26.1% of the individuals in his sample (12/46) were predicted with an accuracy of plus or minus one year. When only dentine translucency, root resorption and cementum apposition were used, his predictions yielded a correlation of 86% with known age at death (+/­ 6.55 years).
The Gustafson (1950) and Johanson (1970) methods have been tested, critiqued and improved upon for use in forensics over the subsequent 50 years (Burns and Maples 1976; Maples 1978; Maples and Rice 1979; Solheim and Sundnes 1980; Kashyap and Rao 1990; Lopez­Nicolas 1989, 1990; Lopez­Nicolas and Luna 1991; Lamendin et al. 1981 and 1992; Solheim 1993; Lucy et al. 1995, 1996; Akroyd et al. 1997). The methods have not been widely adopted for archaeologically derived samples, partially because of the destructive and cumbersome process of preparing the sections. The destructive nature of histological analysis is somewhat justified by the presence of the antimere and through proper documentation including photographs and casts. It is also possible that the methods are not suitable for archaeological material because the effects of uncontrolled temporal, geographic, genetic, cultural, environmental, and taphonomic variables limit the accuracy. This study is an attempt to assess the feasibility of using these histological methods on an archaeological sample. The Present Research This project represents an initial attempt to apply dental histological aging methods to a prehistoric skeletal sample from India. For this analysis, age at death was assessed using methods based on dental attrition, root dentine translucency, and cementum annulations (Johanson 1971; Maples 1978; Charles et al 1986, Lorentsen and Solheim 1989; Kashyap and Rao 1990; Drusini 1990). To determine whether histological methods that were developed from forensic samples and dental extractions, are applicable to prehistoric archaeological material, the following research questions were posed: 1.) Are the methods relatively accurate in relationship to one another and to the multifactorial macroscopic age estimates made previously? 2.) Are the methods internally consistent in tests of observer error? 3.) Are there significant differences between multiple teeth available from the same individuals?
4.) Are there detectable systematic biases within the methods, such as over­aging young individuals and under­aging older individuals? 5.) As all of these methods use the same few anatomical structures, what do any differences between the resulting estimates say about the methods themselves? 6.) Given diagenesis, are the original protocols directly applicable to this sample or are there necessary modifications? 7.) Can any of these methods improve the accuracy of the paleodemographic profile for Damdama? 8.) Can the demographic profile be expanded through the inclusion of individuals for whom age could not previously be estimated specifically? It is expected that if the methods are accurate, the age estimates will closely resemble one another in statistical tests and there will be no obvious trend or bias in estimation. Similarly, if the methods are precise, age estimates from independent observations will not be significantly different and multiple teeth from the same individual will yield similar estimates. The original published protocols were used in the test for each method, followed as closely as possible and without major modifications. If a combination of histological and macroscopic techniques can be used to converge on more precise estimates of age at death, the paleodemographic characteristics of Damdama’s skeletal population can be more accurately assessed. The paleodemographic profile for Damdama is an integral component to understanding Mesolithic culture on the Gangetic Plain in India. The burial sites on the Gangetic Plain have yielded the most numerous and best preserved human remains for this period in India. The hunting and gathering cultures of the Mesolithic period provide an opportunity for reconstructing relationships between environment, subsistence, and settlement prior to the development of agriculture in the Chalcolithic and Neolithic periods in Indian prehistory. These materials can potentially provide clues to the nature of relationships between the Mesolithic cultures of the Gangetic Plain and contemporaneous populations in surrounding areas, as well as affinities with later
peoples. Chapter 1 summarizes some of the context and the issues involved with the archaeological record for this period in India. Age estimation techniques should be developed and applied with an understanding of the biological basis for those age related changes. Unfortunately there are many questions remaining about the ultrastructural nature and the biological processes underlying the phenomenon of cementum annulations. Despite a long history of research into this subject, cementum is one of the least understood mineralized tissues. Chapter 2 summarizes what is known about the biological basis of cementum, the nature of the increments, and the effects of pathology on age estimation. Chapter 2 also summarizes the literature on age estimation using cementum annulations including the protocols used, the success and/or the relative accuracy in estimating age at death for known age individuals. The process of root dentine formation, mineralization, sclerosis and other degenerative processes are better understood. Chapter 3 summarizes the biological basis for the sclerosis, dentine translucency, and secondary dentine deposition. Although these processes are more fully understood, the most biologically relevant protocol for using dentine translucency in age estimation has yet to be determined. The ideal section thickness, the use of stains, and the most accurate criteria for measuring the translucency vary considerably per study and have yet to be standardized. This variability reduces the possibility for comparative analysis and increases the likelihood of interobserver error. Chapter 3 also summarizes the various protocols that have been developed and their relative accuracy for known aged samples. Chapter 4 provides a discussion of the materials and methods used in this study. The ideal sample selection criteria and the real characteristics of this sample are examined, the methods and protocols used here are also detailed. The results of comparisons between the estimates from all the histological and the macroscopic methods are presented in Chapter 5 and interpretations follow in Chapter 6, including a discussion of problems encountered and implications for further research. Chapter 7 is a revision of the paleodemographic profile for Damdama for the 39 individuals whose age at death could be estimated either macroscopically or histologically.
DAMDAMA: THE ARCHAEOLOGICAL CONTEXT The archaeological record for the Mesolithic period on the Gangetic plain has been extensively investigated by several institutions including the University of Allahabad, Deccan College, the University of Pune, and the Archaeological Survey of India (Misra 1996). Several hundred Mesolithic sites have been discovered in the Vindhyan hills and the Gangetic Plains region of Uttar Pradesh in Northern India, including rockshelter and open air sites (Misra 1977). The Indian Mesolithic is a generally defined as a transitional period between the Upper Paleolithic and the Neolithic and specific dates for this period are varied and controversial (see Table 1). The Mesolithic in northern India stretches from approximately 8000­3000 BP, if one of the earliest date from Sarai Nahar Rai (10,050 +/­ 110 BP) is excluded because it was obtained from a sample of calcified unburned bone (Kennedy et al.1986; Misra 1977). TABLE 1: RADIOCARBON DATES FOR INDIAN MESOLITHIC SITES Damdama 8865 +/­ 65 1 ; 5550 +/­ 60 2 (st 1) ; 5250 +/­ 70 2 (st 6) ; 5430 +/­ 60 2 (st 8) Lekhahia, Phase I 8,000 +/­ 75 1 ; 5410 +/­ 115; 4240 +/­ 110 ; 3710 +/­ 110 ; 3560 +/­ 105 Mahadaha 2880 +/­ 250 ; 3840 +/­ 130 ; 4010 +/­120 ; 4680 +/­ 80 2 (st 1) ; 4110 +/­ 60 2 (st 2) ; 6160 +/­ 60 2 (st 4) Sarai Nahar Rai 2860 +/­ 120 ; 10050 +/­ 110 ; 10395 +/­ 110 ; 5040 +/­ 50 2 (surface); Taken from Misra, V.D. (1977: 67); Kennedy et al. (1992); Allchin and Allchin (1982: 79), Kennedy et al. (1986: 52); Lukacs et al. (1996); Lukacs n.d.. 1 Dates obtained from Accelerated Mass Spectrometry (AMS) techniques on bio­apatite from bone 2 Dates obtained from AMS dating techniques on bio­apatite from bovid teeth
Mesolithic sites in India generally consist of the lithic scatters and artifacts, hearth features (sometimes with burned clay inclusions), faunal remains, some include burials and occasionally grave goods. Microlithic tools are considered the diagnostic artifact for this era but this period also saw the appearance of composite tools such as knives and sickles (Kennedy 1992). Tools were also produced from new raw materials as well including wood, bamboo, shell, bone, horn, ivory, and leather (Kennedy 1996). Cultural information about the Indian Mesolithic has also been derived from paintings and carvings on the walls of rock shelters at Bhimbetka and Lekhahia. The rock art has been interpreted as further evidence for a hunting and gathering lifestyle, with depictions of animals and hunting scenes most commonly represented (Varma 1996). Kennedy (1996) has suggested that the paintings may also indicate technologies not represented in the archaeological record including shelters, watercraft, storage vessels, animal traps, and cordage in the Mesolithic. Burial Sites on the Gangetic Plain Damdama (8750m 2 ), Mahadaha (3900 m 2 ), and Sarai Nahar Rai (2800 m 2 ) were located on the banks of ox­bow lakes on the Gangetic Plains (Figure 1). They are large sites with deep cultural deposits, rich in artifacts and are considered to be semi­ sedentary sites (Pal 1992). Most Mesolithic sites are considered temporary campsites because they are composed of a thin lens of artifacts confined to a small area. These sites are also unique in having yielded a large number of human burials, among the best preserved for this period (Misra 1977). The similarities between the artifact assemblages, burial customs, and skeletal morphology of the remains at these sites may indicate cultural (and possibly genetic) interaction between this community of sites. Lekhahia is located 160 km south of Mahadaha at latitude 24 0 47’ N and longitude 82 0 8’ E in the Mirzapur district (Misra 1977; Lukacs, 1993b). The site consists of five rock shelters, excavated in the 1960’s by Allahabad University. Seventeen graves were discovered and tentatively attributed to eight burial phases based on stratigraphic associations and relative dates (Sharma 1965; Lukacs 1997). These associations are tentative because there is evidence for repeated disturbance of
Figure 1: Map of Mesolithic Sites on the Gangetic Plain the site (Lukacs 1997). The graves contained the remains of 27 individuals, some represented by as little as a single bone or tooth (Lukacs 1997). Radiocarbon dates from Lekhahia range between 5410 +/­ 115 and 3560 +/­ 105 (See Table 1). The site was also dated using Accelerated Mass Spectrometry on bioapatite from bone samples, this technique yielded dates for the Mesolithic layers between 8,370­8000 BP (margin of error was +/­ 75 years for both dates) (Lukacs 1996). These dates support contemporaneity between Lekhahia and Damdama. The
dates also increase the temporal difference between the Mesolithic hunter­gatherers and later agriculturist groups, which may call into question theories about the relationships between the two groups (Lukacs et al. 1996). Mahadaha is located along the banks of an oxbow lake on the Gangetic plain proximate to Damdama (25 0 59’ N latitude and 82 0 11’ E longitude) (Misra 1977). The 3900 m 2 site was discovered during the construction of a canal that disturbed several burials and was excavated by Allahabad University (Kennedy 1992). The site contained 28 graves and 32 burials associated with 35 pit hearths, microliths, burned clay lumps, rubbed ochre, quern and muller fragments, hammer stone fragments, and rubbed hematite pieces (Ibid.; Pal 1992). The skeletons from Mahadaha are relatively numerous and well preserved. The burials have been assigned to four phases of the Mesolithic, based on stratigraphic associations (Kennedy 1992; Pal 1992a, b). The graves were oblong and shallow and the skeletons were in a supine position (Ibid.). The graves were filled with material from the hearths and there was a 4­6 cm thick soil cushion lining (Ibid.). All of the burials were single except for burial I, which contained a male and a female buried side by side and burial V, which contained two individuals, one laying in a prone position on top of the other in a supine position (Pandey 1996). The Mahadaha skeletons were oriented east to west or southeast to northwest, with the skull at either end. Some graves included bone rings and jewelry, rubbed ochre, microliths and animal bones (Kennedy 1992). Sarai Nahar Rai is located 15 km southwest of Pratapgarh on the banks of a fossil ox­bow lake at latitude 25 0 48’ N and 81 0 51’ E longitude, proximate to Damdama and Mahadaha (Misra 1977). The surface of the 2800 m 2 site was “littered” with chalcedony and carnelian geometric microliths (Ibid.; Kennedy 1986). The site was excavated by Anthropological Survey of India in 1970 and Allahabad University beginning in 1972 (Misra 1996). Sarai Nahar Rai was the first Mesolithic site on the Gangetic Plain in which burials were discovered (Lukacs 1993b). The undisturbed oblong graves were lined with a cushion of soil 3 cm deep and filled with ash from the hearths that included bone fragments and microliths (Kennedy 1986). Microliths and shells may also have been offered as grave goods (Misra 1977). The graves contained
fifteen adult skeletons associated with eight pit hearths (Ibid.). The skeletons were in extended and supine position, oriented east to west (Kennedy 1986). Most individuals were buried singly but one grave (VII) contained four individuals, two males and two females (Misra 1977; Kennedy 1986). Damdama Damdama is a habitation site located at 26 0 10’ N latitude and 82 0 10’ E longitude on the Gangetic plain, approximately 25 km northwest of the Mesolithic site of Mahadaha (Lukacs and Pal 1993b). The site is situated on the banks of an oxbow lake formed by two tributaries of the Tambura nala, north of where they meet the Sai River (Pal 1992). The site covers an area of 8750 m 2 , with a 1.5 m deep cultural deposit (Pal 1992). There are 10 stratigraphic layers in the Mesolithic period, the uppermost is post­Mesolithic deposit. Excavations were conducted under the direction of J.N. Pal of Allahabad University and continued over five field seasons (1982­1987) (Pal 1985; Lukacs and Pal 1993b). Burials, microliths, bone objects, querns, mullers, hammer stones, burned clay lumps, charred grains and faunal remains were recovered (Ibid.). Four phases of burial activity were discovered at Damdama and 41 graves were excavated from the western and central areas of the site (Pal 1988). The graves are within the habitation area and were generally near hearths, material from which was used as fill in the graves (Pal 1992a). Grave goods included microliths (in graves VII, XVI, and XVIII) and a perforated ivory pendant in grave VII (Ibid.). The graves are shallow and oblong resembling burial customs at Sarai Nahar Rai and Mahadaha. The orientation, positioning and contents of the graves at Damdama, Mahadaha, and Sarai Nahar Rai are similar and may represent a cultural tradition common to the region in the Mesolithic (Kennedy 1986). The majority of burials were single, oriented east to west with the skull to the west. However, Damdama is exceptional in that there were five double burials (in grave nos. VI, XX, XXVI, XXX, and XXXVI) and one grave (XVIII) contained a triple burial with two males and one female (Pal 1988). The majority of the individuals were buried in a flexed supine position. Two individuals (in graves I and XXVIII) were
buried in a flexed prone position, a custom previously unknown in the Indian Mesolithic (Pal 1996). Some researchers have suggested an ethno­archaeological interpretation for the presence of the flexed position at Damdama and Mahadaha (Ibid.). The microlithic assemblage is considered typical for the Indian Mesolithic period. The collection includes retouched blades, scrapers, points, awls, triangles, and trapezes (Ibid.). Blade tools made up the largest percentage of the assemblage (58.93 %), followed by triangular microliths (13.17 %) (Pal 1985). The tools were primarily manufactured from chalcedony and chert (Ibid.) Pal (1985) has suggested that the raw material for stone tool production is found at a distance of 100 km from the site and economy was vital to their production, with every workable fragment having been used as a tool. Heavily worn grinding stones and sickles were recovered and the use­wear analysis of the heavily polished blades suggests use in cutting grasses and plants (Ibid.; Pal 1996). Kajale (1996) conducted a preliminary analysis of the floral remains from Damdama. Floral remains are scarce in the Mesolithic sites of North India (Kajale 1996). Six plant taxa were identified at Damdama, three to species, including: buckwheat (Polygonaceae sp.), mint (Labiatae), nightshade (Solanaceae sp.), wild grasses (Heteropogon contortus, H. sp.), goosefoot (Chenopodium album), and Purslane (Portulaca oleracia) (Ibid.; Lukacs and Pal 1993b). There is also evidence for the presence of the Indian jujube (Ziziphus). Impressions of caryopsis, glumes, Ziziphus, and charcoal from bamboo have been discovered at Mahadaha and impressions of rice husks (Oryza rufipogon and O. spontanea) have been found at both Lekhahia and Mahadaha (Kajale 1996). The faunal remains (21,000 bone fragments) were analyzed at Deccan College in Pune. Much of the material was not identifiable, but 27% of the bones were identified as belonging to over 30 species of animals (Thomas 1995). Of the identifiable bone, 77% were mammals, almost all of which were species of deer, tortoise, gaur (Indian bison), wild buffalo and wild pigs (Sus scrofa) comprised the majority of the rest of the material (Ibid.). Approximately 90% of the bone fragments (identified or not) were charred, most completely and some were calcined (Ibid.).
Most of the faunal remains were extremely fragmentary due to processing activities. Processing activities were localized around the site in discrete butchering, refuse, and dwelling areas (Ibid.). Bone was the most readily available local source material and a bone tool processing area may be indicated at Damdama by the presence of unburned long bones from large mammals in a location separate from food refuse (Ibid.). Intact bone tools recovered include bifacial points, blades, knives, chisels, scrapers, saws, and harpoons (Ibid.). The skeletal morphology of the Gangetic Plains populations of Damdama, Mahadaha, and Sarai Nahar Rai appears to be relatively homogeneous: tall, robust people with well developed musculature on the appendicular skeleton. The cranial morphology and odontometric profiles further indicate that they may share genetic affinities (Lukacs 1993b). Gene flow between these groups has not been positively established but it is possible, given that they are thought to be roughly contemporaneous (Ibid.; Lukacs 1996). There are few apparent biological affinities between these populations and later Indian peoples, with the possible exception of the people who inhabited Neolithic Merhgarh (Ibid.; Lukacs 1992b, 1993b). At both Mahadaha and Sarai Nahar Rai, pathological lesions were limited to vertebral exostoses and osteoarthritis (Kennedy 1986, 1992). Squatting facets on the male tibiae, perforation of the olecranon fossa, and hypertrophy of muscle attachments, especially on the right arm, in individuals from Mahadaha and Sarai Nahar Rai have been interpreted as evidence for habitual activities (Ibid.). Some slight bowing of limb bones may suggest rickets or osteomalacia, but may also be a product of individual variation. These findings and the lack of evidence for communicable disease, parasites, nutritional deficiencies, and dental caries are generally consistent with the profile expected to accompany hunter­gatherer subsistence. Lukacs and Pal (1993b) analyzed the dental pathological profile for Damdama and found that females had higher prevalence of caries, abscesses, AMTL, and pulp exposure, whereas males had higher prevalence of calculus, alveolar resorption, and enamel hypoplasia. The rates for pulp exposure, abscess, AMTL, enamel hypoplasia, calculus, and resorption were lower for the skeletons from Mahadaha, caries rates were higher. All of the carious lesions at Damdama and Mahadaha were on molar teeth.
The dental pathological profile at Mahadaha and Sarai Nahar Rai could be interpreted as supporting the inference of hunter­gatherer subsistence as well. Lukacs and colleagues found a pattern of few cavities but heavy attrition and hypoplastic defects, indicating episodic dietary stress (Lukacs 1982; Lukacs 1991; Lukacs and Pal 1993b). The skeletal populations also had relatively high rates of antemortem tooth loss (AMTL) and subsequent alveolar resorption, resulting from attrition rather than caries. Dental size has also associated with subsistence, and Lukacs (1993b) found a pattern of large tooth size consistent with hunter­gatherer populations. Lukacs, Pal and Misra (1996) attempted dietary reconstruction based on the carbon and nitrogen stable isotopes in bio­apatite samples from Damdama and Lekhahia. The change in Carbon­13 ratios for Damdama were less negative than those for Lekhahia, meaning that the people at Damdama were eating more C4 foods (grassy plants), while the population at Lekhahia was concentrating mainly on meat and C3 plants (trees, shrubs, and tubers). Dental and isotopic evidence that the primary mode of subsistence for these Mesolithic plains communities was hunting and gathering, supplemented with exploitation of aquatic resources, is supported by floral, faunal remains, and rock art at Lekhahia. At Mahadaha, faunal remains included B. gaurus, rhinoceros, S. scrofa, elephant, stag, deer, antelope, gazelle, turtle, fish and birds (Alur 1980; Kennedy 1992). From the hearths at Sarai Nahar Rai excavators recovered charred and uncharred faunal remains of the following species: Bos indicus, B. bubulus, B. gaurus, Ovis sp., Capra sp., Elephas indicus, as well as tortoise and fish bones (Misra 1977; Alur 1980). Alur (1980) has analyzed the faunal remains and has suggested that the species were wild types, though incipient domestication is possible. The faunal remains from Damdama, Mahadaha, and Sarai Nahar Rai have been interpreted as evidence of hunter­gatherer subsistence with possible incipient domestication (Alur 1980; Thomas 1996). The presence of bone harpoons, aquatic bird, fish, and turtle remains suggest exploitation of aquatic resources. The temporal and spatial distributions of faunal remains indicate that the size of mammalian species tended to decrease through time (Thomas 1995). In addition, the quantity of mammalian species hunted varied inversely with the quantity of avian and reptilian
species exploited, the latter may have been exploited in times of scarcity (Thomas 1996). Time averaging seems unlikely because of the distinct and undisturbed stratigraphic layers, but preservation bias or cultural processes may play a role in these patterns. The paleodemographic profile has yet to be published for Damdama, though the site is crucial for any reconstruction of Mesolithic culture on the Gangetic Plain in India. Estimates of age at death will form the foundation for future biocultural research on this important period in Indian prehistory. The Mesolithic is well represented by numerous, well preserved burial sites, and thus provides an opportunity for population studies. As a transitional period between the Late Paleolithic and the development of agriculture in the Chalcolithic and Neolithic, the site is a key component to characterizing the development of agriculture and initial sedentary cultural systems in India.
CEMENTUM ANNULATIONS AND MULTIVARIATE METHODS OF AGE ESTIMATION This chapter has four main intentions, 1.) to summarize the ontogeny and function of the cementum, 2.) to examine some of the issues surrounding the ultrastructural properties of the annulations 3.) to examine the basis for using cementum annulations for estimating age at death, and 4.) to review the literature on age estimation using cementum apposition in univariate and multivariate analyses. The ultrastructural nature of cementum annulations is unknown and has been described as an artifact of sample preparation, section thickness, and as an optical illusion (discussed further below). However, the number of annulations in the cementum has been correlated with known age at death as high as 98% (Charles et al, 1986, 1989). Thus an examination of what is known about the development and structure of cementum, and a thorough review of the protocols used for age estimation is crucial to successfully applying this method. The Ontogeny and Physiology of Cementum Annulations 1 Cementum is an extracellular matrix composed of calcified collagenous Sharpey’s fibrils, collagen, glycosaminoglycans, proteoglycans, and inorganic hydroxyapatite. Cementum initially develops in utero upon the disintegration of the epithelial sheath of Hertwig surrounding the tooth germ. The disintegration of the sheath exposes the root dentine to the follicle, stimulating the differentiation of the cementoblasts from mesenchymal cells. The surface of the root dentine is initially covered with a non­mineralized hyalin layer formed from ectomesenchymal and epithelial products. This hyalin layer is thought to bind the cementum to the dentine. Once the cementoblasts have differentiated, they insert cytoplasmic processes into the hyalin layer and begin to deposit collagen fibrils and extracellular matrix as they move away from the root surface. As the tooth erupts, acellular cementum slowly develops, eventually covering 1 This discussion of the ontogeny and biology of cementum is based on Ten Cate (1998)
the coronal two­thirds of the root surface. The cellular cementum around the apical portion of the root is formed more rapidly, trapping cementoblasts within lacunae that eventually become cementocytes. Once the tooth has reached occlusion the cementoblasts in the acellular region are resorbed and additional layers of cementum are formed by the fibroblasts in the periodontal ligament. Although cementum can be complex and patchily distributed, in general the outermost bands (closest to the periodontal ligament) are termed intermediate cementum. At the innermost (closest to the dentine), youngest levels, the cementum is unmineralized and is termed the cementoid (or precementum). Acellular cementum meets the enamel at the CEJ and gets progressively thicker towards the apex of the root, where most of the cementum is cellular. Acellular cementum serves as the tooth’s anchor within the alveolus whereas the primary function of cellular cementum is to adapt to tooth movement and wear and keep the tooth in the occlusal plane (Ten Cate 1998). The main difference in the ultrastructure of cellular versus acellular cementum, is the inclusion of cementoblasts within lacunae and their subsequent development onto cementocytes. There are extrinsic and intrinsic collagen fibers present in both cellular and acellular cementum. The collagen fibers synthesized by the cementoblasts during eruption are labeled intrinsic. The fibers produced by the fibroblasts in the periodontal ligament are labeled extrinsic. This collagen is organized into long Sharpey’s fibers running between the periodontal ligament and the root dentine. Cementoblasts deposit intermediate cementum around the fibers that, once mineralized within the cementum, anchor the tooth root to the periodontal ligament. This process occurs slowly until the tooth erupts to occlusion, at which point the cementoblasts stop producing the intermediate cementum. The correlation between age and apposition is considered strongest in the cementum 1/3 of the distance from the root’s apex, where it is less compressed than the cementum near the CEJ but has less cellular cementum than the root apex (Naylor 1985; Charles 1986). In general, the thickness of healthy cementum increases threefold between the ages of 11­76 years (Kvaal et al. 1996). The deposition of acellular cementum is thought to be seasonally controlled. The annulations grow with regularity
throughout the life span and are more easily distinguishable in the acellular component (Lieberman 1992). The bands in acellular cementum are better predictors of age than bands in the cellular cementum because environmental factors and the stresses that cause passive eruption and hypercementosis influence the acellular cementum to a lesser extent. Cellular cementum also forms in increments around the apical portion of the root. However, the bands in acellular cementum are uneven in width and distribution partially because it is deposited more rapidly than the acellular component. The width of cellular cementum also varies by proximity to the dentine, younger layers being thinner as the rate of tooth eruption slows. The cellular cementum has additional rest lines representing periods of slow growth and differential degrees of mineralization. The precise mechanism and regulation underlying this process is not well understood. However, eruption, occlusal stress, tooth size, and attrition appear to influence the rate and amount of cellular cementum deposition (Lieberman 1992). For example, “passive eruption” or hypercementosis can result from a combination of these factors. In this condition, the cementum deposition increases at the root apex in an attempt to keep the clinical crown in occlusion. The Ultrastructural Nature of Cementum Annulations Acellular cementum annulations are visible in transmitted and polarized light microscopy, and in microradiography as alternating translucent and opaque bands. Cementum is a complex and patchy tissue and the exact nature of the incremental structures is unknown. Hypotheses concerning the nature of the annulations include differential matrix deposition rates across the translucent and opaque bands (determining the amount of intrinsic fibers and cells), changes in mineralization or extrinsic fiber orientation. Spinage (1973) suggested that the bands represented hypercalcified areas, periods of hydroxyapatite crystal formation, in contrast to areas of decreased matrix production. Grue and Jensen (1979) suggested that the hypercalcified bands formed during arrested matrix production. The opacity difference between the increments is often attributed to degree of calcification, but microprobe analysis has shown a consistent ratio across the bands (Kvaal 1995). Schroeder (1986) attributed the
annulations to changes in the orientation of collagenous fibres. Gordon (1993) has suggested that metabolic differences in the way that the matrix itself is laid down may determine the shape, size and orientation of the crystals. Renz and colleagues have published two attempts to discover the ultrastructural nature of the cementum increments (1997, 1999). These two studies bring up some interesting issues in histology as well as human biology. Their 1997 research paper can be analyzed in terms of the importance of histological protocol for preservation of ultrastructure. Using protocols given in Stott et al (1982), the premolars from clinical extractions were fixed in formalin and then rinsed in running water for several hours. 100­150 um sections were taken from the middle third of the root using a diamond edged saw and polished with sandpaper. The sections were post­fixed with Osmium, dehydrated with ethanol replacement, and polymerized in resin. This sequence is a standard EM protocol minus one ingredient (glutaraldehyde) in the fixing process. Semi­ (0.5­2.0 um) and ultra­thin (50­100 nm) sections were then cut with the microtome using a diamond knife. This protocol suffers from problems that will destroy ultrastructure as well as poor sampling choices. The authors may have chosen to fix the teeth in formalin because they wanted to decalcify in addition to fixing the tooth’s ultrastructure at the moment of extraction. However, formalin creates only weak cross­links and does not preserve as much ultra­structure as the more commonly used glutaraldehyde. When formalin is used, the tissue should not be washed for several hours in water. This step would reverse the cross­links and buffer solution should be used instead. “The presence of weak cross­links introduced into the tissue by formaldehyde necessitates rather rapid washing and dehydration.” (Hayat, 1970; italics mine) Glutaraldehyde could have been used in combination with the formalin. Renz (1997) dried the specimens, if that drying occurred prior to fixation and dehydration, the ultra­structure could also be damaged. In terms of the location where Renz (1997) chose to make their microtome sections, taking sections from both the cementum nearest the CEJ and nearest the apical portion would have helped to clarify the cellular vs. acellular issue in terms of ultrastructure and potential for age estimation. The authors did not specify what type of cementum was used or where exactly the tooth was sectioned. The sections were
examined using Bright Field Light Microscopy (LM), Confocal Laser Scanning, Transmission Electron Microscope (TEM), and X­ray electron­dispersive analysis in a Scanning Electron Microscope (SEM). In the bright field LM, the authors found that the main factors influencing the resolvability of the incremental lines were the thickness of the sections, the medium in which they were examined, the focus plane (or amount of overlapping information), and the illumination. Resin infiltration also enhanced the visibility presumably because of the refractive difference. The TEM sections were stained only with uranyl acetate and no lead citrate was added. Usually, the two stains are used together as uranyl acetate stains have the most affinity for phosphate groups and stain nucleic acids, membranes, and proteins well. Lead citrate has the greatest affinity for glycogens, staining membranes and proteins best. Renz (1997) published micrographs from the TEM showing oblique sections of the Sharpey’s fibers but no specific ultrastructures corresponding to the increments were seen at higher magnification. The reported thickness of the thin sections is too thick to yield decent resolution in the X­ray SEM analysis so the reported figures about mineralization may not be accurate. In 1999, Renz and colleagues published another attempt to discern the ultrastructure of cementum increments. Transverse sections were removed from the middle third of the root from five premolars. The same section was examined by LM, CSLM, TEM, and in an SEM with an energy dispersive x­ray analysis attachment (EDX). For this study, sections100­150 um were cut on a Buehler saw and polished to 100 um. The sections were rinsed in water for an unspecified amount of time. The teeth were postfixed after sectioning in glutaraldehyde and osmium tetraoxide, then progressively dehydrated in ethanol. The ground sections were then embedded in Spurr’s resin. Once the thin sections were embedded, the ultra­thin sections were made on a microtome using a diamond knife. The ground sections (100 um) were stained with toluidine blue or methylene blue. The cementum increments were clearly discernible in the photographs from the light microscope in the 100 um sections. The structures seemed to disappear in the published photograph of the 1 um sections, leaving only the granular textured substrate. However, inspection of the caption beneath the two photographs reveals that the one
section was left unstained, probably heavily contributing to the absence of discernable structures. The author’s state that whether or not the semi­thin sections were stained, the annulations never materialized. The CSLM picked up lines that had been visible in the bright field light microscopy, as well as the presence of cementocytes, indicating that the region analyzed was at least partially composed of cellular cementum. The TEM micrographs of the ultra thin sections (80 nm) show no evidence of distinct annulations. There were cross­sectioned fiber bundles, however they had no spatial relationship to the location of the annulations seen in bright field. The EDX analysis recorded small fluctuations in the calcium­phosphorous ratio across the cementum, however the authors attribute these small variations to changes in the signal intensities of the beam current. The authors conclude that the nature of the structures is still unknown and that the possibility remains that they represent an artifact of section thickness and optical properties. As the latter study used a properly designed histological protocol, their case is certainly strengthened but more research is obviously necessary in this area. The disappearance of the incremental structures in section 1 um thick is questionable given the success of Charles and colleagues (1986, 1989) in evaluating cementum annulations in demineralized sections 7 um thick. The sections examined in the TEM may have been located in areas of ground substance matrix between mineralized areas and may simply have bypassed any ultrastructural evidence. If the increments represent changes in mineralization, then an unmodified TEM may be inappropriate for observing the ultrastructure of the increments. Studies of ultrastructure in dental tissues are difficult due to the hardness of the materials and the need for ultra­thin microtome sections, requiring the use of expensive diamond knives. There are communication and interdisciplinary barriers preventing studies using biologically relevant protocols by researchers specifically interested in aging and other archaeological applications. The studies are crucially important and electron microscopic investigations, elemental signature analyses, and microprobe studies will eventually unlock the biological basis behind this seemingly age­related phenomenon. The relationship between the cementum and the periodontal ligament,
biochemistry, biological cycles, and dietary stress are other factors considered important to better understanding the increments. Cementum Apposition and the Estimation of Age at Death Although the structure is not well understood, in almost all mammalian species the number of bands in the dental cementum are correlated with the age at death (Gordon 1993). Studies of incremental structures in the dental cementum can be traced back to Malpighi’s study of cementum in the 1600’s (cited in Gordon 1993). Retzius (1837) investigated the ‘striae’ in cementum and Tomes (1904) identified the granular inclusions that bear his name (cited in Gordon 1993). During this century there has been considerable research on these annulations in mammals (Bosshardt and Schroeder 1996 for rodents; Beasley 1992 for Bos taurus) and in humans (Gustafson 1950; Dalitz 1963; Johanson 1971; Maples 1978; Solheim and Sundnes 1980; Stott 1982; Naylor 1985; Charles et al. 1986; Condon et al. 1986; Lipsinc 1986; Miller 1988; Lorentsen and Solheim 1989; Kashyap and Rao 1990; Lopez­Nicolas et al. 1990, 1991, 1996; Solheim 1990; Solheim and Kvaal 1993; Stein and Corcoran 1994; Kvaal and Solheim 1995; Kvaal 1996). Cementum annulations are used by ecologists to study demographics in modern mammalian species. The annulations are commonly used as an aging method for archaeologically derived and forensic human skeletal remains, and to determine age and season at death in archaeologically derived faunal remains (Lieberman and Meadow 1992, for Gazella gazelle). In many mammalian species, the refractive nature of the outermost cementum band is considered representative of season at death (Beasley 1992; Lieberman 1992; Gordon 1993). When the outermost bands are opaque, they indicate death in the non­growth season (rest lines). Translucent bands are used to indicate that the organism died during the growth season. Archaeologists use cementum increments on faunal collections to ascertain human subsistence patterns. The history of cementum studies as presented here is focused on aging methods for humans. Cementum is an attractive tissue for age estimation in human skeletal populations because: 1.) The thickness of acellular cementum is proportional to the age of the tooth (Stein 1994). The number of cementum annulations added to the age at root
completion, or age of eruption, has an approximately linear relationship with age at death. The tissue is continuously deposited throughout the life span of the tooth and is rarely remodeled or destroyed in vivo in the absence of other pathological conditions (Lieberman 1992). Unlike bone, cementum is not regularly resorbed and there are no cementoclasts in the permanent dentition. 2.) Cementum has a protected location within the alveolus, which makes this delicate material less sensitive to the oral environment and pathological conditions. This protective function is negated in cases of periodontitis, caries, and abscess. SEM analyses of the cementum surface have shown significant modifications to its structure in those situations. The linear relationship between cementum annulations and age at death is not simple and has been questioned by many authors (Solheim and Sundnes 1980; Miller 1988; Lucy 1995, 1996). There is a tendency for overestimation of age in younger individuals and underestimation in older individuals (Lucy 1996). Several investigators have noted an opposite tendency toward underestimation in individuals over thirty years of age (Solheim and Sundnes 1980; Miller 1988). Some of these problems may be attributed to inappropriate analyses such as the use of linear regression for discrete data. The use of linear regression requires several assumptions about the data, 1.) that the variables have a linear relationship to age, 2.) the data is normally distributed, 3.) the error is normally distributed about the mean, 4.) and that all variables are weighted equally (Hillson 1992; Lucy et al. 1996). Many studies have used age as the dependent variable, a tendency that may cause systematic, age­dependent errors in least squares regression analysis (Akroyd 1997). If the data is collected in a discrete, or categorical rather than an ordinal scale, a non­parametric Bayesian approach may be a better measure of accuracy (Lucy 1996). Instead of giving an age estimate and confidence interval, this method gives the probability that an individual falls within a 10 year range around the estimate (Hillson 1992; Lucy 1996). Thus the linear relationship between age at death and the number of annulations is not straightforward and there is some evidence to the contrary. Condon and colleagues (1986) found that in a reduced major axis analysis the slope of their regression line was less than one, indicating that age was accumulating faster than the annulations. When analyzed by sex, the males in the sample were more responsible for
the decreased number of annulations as compared to known age (Charles 1989). There are anomalous cases in which individuals appear to have no discernable annulations or the number is obviously doubled (Condon 1986). The reasons for these anomalies are thus far unknown. Questions have also arisen regarding other statistical methods used in histological aging studies. Gustafson’s (1950) work in particular has been criticized because he tested his model using 19 individuals from the original sample used to develop the model. He has also been criticized for using the average difference between estimates and known age, an “average error of estimation,” rather than standard error or dispersion of the estimates about the regression line (Lucy 1995). Gustafson (1950) calculated his error to be +/­ 4.5 years, a figure that has never been reproduced by any researcher using his method on an independent sample. Gustafson published his original data, which Maples and Rice (1979) used to calculate a standard error of 7.03 years for his regression line. Johanson’s (1971) method has been determined to be more accurate and his regression formulae give estimates closer to known age at death in independent tests (Lucy 1995). The use of cementum annulations to estimate age at death is further complicated by the presence of pathological conditions, particularly periodontal disease. Periodontitis has been shown to have a significant effect on the number of annulations present in human teeth (Solheim and Sundnes 1980; Condon 1986; Charles 1989). Periodontal disease exposes cementum to the oral environment, weakens periodontal attachments, and increases cementum apposition at the root apex. Charles and colleagues (1989) found that eliminating 18 cases of periodontal disease from their sample greatly increased the correlation between the number of annulations and the known age at death, moving the slope of the regression line in a reduced major axis analysis closer to one. Periodontitis not only effects cementum thickness, it impacts the ultrastructure and elemental composition as well. In contact microradiographs, healthy cementum has alternating layers of high and low mineralization (Simon 1981). When periodontal disease exposes the cementum to the oral environment, a thick outer layer (10 um) of
hypermineralization develops, characterized by dense “tablet­shaped” hydroxyapatite crystals (Ibid.). In the SEM, teeth affected by periodontal disease show a haphazard arrangement of the cellular cementum matrix. There are more calcification projections and lacunae depressions, and decreased numbers of fibers overall. This indicates an increase in the level of reparative and regenerative activity. There is also an increase in collagen destruction with exposure to the oral environment, and mineralization of remaining collagen fibers increases as the tooth loosens. The increase in projections and depressions may be due to increased calcification resulting from inflammation. Quickly formed cementum, caused by both faster paced mineralization at the ‘resorption bays’ along exposed areas (Hillson 1986) as well as hypercementosis at the apex in numbers of fibers, could also cause a less organized structure to be laid down. In x­ray spectrometry analysis, healthy cementum has a relatively equal proportion of phosphorous and calcium (Simon 1981). Periodontally diseased cementum has a preponderance of calcium and a deficit of phosphorous. This increased level of calcification has been found in periodontally diseased acellular cementum as well. The incremental bands are observed in healthy and periodontally diseased cementum, indicating that the increments are not solely a product of changes in mineral content, though they might still represent changes in the crystalline structure across the bands, as Gordon (1993) suggested. Two major ultrastructural changes have been observed on periodontally diseased cementum examined using electron microscopy: tablet­shaped crystals at the surface of exposed cementum that have an x­ray diffraction pattern consistent with hydroxyapatite, and hypermineralization (Armitage and Christie 1973b). The surface area of the exposed cementum also often has microorganisms from calculus deposits and destruction of the remnants of periodontal ligaments (Ibid.). In light microscopy, no major changes have been observed with the exception of granular structures on the surface of exposed cementum (Bass 1951, cited in Armitage and Christie 1973b). These granular structures may be explained by the vacuoles discovered by Armitage and Christie (1973b). The ‘vacuoles’ were not observed in any of the sections from unexposed cementum. There were four common patterns for the ‘vacuoles’: 1.)
isolated occurances, 2.) grape­like clusters, 3.) chain­like aggregates, and 4.) long fissured areas in one specimen. These ‘vacuoles’ tend to follow the pattern, orientation, and configuration of the collagen bundles. The ‘vacuoles’ may represent demineralized areas caused by periodontal disease. This speculation is supported by x­ray diffraction studies (Simon 1981) in which hypermineralization occurred at the surface in exposed cementum, but overall there was a lack phosphorous. The hypercalcification at the surface of exposed cementum may be a product of the oral environment and a response to an overall decrease in mineralization. The regenerative and other fluid processes in cementum that occur within the life span warrant further investigation. The formation rate of cementum has been calculated for certain mammals and has been found to accumulate at a constant rate barring pathological disruption. In cases of pathological disturbance, the cellular cementum may regenerate and the overall width may be maintained. If the root has been exposed to the oral environment, the tooth should be included in histological analyses with caution, if at all. A better understanding of the pathological changes to the ultrastructure is needed to understand any impact on age estimations. Review of the Literature on Age Estimation Despite the fact that the biological basis for cementum annulations in humans is not well understood, and though Gustafson (1950) and Johanson’s (1971) methods have been criticized, they are still considered the most accurate methods for aging adults from dental histology in forensic circumstances (Lamendin 1992). A summary of studies employing cementum increments, or thickness, is presented here to demonstrate the variety of protocols employed and the range of success correlating cementum with known age at death. Multivariate methods are included in this section as well. Dalitz (1963) successfully applied Gustafson’s (1950) scale for attrition, periodontitis, secondary dentine, and translucency to an independent sample of known age at death (r = 0.88). Johanson (1970) refined the Gustafson technique and added a multivariate analysis (discussed in the introduction). However, Burns and Maples (1976), Maples (1978) and Maples and Rice (1979) were among the first investigators to suggest that tooth class, pathology, and ethnicity influenced age estimates from
cementum annulations. These authors were also the first to critique Gustafson’s statistical methods. Burns and Maples (1976) applied the Gustafson method to a sample of 355 individuals of known age, sex, race, and socio­economic class. They found that these variables had significant effects on the results, as did periodontal disease. Maples (1978) found that root resorption had the worst correlation with age. This result is not surprising given that in general age estimation methods that rely on degenerative processes are less useful than are those that rely on developmental or even appositional processes.
Maples (Ibid.) found that the two variables with the highest correlation with known age at death were root translucency and secondary dentine. He suggested that periodontitis should be eliminated as a criterion because it is difficult to determine the extent of periodontal disease after decomposition of soft tissues. He also tested the correlation between estimated and known age by tooth class and found that the most accurate results came from estimates made using central incisors, lateral incisors, and second molars. Solheim and Sundnes (1980) compared the age estimates obtained from traditional macroscopic observations with those obtained using the intact tooth method of observing root translucency (Bang and Ramm 1970) and the Gustafson method (Dalitz 1963; Miles 1963; Johanson 1971). They found that Johanson’s (1971) method was the most accurate among the histological techniques, and compared most closely with traditional macroscopic estimates coming within one or two years. The standard error was calculated at 10 years, much less than the error for the Bang and Ramm (1970) method. The authors found no significant differences in accuracy of estimates from pathological specimens, by sex or tooth class. Stott (1982) was the first researcher to test the correlation between known age at death and a count of the number of increments, rather than the thickness of cementum. He made serial sections of 80 um thickness from a sample of 19 canines from cadavers. He stained the sections with 2% alizarin red for 4 minutes. His correlation coefficient was very low (r = 0.263). The standard error was 4 years in his examination of 10 teeth derived from three cadavers, aged 57, 67, and 76.
Naylor (1985) conducted a study to determine the optimal section thickness and stain for observing the increments. He did not report his sample size nor did he give full details about his technique. He determined that transverse sections should be taken from 15­45% down the root from the CEJ, in order to avoid both cellularity and compression. He sectioned the teeth to thicknesses of 50, 75, and 100 um. He also tested the following stains: Mayer’s haematoxylin, luxol fast blue, celestine blue, Congo red, Harris’ haematoxylin, thionin, eosin, Bibrich scarlet acid fuchsin, chlorazol black E, nuclear fast red, aniline blue, alcian blue, alizarin red S with ammonium hydroxide, gallocyanin, toluidine blue, and silver. He found that 0.1% Cresyl fast violet for 3 minutes provided the best resolution in mineralized sections. His annulation count was made from black and white photographs at 200x magnification. He found that the annulations were clearest on the labiomesial and linguodistal aspects of the root. Condon and colleagues (1986) focused on calculating the inaccuracy and bias of cementum annulation studies. They demineralized 80 premolars from clinical extractions and sectioned them to 7 um thickness. The sections were stained with haematoxylin and photographed at 400x magnification. The increments were counted from a slide projection of the section. Their sample had originally included extracted canines but these teeth were eliminated from the final analysis because they systematically overestimated age at death by 10 years. The authors performed a least squares regression of cementum increments (y) on known age (x). Measures of inaccuracy (average absolute error) and bias (mean over or under prediction) were calculated for each decade of known age. The standard error was calculated using a jackknife technique, in which one tooth was eliminated from the sample in a series of regression analyses, and these teeth were used as the independent sample. The authors found that 4% of their sample teeth showed no evidence of annulations. It is likely that the increments were not distinct enough to be counted, or did not stain properly. The individuals without annulations, 2 females (24 and 37 years old) and one male (63 years old) were eliminated from the sample. The overall thickness of the cementum was not measured; it is unknown whether it was correlated with known age for these individuals.
An additional four individuals were eliminated from the sample because they showed clear evidence that the number of annulations had been doubled. The authors do not indicate if the thickness of the cementum was doubled as well. This doubling phenomenon is difficult to detect unless the individual is older and the count exceeds 100, making that case an extreme outlier. The reasons behind doubling are unknown, however it is also found in indigenous tropical species and is thought to be related to seasonal fluctuations (Charles 1989). Cementum annulations are absent in domesticated animals whose exposure to seasonal fluctuations has been lessened (Grue and Jensen 1979). Exposure to seasonal and environmental fluctuations in modern human forensic samples could generally be considered extremely modified and reduced. Of the 73 individuals remaining in this sample, the authors achieved a correlation of 97.3% between their estimates and the known age at death (the standard error was 9.6 years). There were no significant differences between the sexes in terms of the accuracy of the technique. Condon and colleagues (1986) also found that periodontal disease had a significant effect on the number of annulations. There were 18 cases of periodontal disease, when these individuals were removed from the sample, the correlation surprisingly decreased to 96.5% between estimates and known age but the standard error was also significantly reduced to 7.4 years. The authors found that the residuals increased in individuals over 30 years of age, meaning that the number of increments represented biological age rather than chronological age, which was accumulating faster than the increments. The authors suggest the difference may be attributed to population differences in the rate of annulation formation. Charles and associates (1986, 1989) attempted to define a standard for estimating age at death using cementum annulations in response to criticisms of paleodemography discussed above (Bouquet­Appel and Masset 1982). The authors used 52 canines and premolars sampled from a forensic population with known age at death. They tested several methods for preparing the sections using decalcification, different thicknesses and stains (Condon 1986; Charles 1986; Charles 1989). The first method most closely resembles the technique employed in this study: the teeth remained mineralized, they were sectioned to a thickness between 80­150 um and stained with a 1­2 % solution of Alizarin Red for 1­4 minutes, washed in ETOH,
cleared in xylene, and mounted on glass slides. Charles and colleagues (1986) also examined mineralized sections that had been left unstained and others stained with Cresyl Fast Violet. Demineralized sections were also prepared and sectioned to 7 um using a microtome. These sections were stained with haematoxylin. The authors found that a magnification of 400x was necessary to clearly delineate and count the cementum annulations. Magnification of 100x is associated with under­estimation unless the overall thickness of the cementum is being measured. Color photomicrographs were used to analyze the sections because of the condensed nature of human cementum. The slides were projected to a size of 22 x 28 cm and counts were made in short transects in the area of the root with the clearest definition, and then the transects were linked to form a total cross section. The authors only had access to canines and premolars; they found the latter to be better indicators of age at death because the canine cementum was generally thicker. The canines systematically overestimated the age at death by 10 years. In this study variability created by interobserver error accounted for 17% of the total variation between the estimates and known age. Intratooth and intraobserver error only accounted for 3­5% of the variation. Only 58% of the sections in the mineralized sample and 76% of the demineralized sections were scorable. Since sectioning a tooth perfectly perpendicular to the cementum increments is not possible, it is preferable to use a demineralized section of 7 um because there will be less overlapping information. However, the researchers also tried both techniques on an archaeologically derived tooth and the sections from the demineralized sample appeared to be macerated (Condon 1986). Lipsinc (1986) examined mineralized and demineralized sections from 31 teeth that had been extracted in clinical practice. Formic acid (10% solution) was used to decalcify sections. The sections were made one­third down the length of the root in the transverse plane to 5 um and stained with haematoxylin and eosin. The sections were examined at 100x magnification at two loci. The counts from three investigators were averaged and added to the age at eruption. The correlation was only 50.98% between known age (y) and the count of cementum annulations (x). However, when the one individual over the age of thirty was eliminated from the sample, the correlation was
increased to 84.47% (standard error was 8.04 years). When age was used as the independent variable (x) and the count of cementum annulations as the dependent variable (y), the accuracy was improved significantly (r = 0.9294, SE = 2.2). Lipsinc (1986) concluded that the technique was unreliable because of the systematic underestimation of age in individuals over the age of thirty. Miller (1988) examined cementum annulations in 100 clinically extracted teeth from individuals between 9­78 years old. Teeth were sectioned to 200­400 um thickness in the transverse plane through the middle of the root. The sections were polished using 6 um diamond paste. Black and white photomicrographs were taken at 90x magnification and the observations of four researchers were averaged and added to the age of eruption. Twenty­nine percent of the teeth had obscured or indistinct annulations and were eliminated from the sample. Only 5.7% of the age estimates were accurate within 5 years. The standard error was 10 years for 85% of the age estimates. The regression line did not account for most of the variability in the estimates and there was a large amount of scatter about the regression line. Ages were most accurate in individuals under the age of 35. Lorentsen and Solheim (1989) used a sample of 500 teeth, 50 of each type excluding molars. They tested the Gustafson (1950) technique with teeth that were longitudinally sectioned once in the buccolingual plane. The specimens were photographed and the images projected for scoring each characteristic. Rather than using Gustafson’s (1950) subjective ordinal scale for the root dentine translucency, the authors created an index using the area of translucency, the total root area and the total tooth area. They found that dentine translucency, secondary dentine formation and cementum thickness were the variables most correlated with age. Kashyap and Rao (1990) conducted a study designed to minimize the difficulties in quantification of attrition, secondary dentine, translucency and cementum annulations. They collected 25 teeth from cadavers in Hyderabad, India. The individuals were between 18­45 years of age. The authors applied the Gustafson (1950) technique to their sample and obtained an error in estimation of +/­ 8.13 years. The authors also developed their own method of age estimation using indices for four age­ related changes. Their index for attrition was the width of the worn area divided by the
width of the tooth at the CEJ. The index for secondary dentine deposition was the length of the secondary dentine divided by the length of the entire pulp cavity. Translucency was indexed by dividing the length of the translucent area by the length of the entire tooth. The thickness of cementum was measured at the thickest point and divided by the width of the tooth at that point. The idea was to create indices for each measurement, to make the technique more specific to individual variation. The method was highly accurate and precise in their study. The index values showed a linear relationship with known age at death. The cementum index required a square root transformation. Their estimates, derived from the mean of the four indices, were very highly correlated with known age at death (r = 0.998). The standard error was 1.59 years, a smaller number than any produced thus far by any other study. The precision of the technique warrants further testing of this method on samples of known age, particularly given that the original sample size was 25 individuals. Lopez­Nicolas and associates (1990) used digital image analysis in their test of the Gustafson method. They collected 173 incisors from clinical extractions from individuals between 13­83 years. The teeth were halved and a central section 1 mm thick was removed. The sections were photographed with color film at a magnification between 9­18x through a binocular microscope. The following measurements were estimated using the IBAS image analysis program: crown hemidiameter at the CEJ, root hemidiameter at half the length from the apex to the CEJ, the width of the pulp cavity at the CEJ, the thickness of the pulp cavity at half the length of the root, the distance from the end of the pulp cavity to the apical foramen. The authors also estimated the following areas using the IBAS image analysis program: secondary dentine, root transparency, complete pulp area, pulp area from the apex to the pulp­ cementum limit, apical resorption, cementum thickness, ideal crown area, and attrition. The individual measures (y) were regressed on the known age at death (x) and the following variables were found to have a significant positive correlation with age at death: width of the pulp canal at the CEJ (x1), area of root transparency (x2), complete pulp area (x3), the area of secondary dentine, and the area of estimated attrition. Their regression formula is: Age = 56.4837 – 0.2757 (x1) + 07.3547 (x2) – 4.632 (x3) –
0.844 (x4) + 1.0461 (x5). The crown length (x4) and the estimated distance from the periodontal attachment to the CEJ (x5) did not produce significant correlations in univariate analyses. Unfortunately, the measurement of periodontitis is not reliable once the soft tissue has decomposed, making this method less useful for archaeological teeth. Solheim (1990) tested Gustafson (1950) and Johanson’s (1971) methods for measuring cementum thickness and root dentine translucency on a sample of 1000 teeth, 100 of each type excluding molars. In addition, the authors included a measurement of root color. The teeth were halved and stained with carbol fuchsin. They found a poor correlation between estimates and age at death (r = 0.31 to r = 0.72). The authors provided no possible explanations for the poor correlation, though it might be partially attributed to the use of cementum thickness at 1/3 the length of the root from the apex, without regard to the cellularity of the cementum at that point. A count of increments in acellular cementum may have produced better results. Solheim (1993) published a list of critiques of the original Gustafson method beginning with the subjectivity of scoring each of the criteria into discrete categories. In fact, to account for this subjectivity, Gustafson had recommended that each worker calculate his or her own regression formula from an independent sample (1950). Most of Solheim’s critiques were focused on Gustafson’s statistical procedures. The use of linear regression requires that the variables are truly independent of one another and that there is a direct linear correlation between the independent and the dependent variables. Solheim (1993) questions whether the variables are independent, but offers no alternatives to this dilemma (see Lucy et al. 1995, 1996; Akroyd 1997). Solheim's main critique of Gustafson is the method by which he derived his estimate of the error of his technique. Gustafson used teeth from his original sample to calculate the error of his method. Had he used an independent sample, the amount of error would have been greater. In fact, Gustafson calculated his regression formula incorrectly and the correlation has since been corrected to 91% from the reported 98% (Maples and Rice 1978). Solheim (1993) describes the corrections and improvements on the Gustafson method that have been published by Burns and Maples (1976), Maples and Rice (1978), and Maples (1979). Unfortunately many of these
improvements scattered through the literature have not been widely adopted because a standardized protocol has yet to be developed and tested. Stein and Corcoran (1994) found several problems using cementum increments as an age estimation tool. Their sample consisted of 52 teeth from 42 patients at the University of Michigan Veterans Affairs Hospital. All of the teeth were mandibular premolars and central incisors. The sections were stained with 1% Alizarin Red for 2 minutes, examined with a transmitted light microscope, and photographed using a green filter. The increments were counted from 10X photomicrographs and the count was added to the average age of eruption in Gray’s Anatomy 35 ed. (1973). The regression line for individuals under 55 was Age = x – 1.8. Their results indicated that there is a one­third decrease in cementum apposition in individuals over 60 years of age, which makes the technique less valuable as a tool for aging elderly individuals. They also found that the distinction between light and dark bands seems to disappear at higher magnifications which can lead to double counting (see also Kay 1984 and Miller 1988). Kvaal and Solheim (1995) removed the apical portion of the root and longitudinally sectioned in the mesiodistal plane. The teeth were demineralized, sections made between 5­7 um thick and stained with Cresyl violet (Voigt’s method). They used a fluorescence microscope with a green excitation light. The cementum fluoresced bright red and the incremental lines did not fluoresce. The authors also tested several methods of counting the increments. 1.) All of the lines were counted using a video monitor, beginning in one area and following the line to a new area occasionally. The lines were counted in three different areas for each tooth and the highest count was recorded. 2.) The lines were counted where they were judged easiest to see, whether the cementum was cellular or acellular. 3.) The lines were counted in an area where they appeared to run approximately parallel to one another. The width of the cementum was measured from the dentine border to the outer­most layer and the width of 4­6 of the most easily recognizable increments was measured at the same location. These measurements were taken on the projection screen. The authors estimated the number of increments within the total width of the cementum. A Pearson’s correlation was calculated for the estimates from each procedure
and the known age at death. The number of incremental lines counted in the first method had an 84% correlation with chronological age. The correlation was only 74 % from the second method. The correlation between estimates made by the two researchers was 74 % for the first two methods, though this test may not be as appropriate as a student's t­test for paired samples for evaluating repeatability. In the third method, the number of increments was estimated from cementum thickness, these estimates had a 73 % correlation with known age. The best correlation (88 %) was obtained for teeth extracted for non­ pathological reasons when the lines were counted and not estimated. The accuracy of counting (not estimating) the number of annulations was greatest for the lower fourth premolar at 96 %. The accuracy of the counting method by age group was 93% for those aged less than 30, 54% for those individuals between 30­49, 33% for those between the ages of 50­69, and 45% for those over 69. The regression formula for estimating tooth age from increment counts is tooth age = 3.4 + 1.8C. The predictions from this formula had a 78 % correlation with known age. Kvaal and associates (1996) tested a variety of methods, stains, and microscopes in their study of cementum annulations in 27 molars and premolars from individuals of known age (16­85 years old). They made sections from decalcified and mineralized teeth. The teeth were demineralized in either HCL or Nitric acid and sections were cut at a thickness of 6 um. The mineralized sample was sectioned to 15­ 100 um in the longitudinal plane and 15­35 um thick in the transverse plane. An additional sample was half sectioned and acid etched for examination in the SEM (at 5000x magnification). The cementum in the demineralized sections tended to separate from the dentine or to shred into ribbons. The increments were most visible in demineralized sections stained with contrast haematoxylin (PAS) and observed in a transmitted light microscope. The mineralized sections were examined using phase contrast, interference contrast, and confocal laser scanning microscopy. The best contrast in the annulations was obtained in the mineralized sections stained with Cresyl fast violet and examined with a fluorescence microscope using a green excitation light.
Summary The Gustafson (1950) method of age determination has undergone a substantial amount of testing and revision. Kashyap and Rao (1990) and Lopez­Nicolas (1998) have obtained the most accurate results for a multivariate approach. The authors of revisions have developed methods that standardize each criterion using indices and both studies employed digital analysis as well. However, the technique developed by the latter researchers included periodontitis. Periodontal disease is no longer considered a useful criteria by many researchers because it will vary per tooth and is most influenced by diet, oral and internal environmental factors. Studies of cementum in isolation have been most successful when the number of annulations is recorded, rather than the overall thickness of the tissue. There is disagreement as to which tooth class provides the most accurate estimates, though the anterior teeth may be easier to position for longitudinal sections as they are single rooted. Some researchers have avoided the use of the posterior teeth altogether because the roots tend to be twisted and bent. However, Maples (1978) found that the age estimates from the Gustafson method on second molars were well correlated with known age at death (r = 0.89 +/­ 8.0). In an archaeological sample, the use of molars with straight roots may be necessary in order to achieve a reasonable sample size. Most researchers have discovered that the section should be 100­150 um in thickness if the teeth remain mineralized. The section should be stained with Alizarin Red and observed at a magnification greater than 100x. Cementum annulations should be observed in the acellular region; the area 1/3 the distance from the root apex seems to be the best compromise between cellularity and compression. Periodontal disease has a significant negative impact on the number of annulations present, teeth with obvious evidence of exposure to the oral environment should be eliminated. The sections should be photographed and projected, or analyzed digitally to avoid interobserver error. The number of annulations must be added to the mean age at which the root is completely formed for the given tooth class. Researchers have had varying success estimating age at death from the increments. The annulations are easiest to observe in demineralized sections, and
studies using decalcified teeth have obtained high correlations with known age at death. However, those researchers who have attempted to demineralize archaeologically derived teeth have discovered that the teeth tend to become macerated upon sectioning. Correlations between the number of annulations and known age at death range from 88 % for mineralized teeth (Kvaal and Solheim 1995) to 97.3 % for demineralized teeth (Condon et al. 1986). Despite these difficulties, many researchers consider the cementum annulations to be the most promising method in dental histology for age estimation especially for forensic samples.
ROOT DENTINE TRANSLUCENCY AND SECONDARY DENTINE DEPOSITION IN AGE ESTIMATION Ontogeny and Physiology of Root Dentine Translucency 2 Late in the bell stage of tooth development, the cells of the dental papilla adjacent to the internal dental epithelia differentiate to form odontoblasts, which will form the coronal dentine. The root dentine begins to form upon the disintegration of Hertwig’s sheath. Odontoblasts lay down the dentine’s organic matrix of collagen and ground substance, as the collagen fibers are secreted they increase in diameter until the ground substance between them is obliterated. The odontoblasts move towards the center of the papilla and the odontoblast process begins to form. The mineralized matrix begins to form as hydroxyapatite crystals are deposited. Calcium and phosphorous ions are present in the cytoplasm of the tissue. Calcium channels on the dentinal cell membranes are activated by the production of ALP (alkaline phosphatase) and CaATPase. Mineralization proceeds by “globular calcification” by which crystals are deposited in discrete areas, which are enlarged until they eventually fuse. Dentine mineralizes incrementally at a rate of approximately 4 um per day and small shifts in the orientation of the fibers are visible at these increments. Greater changes in orientation occur on a five day cycle (approximately 20 um apart) and are known as von Ebner’s lines. The rate of deposition for root dentine is slower, the orientation of collagen is different from that of the coronal dentine, and this dentine is mineralized to a lesser degree. The Ultrastructural Nature of Root Dentine Translucency Root dentine becomes increasingly translucent with age, the process commencing at the root apex and progressing towards the CEJ (Nalbandian and Sognnaes 1960). Dentine normally appears opaque because of different refractive indices of the crystalline ‘fundamental matter’ and the intratubular organic matrix (Drusini 1989). With advancing age, the continued deposition of intratubular dentine 2 This discussion of the ontogeny and physiology of Root translucency is based on Ten Cate (1998)
can lead to the complete obstruction of tubules (Ten Cate 1998). The dentinal tubules in the mat dentine are approximately 3.2 um in diameter and are reduced to 1.0 um as they become increasingly calcified with age (Williams 1985). As the tubules calcify, the refractive index of the fundamental matter becomes increasingly similar to the intratubular matrix (Drusini 1989; Ten Cate 1998). Eventually the dentine appears transparent in the transmitted light microscope. Secondary ion microscopy studies have shown that sclerosed tubules have a higher content of calcium, phosphorous, magnesium, potassium, and sodium than the intertubular dentine (Berkovitz 1989). The sclerosis usually begins at the root apex after an individual has reached the age of 20 and the teeth are fully erupted (Drusini, 1990; Ten Cate 1998). The amount of sclerosis of the dentinal tubules is linearly correlated with age and is generally assumed to be unaffected by tooth function, pathology, or other external processes (Vasiliadis 1983a). However, Johanson (1971) found some translucency in individuals between the ages of 15­19 years old. Lamendin (1992) found that translucency was rarely present in individuals under the age of 30. There are clearly some factors other than age responsible for this variation, although they are unclear at this time. Secondary dentine and reparative, or tertiary dentine formation is a function of age, attrition, and injury. Secondary dentine forms in the same manner as described above once the root is completed. However, secondary dentine has been found in unerupted teeth, indicating that its production is not solely a product of functional requirements (Ten Cate 1998). This type of dentine is also produced by odontoblasts but there are indications that the secondary dentine does not mineralize fully and has a different affinity for stains. The rate at which secondary dentine forms is dependent upon the extent of the attrition or injury, when dentine is deposited quickly it is laid down in an increasingly disorganized fashion (Ibid.). Root dentine translucency, in combination with secondary dentine formation, is considered by some researchers to be the most accurate bivariate technique for age estimation from Gustafson’s original method (Maples 1978; Hillson 1992; Lucy 1995; Sengupta 1998). However, there are difficulties specific to this technique which can limit its usefulness: 1.) within the constraints of the root morphology, the sections must be positioned in such a way that the translucency is visible from apex to CEJ and 2.) the
most useful section thickness has yet to be standardized. Most researchers have reported that it is difficult to make sections that are representative of the real level of translucency in the root. The translucency may develop in a butterfly shaped pattern in the transverse plane, which would be misrepresented by longitudinal sections (Darling and Levers 1983). This difficulty is also partially due to twisted and bent roots that can complicate sectioning through the ‘center’ of the root. Workers do not agree on the optimal section thickness for accurate use of this method and thus there has been a wide range of variation in the accuracy and precision of age estimations made using root dentine translucency. Another difficulty for the purposes of this study, is that the methods have mostly been tested on samples derived from clinical extractions and cadavers. The effects of long term burial have yet to be explored. Some workers have tested the method on recent archaeological material (Drusini 1989, 1991; Sengupta 1998) and others have avoided ancient remains because of discoloration in the dentine (Lucy 1995). In the case of archaeologically derived material which is heavily mineralized, or in which the root surface has suffered post­depositional destruction, there may not be evidence of translucency in thicker sections (1 mm to 300 um). In this study, the extent of the translucent zone was not apparent until the sections were ground to a thickness of 250 um. Review of the Literature on Age Estimation for Intact Teeth It is possible in to evaluate the dentine translucency of modern intact teeth by rotating the tooth in front of a diffuse light source (Bang and Ramm 1970; Drusini 1989; Lamendin 1992) or through the use of radiographs (Ikeda 1985; Kvaal and Solheim 1994). Bang and Ramm (1970) developed a method of judging translucency in intact teeth in order to eliminate the sectioning difficulties of Gustafson’s (1950) method. However, their method was not highly accurate, the correlation between estimate and known age at death was between 61 and 83%. The technique is not precise and there is a large amount of interobserver error in measuring the length of the translucent zone in whole teeth (Solheim and Sundnes 1980; Solheim 1989; Drusini 1989; Lucy 1995).
The standard error in studies of intact teeth is also higher than that achieved in sectioned teeth. The lowest estimate of standard error is +/­ 10 years, achieved by Lamendin et al. (1992). Lamendin’s (1992) technique uses root dentine translucency and periodontitis, indexed by root length from the CEJ to the apex. The use of periodontitis as a criterion has been criticized because the amount of gingival recession varies between teeth and is more influenced by diet and hygiene than age (Sperber 1992). In addition, periodontitis cannot be accurately measured in archaeological material in comparison to a forensic reference population. Drusini and colleagues (1989) also tested the macroscopic method of evaluating dentine translucency developed by Bang and Ramm (1970). The teeth were examined under a tungsten light and sliding calipers were used to measure the zone of transparency. The length of the transparent zone was not symmetrical on two sides of the root so an average was calculated. An average was also calculated for all sides of the roots in multi­rooted teeth. They tested the method on 382 teeth (32 anterior, 33 premolars, 81 molars) from 311 individuals of known age and sex. The samples were from both living people and 100 of the individuals were from a skeletal population. The root translucency was measured, multiplied by 100 and then divided by the total root length. Their standard error was comparable to that of Lamendin (1992) in 50% of observations (+/­ 10 years). However, 50% of the measurements had a standard error of greater than 20 years. They found the method showed the best correlation with known age at death in second molars (r = 91%). Drusini et al. (1991) based their method on that of Lamendin (Lamendin and Cambray 1981). The authors created four indices 1.) translucent area/length of translucent zone; 2.) root area/translucent area; 3.) translucent area x length of translucent zone; and 4.) translucent area x translucent zone. They made their measurements on 366 intact teeth from clinical extractions and buried remains 100 years old. The correlation between known age and estimated age was 86% with a standard error of 7.10 years. Drusini (1997) studied secondary dentine formation in a sample of intact teeth using panoramic radiographs. The radiographs were collected from 433 living individuals of known age and sex. They measured the secondary dentine in 846 teeth. They found a relatively high correlation between known age and
estimated age at death (r = –89.5), the standard error was 5 years in 60% of the cases. Baccino et al. (1998) collected 19 teeth from autopsied individuals ages 19­54. The authors used Lamendin’s (1992) method and measured root translucency in addition to periodontitis. They compared the age estimates derived from these criteria with those from the pubic symphysis using Suchey­Brooks (1990), methods developed by Iscan and Loth for the sternal end of the fourth rib, and Kerley’s (1965, 1978) method based on cortical bone remodeling (all cited in Baccino, et al. 1998). They found that the Lamendin method was the best single technique, with negligible intra­ and interobserver error, however the method was most accurate when combined with the other techniques. Review of the Literature on Age Estimation for Sectioned Teeth The accuracy and precision of age estimates from root translucency also varies widely for sectioned teeth. Much of this variability is probably related to differences in section location and thickness, as well as tooth type. Johanson (1971) suggested that sections 250 um thick gave the most accurate resolution. Mineralized sections at between 350­100 um thickness are most commonly used (Gustafson 1950; Miles 1963; Johanson 1971; Burns and Maples 1976; Maples 1978; Solheim and Sundnes 1980; Sengupta 1998). Age estimates from thin sections are associated with reduced margins of error, ranging from +/­ 3 to 5 years (Miles 1963; Solheim and Sundnes 1980; and Drusini et al. 1989). Metzger and colleagues (1980) suggested that sections 250 um through the center of the root would bypass large areas of translucency. He suggested that the sections be made 1 mm thick. Lopez­Nicolas et al. (1990) measured transparency, among other variables, in 1 mm sections. Their age estimates had a prediction error of 2.05 years with a confidence interval of 95%. However, 1mm thick sections have been criticized as difficult to examine due to overlapping information. This danger is supported by studies of half­sectioned teeth that have produced low correlations between known age and estimates, ranging from 31­72%. (Solheim 1989, 1990, 1993; Lorentsen and Solheim 1989). Solheim (1989, 1990, and 1993) conducted a series of tests using the Gustafson
(1950) and Johanson (1971) criteria on 1000 teeth, 100 from each tooth class except for the molars. Johanson (1971) had found that root dentine translucency was the single variable that was most correlated with age at death (r = 0.84). In a test of root dentine translucency alone, the authors measured the length of the translucent zone for intact and half­sectioned teeth (1989). They found the sectioned teeth to be more useful, as the measurements made on intact teeth had large interobserver error. The sectioned teeth showed little variation between tooth class and the influence of pathological conditions was negligible. The same sample was used to test the correlation of secondary dentine deposition, the width of the pulp chamber, and known age (1992). The authors only found a 60% correlation using these two criteria. Drusini et al. (1990) tested the idea that root dentine translucency shows the best correlation with age of all the criteria in the Gustafson (1950) method. The authors looked at 70 teeth (33 premolars, 37 molars) from 46 adults of known age and sex. They sectioned one root from each tooth to 600 um thickness (in the buccolingual plane). The authors used a light microscope at 6 times magnification and measured total root length as well as root transparency. Two observers took the measurements and there was no significant difference between the two scores. The authors noted a tendency to overestimate age in young individuals and to underestimate in older individuals. The margin of error around their estimates was +/­5 years in 21% of cases, +/­ 10 years in 26% of cases, and the other half of the estimates had a margin of error between 10­20 years. The best correlation between the estimate and known age was 58 %. The authors attribute their low success rate to the difficulties of getting the full zone of transparency included in the section. The average correlation for the premolars was 49%, for the molars it was slightly higher at 55%. Sengupta and colleagues (1998) tested the applicability of root dentine translucency studies for archaeologically derived teeth. They tested several methods on both modern, clinically extracted teeth and on two historic period skeletal assemblages. The teeth were examined intact, following Bang and Ramm (1970) and then embedded. They collected three buccolingual sections 250­150 um thick from the center of each tooth. A test of six stains showed unstained teeth to provide the most resolution. The authors took monochrome digital photographs of each section over a light box with a
macrolens. They used Microscale TM/TC program for digital analysis, tracing all of the areas to be measured using the mouse. They measured the length and area of root transparency, the percent length of root translucency (length of translucency divided by the total root length), and the percent area of root translucency (translucent area divided by the total root area). Sengupta et al. (1998) found that intra and interobserver error were not significant in measurements of sectioned teeth, interobserver error was significant for intact teeth. The canine was the preferred tooth class for sectioning because the roots are generally broad and straight. The teeth from the archaeological sample tended to fracture and fragment upon sectioning unless they were infiltrated with the methyl methacrylate. The authors also found that the stains that they tested did not improve resolution of the translucent area so teeth were examined without the aid of stain. The paper is a preliminary report and the authors do not report here the age estimates obtained or the resulting correlation with known age at death. Summary A review of the literature on root translucency reveals that the method is employed with varying success using a variety of protocols. The methods developed for intact teeth by Band and Ramm (1970) and Lamendin et al. (1981) are useful in that they do not require sectioning. However, the standard error for age estimates made using these techniques is greater than ten years and interobserver error is generally highly significant. The methods have not been fully tested on archaeological material. Sectioning has also been employed for the study of root translucency using a plethora of techniques, thickness’ and measures. The most accurate measures seem to have been derived from teeth sectioned to 250­150 um, left unstained, examined using digital analysis. The most accurate results have been derived from measures of translucent area rather than the length of the translucent zone. Almost all of the tests of root translucency in age estimation were conducted using teeth derived from forensic contexts or from clinical extraction. Archaeological samples present additional challenges. Heavy amounts of post­depositional mineralization and damage to the root surface could make observations on intact
prehistoric teeth difficult. In addition, archaeologically derived teeth tend to fracture and fragment upon sectioning. It is unknown how much the standard error increases for age estimates based on methods developed from modern forensic reference samples and used on teeth derived from different temporal and geographical contexts. Periodontitis cannot be accurately measured on teeth when soft tissue has decomposed, so multivariate methods employing measures of periodontal disease in addition to translucency are less useful.
MATERIALS AND METHODS This section provides detailed description of the sample available for this study, including description of the sample selection criteria, stratigraphic context, pathological profile and sex estimates. Each method used in this analysis had different specifications for section thickness, stains, and data collection. The protocols for preparing the sections for each phase of analysis, equipment, methods for data collection, and regression formulas for predicting age at death are included in this section. Discussion of the statistical methods is reserved for the following chapter on results. Sample Derivation and Characteristics Forty­one graves were excavated at Damdama from a deposit 1.5 m in depth, comprised of ten stratigraphic layers. Layers 2 and 3 were composed of hard, ashy black soil and the rest of the layers were black clay with occasional bands of yellow clay (Varma et al. 1985). The Damdama skeletal collection consists of the remains from forty­eight individuals. The sample is comprised of 2 juveniles (age 2­2.5), 9 adolescents (14­19), 10 young adults (20­29), 10 adults (30­45), 6 older adults (45­60). Due to poor preservation 6 individuals were aged only as “adult” and age estimation was not attempted macroscopically for 3 additional individuals. J.R. Lukacs collected twenty­nine teeth from 18 adult individuals from the Damdama skeletal collection with permission from the Department of Ancient History, Culture, and Archaeology, University of Allahabad. Table 2 shows the sample collected and the stratigraphic context. Five out of the eighteen individuals included in this analysis (28%) were recovered from double burials, individual 18a was in a triple burial. The selection of teeth for histological aging should ideally be determined by preservation, homogeneity, root morphology, and absence of pathology. Teeth that have remained within the alveolus throughout the depositional period are protected, and thus less likely to suffer damage from soil chemistry and other taphonomic processes. The depositional environment may affect dentine translucency through postmortem mineralization. Tears and other damage to the periodontal ligament and to the cementum are also more likely to occur in isolated teeth. Stott (1982) recommended
TABLE 2: CLASS OF TOOTH AVAILABLE FOR ANALYSIS FROM DAMDAMA Individual 6b 2 7 8 11 12 13 15 2 16a 17 3 18a 2 20a 28 2 30a 2 30b 32 34 2 36a 37 Total # Ind 18 Teeth LRP3, URM2 LLC, URM2, URM3 LLI2, ULM3 URM2 ULM3 LRC, LRM3 URM3 ULI2 ULM1, ULM3 ULM2 ULM1 URI1, LRM3 URM3 ULC, LRM1 ULI1, ULP4, LRM3 ULM2, ULM3 LRP4 URM3 Total # Teeth 29 Stratigraphic VII VII VII IV I IX VI III III VII V VII VIII VIII VIII IX VIII VIII Orientation 1 North to South West­East West­East West­East West­East West­East West­East West­East East­West West­East West­East NW­SE West­East West­East SW­NE SW­NE West­East West­East Information on stratigraphic context derived from Pal 1988 and 1992. Individuals are numbered using the original grave numbers. 1 Orientation is given with the position of the skull listed first 2 Individuals/graves numbered 6, 16, 20, 30 and 36 were double burials 3 Grave XVIII contained three individuals sectioning and examining teeth within the alveolar bone because extraction may damage the cementum. This advice seems especially relevant for archaeological samples. The Damdama skeletons were heavily encrusted with a calcareous matrix, making teeth difficult to extract. This concretion had the greatest influence on the selection of teeth available for analysis. Eight teeth had portions of alveolar bone protecting the root surface (30%), the rest of the sample consists of isolated teeth (Table 3).
The sample is not large and it is not homogenous in the sense that the individuals were recovered from nine different stratigraphic layers and the teeth are not of the same tooth class. Small sample size is unfortunate but it is one of the common limitations faced in bioanthropological research. The small size of this sample may be considered part of the test to see if the methods are feasible for archaeological populations. The sample size is also not so small as to be unprecedented, even for tests of dental histological aging methods in forensic contexts (Cook 1984). In order to
maximize samples size, the temporal context of the nine stratigraphic layers is considered roughly contemporaneous. The teeth were originally collected for paleodietary analysis. The sample is thus biased toward molar teeth, particularly third molars. Single rooted teeth are preferred for histological study because they are generally easier to position and section, though there may be few differences in accuracy between anterior and posterior teeth in evaluating root dentine translucency (Drusini 1989) and cementum annulations (Maples 1978). Most of the methods employed in this study do not differentiate between tooth types and in order to maximize sample sizes for archaeologically derived material, it is not always possible to consider tooth class as a primary selection criteria. For the purposes of this study, the main question involved the feasibility of employing histological techniques on an archaeologically derived sample. All of the teeth available were used. The teeth should also be free of pathological conditions such as periodontitis, to the extent that the condition can be judged in archaeological remains. Periodontitis, alveolar resorption, passive eruption, AMTL, and caries can expose the tooth root to the oral environment. Exposure of the root surface to the oral environment can cause hypermineralization of exposed surfaces, resorption bays on the cementum, and may have a significantly negative impact on age estimation. In this sample most of the teeth were missing portions of the periodontal ligament, making periodontitis impossible to judge. The cementum annulations were only counted in areas where the ligament remained intact. However, the possible effects of periodontitis on the results of this study are unfortunately unknown. Table 3 gives details on the condition and the pathological profile of the teeth used in this sample. Attrition had reached the level of dentine exposure in 10 teeth (66.67%) and had resulted in pulp exposure in 7 teeth (25.93%). There were 13 teeth (48.15%) which had large interproximal wear facets, and 1 tooth (3.7%) the LRP3 from individual 6b had an antemortem interproximal groove located at the CEJ, probably resulting from some habitual idiosyncratic behavior such as tooth picking (not included in Table 3). There was clear evidence of postmortem damage to the enamel of 8
TABLE 3: DENTAL PATHOLOGICAL PROFILE FOR DAMDAMA SAMPLE Ind # Alveolar bone caries dentine exposure pulp exp interprox. wear p.m. damage 6b URM2 LRP3, URM2 LRP3, URM2 URM2 7 URM2 URM2 LLC 8 ULM3 LLI2, ULM3 LLI2, ULM3 LLI2, ULM3 11 URM2 URM2 12 ULM3 13 ULI1, LRM3 ULI1, LRM3 LRM3 ULI1 15 URM3 17 ULI1, ULM3 18a ULM2 ULM2 28 LRM3 URI1, LRM3 LRM3 URI1 30a URM3 LRM3 30b ULC, LRM1 ULC, LRM1 LRM1 32 ULP4, LRM3 ULI1 ULI1 ULI1, ULI1 34 ULM2, ULM3 ULM2, ULM3 36a LRP4 LRP4 LRP4 37 URM3 URM3 Tl # 8 2 10 7 13 8 % 30% 7% 67% 26% 48% 30% additional teeth (29.63%). Two teeth had occlusal caries (7.4%), however the root was unaffected. Protocol Age at death was estimated macroscopically by dental eruption timing, attrition, changes in pelvic morphology (the auricular surface and the pubic symphysis), cranial sutures, epiphyseal suture closure, and degenerative changes to postcranial morphology (Lukacs n.d.). Sex was estimated using the shape of the sciatic notch when available, the diameter of the acetabulum, mandibular and cranial morphology, as well as metric and morphological observations of the postcranial skeleton (Lukacs n.d.). Tables 4 and 5 give details of the macroscopic methods used for age and sex estimation for each individual. For comparative purposes, the mean for each range was used. Estimating age at death from cementum increments requires thin sectioning and histological analysis. Root translucency can be judged from observations on whole teeth (Bang and Ramm 1970; Lamendin and Cambray 1981; Drusini 1989). The whole tooth technique may be less accurate than observations made on sectioned teeth and is
subject to higher intra­ and interobserver error (Lucy 1995; Sengupta 1998). Translucency is difficult to observe at a thickness greater than 200 um in archaeological samples, which may have suffered from postdepositional mineralization. Six histological methods of age determination were used in this study. The multivariate methods include those developed by Kashyap and Rao (1990), Johanson (1970), and Maples (1978). Two methods based solely on root translucency were used, those developed by Lorentsen and Solheim (1990) and Drusini (1989). Finally, age was estimated using a count of cementum annulations, following Charles and colleagues (1986, 1989). Development of standardized protocols for embedding, sectioning, staining and observing the age related changes could reduce the margin of interobserver error (Table 6). This protocol for preparing and sectioning the teeth was developed with Dr. Jeanne Selker, a biologist who runs the electron microscope facilities at the University of Oregon. In discussing the problems of working with a hard and intractable material, Dr. Selker recommended using Spurr’s resin (Buehler, Ltd., Lake Bluff, Ill.). Spurr’s resin is an embedding material with very low viscosity, in this case mixed for medium hardness (through the addition of a smaller amount of catalyst). The use of Spurr’s resin also leaves open the possibility of work with the cementum on the EM. The teeth used in this study did not require fixation because of their archaeological derivation. In terms of demineralization, staining and sectioning, the various age estimation techniques have different requirements. The multivariate methods, and those based on root translucency do not require decalcification or staining. The section thickness varies per method, so the sections in this study were ground thinner for each succeeding method to be tested, as will be discussed below. For studying cementum increments, many authors feel that the best method is to microtome section decalcified teeth and to stain them with haematoxylin (Condon 1986; Hillson 1996). Though this protocol produces the best resolution of the increments, Lieberman and Meadow (1992) have suggested that this procedure is often too harsh for archaeological remains and other researchers (Condon 1986; Charles 1989) have found that decalcification tends to produce macerated sections in archaeological material. For the purposes of this study, several methods for estimating
age at death were applied to each section and the teeth were not demineralized prior to dehydration and embedding. Each tooth was placed in 100% acetone for 24 hours, then 75% acetone mixed with 25% Spurr’s resin for 24 hours. The concentration of resin was increased to a 50­ 50% solution for the next 24 hours, then increased to 75% for 24 hours. Finally each tooth was placed in 100% Spurr’s resin for 24 hours. The teeth were embedded in the resin in a plastic mold and polymerized at 60 degrees for 24 hours. This protocol is similar to that used by Stein and Corcoran (1994). Each tooth was longitudinally sectioned in the bucco­lingual plane to an initial thickness of 1 mm using a Buehler Isomet low speed saw with a diamond impregnated blade. The section that passed though the center of the root was used to test each method of age estimation. Some authors section in the transverse plane for studies of cementum annulations, however the multivariate methods and those for root translucency required longitudinal sections. Kashyap and Rao (1990) sectioned teeth to 1 mm thickness; in the interests of repeatability, their method was tested on the sections at their original 1 mm thickness (Figure 2). A trial run also indicated that the teeth were easily fractured and occasionally disintegrated when sectioned less than 1 mm. FIGURE 2: MEASUREMENTS FOR MULTIVARIATE AGE ESTIMATION (KASHYAP AND RAO 1990) T t
TABLE 7: REGRESSION FORMULAS FOR AGE ESTIMATION (JOHANSON 1970) Johanson’s formulae Attrition Dentine translucency Secondary dentine Root resorption Attrition & translucency Regression equations Correlation 23.25 + 14.96(A) .49 25.32 + 14.91(T) .86 25.32 + 13.13(S) .66 48.28 + 11.04(R) .24 19.07 + 7.74(A) + 11.17(T) .88 St. dev. 10.75 7.1 10.26 13.32 6.14 The sections were not stained for the multivariate methods, nor for measuring the translucent area, as Sengupta (1998) found a decrease in the contrast between translucent and opaque dentine in comparison with unstained sections in their test of the following stains: Ammoniated Indian ink, Solochrome cyanin, Schmorl’s picro­ thionin, Methylene blue, von Kossa, and P.A.S. Each observation was repeated after one week to test intra­observer error. The individual ages at death were calculated using the following formula: {[(a/A) x 100] + [(d/D) x 100] + [(t/T) x 100] + {[(c1 + c2)/C] x 100}}/ 4. Kashyap and Rao (1990) obtained a correlation of 99.8 % between their estimates and known age at death. The standard error was +/­ 1.59 years. The sections were subsequently ground to a thickness of 200 um using a series of sandpapers (grit 200­600) and finally 9 um diamond paste on a Buehler Minimet automatic polisher. The sections were examined using Johanson’s (1970) criteria for age estimation (Figure 3). For this study, four of Johanson’s six original criteria were used: attrition, secondary dentine, root dentine translucency, and root resorption. Periodontitis was excluded as it is not reliably measured in archaeological specimens. Cementum thickness was also excluded because it is measured at the thickest point, an area generally found in the cellular region that is heavily influenced by environment and is not an accurate indicator of age. Each observation was repeated after one week to test intra­observer error. Age at death was calculated using four of Johanson’s (1970) univariate and one multivariate regression formulae (Table 7). The 200 um sections were also evaluated using the regression formulae from Maples (1978) with Gustafson’s (1950) criteria for scoring secondary dentine and root translucency (Figure 3). Maples (1978) found that these two criteria had the best
TABLE 8: REGRESSION FORMULAS FOR AGE ESTIMATION (MAPLES 1978) Tooth Class I1 I2 C P3 P4 M1 M2 M3 Regression formula 3.89S + 14.23T + 15.28 6.51S + 12.55T + 25.16 18.67S + 11.72T + 21.94 2.82S + 15.25T + 19.65 4.79S + 15.53T + 17.99 11.28S + 5.32T + 10.86 6.99S + 10.86T + 19.31 4.71S + 12.30T + 24.57 Correlation .89 .88 .76 .77 .83 .85 .88 .83 S.E. 9.1 9.6 11.0 12.2 7.6 11.1 6.8 12.0 correlation with age when weighted by tooth class (Table 8). He achieved an overall weighted correlation of r = 0.86 with known age at death. These formulas also include estimates of standard error by tooth class, which range from +/­ 6.8 to +/­ 12.2 years. Each observation was repeated after one week to test intraobserver error. Recently digital imaging analysis has been employed to measure the area of dentine translucency. The use of digital image analysis can reduce the level of intra and interobserver error as the imaging program can be used to precisely measure the area of dentine translucency, in relationship to the length and total area of the root (Lorentsen and Solheim 1989; Drusini 1991). The area is an important unit of measurement if the root translucency does not proceed linearly from the apex, but in a butterfly shape in the transverse plane (Darling and Levers 1983). To measure the area and length of root translucency, the 200 um sections were photographed through a Zeiss 2000C stereomicroscope at 6.5 x magnification and a Polaroid digital camera (DMC1) at 1600 x 1200 resolution. The images were analyzed using the SigmaScan Image program on a PC running Windows 98. Following Lorentsen and Solheim (1989) the area of translucency and total root area were calculated in pixels using the mouse to demarcate the area to be measured (Figure 4). Measurements were converted to millimeters using the calibration function on a 0.5 mm scale included in the photographs. Each observation was repeated after one week. Lorentsen and Solheim (1989) developed their regression formulae for each anterior tooth class by jaw, producing a total of ten formulae (Table 9). They calculated the correlation between the translucent area and known age at death and the standard
TABLE 9: REGRESSION FORMULAS FOR AGE ESTIMATION FROM AREA OF TRANSLUCENT DENTINE (LORENTSEN AND SOLHEIM 1989) Tooth class Maxilla I1 I2 C P3 P4 Mandible I1 I2 C P3 P4 Regression formulae Correlation SD for Age 37.27 + 2.71ATD – 0.03ATD 2 – TA 31.14 + 1.30ATD – 8.28S 19.19 + 1.46ATD – 0.01ATD 2 21.90 + 0.69ATD 22.89 + 0.90ATD 82 84 86 75 70 9.0 9.2 8.7 13.1 12.1 23.96 + 2.06ATD – 0.02ATD 2 – 9.74S 25.44 + 1.68ATD – 0.02ATD 2 23.51 + 1.60ATD – 0.02ATD 2 27.85 + 1.04ATD 21.60 + 1.59 ATD – 0.01ATD 2 76 64 78 68 81 10.9 12.9 12.0 12.0 11.4 ATD = Area of translucent dentine; TA = Total area; S = Sex (male = 1, female = 0) error for each formula. The correlation coefficients ranged from r = 0.64 for mandibular lateral incisors (S.E. = 12.9) to r = 0.86 for maxillary canines (S.E. = 8.7). Only ten individuals (11 teeth) could be included in this analysis, as the authors did not publish regression formula for molar teeth. The root length and the length of the translucent area were also used to calculate age at death following Drusini (1991). The age estimates are based on an index designed by Lamendin and Cambray (1981). The length of the translucent area (h) was multiplied by 100 and then divided by the length of the root (H). Both measurements were made using a ruler function in SigmaScan on the digital images, they were calibrated using a scale included in the photograph (see Figure 5). Age at death was estimated using two regression formulae, one developed for premolars and one for molars. For this study, the anterior teeth were evaluated using the premolar formula, a compromise somewhat justified by the single straight roots for these classes of teeth. The formula for premolars (Age = 23.7329 + 0.1262x +0.0089 x 2 – 0.1046 x 3 ) had a 58% correlation with known age at death and the standard error was 8.4. The formula for premolars (Age = 23.7329 + 0.1262x +0.0089 x 2 – 0.1046 x 3 ) had a 57% correlation with known age at death with a standard error of 6.1 years. The sections were subsequently ground to a final thickness of 100 um using a
TABLE 10: DENTAL EMERGENCE TIMING FOR CHILDREN IN CHANDIGARH, INDIA (YEARS) Maxilla Central incisor Lateral incisor Canine Third premolar Fourth premolar First molar Second molar Males 7.08 8.13 10.97 10.47 11.48 6.61 12.02 Females 6.92 8.13 10.47 10.23 11.22 6.03 11.22 Mandible Central incisor Lateral incisor Canine Third premolar Fourth premolar First molar Second molar Males 6.61 7.59 10.71 10.91 11.75 8.17 11.18 Females 6.96 7.59 9.77 10.00 11.22 5.62 10.72 Table based on original data published in Kaul et al. (1975), taken from El­Nofely and Iscan (1989). series of sandpapers (grit 200­600) and finally 9 um diamond paste on a Buehler Minimet automatic polisher. Each section was stained with 2% Alizarin Red to examine the cementum increments, following Charles (1986, 1989). Image processing software may also be used to provide a more objective analysis of the annulations through contrasting pixels (Drusini 1990; Lieberman and Meadow 1992; Stein and Corcoran 1995; Lopez­Nicolas 1996). The sections were photographed at 200x magnification through a Zeiss transmitted light microscope using a Polaroid digital camera (DMC1) at 1600 x 1200 resolution. The number of cementum annulations was counted on a 14” Sony digital monitor (see Figure 6). The measurements were collected twice a month apart to test for intraobserver error. Measurements were also collected by Dr. J. Lukacs to test for interobserver error. Following Charles (1986, 1989), the number of annulations was added to the age at which the tooth generally completes eruption and reaches the occlusal plane. Eruption times by tooth class for children from Chandigarh, India were chosen and are given in Table 10.
RESULTS This study is a comparison of age estimates from macroscopic and microscopic methods in a sample of 29 teeth from 18 individuals from Damdama. Macroscopic age estimates were made by Lukacs (n.d.) based dental eruption times and attrition, changes in the pelvic morphology (the auricular surface and the pubic symphysis), cranial sutures and epiphyseal closure, and degenerative changes to postcranial morphology. Sex was estimated using the shape of the sciatic notch when available, the diameter of the acetabulum, mandibular and cranial morphology, as well as metric and morphological observations of the postcranial skeleton (Table 11). Observer Error and Comparisons within Individuals Intraobserver error indicates the precision with which estimates are made from each method on two separate occasions; interobserver tests the precision with which the estimates can be collected between two observers. Intraobserver error was tested for TABLE 11: MACROSCOPIC AGE AND SEX ESTIMATES FOR DAMDAMA SAMPLE Age 1.5­2.5 2.5­3.5 14­18 16­18 16­20 17­20 18­20 18­22 19 20­23 20­24 20­25 21 22­25 25­29 25­35 27­33 Indet total Male 36b 32 7 33 19 8 22 18a 16a 18b 34 16b 30b 24 Female Unknown total 4 1 5 1 1 17, 35 2 36a 2 20a 1 1 1 1 1 1 1 1 1 1 29 2 1 21 14,31 3 18 4 46
Age > 30 30­35 30­40 30­45 35­39 35­45 36­50 37­43 40­45 43­51 45­50 45­55 45­60 45­55 50­60 Male 6b 27 11 40 20b 39 28 18c Female Total 2 1 1 1 6a 1 30a 1 12, 13 3 1 1 1 1 1 23 2 1 26 1 37 1 3 1 Adult 9, 24, 25 10, 15 6 TABLE 12: DESCRIPTIVE STATISTICS FOR CEMENTUM ANNULATION COUNTS Annulation count 1 (Robbins) Annulation count 2 (Robbins) Annulation count (Lukacs) N 17 16 12 Min Max Mean S.E. 18 38 26.95 1.55 17 38 26.81 1.57 11 38 21.33 2.08 S.d. 6.38 6.30 7.20 t p ­.226 .824 ­.392 .700 ­2.813 .017 all of the methods and was found to be insignificant (µ = 0.05). Intraobserver error between the two sets of age estimates from my counts of cementum annulations were not significantly different (p = 0.597). Interobserver error was tested only for the cementum annulation method because it is the method that appears to produce the most accurate results (see below). The cementum increments were counted independently by Dr. J.R. Lukacs and a student’s t­test for paired samples shows that both sets of observations that I collected were significantly different from the count made by Dr. Lukacs (p = 0.000 and p = 0.003 respectively). There were no significant differences between the two sets of observations that I collected for the cementum annulations and the macroscopic methods (Table 12). TABLE 13: AGE ESTIMATES FOR INDIVIDUALS WITH MULTIPLE TEETH AVAILABLE Ind 6b 6b 7 7 7 8 8 13 13 17 17 28 28 30b 30b 32 32 32 34 34 Tooth LRP3 URM2 LLC URM2 URM3 LLI2 ULM3 ULI1 LRM3 ULI1 ULM3 URI1 LRM3 ULC LRM1 ULI1 ULP4 LRM3 ULM2 ULM3 N MinimumMaximum 9 20.00 56.14 7 24.00 56.14 9 16.00 83.67 7 14.00 70.05 7 15.00 70.89 9 19.00 68.13 8 5.00 70.89 9 22.00 88.66 8 38.00 75.60 8 19.00 58.88 7 19.00 47.69 8 22.00 70.05 7 32.00 68.82 9 30.00 75.77 8 23.00 74.78 9 7.00 62.60 8 18.00 75.97 7 18.00 55.14 8 24.00 40.20 7 12.00 55.14 Mean 40.53 43.93 47.98 44.36 43.31 45.86 45.03 57.75 57.29 40.75 35.29 53.56 53.81 56.53 48.18 39.99 46.25 39.81 32.91 39.93 S.E. Std. Dev. Variance 4.06 12.18 148.30 4.63 12.24 149.84 8.60 25.80 665.83 8.14 21.54 464.07 7.54 19.96 398.41 5.97 17.90 320.31 8.37 23.66 559.98 6.74 20.21 408.53 5.80 16.40 269.00 5.33 15.07 227.05 4.22 11.15 124.38 5.41 15.28 233.35 5.17 13.69 187.35 5.73 17.18 295.02 7.36 20.83 433.83 6.85 20.55 422.41 6.82 19.29 372.09 5.80 15.35 235.61 2.02 5.70 32.51 5.91 15.63 244.26
TABLE 14: PAIRED T­TEST FOR SIGNIFICANT DIFFERENCES FOR INDIVIDUALS WITH MULTIPLE TEETH Ind 6b 7 8 13 17 28 30b 32 34 Teeth LRP3­ URM2 LLC­ URM2 LLC­URM3 URM2­ URM3 LLI2­ ULM3 ULI1­ LRM3 ULI1­ ULM3 URI1­ LRM3 ULC­ LRM1 ULI1­ ULP4 ULP4­ LRM3 ULI1­ LRM3 ULM2­ ULM3 n Mean Diff ­2.58 1.95 3.00 1.05 7 1.38 7 ­3.40 6 7.28 6 ­.87 7 7.68 6 ­.20 2.19 1.99 6 ­5.75 6 6 s.d. 3.99 11.23 5.73 9.20 15.37 12.84 8.00 8.72 17.41 15.34 7.09 10.20 15.20 S.E. 1.51 4.25 2.17 3.48 5.44 4.54 3.02 3.30 6.16 5.80 2.68 3.86 5.74 t ­1.708 .459 1.384 .302 .254 ­.749 2.408 ­.263 1.248 ­.035 .818 .517 ­1.001 Sig. .138 .663 .216 .773 .807 .478 .053 .802 .252 .974 .445 .624 .355 Dr. Lukacs eliminated five sections because they appeared unscorable. This indicates that the method cannot be applied reliably by two different observers with a different level of familiarity with cementum annulations in general. There were seven individuals with two teeth available, and two individuals with three teeth available (Table 13). The precision of all of the estimation methods was also tested by comparing estimates for different teeth from the same individual. The estimates from the Lorentsen and Solheim method (1989) and the cementum annulations were eliminated from the comparisons between teeth unless they were present for both, or all three teeth. There were only two individuals (13 and 30b) for whom the cementum could be included in the analysis; none of the Lorentsen and Solheim estimates could be included. The results of the paired t­test for significant differences in the mean estimates for age at death are given in Table 14. The largest difference in the mean estimates was 5 years, the smallest difference was 0.18 years. For all of the individuals with multiple teeth available, the differences in the estimates from those teeth were not significant. The relationship between teeth was also plotted in scatterplots (Figure 7). Had there been significant differences between the estimates derived for different teeth, possible biases between tooth classes would have been indicated for
each method. However, similar ages were derived despite tooth class for all of the methods. The test of differences “within” individuals also speaks to the small amount of intra­observer error for observations within each method. As there were no significant differences between the estimates from different teeth for the same individual it is possible to state that 1.) The methods can be applied to the same sections with an insignificant level of intraobserver error, 2.) estimates for different teeth from the same individuals are internally precise, whether or not they are accurate, and thus 3.) tooth classes do not seem to influence the accuracy of results. Age Estimates from Dental Histological Methods As the true age at death is not known, the accuracy of the histological methods can only be approximated by the significance of differences between the means, analysis of variance, and the significance of correlation coefficients. Age estimates for the macroscopic and all the histological methods are given in Table 15. Boxplots were created for an initial, rough comparison of the range of age estimates for each method and for each individual (Figure 8). To examine the density and distribution of the age estimates, histograms were produced for each method (Figure 9) and each individual (Figure 10). Normal quantile plots were produced for each method to test the normality of their distribution (Figure 11). Scatterplots were produced comparing each histological method with the macroscopic age estimates (Figure 12). To evaluate the similarities between the histological methods, a student’s t­test for paired samples was used to compare the mean age estimate from each method with the mean from the macroscopic methods (Table 16). An analysis of variance (one way ANOVA) was calculated to measure the amount of variance between estimates as opposed to the amount of variance within the estimates from each method (Table 17). A Pearson’s correlation matrix was used to evaluate the significance of similarities between sets of estimates from the different methods (Table 18). The Euclidean distance was also calculated between the methods (Table 19). The estimates from the Kashyap and Rao method and from the cementum annulations were the most closely
TABLE 15: MEAN ESTIMATES FOR AGE AT DEATH Ind 6b 7 8 11 12 13 15 16a 17 18a 20a 28 30a 30b 32 34 36a 37 N Mean s.d. Min Max 1 30­35 18­20 20­23 35­45 35­45 35­45 30 21.00 16­18 20­25 17­20 45­50 35­39 27­33 16­20 25­29 16­20 45­55 2 32.5 19 21.5 40 40 40 30 21 19 22.5 18.5 47.5 37 30 18 27 18 50 18 29.72 10.95 18.0 50.0 3 22 15 31 64 26 50.5 20 20 24 16 27 27 25 48.5 20 23.5 19 28 18 28.14 13.07 15.0 64.0 4 53.2 43.4 56.9 68.1 53.2 68.1 45.7 30.7 38.2 38.2 38.2 60.7 68.1 68.1 40.7 38.2 45.7 68.1 18 51.31 13.04 30.7 68.1 5 55.1 62.6 62.6 55.1 70.1 70.1 62.6 32.8 51.4 55.1 55.1 66.3 70.1 70.1 60.1 47.7 62.6 70.1 18 59.97 9.80 32.8 70.1 6 56.1 57.6 63.8 62.9 68.1 74.8 58.8 28.2 46.5 49.4 49.4 68.5 74.8 74.8 54.5 43.5 58.8 74.8 18 59.17 12.75 28.2 74.8 7 39.4 70.8 67.1 58.9 58.6 62.4 66.2 44.2 50.2 48.0 32.8 57.9 66.2 51.6 53.4 42.0 69.4 36.9 18 54.22 11.81 32.8 70.8 8 41.3 83.7 41.5 . . 88.7 . 34.1 . . . 57.8 . 61.9 62.6 . 62.2 . 10 59.30 18.59 34.1 88.7 9 40.2 44.4 44.2 49.6 45.1 48.9 41.6 36.0 43.2 40.0 34.1 45.9 46.4 42.6 41.7 37.0 41.6 46.1 18 42.70 4.20 34.1 49.6 10 34 24 27 30 30 27 28 23 28 28 31 18 24 19 17 26.94 6.37 18.0 38.0 1.) Macroscopic age estimate range, 2.) Mean macroscopic estimate, 3.) Kashyap and Rao, 4.) Johanson (Attrition), 5.) Johanson (Translucency), 6.) Johanson (Attrition and Translucency), 7.) Maples, 8.) Lorentsen and Solheim, 9.) Drusini, 10.) Cementum. correlated with the macroscopic estimates. These two methods were further tested by dividing the sample into young (16­29) and older (30­55) adult groups to test for systematic biases related to age (Tables 20­21). The mean age estimates from the Kashyap and Rao and the Charles method for counting cementum annulations were not significantly different from the mean age of the macroscopic methods (Table 14). The difference between the means was 1.37 between Kashyap and Rao and the macroscopic estimates (p = 0.670); the mean difference was –0.44 between the cementum annulation estimates and the macroscopic
FIGURE 8: RANGE OF AGE ESTIMATES PER METHOD AND PER INDIVIDUAL 100
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10 Indiv iduals age estimates (p = 0.824). All of the other methods tested here, produced age estimates that were significantly different from the macroscopic estimates, in fact they substantially overestimated age at death (Figure 8). The paired t­test only tests for significant differences between the means of two samples. A sample could be highly internally variable and the t­test will allow those differences to be averaged out. The standard deviation around the mean gives some idea of the variability within methods. A one­way ANOVA was calculated to compare the variability between methods (Table 15). The one way analysis of variance measures the amount of variation between methods in comparison to the amount of variation within methods. Quantile plots were made for each method because the ANOVA requires that the estimates have a normal distribution. Again, the age estimates from TABLE 16: PAIRED T­TEST FOR MEANS OF THE MACROSCOPIC AND HISTOLOGICAL METHODS Method Mean Difference Std. dev. K & R 1.37 12.71 Attrition ­21.47 7.88 Translucency ­29.80 10.28 Attrition & Trans ­29.06 9.75 Maples ­22.82 15.56 L & S ­33.28 20.73 Drusini ­12.83 8.56 Cementum ­.4412 8.05 t .43 ­10.89 ­11.59 ­11.92 ­5.87 ­4.54 ­6.00 ­.226 df 15 15 15 15 15 7 15 16 Sig. .670 .000 .000 .000 .000 .000 .000 .824
TABLE 17: ANALYSIS OF VARIANCE (ANOVA) FOR METHODS Method Kashyap & Rao Attrition Translucency Attrition & Translucency Maples Drusini Cementum SSG SSE SST SSG SSE SST SSG SSE SST SSG SSE SST SSG SSE SST SSG SSE SST SSG SSE SST SOS 2985.007 2375.200 5360.207 3721.340 1326.025 5047.365 1784.064 784.919 2568.984 3391.613 717.094 4108.707 2686.684 3214.038 5900.722 379.973 248.385 628.358 265.775 385.167 650.941 df 12 16 28 12 16 28 12 16 28 12 16 28 12 16 28 12 16 28 12 4 16 MS 248.751 148.450 F 1.676 Sig. .166 310.112 82.877 3.742 .008 148.672 49.057 3.031 .020 282.634 44.818 6.306 .000 223.890 200.877 1.115 .412 31.664 15.524 2.040 .092 22.148 96.292 .230 .979 SSG: Variance between groups (between mean for each method and overall mean); SSE: Variance within groups (between mean and individual observations); SST: Total variance (within and between groups); SS: Sum of Squares; MS: Mean Square Lorentsen and Solheim’s method for area of root translucency were excluded from this ANOVA because there were too many missing variables. The variance within each method (MSE) exemplifies the range of ages produced by each method. The level of variance between groups (MSG) represents of the amount of variance between the methods. The estimates from the count of cementum annulations had the least significant amount of within and between method variance. The estimates from the Kashyap and Rao and Maples’ method also had insignificant levels of variation within and between methods. The rest of the methods produced significant results for the analysis of variance between groups. The variation between groups was highest for the estimates from Johanson’s methods for attrition and translucency, and Drusini’s estimates from length of root translucency. The level of correlation between age estimates from the various methods and
the macroscopic methods was also calculated using Pearson’s correlation for similarity (Table 16). Euclidean distances between the methods illustrate the relative differences (Table 17). These correlation and distance statistics confirm close associations between the macroscopic methods and both Kashyap and Rao as well as estimates from cementum annulations. The cementum annulations were most closely related to the macroscopic estimates and were not significantly correlated with any other methods. The methods based on categorical measures of root translucency and attrition consistently cluster together. These similarity and distance statistics also show the divergence between the Maples method and those of Johanson and Drusini. While Maples method is not an accurate predictor in comparison with the macroscopic, Kashyap and Rao, and Charles methods, it does appear to be more precise than the other multivariate methods. Maples’ method has a smaller standard deviation, a less significant amount of variance, and a higher correlation coefficient than Johanson and Drusini’s methods. Though the mean age from the metric measurement of root translucency (Drusini’s method) was significantly different from the mean age for the macroscopic estimates, the method also produced a significant Pearson correlation to and a relatively small Euclidean distance from the macroscopic estimates. Thus it appears that while translucency may not be a good measure of age at death for this sample, it is somewhat TABLE 18: PEARSON CORRELATIONS AND P­VALUES FOR METHODS Macro K & R Attrition Trans A & T Maples Drusini Cement Macro .009** .000** .012** .000** .356 .011** .043* K & R .434 000** .174 .011* .344 .136 .281 Attrition .695 .619 .001** .000** .235 .007** .270 Trans. .417 .181 .561 .000** .017* .000** .108 A & T .609 .422 .852 .911 .046* .000** .143 Maples ­.071 ­.078 .139 .396 .319 .007** .128 Drusini .424 .211 .454 .616 .616 .449 .156 Cement .428 .151 .160 .316 .275 .292 .261 * Correlation (1­tailed) is significant at the 0.05 level ** Correlation (1­tailed) is significant at the 0.01 level TABLE 19: EUCLIDEAN DISTANCE MATRIX Macro K & R Attrition Trans A & T Maples Drusini
K & R Attrition Trans. A & T Maples Drusini Cement 48.485 114.948 147.632 147.244 142.028 66.266 32.261 109.742 147.753 51.795 144.789 41.668 18.093 147.214 76.189 64.676 68.995 75.005 62.686 86.690 88.560 84.467 58.975 121.006 149.488 151.005 138.473 66.968 more accurate to measure the translucency than to use discrete categories for data collection. The categorical measures of attrition in the Johanson method also substantially overestimated age at death. Johanson had only achieved a correlation of less than 50% from his univariate formula for attrition, thus it is not surprising that the method was not accurate in this study. The Kashyap and Rao method also used attrition and translucency in age estimation. However, their method was based on a metric measurements of these variables, rather than a discrete score. In addition, their method standardized attrition TABLE 20: TEST FOR SIGNIFICANT DIFFERENCES IN INDIVIDUALS 16­29 YEARS OLD Ind Macro. Range 7 18­20 8 21.50 16a 21.00 17 16­18 18a 20­25 20a 17­20 32 16­20 34 25­29 36a 16­20 Mean (St. Dev.) t p Correlation (r) p Macro. Mean 19.0 21.5 21.0 19.0 22.5 18.5 18.0 27.5 18.0 20.56 (3.07) Kashyap & Rao 15.00 31.00 20.00 24.00 16.00 27.00 20.00 23.50 19.00 21.72 (5.17) ­.619 .553 .131 .736 Cementum 24.00 27.00 . 28.00 23.00 28.00 24.00 19.00 24.00 24.63 (3.02) ­2.014 .084 .693 .056 by dividing the measure by the width of the CEJ. Their measure of the length of the translucent zone was standardized by tooth height. The standardized nature of their
method, and the metric system of data collection may contribute to the small margin of error and to the accuracy in relation to the macroscopic estimates. The Kashyap and Rao method was based on samples from Hyderabad, India and environmental similarities may also partially account for some of this increased level of accuracy. In younger individuals, for whom the macroscopic age estimates were based on eruption of the third molar and on epiphyseal union, the estimates have a smaller standard error and might be expected to be the most accurate. A separate comparison of the estimates from Kashyap and Rao and the cementum annulations was made with the macroscopic estimates for individuals aged 16­29 and 30­55 (Tables 20, 21). For this comparison, the sample sizes were small and a student’s t­test was used though this test is not specifically designed for small samples and some error must be expected in tests of these subsets. A Pearson correlation coefficient was also calculated to compare the significance of similarities of estimates between methods. TABLE 21: TEST FOR SIGNIFICANT DIFFERENCES IN INDIVIDUALS 30­55 YEARS OLD Ind 6b 11 12 13 15 28 30a 30b 37 Mean (St. Dev.) t p Correlation (r) p Macroscopic Range 30­35 35­45 35­45 35­45 30.00 45­50 35­39 27­33 45­55 Macroscopic Mean 32.5 40.0 40.0 40.0 30.0 50.0 37.5 30.0 50.0 38.89 (7.51) Kashyap & Rao 22.00 64.00 26.00 50.50 20.00 27.00 25.00 48.50 28.00 34.56 (15.61) .745 .478 ­.018 .963 Cementum Annulations 34.00 . 30.00 30.00 27.00 28.00 31.00 18.00 . 28.29 (5.06) 3.149 .020 .292 .525 For the young adult category, the estimates from both Kashyap and Rao and from the cementum annulations were not significantly different from the macroscopic
estimates (p = 0.553 and 0.084 respectively). The estimates from the cementum increments were significantly correlated with the macroscopic estimates (r = 0.693, p = 0.056). For the older adult age category, the cementum estimates were significantly different from the macroscopic estimates (p = 0.020), the estimates from Kashyap and Rao’s method were not (0.478). For this subsample, the Kashyap and Rao method had the highest standard deviation, reflecting a broad range of estimates for the older sample (22­50.5). Neither of the histological methods produced a significant correlation coefficient with the macroscopic estimates for the older age category. Though the mean of the estimates from the Kashyap and Rao method were not significantly different from the macroscopic mean in either age category, the variability for this method increases in the older age category (Figure 13). There was no dominant trend in the direction of the differences (towards over or under estimation) for the Kashyap and Rao method for the young or older adult samples. The estimates from the cementum annulations had a smaller range of difference than the estimates from Kashyap and Rao. However, three individuals in the older adult category have age estimates differing by 10 years or more from the macroscopic estimates. The cementum annulations tended to overestimate the age of younger individuals and underestimate the age of older individuals (Figure 13). Charles and colleagues (1986) found a similar centrist trend in the age estimates from cementum for known aged individuals. Despite this trend, they found that the estimates were highly correlated with known age at death. As the cementum has a margin of error less than +/­ 10 years for older individuals, an error similar to the macroscopic estimation methods, a mean of the two estimates will be used to revise the demographic profile for this population. The sample was also subdivided into male (n = 9) and female (n= 9) sub­ samples to test for significant differences between the sexes (Table 22). The mean age at death is slightly higher for females than for males, but the results of the t­test for independent samples show that the differences between the sexes for the macroscopic estimates are not significant (p = 0.286). There were no significant sex differences in either histological method, Kashyap and Rao (p = 0.289) and the estimates from the FIGURE 13: DIFFERENCES BETWEEN MACROSCOPIC AND HISTOLOGICAL AGE ESTIMATES
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Macro 10 Kas hy ap & Rao Macro 10 C em ent 32 36a 20a 7 17 16a 8 18a 34 15 30b 6b 30a 11 12 13 28 37 32 36a 20a 7 17 16a 8 18a 34 15 30b 6b 30a 11 12 13 28 37 Indiv iduals Individuals cementum annulations (p = 0.107). An examination of the estimates from all three methods for males and females shows that the amount of variance in the estimates was again greater for the method developed by Kashyap and Rao than for the cementum annulations. The variance was equivalent for both males and females. Thus there appear to be no significant differences in the mean ages at death and no significant sex­ based systematic biases within these age estimation methods. The Damdama sample was also divided by anterior (n = 11) and posterior (n = 18) tooth classes. Anterior teeth were defined as incisors, canines, and premolars. Posterior teeth included all molars. The two groups were tested using a student’s t­test for independent samples. There were no significant differences between anterior and posterior tooth class in the estimates from the macroscopic (p = 0.306), Kashyap and Rao (p = 0.948), or cementum (p = 0.318) methods. The cementum and the Kashyap TABLE 22: DESCRIPTIVE STATISTICS FOR AGE ESTIMATES FOR MALES AND FEMALES Female Macroscopic Kashyap & Rao Cementum Male Macroscopic Kashyap & Rao Cementum N Min Max Mean s.d Variance 9 9 7 18.00 19.00 24.00 50.00 64.00 31.00 32.56 31.50 28.29 11.70 15.28 2.36 136.84 233.357 5.57 9 9 8 18.00 15.00 18.00 50.00 48.50 34.00 26.89 24.79 24.63 10.00 10.21 5.13 99.99 104.26 26.27 and Rao method are thus not biased by tooth class because the macroscopic methods
were also based on skeletal observations. The Kashyap and Rao method was the least sensitive to real differences between the ages of individuals included in both samples. In comparisons between teeth from the right and left sides, the macroscopic estimates were significantly different (p = 0.043), with a difference of 7.71 years between the means for the two sides. This indicates only a coincidental difference in age at death between individuals for whom right and left teeth were available. Neither the Kashyap and Rao method, nor the cementum recorded significant differences between the right and left sides (p = 0.986, and 0.575 respectively), indicating that these methods are not biased by side. The sample was also divided by maxillary teeth (n = 21) and mandibular teeth (n = 8). In a t­test for independent samples, there were no significant differences between macroscopic, Kashyap and Rao, and cementum estimates from either jaw (p = 0.971, 0.588, and 0.724 respectively). Discussion and Interpretation The histological methods compared in this study are based on attrition, root translucency, secondary dentine, and cementum annulations. The Kashyap and Rao method and the cementum annulations produced the most promising results. Methods based on root translucency produced results very similar to one another and very different from the macroscopic age estimates. The multivariate method developed by Kashyap and Rao (1990) produced a mean that was not significantly different from that of the macroscopic estimates and the correlation coefficient was significant. However, an analysis of the variance within and between the estimates from the macroscopic and the Kashyap and Rao method showed that there was a large amount of variance for estimates from this method, which the mean difference could not demonstrate. Eleven estimates from this method differed from the macroscopic estimate by greater than plus or minus fifteen years. The amount of variation in the estimates from this method may be related to the way the indices were constructed. Some of the measurements used as fixed variables are not themselves independent of age related changes. The length of the secondary dentine was divided by the length of the pulp cavity. It might be better to divide the length of the secondary dentine deposit by the length of the root, which is a more
independent variable. The length of the translucent area is divided by the length of the entire tooth, which is also effected by the amount of attrition, root resorption, and cellular cementum. The method might be more accurate if the area of translucency were divided by a variable more independent of age. Their method used the width of the cementum deposit at the thickest point, which would generally be at the level of the cellular cementum that is not solely deposited as a function of age. A count of cementum annulations also might be more accurate. It should be mentioned that the method was precise within 1.59 years in modern teeth. It remains to be seen whether these changes would provide more precision to the method for prehistoric material. This method was originally developed on a small sample of 25 individuals. It has not been independently tested on modern teeth of known age at death or extraction. Thus the standard error for independent samples and the repeatability of this method are not known. Another possible contributing factor to the variability of this method is that the sections were examined at a thickness of 1 mm. This thickness may be more useful in clinically extracted or forensic material, but may be too thick to represent the full extent of translucency in prehistoric archaeological samples. It appears that the rest of multivariate and univariate methods based on root translucency are not useful for this sample. Johanson’s methods for attrition and translucency consistently overestimated the age at death in individuals from Damdama. Though Johanson’s univariate formula for attrition was only correlated with known age around 50% in his original study, the method combining attrition with translucency was correlated at 86%. Not only was Johanson’s method overestimating age at death in comparison with the macroscopic, Kashyap and Rao and cementum methods, it was strongly clustering with the other methods based on multivariate methods utilizing categorical scores (Maples) and the methods based on root translucency (Lorentsen and Solheim and Drusini). One explanation for the lack of accuracy for these methods in this sample then involves the application of a discrete, or categorical scale. Although tooth types did not contribute to a lack of precision in the test of individuals with multiple teeth available, the lack of distinction between tooth types may complicate the data collection in
discrete categories. In anterior teeth, wear can be easily scored on one cusp. In posterior teeth the wear does not generally affect the entire occlusal surface evenly. If for example, the lingual cusps of a maxillary molar are worn to dentine exposure and the buccal cusp is worn to half the height of the enamel, there is no intermediate grade to average the scores. Methods that do not distinguish tooth type can have problems caused by differential amount of wear being complicated by eruption timing. If eruption timing is not considered in scores of attrition, there can be a wear gradient caused not by differences in the age of onset of wear, or the age at which the tooth reached the occlusal plane. However, differences in tooth type are not supported in this study, given the close resemblance of estimates for different teeth available from the same individual for all of the methods. For individuals with two or three teeth available for analysis, there were no significant differences between the estimates based on root translucency. Thus the methods based on root translucency were as internally consistent as the other methods. This indicates several things: 1.) the methods were consistently applied in independent assessments and between tooth classes, 2.) while the accuracy of the methods may be questionable in this study, the methods are precise. In other words, whether or not the estimate is accurate, the method yields estimates within a narrow margin of error for teeth from different classes from the same individual. There may be an effect in Johanson’s attrition scores from the obvious differences in diet between the reference and the sample populations. Attrition will proceed much slower in Johanson’s sample of modern Norwegians than hunter­ gatherers from Mesolithic India. Lukacs’ study of dental pathology profiles in Mesolithic India has demonstrated that these groups tend to have high levels of attrition, most likely due to a coarse textured wild food and excess grit in the diet in comparison to processed agricultural foods. Dietary differences in childhood may also be implicated in differential degrees of mineralization. These problems with attrition are recognized in macroscopic age estimation methods as well and are responsible for the general consensus that specific rates should be calibrated for each sample, based on comparisons between attrition and independent age estimates.
Johanson’s criteria for root dentine translucency suffers from similar problems. All of the methods based on root translucency were inaccurate whether or not a categorical system was employed for scoring. The age estimates from methods developed by Maples (1978) from Gustafson’s (1950) criteria, Lorentsen and Solheim (1990) for translucent area, and Drusini (1989) for the length of the translucent zone consistently and substantially overestimated age at death in comparison with the macroscopic methods. Maples’ method produced results that were more closely correlated with the macroscopic estimates than the other methods, but the method still most closely resembled this cluster of methods. The fact that all of the methods based on root translucency closely resembled one another suggests that the use of categorical scores only partially explains problems in using root translucency in this prehistoric sample. Much more work is needed to understand the effects of diagenesis on root dentine translucency. The teeth in this sample had been affected by postdepositional processes and were heavily mineralized. Vlcek and Mrklas (1975) found that neither periodontitis nor root translucency could be scored in archaeological samples. In their test of the Gustafson method, root resorption and attrition were not useful for estimating age at death either. The count of cementum increments, based on the method developed by Charles and colleagues (1986, 1989; Condon et al 1986) produced the most promising results. The mean estimate was not significantly different from the mean for the macroscopic method and the variance between the two groups was not significant. The Pearson’s correlation coefficient for similarities between macroscopic estimates and cementum counts was 42.8%, significant at the 5% level (p = 0.043). The Pearson correlation is much higher for young adults, 16­29 year olds (r = 0.693, p = 0.056) and is not significant in the 30­ 55 year old category (r = 0.292, p = 0.525). The differences in the mean age estimates were not significant for the young adult category (16­29) but there was a significant difference for the older adult category (30­55). In comparison with the macroscopic estimates, the cementum tends to overestimate age for individuals in the younger category and underestimate age in the older adults, demonstrated by the reduced amount of variance within the cementum estimates as compared to the macroscopic estimates. However, Charles and colleagues (1986) also observed this
trend and their age estimates from the cementum were still highly correlated with known age at death. The method for cementum annulations does not pose the same problems as the other dental histology methods. The method is metric, not based on categorical scores, thus the full range of variation is represented in the observations. Dental eruption timing was taken into consideration because the estimates represent the sum of increment counts and eruption times by tooth class. Differences between sample populations are controlled by using eruption timing specific for a modern Indian population. There will still be an unavoidable amount of error introduced by differences between eruption times in modern individuals and a Mesolithic population. This error is common to all bioanthropological estimates for age at death. The amount of interobserver error was statistically significant for the cementum annulation counts. The level of error between observers was higher than the level of intraobserver error. The criteria whereby sections were eliminated as unscorable were highly variable between observers as well. The significant amount of interobserver error indicates that the accuracy of the method is highly dependent on the experience of the observer. The insignificant amount of intraobserver error demonstrates that an experienced observer can precisely apply the method. These results for observer error are also consistent with those obtained by Charles and colleagues (1986). Implications for Further Research In the next phase of this research, the methods developed by Kashyap and Rao and the Charles method for counting cementum annulations in mineralized teeth will be tested on an independent sample of modern teeth from India. As the age of extraction will be known, regression formulas can be developed for each tooth class for the Kashyap and Rao method and changes outlined above can be tested as well. The original method will be tested on thinner section, as the 1 mm thickness is less appropriate for prehistoric than for modern samples. New indices used for standardizing age related changes will also be tested in the modern sample. As was mentioned in the conclusions section, some of the measurements used as fixed variables in the Kashyap and Rao method are not themselves independent of
age related change. The following modifications will be tested: dividing the length of the secondary dentine deposit and the length of the translucent area by the length of the root. The cementum will be measured at one­third the distance from the apex, rather than at the thickest point. If the revised method is accurate at a thickness of 100 um, a count of cementum annulations may also be factored in instead of the thickness. The centrist tendency noted in the cementum annulations will be tested as well. The Lopez­ Nicolas methods (1990, 1991, 1996) described above will also be tested and a regression formula can hopefully be developed that does not include periodontitis. These histological methods will also be tested on archaeological samples from a different chrono­cultural context, the Indian Chalcolithic (3500­2700 BP). As this material is derived from a different deposit, it has suffered very different diagenetic changes. Testing these methods on Chalcolithic samples for trends in the variance between macroscopic and microscopic methods will hopefully provide a more detailed understanding of the effects of taphonomic processes as well as the effects of temporal distortion in histological aging. Summary of Results and Conclusions 1. The Kashyap and Rao (1990) and Charles (1986) methods produced estimates most closely resembling the macroscopic estimates. The other multivariate methods developed by Johanson (1971) and Maples (1978), and the methods based solely on root translucency developed by Lorentsen and Solheim (1989) and Drusini (1990) consistently and substantially overestimated age at death by comparison. 2. The mean for the method developed by Kashyap and Rao (1990) was not significantly different from that of the macroscopic estimates. However, the method had a large standard deviation, produced a substantial amount of internal variance, and there were eleven estimates that differed from the mean macroscopic estimate by 15 years or more. The method was developed using a small modern sample (25 individuals) of known age. This may have affected the accuracy of the regression formulas for estimating age in an independent sample. The method has yet to be tested on an independent sample of known age. The method shows promise for use in archaeological samples, though the method might benefit from changing some of
the index variables that are not themselves independent of age at death. The 1mm thickness of the sections may also negatively impacted the accuracy of the results. 3. The methods developed by Johanson (1971) and Maples’ (1978) revision of the Gustafson method rely on categorical scores, rather than metric measurements. The use of categorical scores may partially account for the inflated age estimates. The scores for attrition are probably inflated for this sample due to the acceleration of wear accompanying a hunter­gatherer diet in the Damdama sample. These methods were developed from modern sample populations with a diet higher in processed food. The attrition scores in Johanson’s original study were not highly correlated with age at death unless combined with translucency in a bivariate formula. 4. The estimates from root translucency in the multivariate methods and the methods developed by Lorentsen and Solheim (1989) and Drusini (1990) were all elevated to a similar degree and strongly correlated with one another. This indicates that root translucency, whether measured categorically, by area, or by length is not a useful tool for age estimation in this sample. It is possible that the translucency was increased by diagenesis. Soil samples were collected and await further analysis. Increased levels of mineralization in prehistoric samples that precluded analysis of root translucency have been reported elsewhere (Vlcek and Mrklas 1975; Lucy et al. 1996). It is also possible that a section thickness of 500 um is not practical for prehistoric, archaeologically derived material. 5. The age estimates derived from cementum annulations were not significantly different from the macroscopic estimates. The macroscopic age estimates for younger adults (16­29) had a smaller margin of error than estimates for the older adults (30­55) in comparison with the macroscopic estimates. An analysis of the variance within and between methods and an examination of the differences between age estimates for the two age categories indicates that the cementum tends to overestimate age at death in younger individuals and underestimate age for older individuals. Despite finding a similar trend, Charles et al. (1986) found a strong relationship between the estimates from the cementum and known age at death. 6. There were no significant differences between the macroscopic, Kashyap and Rao,
and cementum methods in comparisons between males and females, anterior and posterior tooth classes, and upper and lower jaws. The macroscopic estimates were significantly different between teeth from the right and left sides, reflecting real differences in the age profiles for these two subsamples not bias in the macroscopic methods. The macroscopic ages were derived from skeletal and dental age estimates. The histological methods were not significantly different in this comparison. This last comparison indicates that the histological methods were insensitive to a real trend in age at death between two subsamples. The cementum method was considered accurate enough to be included in the revised ages at death for the demographic reconstruction of the Damdama sample.
PALEODEMOGRAPHIC PROFILE FOR DAMDAMA The age estimates from the cementum annulations closely compared with the mean estimates from the macroscopic methods. There was a tendency for the cementum annulations to overestimate age at death for younger adults and to underestimate age at death for older individuals, in comparison with the macroscopic estimates. Charles and colleagues (1986) also noted this tendency, but they found that the estimates from the cementum annulations were accurately predicting age at death never the less. It was thereby determined that the most accurate age profile for the Damdama skeletal sample would be derived by averaging the estimates from the cementum annulations with the macroscopic estimates (Table 23). There were no estimates from the cementum annulations available for three individuals (11, 16a, and 37). For those three individuals and for twenty­one individuals for whom teeth were not available for this analysis, the macroscopic estimates were used. TABLE 23: AGE ESTIMATES FOR THE SKELETAL POPULATION FROM DAMDAMA Age 1.5­2.5 2.5­3.5 14­18 16­18 18­22 19 20­24 20­25 21 22­25 24­25 26.5­30.5 30 30­36 Male Female Unknown 4 5 36b 35 33 19 22 18a 7, 16a, 32 36a 18b,34 17, 20a 8, 30b 15 16b 2, 29 6b Indet total (46) 25 21 17 14,31 4 FIGURE 14: AGE PROFILE FOR DAMDAMA
Age 32.5­37.5 30­40 35­40 30­50 40 40­45 36­50 43­51 45­50 45­55 45­60 50 50­60 Adult Male 27 Female 30a 12, 13 6a 28 11, 40 20b 1 39 23 18c 26 37 3 9, 24, 25 10 8 6 4 2 Std. Dev = 12.62 Mea n = 30 N = 39.00 0 2 6 10 13 17 21 25 29 33 36 40 44 48 52 56 Age at Death As is common in prehistoric skeletal samples, juveniles (defined here as birth to 16) are under­represented in the Damdama population. There were only two subadults recovered from excavations at Damdama, both young children aged 2 and 3 years old based on dental development and eruption timing. The mean age at death for the entire sample, aside from individuals aged only as adult or for whom age could not be determined, was 30 years of age (n =39). The mode is 21 years of age (7 individuals, 15%) and the distribution appears fairly normally distributed between the ages of 16 to 55. The age distribution is biased towards younger adult individuals. Of the 39 individuals for whom age at death could be assessed, 24 individuals were in the young adult category (18­34), there were 11 older adults (35­50). There are only 4 individuals over the age of 50. Of these 39 individuals, 21 (53.85%) died before the age of 25. The 37 adult individuals for whom age was estimated in the Damdama sample were also divided by sex. The mean age at death for males (n = 22) was 29.25 years of age (standard deviation = 9.94). The mean age at death for females (n = 15) was 35.63 years of age (standard deviation = 11.93). The distributions were significantly different in a chi­square test (X 2 = 68.27, p = 0.015). An examination of the distribution of ages shows this difference in the patterning of age at death for females and males (Figure FIGURE 15: DISTRIBUTION OF AGE ESTIMATES FOR FEMALES AND MALES
4 7 3 6 3 5 2 4 2 3 1 2 Std. Dev = 11.93 1 Std. Dev = 9.94 1 M ean = 35.6 N = 15.00 0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 M ean = 29.3 N = 22.00 0 15.0 Age at Death­ Fem ales 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Age at Death ­ M ales 15). The age at death for males was skewed toward early adulthood, with peaks at 20­ 25 and a small standard deviation reflecting minimal spread about the mean. For females however, there was more of a bimodal distribution with a substantial peak for death between ages 25­35 and a smaller peak at age 50­55. The sample sizes are small and unequal, so caution must be exercised in relying on the results of this comparison. However, some broad trends can be extrapolated. Life expectancy for this population in general was not long, with 30 years old being the average age at death. It also appears that there was a sex related difference in age at death, with more females living somewhat longer than males. Kennedy and colleagues (1992) noted a similar trend in the skeletal population from Mahadaha (Table 24). For Mahadaha, Kennedy attributes the young adult mean age at death to TABLE 24: DAMDAMA, MAHADAHA, AND SARAI NAHAR RAI AGE DISTRIBUTION n DDM MHD SNR Total Sub­adult <18 yrs 46 4 (8.7 %) 26 3 (11.5 %) 10 0 82 7 (8.5 %) Young adult 18­34 yrs 19 (41.3 %) 11 (42.3 %) 10 (100 %) 40(48.8 %) Middle adult 35­50 yrs 13 (28.3 %) 1 (3.8 %) 0 14 (17.1 %) Older adult >50 yrs 3 (6.5 %) 2 (7.7 %) 0 5 (6.1 %) Indet. 7 (15.2 %) 9 (34.6 %) 0 16 (19.5 %) DDM= Damdama (this study and Lukacs, n.d.); MHD = Mahadaha (Kennedy et al. 1992); SNR= Sarai Nahar Rai (Kennedy et al. 1986) sampling error and/or age estimation bias. Damdama has a more evenly distributed
profile for age at death. A chi­square test shows that the distributions for the two groups are significantly different at a 1% level (X 2 = 48.85, p = 0.0205). The depth of deposits at Damdama has been interpreted as evidence for a stationary lifestyle, though the depth could also certainly represent a series of successive temporary occupations. The construction of a stationary life table (following Storey 1992) assumes that the population was not only stationary, but stable in physical size and in size of the population. Though it might be oversimplifying the evidence from Damdama to construct such a table, it is also possible that interesting insights can be tentatively derived from overstepping the boundaries of conservatism, as long as the limitations and potential problems are addressed. The table is probably biased from invisible demographic characteristics resulting from time averaging and the relatively small sample size, so any inferences or conclusions drawn from the table should be considered estimates about general patterns in somewhat faulty data. Further extrapolations such fertility and fecundity are considered beyond the potential of the data from this sample and will not be attempted. The table was constructed for adult individuals and inference will be limited to broad patterns, with the caveat that the results may be inaccurate if this sample population did not cross­sect a single temporal period of relative stability (Table 25). Juveniles under 15 years of age were not included because they were obviously under­ represented in the sample (there were only two individuals between the ages of 1­4). There is no direct evidence that these individuals were interred elsewhere, although separate burial customs for infants and juveniles have been recorded for Chalcolithic sites in central Western India (Lukacs and Walimbe 1986). The calculations of life expectancy are based only on individuals who had already survived to the age of fifteen. Five year intervals were used to account for errors in age estimation. To briefly explain the table: 1.) Dx represents the raw number of deaths in each age class, 2.) dx is the simple proportion of all deaths in each age category, 3.) Ix is an estimate of survivorship (the percentage of individuals surviving to the beginning of the age class), 4.) qx is the percentage of individuals who reach a given age class and die within that class (mortality), 5.) Lx is the total number of years lived in each age TABLE 25: STATIONARY POPULATION LIFE TABLE FOR DAMDAMA
% Prob of Yrs lived Yrs left Survivors death in class in life lx qx Lx Tx 100.000 .1081 472.975 1763.80 89.1900 .3636 364.875 1290.83 56.7600 .0476 277.050 925.953 54.0600 .2000 243.275 648.903 43.2500 .3125 182.475 405.628 29.7400 .4544 114.925 223.153 16.2300 .3331 67.625 108.228 10.8200 .7494 33.825 40.6025 2.7100 1.000 6.778 6.7775 0 0 0 0 Age # deaths % deaths Dx dx 15 4 10.8108 20 12 32.4324 25 1 2.7027 30 4 10.8108 35 5 13.5135 40 5 13.5135 45 2 5.4054 50 3 8.1081 55 1 2.7027 60+ 0 0 total 37 Life Expect. ex 17.64 14.48 16.31 12.00 9.38 7.50 6.67 3.75 2.50 0 100.00 category (assuming that the number of deaths per year is equivalent), 6.) Tx is the total number of years left in life for individuals in a given age class until all individuals are dead, and 7.) ex is the life expectancy at a given age (calculated from Tx) beginning with eo or life expectancy at birth. For individuals who had reached the age of fifteen life expectancy was 17.6 years. Fewer than 50% of the population lived beyond the age of thirty and 30% lived past 40 years old. For those individuals who did make it to thirty, life expectancy was FIGURE 16: ADULT MORTALITY CURVE AND LIFE EXPECTANCY FOR DAMDAMA ADULTS 100
20 90
89 18 80
16 Life Expectancy (years) % Survivors 70
60
57 50
54 40
43 30
14 12 10 9 8 7 30 20
4 16 10 3 11 0 0 16­20 21­25 26­30 31­35 36­40 Age (in years) 41­45 46­50 51­55 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 AGE only 12 years. As there are very few individuals in the age classes above fifty, a particular bias involving underestimation of age at death in older individuals should be
mentioned. The four individuals over the age of fifty were all aged using macroscopic estimates of degenerative change. There was one older adult (37) which was originally intended to be included in the dental histology portion of this study, but the root surface was damaged and the cementum annulations could not be counted. If this individual could have been included in the cementum age estimations, the method demonstrated a tendency to underestimate age in comparison with the macroscopic estimates in individuals over the age of 35. The trend has also been noted in studies of modern teeth, of known age, but the centrist trend has generally been ascribed to the use of multiple regression when a Bayesian approach might be more appropriate (Lucy et al 1996). A contradictory trend has also been noted, whereby the annulations accumulate faster than age at death, leading to overestimation (Condon et al. 1986). Further work on archaeological samples and in teeth of known age at death or extraction will be required to determine if this trend is a permanent bias in the annulations, or if there is a solution in statistical approaches. If age was underestimated for these older individuals, the estimates for mortality per age category will be overestimated and the life expectancy will be underestimated (Storey 1992). The construction of demographic profiles and intra­regional comparison allows broad inferences to be made about the skeletal population from Damdama. The life expectance was generally low, about 17.6 years for individuals who had survived to the age of 15. The mean age at death was also low (30 years) but 30% of individuals lived past the age of 40. Females tended to live longer, up to 50­55 years of age, than males, few of whom lived beyond the age of 35. These statistics are somewhat comparable to the skeletal population from Mahadaha, though the middle adult (35­50) category in that sample is substantially under­represented. Sarai Nahar Rai does not offer much potential for comparisons of demographic characteristics as the collection consists of 10 young adults. As the Damdama sample was fairly normally distributed above the age of 15, the profiles might be considered reasonable approximations of the actual demography of the population and therefore partially representative of life in the Indian Mesolithic.
TABLES TABLE 4: MACROSCOPIC METHODS FOR AGE ESTIMATION Ind Auricular Surface 1 36­50 50­60 2 3 4 5 6a 6b 7 8 9 10 11 12 13 14 15 16a 16b 17 18a 18b 18c 19 20a 20b 21 22 23 24 25 26 27 28 29 30a 30b 31 32 33 34 35 36a 36b 37 39 40 Pubic Symph Dental Eruption Dental Attrition Sutures & Epiphyses Summary Age Adult 22­49~35 36­50 Adult Adult > 30 > 30 50­60 1.5­2.5 2.5­3.5 30­45 30­35 ~20 Adult Adult Adult Adult 35­45 35­45 Frgmt. Adult 21 25­35 16­18 20­25 22­25 45­55 19 17­20 40­45 ind 20­24 45­50 adult adult 45­60 30­40 45­50 25­35 35­39 27­33 ind 16­20 18­22 25­29 16­18 16­20 14­18 45­55 43­51 37­43
2+/­6mo 30­35 40­45 43­46 20­24 30­39 20­24 22­26 45­55 30­47 22­47 27­35 2­3 Adult Adult Adult Adult 30­45 30­35 18­20 Adult Adult Adult Adult Adult 35­45 35­45 Adult Adult Adult Adult 16­18 >18 >18 Adult 18­24 17­20 40­44 18 20­22 20­24 adult Adult ~20 Adult Adult Adult 17­24 Adult 56.4 17­21 17­20 40­45 20­24 >20 45­50 adult adult 52­56 30­40 45­50 25­35 35­39 27­33 35­39 16­18 20­24 16­20 18­22 25­29 16­20 16­18 16­20 15­18 45­55 40­55 37­43 23­76 TABLE 5: MACROSCOPIC METHODS FOR SEX ESTIMATION Ind Sciatic Notch Diameter of Acetabulum PC Measures PC Morph Cranial Morph Mand Morph Sex 1 2 3 4 5 6a 6b 7 8 9 10 11 12 13 14 15 16a 16b 17 18a 18b 18c 19 20a 20b 21 22 23 24 25 26 27 28 29 30a 30b 31 32 33 34 35 36a 36b 37 39 40 F M/F F M/F F F F F F F F F M/F M M/F M M M Female Female Female Ind. Ind. Female Male Male Male Male Female Male Female Female Frgmt. Female Male Male Female Male Male Male Male Female Male Female Male Female Male Male? Female Female Male Female Female Male ind Male? Male Male Female Female Male Female Male Male
F F M M M/F M F M F F M F M M M M/F M M/F F M F M F M M M M M M M F? M F M M/F M M M M M/F M? F F M F M M M M F M F M/F M/F M M? M F M M/F M/F M M M M F M F M/F M F F M/F F F M M F M M M M M F M M/F M M/F M M F? M F? M F F F M M F F F M M F F M? M/F F? F M/F F M M F M/F F M/F M? M F F M/F M M M TABLE 6: PROTOCOL FOR PREPARING AND SECTIONING TEETH 1. Sample selection
· Teeth should be unaffected by caries, periodontitis, abscess, or other pathological processes that expose the root to the oral environment
· Anterior teeth are easiest to section given the tendencies for molar teeth to have roots that are crooked, twisted, or bent.
· Several teeth should be processed for each individual when possible 2. Documentation
· A thorough dental anthropological analysis should be recorded prior to removal from the jaw.
· Photographs: black and white photographs, color slides, and/or digital photos of each arcade from which samples will be derived, as well as 5 views of each individual tooth (occlusal, medial, distal, buccal, and lingual surfaces)
· Casts­ negative and positive molds should be made for each tooth 3. Embedding
· Labels with the tooth’s identification number (assigned from a random number table) should be embedded at a permanent face in the mold
· Teeth must be positioned carefully in embedding molds for buccolingual sections; teeth that are more difficult to position can be mounted with wire
· For light and electron microscopy, many embedding media will suffice. (In this study Buehler’s Spurr’s Resin (medium hardness formula) was used.)
· Infiltration 1. 25% resin and 75% acetone for 24 hours 2. 50% resin and 50% acetone for 24 hours 3. 75% resin and 25% acetone for 24 hours 4. 100% resin for 24 hours 5. Fresh 100% resin heated to 60 degrees Celsius for 24 hours, or until resin has hardened completely. 4. Sectioning and polishing
· Serial sections should be made in the buccolingual plane using a diamond edged saw (Buehler Isomet) to a thickness of 200 um (for translucency) to 100 um (for cementum) based on method to be employed.
· The sections can be bathed in HCL to soften the edges of large scratches. The sections should be polished to remove fine scratches (Buehler Minimet).
· Sections should be dehydrated in 70% ETOH, attached to glass slides with methyl methacrylate or Canadian Balsam, labeled, and stored carefully. 5. Microscopy and section photography
· No stain is necessary for observations of root translucency. For counting cementum annulations, mineralized sections should be stained with Alizarin red
· Root dentine translucency should be examined at 65­100 x magnification. Cementum annulations should be counted at 200­400x magnification
· Photographs of sections can be taken with a digital camera mounted on the light microscope. Images should be saved at 1200x1600 resolution, in a high quality format (such as .tiff).
FIGURES FIGURE 3: JOHANSON’S SCORING CRITERIA
FIGURE 4: AREA OF ROOT TRANSLUCENCY (LORENTSEN AND SOLHEIM 1989)
FIGURE 5: LENGTH OF ROOT TRANSLUCENCY (DRUSINI 1990)
FIGURE 6: CEMENTUM ANNULATIONS
FIGURE 7: COMPARISON OF MULTIPLE TEETH FROM THE SAME INDIVIDUAL 1 80
90
8 6 80
70
70
6 7 5 5 3 4 Individual 7 LLC Individual 6B LRP3 60
50
40
8 10 1 7 60
4 50
3 10 40
30
30
2 20
1 20
10 Rsq = 0 .9154 10 20 30 40 50 60 70 2 10 80 Rsq = 0 .6806 10 20 30 Individual 6B URM 2 40 50 60 70 90 Individual 7 URM 2 90
90
8 6 80
80
70
3 70
6 4 7 5 7 60
Individual 8 LLI2 Individual 7 LLC 80 4 50
3 1 0 40
30
8 60
5 50
10 2 40
30
1 1 20
10 10 1 1 20
2 Rsq = 0 .8542 20 30 40 50 60 70 80 10 90 Rsq = 0 .5956 20 30 40 Individual 7 URM 3 50 60 70 80 90 Individual 8 ULM 3 90
90
6 80
80
7 70
5 Individual 13 ULI1 Individual 17 ULI1 3 70
2 60
50
8 10 1 40
6 8 60
5 7 50
3 4 1 0 40
4 30
2 30
1 20
1 1 20 30 Rsq = 0 .5117 40 50 60 70 Individual 13 LRM 3 1 80 90 10 10 Rsq = 0 .5474 20 30 40 50 60 70 80 90 Individual 17 ULM 3 1 = Macroscopic methods, 2 = Kashyap and Rao (1990), 3 = Johanson (1971) Attrition, 4 = Johanson (1971) Translucency, 5 = Johanson (1971) Attrition and Translucency, 6 = Maples (1978), 7 = Lorentsen and Solheim (1989), 8 = Drusini (1990), 9 = Charles (1986, 1989) method for cementum annulation counts
FIGURE 7: COMPARISON OF MULTIPLE TEETH FROM THE SAME INDIVIDUAL (CONTINUED) 1 90
90
6 6 80
80
8 7 5 5 8 60
3 50
10 3 70
7 Individual 30B ULC Individual 28 URI1 70
1 4 60
4 50
2 1 0 40
40
30
30
1 1 1 2 20 Rsq = 0 .7903 30 40 50 60 70 20 80 Rsq = 0 .4381 20 30 40 Individual 28 LRM 3 50 60 70 Individual 30B LRM 1 100
100
6 6 80
80
5 7 Individual 32 ULI1 Individual 32 ULI1 80 4
60
3 9 8 10 40
1 20 7 3 8 1 0 40
20 2 Rsq = 0 .4366 20 1 2 0 10 5 4 60
30 40 50 60 70 80 90 0 10 Individual 32 ULP4 Rsq = 0 .8334 20 30 40 50 60 70 80 90 Individual 32 LRM 3 60
6 Individual 34 ULM 2 50
4 5 40
3 7 2 10 8 30
1 20 10 Rsq = 0 .2318 20 30 40 50 60 70 Individual 34 ULM 3 1 1 = Macroscopic methods, 2 = Kashyap and Rao (1990), 3 = Johanson (1971) Attrition, 4 = Johanson (1971) Translucency, 5 = Johanson (1971) Attrition and Translucency, 6 = Maples (1978), 7 = Lorentsen and Solheim (1989), 8 = Drusini (1990), 9 = Charles (1986, 1989) method for cementum annulation counts
FIGURE 9: DISTRIBUTION OF ESTIMATES FOR EACH METHOD 70
3.5 60
3.0 2.5 Age Estim ates 50
2.0 40
1.5 30
M acro scopic 1.0 20
Kashy ap an d Rao Std. Dev = 10.95 .5 10 M ean = 29.7 C harles 11 13 12 16a 15 18a 17 28 20a 30b 30a 34 32 37 36a 7 6b N = 18.00 0.0 8 17.2 19.6 22.0 24.5 26.9 29.3 31.7 34.1 36.5 39.0 41.4 43.8 Individuals M ACROSCOPIC 7 6 6 5 5 4 4 3 3 2 2 Std. Dev = 13.07 1 1 Std. Dev = 13.04 Mean = 28.1 N = 18.00 0 M ean = 51.3 N = 18.00 0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 30.0 35.0 40.0 45.0 KASHYAP 50.0 55.0 60.0 65.0 70.0 ATTRITION 6 5 5 4 4 3 3 2 2 1 1 Std. Dev = 9.80 Std. Dev = 12.76 Mean = 60.0 N = 18.00 0 35.0 40.0 45.0 50.0 55.0 TRANSLUCE NCY 60.0 65.0 70.0 M ean = 59.2 N = 18.00
0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 ATTRITION & TRANSLUCE NCY 70.0 75.0 FIGURE 9: DISTRIBUTION OF ESTIMATES FOR EACH METHOD (CONTINUED) 5 5 4 4 3 3 2 2 1 1 Std. Dev = 18.59 Std. Dev = 11.81 Mean = 59.3 M ean = 54.2 N = 18.00 0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 N = 9.00 0 30.0 70.0 40.0 50.0 MAPLES 60.0 70.0 80.0 90.0 LORENTSEN 6 5 5 4 4 3 3 2 2 1 Std. Dev = 4.19 1 Std. Dev = 4.37 Mean = 26.3 M ean = 42.7 N = 18.00 0 34.0 36.0 38.0 40.0 42.0 DRUSINI 44.0 46.0 48.0 50.0 N = 15.00
0 17.5 20.0 22.5 25.0 27.5 CEMENTUM 30.0 32.5 35.0 FIGURE 10: HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL 4 3 3 2 3 2 2 2 1 1 1 Std. Dev = 11.51 1 Std. Dev = 24.97 M ean = 41.5 N = 9.00 0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 M ean = 46.1 N = 9.00 0 55.0 10.0 20.0 30.0 6B 40.0 50.0 60.0 70.0 80.0 7 4 3 3 2 3 2 2 2 1 1 1 Std. Dev = 9.64 Std. Dev = 17.19 1 M ean = 46.2 N = 9.00 0 20.0 30.0 40.0 50.0 60.0 Mean = 56.9 N = 7.00 0 70.0 40.0 45.0 50.0 8 55.0 60.0 65.0 70.0 11 3 4 3 2 3 2 2 2 1 1 1 Std. Dev = 16.49 Std. Dev = 18.45 1 M ean = 48.9 N = 8.00 0 30.0 40.0 50.0 12 60.0 70.0 Mean = 59.3 N = 9.00
0 30.0 40.0 50.0 60.0 13 70.0 80.0 90.0 FIGURE 10: HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL (CONTINUED) 3 4 3 2 3 2 2 2 1 1 1 Std. Dev = 17.42 Std. Dev = 7.94 1 Mean = 44.0 N = 8.00 0 20.0 30.0 40.0 50.0 60.0 Mean = 30.9 N = 8.00 0 20.0 70.0 25.0 30.0 35.0 40.0 45.0 16A 15 3 4 3 2 3 2 2 2 1 1 1 Std. Dev = 12.44 Std. Dev = 14.45 1 Mean = 37.6 N = 8.00 0 20.0 25.0 30.0 35.0 40.0 45.0 Mean = 36.5 N = 8.00 0 50.0 20.0 30.0 40.0 17 50.0 60.0 18A 4 5 3 4 3 2 3 2 2 1 1 Std. Dev = 12.03 Std. Dev = 15.29 1 M ean = 51.1 Mean = 35.4 N = 8.00 0 20.0 30.0 40.0 20A 50.0 60.0 N = 9.00
0 25.0 30.0 35.0 40.0 45.0 50.0 28 55.0 60.0 65.0 70.0 FIGURE 10: HISTOGRAMS FOR ESTIMATES FOR EACH INDIVIDUAL (CONTINUED) 4 5 3 4 3 3 2 2 2 1 1 Std. Dev = 19.70 Std. Dev = 19.18 1 Mean = 51.7 Mean = 52.4 N = 8.00 0 30.0 40.0 50.0 60.0 N = 9.00 0 20.0 70.0 30.0 40.0 50.0 60.0 70.0 30B 30A 4 3 3 2 3 2 2 2 1 1 1 Std. Dev = 17.41 1 Std. Dev = 10.28 M ean = 41.7 N = 9.00 0 20.0 30.0 40.0 50.0 M ean = 34.8 N = 8.00 0 60.0 20.0 25.0 30.0 32 35.0 40.0 45.0 50.0 34 4 4 3 3 3 3 2 2 2 2 1 1 Std. Dev = 20.14 1 Std. Dev = 17.95 1 Mean = 44.6 N = 9.00 0 20.0 30.0 40.0 50.0 36A 60.0 70.0 M ean = 53.4 N = 7.00
0 30.0 40.0 50.0 37 60.0 70.0 FIGURE 11: NORMAL QUANTILE PLOTS FOR METHODS Normal Quantile Plot Estimates Normal Quantile Plot Estimates from from Macroscopic Methods Expected Cum Prob Kashyap and Rao Method (1990) 1.00 1.00
.75 .75
Expected Cum Prob .50 .25 .50
.25 0.00 0.00 0.00 .25 .50 .75 1.00 0.00 Observed Cum Prob .75 1.00 Normal Quantile Plot for Translucency from Johanson's Method (1970) from Johanson's Method (1970) 1.00
1.00
.75
.75 Expected Cum Prob .50
.50 .25 .25 0.00 0.00 0.00 .25 .50 .75 0.00 1.00 Observed Cum Prob .25 .50 .75 1.00 Observed Cum Prob Normal Quantile Plot for A & T Normal Quantile Plot for Estimates from Johanson's Method (1970) Expected Cum Prob .50 Observed Cum Prob Normal Quantile Plot for Attrition Expected Cum Prob .25 from Maples Method (1978) 1.00
1.00
.75 .75 Expected Cum Prob .50 .25 .50 .25 0.00 0.00
0.00 .25 Observed Cum Prob .50 .75 1.00 0.00 .25 Observed Cum Prob .50 .75 1.00 FIGURE 11: NORMAL QUANTILE PLOTS FOR METHODS (CONTINUED) Normal Quantile Plot for Estimates Normal Quantile Plot for Estimates from Drusini's Method (1990) Lorentson and Solheim Method (1989) 1.00
1.00 .75 .75 Expected Cum Prob Expected Cum Prob .50 .25 .25 0.00 0.00 0.00 .25 .50 .75 1.00 Observed Cum Prob from Cementum Annulations 1.00 .75 .50 .25 0.00
0.00 .25 Observed Cum Prob 0.00 .25 Observed Cum Prob Normal Quantile Plot for Estimates Expected Cum Prob .50 .50 .75 1.00 .50 .75 1.00 FIGURE 12: SCATTERPLOTS FOR EACH METHOD 70
80 70 60
Attrition (Johanson) Kashyap and Rao 60 50
40
30
50 40 30 20
20 10 10 10 Rsq = 0.2046 20 30 40 50 60 10 70 Rsq = 0.6093 20 30 40 Macrosco pic 70 80 60 70 80 80 Attrition and Translucency (Johanson) 70 Translucency (Johanson) 60 Macroscopic 80 60 50 40 30 70 60 50 40 30 20 20 20 50 10 Rsq = 0.2631 30 40 50 60 70 80 10 Rsq = 0.4497 20 30 40 50 Macroscopic Macroscopic 95
80 85
70 75
Lorentsen and Solh eim Maples 60 50 40 30 55
45
35
20 25
10 10 65
Rsq = 0.0009 20 30 40 50 Macroscopic 60 70 80 15 15 Rsq = 0.0262
25 35 45 55 65 Macrosco pic 75 85 95 FIGURE 12: SCATTERPLOTS FOR EACH METHOD (CONTINUED) 60
40
50
30
Drusini CHARLES 40
30
20
20
10 10 Rsq = 0.3911 20 30 40 Macroscopic 50 60 10 10 Rsq = 0.1557
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