Paleodemography Research Paper

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1. Introduction

How long humans lived in the past is a question that has challenged researchers for centuries. Once the dominion of philosophers, theologians, and then historians, physical anthropologists have also struggled to answer this question over the last century. For the physical anthropologist, understanding long-term patterns of the human life span aids in placing observed changes within an evolutionary framework, linking both biological and cultural pressures and adaptations to a changing pattern of life expectancy in populations.

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Exploration of this question by physical anthropologists has been primarily in the analysis of skeletal remains recovered archaeologically, and has fallen under the purview of a field developed as a subspecialty of physical anthropology—paleodemography.

The early days of paleodemography represented an exploration of modern demographic theory applied to ancient populations and the use of the abridged life table as a tool to aid interpretations of age-at-death profiles from cemetery samples. The 1980s marked a pivotal point for paleodemography. While there had been the occasional critique prior to the 1980s, it was not until 1982 that the great debate over the merits of paleodemography began, sparking several years of controversy within the literature (e.g., Bocquet-Appel and Masset 1982, Buikstra and Konigsberg 1985, Wittwer-Backofen 1987). Subsequent to this period, the two critical issues explored most in paleodemography have been (a) the accuracy of aging techniques, and (b) representativeness of samples.

2. Sources Of Data

Paleodemographic studies have focused primarily on the reconstruction of human skeletal samples recovered archaeologically. The basic assumption has been that mortality statistics derived from the skeletal sample are sufficient to make inferences about mortality in the once living population. In conjunction with and sometimes ancillary to skeletal data, archaeological evidence for settlement size and distribution have also been used to estimate population growth or to address more broadly-based questions of population structure in the past. Anthropological demography, of contemporary or recent historic hunter-gatherer and foraging populations, can also provide us with models for prehistoric populations. However, the range of fertility and mortality patterns among populations is so wide and overlapping that ethnographic analogy from subsistence base and mobility is extremely problematic. More recently, evidence from genetic studies has begun to be used to make inferences about long-term evolutionary changes in demographic structure in modern human populations over the past 250,000 years or so.

2.1 Skeletal Remains

The accuracy and reliability of age estimation techniques in particular have been central problems in paleodemography, particularly with respect to underestimation of the ages of older adults. A further issue is the necessary separation of techniques used to estimate age from the skeletal remains of adults vs. children. The latter are based on a various criteria related to known rates of development, whereas the former rely on more variable patterns of rates of development and degeneration of the bony tissues. In addition, differential rates of change between the sexes have made most techniques sex-specific. The scope of this research paper prevents an in-depth review of such methods, and readers are referred to such classic monographs as Acsadi and Nemeskeri (1970) or Krogman and Iscan (1986) for more detailed descriptions.

2.1.1 Estimation Of Age. Determination of age from the adult skeleton can be undertaken using a variety of methods, including both quantitative and qualitative measures of continuous and discrete data. Such methods are referred to as skeletal ageindicator techniques. Adult aging techniques examine both macroscopic and microscopic changes in the morphology and structure of bone. Many macroscopic techniques focus on the pattern of age-related degeneration of bone (e.g., pubic symphysis, auricular surface, sternal end of the fourth rib). Others focus on the remodeling of bone as a biomechanical response to microfractures from every-day wear and tear. The physical anthropologist evaluates multiple criteria for each skeleton and then combines all estimates from each age-indicator method into an overall estimated age range for the individual.

Age estimation from the skeletal remains of children is based on the development of dental and skeletal tissues, including tooth formation and eruption. Estimation of age in the nonadult is much easier and more accurate than in the adult. While there are fewer techniques than are available for adult age estimation, each has a smaller range of error. Overall, estimation of age from a juvenile is accurate within a range of about plus or minus half a year.

2.1.2 Estimation Of Sex. It is recognized widely in the anthropological literature that the pelvis or hipbone is the most reliable part of the skeleton for determination of sex. Both metric and nonmetric or morphological techniques have accuracy rates of better than 95 percent for correct sex, while accuracy rates based on other parts of the skeleton are usually lower. While morphological variables are often preferred because of better preservation in skeletal samples, metric variables are considered by many to be more reliable because of their greater precision.

To the regret of many researchers, however, determination of sex from the skeleton has most often been restricted to those who have survived past adolescence and who then manifest changes in the skeleton reflective of sex. While a variety of studies have investigated traits that might be sexually dimorphic in infants and juveniles, only a few have had sufficient levels of accuracy to warrant their application in osteological analyses. More promising, but still restricted by time and money, is the determination of sex by extracting ancient DNA from the bones or teeth of individuals.

2.2 Archaeological Evidence

While paleodemography has been focused primarily within the realms of skeletal biology, other forms of evidence have also been explored to answer demographic questions. In particular, archaeological demography has often shed light on issues of population structure in the distant past. Estimates of population size and growth have been attempted from settlement data by examining features such the size and area of the living site, number of dwellings, the density and distribution of artifacts and food remains, as well as from ethnohistoric estimates of population size. Even when data are available, estimates of population size must often be made through ethnographic analogy, whereby the relationship between population size and material remains observed in modern or historic groups is imposed on the archaeological site.

2.3 Genetic Evidence

Recent studies of DNA have proposed some interesting hypotheses regarding demographic changes in the past that are of interest when exploring the issue of long-term trends in human prehistory. In particular, advances in nucleotide divergence theory have provided information on human demographic behavior since patterns of gene differences contain information about the demographic history of a species (Harpending et al. 1998). Genetic data have been used specifically to tackle questions of modern human origins, and the evidence implies human demographic expansion from a population of only several thousand about 250,000 years ago.

3. Representativeness

The representativeness of a skeletal series is a crucial factor in paleodemographic studies. While anthropologists do not expect samples to represent the population in absolute numbers, the fundamental assumption governing the analysis and interpretation of skeletal samples is that the distribution and pattern of any parameter is the same in the skeletal sample as in the living population that contributed to it. That is, the pattern observed in the skeletal sample can be used as a proxy for the real pattern within the population. However, a variety of factors serve to bias paleodemographic reconstructions relative to the true demography of the once living population. Biological factors related to the fact that skeletal samples are the nonsurvivor portion of a population, cultural factors related to the burial of the body, environmental factors related to postdepositional processes, and methodological factors related to the excavation and analysis of skeletal samples all serve to potentially bias interpretations.

Until the 1990s few attempts had been made to directly assess the validity of interpretations from paleodemographic reconstructions because of the obvious difficulty in obtaining a skeletal sample that is known to be representative of its larger population. With the more recent, detailed studies of historic cemetery skeletal samples, researchers have begun to test the representativeness of their samples by comparing the mortality data derived from the skeletal sample with the documentary mortality data associated with the cemetery from which the sample was drawn. In most cases, clear differences in demographic parameters estimated from the two sources became apparent. As a result, recognizing and controlling for these biases has become an important part of paleodemographic reconstructions.

4. Reconstructing The Demography Of Prehistoric Populations

Traditionally, the statistical tool used by paleodemographers has been the abridged life table. Under the assumption of a stationary population, paleodemography uses mean age at death to estimate expectation of life at birth. Generally, mean age at death is considered approximately equivalent to the inverse of the birth rate in a population, but is independent of both life expectancy and the death rate. However, when the conditions of a stationary population are not met, this calculation simply represents mean age at death.

Traditionally, the abridged life table has been used by paleodemographers to estimate general mortality patterns with fertility being a by-product. However, several estimators of fertility from mortality profiles have been used. Jackes (1992) has noted that comparisons of the age structures across populations are based on the assumption that there is a relationship between juvenile and adult mortality, and that age-at death data within very broad age categories will carry some information about the fertility rate of the population. However,

in nonstationary populations, age-at-death distributions are extremely sensitive to changes in fertility but not to changes in mortality … . Thus, if a population is not stationary—and changing populations never are—small variations in fertility have large effects on its age-at-death distribution, while even quite large modifications of mortality have virtually none (Wood et al. 1992, p. 344).

As a result, many researchers have concluded that the age distribution of skeletal samples provides less information about mortality than it does about fertility, a position supported very early on in the demography literature. In fact, the same fertility and mortality schedules can produce different birth and death rates in populations with different age structures.

4.1 Model Life Tables

In the 1970s demographers expressed concern over the paucity of evidence from which to make statements regarding paleodemographic parameters, forcing investigators to extrapolate from models derived from other sources. Subsequently, model life tables from modern demographic studies formed the basis from which anthropological demographers began to develop model life tables for past populations. Skeletal samples can be compared with a model life table and the fit between model and observed mortality distributions can then be assessed statistically.

The use of model life tables in anthropological demography is twofold. First, it provides a means of assessing or compensating for biased and incomplete data, and second, it allows for the estimation of fertility rates and construction of an initial population at risk. In the early 1970s, Weiss (1973) developed a set of model fertility and mortality schedules derived from ethnographic samples of contemporary hunter-gather societies and prehistoric skeletal samples. Many investigators agree that demographic statistics derived from contemporary non-Western societies represent an effective means of assessing skeletal age profiles of past populations. However, given the variety of conditions under which many contemporary populations live, it is difficult to be certain that ethnographic analogy will always be appropriate. Further, the application of ethnographic estimators to samples for which related sociocultural information is sparse further compounds the problem.

4.2 Hazard Analysis

Although a potentially powerful tool for paleodemographic analyses, model life table fitting techniques are still subject to potential biases resulting from the use of inappropriate model populations. As an alternative, Gage (1988) proposed the use of a hazard model of age-at-death patterns that can be fitted to survivorship, death rate, and age structure data. As Gage has noted, the Siler model of mammalian mortality is useful for studying human mortality because it reduces the number of variables while maintaining interpretive power. The model for survivorship, which can be fitted to the lx column in a life table using nonlinear techniques to estimate the parameters, is:

Paleodemography Research Paper Formula 1

where t is age, a1 is the risk of immature mortality at the moment of birth, b1 is the rate of decline with age of this risk, a2 is a constant risk factor, a3 is the risk of senescent mortality at the moment of birth, and b3 is the rate of increase in senescent mortality with age (the rate of aging).

The instantaneous death rate or force of mortality at age t of this model is

Paleodemography Research Paper Formula 2

and the proportion of individuals surviving the immature component is exp{-a1 / b1} (Gage 1988). This technique provides a method of estimating age-specific mortality and fertility directly from anthropological data, and it can smooth demographic data from a variety of populations without imposing a predetermined age structure (Gage 1988).

Ultimately, the focus of paleodemography has been to refine methods to improve estimates of age at the individual level in order to get some aggregate estimate of the age structure in the population. More recently, however, researchers have begun to try and estimate the mortality distribution of a sample directly from the distribution of skeletal age-indicator stages scored. While the difference is subtle, it is important in that such techniques attempt to avoid the broad range of error associated with estimates at the individual level. If one is interested in estimating an individual’s age, as in the context of forensic anthropology, then such techniques fall into the category of prediction models. However, if one is interested in shape of mortality or the force of mortality within a population then an alternative theoretical approach is required—one that begins with the overall age structure being estimated, and estimates of individual age being derived secondarily. This apparent paradox in paleodemography was noted by Konigsberg and Frankenberg (1994, p. 96):

Thus, for any single skeleton from an archaeological sample, the posterior probability that the individual is a particular age conditional on the observed indicator is proportional to the prior probability that the individual is that particular age times the probability that an individual that particular age would be in the observed indicator state. However, we do not know this prior probability. If we knew the prior probability of death at particular ages, then we would know the age-at-death distribution, which is precisely what we are trying to estimate. This paradox can be solved by using a common statistical method known as maximum likelihood estimation.

While the precise statistical approach to estimating the mortality structure from skeletal age-indicator data may vary, it is clear that the emerging analytical framework on which paleodemography must move forward begins first with an estimate of the age structure, from which estimates of individual age can subsequently be calculated (cf. Hoppa and Vaupel 2001).

5. Summary

The biological basis for human survival and longevity evolved during the tens of thousands of years of human experience under premodern conditions. A deeper understanding of this basis thus depends on knowledge of mortality and fertility patterns over the long stretch of human existence. Furthermore, deeper understanding of the plasticity of survival and longevity depends on knowledge of how different mortality is today from mortality before the modern era. Paleodemography attempts to address this question by integrating evidence from a variety of sources, but focusing primarily on skeletal, archaeological, and even genetic data. Long-standing interpretations have suggested that the bulk of premodern populations had very short life spans with only a few individuals surviving to middle adulthood. However, new methodological approaches are beginning to readdress this position, and future paleodemographic studies will be able to test this hypothesis more reliably.


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