1 INTRODUCTION

Paleodemography is the window through which many aspects of the past human experience can be seen, including life expectancy, fertility, risk of dying, and survival in diverse cultural and natural settings. Our predecessors' life experiences have long been ripe for speculation. According to what many consider divinely inspired text, people lived as long as “threescore years and ten” (Psalms 90:10). Or perhaps the situation was entirely different, and lives were “poore, nasty, brutish, and short” (Hobbes, 1904 [1651], p. 84). Only one thing is certain. There is no shortage of seemingly definitive, but ill-informed, pronouncements about such topics. This interest, long predating today's paleodemography, stems in part from an inchoate recognition that skeletons can tell us much about life in the past.

Paleodemographers are principally concerned with clarifying how people once lived, why ways of life changed over time, and the conditions under which they did so. While there is more to the field than skeletons, in this article we focus on human remains. Even when restricting ourselves to bones, it is only possible to highlight current problems, controversies, and directions for future work. Doing so involves first diving into how data are obtained and their limitations. Beyond knowing how a skeletal sample came about, paleodemographic work requires morphological indicators that accurately reflect sex and age. Unfortunately, conventional adult age-estimation methods perform poorly, although there is reason to be optimistic about ongoing research that promises to yield unbiased estimates of age throughout the lifespan. Moving beyond individual skeletons, generating age-at-death distributions and figuring out what they might mean are not straightforward tasks. Producing a distribution of deaths is heavily weighted by assumptions and decisions that affect the results, including data comparability.

A balance must be struck between a need for better data and innovative methods to handle them, and how that information can be used to advance understandings of evolutionary processes, community organization and function, and human-disease interaction, among other topics that require accurate appraisals of population structure. Being able to address such issues is why paleodemography is important, although the road getting to something useful has been long and tortuous. In fact, the foundations of much of what we would like to know about life in past communities—estimating age, generating age distributions, and interpreting them—have been the subjects of considerable work and intense debate.

With regard to the experiences of past peoples, we first turn to inferences that can be drawn from skeletal age distributions and single figure summaries of them. We then cover what can be said about the course of life from childhood to old age, the latter much-neglected in research focused on archaeological skeletons. Skeletons also provide the primary means of identifying the principal characteristics of preindustrial demographic regimes, as well as when changes took place that led to the world as we know it today. For example, the representation of juveniles in archaeological samples, interpreted as a fertility indicator, provides clues concerning what took place and the pace of change from hunter-gatherer to agricultural ways of life. Finally, the role of paleodemographic findings in allied fields is highlighted through ongoing developments in paleoepidemiology, a quantitative assessment of the disease experience of past people.

2 PALEODEMOGRAPHY 2.1 Coverage and development

Human skeletons are only part of what is covered by a broad paleodemographic umbrella (Chamberlain, 2006; French, 2016; French et al., 2021; Hassan, 1981; Schmidt et al., 2021; Williams, 2012). Studies of population size, distribution, and growth also involve sites, artifacts, and radiocarbon dates. These topics, in fact, are more directly addressed through archaeological material than skeletal remains. Nevertheless, skeletons have been used to estimate settlement size and duration, and this work dates back to a time when there were few such estimates about excavated communities (Hooton, 1920, 1930). Demographic information for past societies with written records has also been derived from funerary inscriptions, among other source material (Floris et al., 2021; Hin, 2013; Scheidel, 2012; Woods, 2007). These data have their own biases and interpretive ambiguity, so much so that a critique of funerary epitaph information has been aptly titled Graveyards for Historians (Hopkins, 1987).

Prior to the mid-20th century, characterizations of the age and sex composition of past populations, regardless of whether data came from skeletons or funerary inscriptions, were few and far between (Angel, 1947; MacDonnell, 1913; Pearson, 1902; Todd, 1927). But since about 1970, paleodemography has played a central, if controversial, role in studies of archaeological skeletons (Acsádi & Nemeskéri, 1970; Angel, 1969; Asch, 1976; Bennett, 1973; Blakely, 1971, 1977; Buikstra, 1976; Green et al., 1974; Lovejoy et al., 1977; Owsley & Bass, 1979; Swedlund & Armelagos, 1969; Ubelaker, 1974).

Paleodemography's origin as a coherent field, in all its various dimensions, owes much to a reorientation of research that accompanied the 1960s New Archaeology. An emphasis was placed on the people of the past rather than mere descriptions of artifacts and architectural remains. Researchers accordingly focused on people's lives, with the structure of populations as recoverable through skeletons being an important part of that work (Angel, 1971; Buikstra, 1976; Green et al., 1974; Ubelaker, 1974).

Initial enthusiasm about paleodemography's potential was soon dampened by problems posed by skeletal data, their analysis, and interpretation (Bocquet-Appel & Masset, 1982, 1985, 1996; Hoppa, 2002; Hoppa & Vaupel, 2002a; Konigsberg & Herrmann, 2006; Masset, 1976; Petersen, 1975). Some of the issues have been addressed, whereas others have not. Nevertheless, an early announcement of the field's death was surely premature, but in Bocquet-Appel and Masset's (1982) Farewell to Paleodemography the research effort suffered a direct, telling, and much-deserved hit. Four decades on, their challenge coupled with issues raised by other researchers continues to inspire the conceptual and methodological work necessary to estimate age from skeletal remains, generate age-at-death distributions, and glean information from them about life in the past (Frankenberg & Konigsberg, 2006; Hoppa & Vaupel, 2002a; Milner et al., 2019, 2021; Séguy & Buchet, 2013).

2.2 Contributions

Demography is a driving force of evolution and societal development. Of the many potential contributions of paleodemography to our understanding of the human experience, several that resonate in today's world are of special interest. They include how different categories of people were perceived and treated in the past, our predecessors' life courses, and the impact of diseases, trauma, and migration on the processes behind major societal transformations, including the development of more intensive subsistence systems and organizationally complex societies.

2.2.1 Life in past communities

Without a grasp of a group's age and sex composition, one important objective of contemporary bioarchaeological work (Larsen, 2015), the characterization of what specific groups of people, up to entire communities, experienced, would be severely compromised. In the absence of such information, one cannot hope to appreciate, let alone measure, the challenges people faced in terms of dependents to producers; the allocation of household and community labor to essential survival tasks; and the capacity to siphon off time and effort to construct monuments, defensive structures, and other public works. Archaeological findings are testimony to what people in the past achieved, but not the toll it took on past communities or specific segments of them.

One of the best means to delineate the organizational structure of past societies is through a combination of skeletal information and archaeological data, including grave form and location, body treatment, and associated items. Of particular interest are age and sex-related roles and status, as well as the nature of, and interactions among, the social groups that constructed and maintained specific mortuary contexts. Extracting the most information possible from mortuary sites requires, as a first step, knowledge about who was buried in them, notably their age and sex. These biological characteristics of the deceased, when combined with variation in body treatments, grave forms, and burial artifacts, provide perspectives on how general categories of people were regarded in their communities.

In recent years, bioarchaeologists have increasingly focused attention on group and individual identities, as reconstructed from skeletal remains and contextual information (Hosek & Robb, 2019; Knudson & Stojanowski, 2008; Torres-Rouff & Knudson, 2017; Zakrzewski, 2015). The emphasis is on understanding how people, often specific individuals, experienced their lives. Although rarely acknowledged, or perhaps even recognized, this work owes much to Goodenough's (1965, p. 7) fieldwork on the Micronesian island of Chuuk (then Truk). He detailed how a person's multiple identities, only some of which come into play in specific circumstances such as funerary proceedings, form an individual's situationally determined “social persona.” Age, chronological or otherwise, and gender are among the identities that shape social relationships.

Turning first to sex and gender, one can estimate the former from adult skeletons, but not the latter (Garofalo & Garvin, 2020; Walker & Cook, 1998). An attribution of sex means the observed morphological characteristics conform to a greater or lesser degree to those expected of males or females. It is, therefore, an inherently probabilistic statement, although not often expressed as such. The biological and social components of gender are receiving increasing attention in anthropology (DuBois & Shattuck-Heidorn, 2021). For archaeological samples, doing so involves contextual information beyond bones. That is, it requires simultaneously examining both bones and other information, such as grave form, burial accompaniments, and what remains of garments. That work is necessarily undertaken on a case-by-case basis, so however much it tells us about life in the past these individual-oriented analyses are not susceptible to quantitative analyses of numerous individuals, the basis of paleodemographic investigations.

The social significance of age is more amenable to paleodemographic studies. Here the familiar distinction between biological and chronological age is augmented by evidence for the roles that people adopted, as well as how they were perceived and treated by others (Gowland, 2006; Sofaer, 2011). Coming to grips with social age involves, once again, the use of both skeletal and contextual data. From a paleodemographic perspective, investigations of social age have the advantage of being amenable to quantitative analyses of large skeletal samples. For example, different stages in the subadult years, specifically a “middle childhood” where limits were defined by an individual's characteristics rather than a strict accounting of age, have been identified in Neolithic Europe (Bickle & Fibiger, 2014). In a late prehistoric village in the American midcontinent, people in their late teenage years apparently assumed the roles of adults (Milner et al., 1991). They were ambushed by their enemies much like older people, presumably while engaged in subsistence-related tasks that exposed them to settings where they risked being killed. Behavior associated with the transition to adulthood—something that cannot be observed directly—can be inferred from an enhanced risk of dying at the hands of their enemies. A modern example would be legal age, specifically the attainment of adulthood, in medicolegal investigations (Arge et al., 2020; Konigsberg et al., 2019; Thevissen et al., 2010). But, despite its importance in enriching community-level studies, social age is not considered further because age-related roles and how people were regarded by others require context-specific archaeological information beyond what bones alone can provide.

2.2.2 Life histories

The risks (probabilities) of dying experienced by once-living people, as estimated from contextually well-defined archaeological samples, is an essential part of describing the lives of people in the past and how they changed over time. For example, such information can provide a deep history to our understanding of the human lifespan (Wilmoth, 2000). That includes variation in age-specific mortality, differences in male and female life courses, and longevity relative to that of nonhuman primates (Colchero et al., 2016). Without this paleodemographic information, it would be difficult, if not impossible, to characterize transformations in the human experience from our hunting-and-gathering predecessors to the societies of today.

Age-at-death is affected by the conditions people face, with one readily appreciated outcome being longer life expectancies over the past century or two that accompanied improvements in public health measures and internal medicine. Twin studies show that about a quarter of the variance in the lifespan is explicable in terms of genetics (Christensen et al., 2006; Herskind et al., 1996). Notably lower estimates have been generated from a larger and more diverse sample that was not based on twins (Ruby et al., 2018). Because of the strong environmental influence, it comes as no surprise that age-at-death patterns can further understandings of the mortality consequences of our ancestors' diverse ways of life.

Unfortunately, and for reasons explained later, we have only a poor grasp on the length of life in the past when conditions were quite unlike those of today. Much depends on having good skeletal age estimates from many archaeological samples. If available, the relationship between life expectancy and lifespan equality, the latter capturing variation in age at death, as it is understood for relatively recent societies (Aburto et al., 2020) could be extended farther into the past. It might even be possible to determine if in the distant past there was a deceleration in the risk of dying among the elderly, which today is most notable for supercentenarians (Gampe, 2010; Thatcher, 1999; Vaupel, 2010). Perhaps this aspect of mortality occurred at an earlier age in preindustrial populations when life experiences were much different from those of today.

2.2.3 Disease experience

A community's experience with trauma and disease represents a critical part of characterizations of life in the past (Cohen & Armelagos, 1984; Larsen, 2015). That is particularly true when multiple lines of evidence, skeletal remains and much else, are combined when interpreting what happened and why it did so (Larsen et al., 2019). Although traditionally the domain of paleopathology, a largely descriptive endeavor, age and sex information is an essential component of estimating the impact deleterious conditions had on past communities, not just their presence in certain times and places. Going beyond simple counts of fractured or diseased bones in mortality samples is the objective of quantitatively rigorous paleoepidemiology that combines indicators of risk, notably skeletal or dental lesions but also including sex and stature, with age at death information (Boldsen, 1997, 2001, 2005b; Boldsen, Milner, & Weise, 2015; DeWitte, 2018; DeWitte & Hughes-Morey, 2012; DeWitte & Wood, 2008; Gamble et al., 2017; Godde et al., 2020; Godde & Hens, 2021; Kelmelis et al., 2017; Kelmelis & Dangvard Pedersen, 2019; Milner & Boldsen, 2017; Usher, 2000).

Early death from illness and injuries undoubtedly produced age-at-death distributions unlike those of developed nations today where adult mortality is dominated by chronic, late-onset diseases, such as coronary disease and many cancers. At present, standard adult age-estimation methods are not equal to the task of documenting the magnitude of such differences, although the means of getting sufficiently precise information are on the horizon (Hoppa & Vaupel, 2002a; Milner et al., 2021; Séguy & Buchet, 2013). Nor are adult age estimates produced by standard methods a satisfactory basis for investigating the deep history of age-related mortality of concern to medical science, such as the lingering effects of early illness on later well-being and survival (Barker, 2004; Gluckman & Hanson, 2004; Hoffman et al., 2017). A modern world example is the long-term health consequences of the Dutch Hongerwinter (Roseboom et al., 2001), quite aside from the excess mortality that occurred at that time (Ekamper et al., 2017). Much earlier populations were surely subject to periodic privations with similarly severe immediate and downstream impacts on morbidity and mortality that are potentially measurable (Gowland, 2015).

2.2.4 Migration

Population movement, despite its prominence in world news today, has had a checkered history in archaeology (Adams et al., 1978; Anthony, 1990; Burmeister, 2000; Snow, 2009). Once considered a primary driver of change—separate groups of people carried equally distinctive cultural baggage—migration's significance in regional cultural trajectories was largely dismissed with the focus of New Archaeology on autochthonous change. Recently, however, increasing interest has been directed toward the part played by population mobility in societal transformations (Gregoricka, 2021; McSparron et al., 2020; Smith, 2014).

There is indeed reason to direct more attention toward mobility in the past, regardless of a society's structural form. To date, archaeological work on population mobility has emphasized the identification of migrants through the stable isotope, and to a lesser extent the trace element, composition of bones and teeth (Bentley, 2006; Katzenberg Waters-Rist, 2019; Knudson & Price, 2007; Price et al., 2000; Rasmussen et al., 2020). The reconstruction of individual life histories through analyses of isotopes and trace elements, when combined with good skeletal age estimates, facilitates the estimation of migration frequencies on a societal level.

Modeling the effects of migration on population parameters is a daunting task. Of concern is not only the effect on recipient settlements, such as developing urban centers, but also its impact on donor communities. The difficulty here can be readily appreciated: one could have the skeletons of migrants included in samples from the communities where they died, but not from where they originated. Identifying and quantifying the effects of the arrival or departure of appreciable numbers of people, in most instances likely dominated by young adults, is a potentially important, but as yet unrealized, dimension of paleodemographic research.

2.2.5 Evolution and the life course

Evolution acts through the genetic consequences of demographic processes, including differential fertility and survival, as well as population structure, growth (or decline), movement, and interaction (Carey & Vaupel, 2005; Ward & Weiss, 1976). Knowledge about the structure of past populations furthers our understanding of human evolution, including past and present geographical variation in our species.

Indeed, one early objective of paleodemography was the characterization of mortality patterns for much of human existence (Acsádi & Nemeskéri, 1970; Hassan, 1981). This work extends back to our distant hominin ancestors, especially with regard to expected lifespans and the durations of life-history stages (Bermúdez de Castro et al., 2004; Bocquet-Appel & Arsuaga, 1999; Caspari & Lee, 2004; Lovejoy, 1981; Migliano & Guillon, 2012; Monge & Mann, 2015; Riga et al., 2019; Trinkaus, 1995, 2011; Trinkaus & Thompson, 1987). The overwhelming amount of paleodemographic research, however, focuses on anatomically modern humans from the mid-Holocene onward. Populations following the development of agricultural economies are particularly well represented because cemeteries associated with villages and, later, urban centers often yield large skeletal samples.

While the remains of our hominin ancestors are poorly represented in geological deposits, studies of Holocene skeletons can contribute to knowledge about the forces that shaped human evolution. They include the capacity of old individuals to transmit long-remembered knowledge that benefited their social groups (Rosenberg, 2004). Much discussed is the Grandmother Hypothesis in which women past reproductive age made contributions that enhanced the well-being and survival of younger family members (Hawkes, 2006; Hawkes et al., 1998; Hawkes & Blurton Jones, 2005). The question naturally arises if there were enough women of post-reproductive age to make a difference (Crews & Gerber, 2003; Kennedy, 2002). If few people during the past several millennia survived to old age, then surely such longevity, and any benefits it afforded, could not have played a major role in the evolution of our species. Holocene skeletal samples are all but truncated at about 50 years of age. If published results are accepted at face value, one must conclude that virtually all adults in the more distant past also died early deaths. Alternatively, the remarkably consistent results are an artifact of widely used, but deeply flawed, age estimation methods.

2.2.6 Demographic and socioeconomic transitions

Holocene population growth lies at the heart of what we want to know about how we got to where we are today. Attention has accordingly focused on when and why populations increased, notably during the shift to an agricultural (Neolithic) way of life and later to organizationally complex societies. As indicated above, tracking an increase in regional population size and density is most effectively done with archaeological evidence, particularly through compilations of sites and radiocarbon dates. There is, however, still a place for skeletal studies in this aspect of paleodemographic research.

Where food-production systems developed independently, this transition toward an increasingly settled existence, plant cultivation, and animal husbandry spanned millennia (Smith, 2001, 2011). But, we do not know if the shift was gradual, involving innumerable incremental and individually imperceptible changes, or if it occurred in a stepwise fashion. It is here where a skeletal measure, which is based on subadult remains relative to other skeletons, indicates that there was at least one such episode of, archaeologically speaking, rapid change interpreted as an increase in Neolithic fertility (Bocquet-Appel, 2011; Bocquet-Appel et al., 2008). It remains to be seen how closely this skeletal indicator corresponds to shifts in morbidity and mortality, as identified through paleodemographic and paleoepidemiological studies of multiple skeletal samples with appropriately tight temporal controls. Nevertheless, evidence for stepwise changes is consistent with Wood's (1998) model of population growth and production strategy intensification in the preindustrial world.

3 DEATH ASSEMBLAGES

It is best to begin with the obvious: when examining archaeological skeletons, one is looking at a sample of the dead, not the living. Here there are actually two issues of interest—samples and deaths—and they affect all that follows from the initial collection of data to how they are interpreted.

3.1 Sampling bias

Turning first to skeletons as samples, the word population is commonly used in studies of archaeological skeletons. But what comes to a bioarchaeologist's attention are skeletons that have undergone a lengthy selection process that reduces the individuals who died to the skeletons that happen to be examined (Dutour et al., 2003; Hoppa, 1999; Knüsel & Robb, 2016; Konigsberg & Frankenberg, 1994; Milner et al., 2019; Pinhasi & Bourbou, 2008; Saunders et al., 1995; Séguy & Buchet, 2013). For the following reasons, it is best to refer to groups of archaeological skeletons as samples, not populations.

Starting with deaths, often group members are not interred in the same way or place, so the likelihood of their skeletons being discovered and excavated differs accordingly. Some of these remains are more likely to have lasted to the present than others. In heavily used graveyards, it is not uncommon for skeletons to have been disturbed when later graves were dug, with bones being displaced or discarded. Much the same can be said of skeletons in contexts that attracted looters searching for artifacts. Burial conditions, including soils that are even slightly acidic, affect the preservation of bones (Bello et al., 2006; Djurić et al., 2011; Gordon & Buikstra, 1981; Guy et al., 1997; Manifold, 2015; Stanton & Hefner, 2016; Walker et al., 1988; Willey et al., 1997). The small bones of children are more likely to be lost than the bigger bones of adults, with infant underrepresentation being the norm in archaeological samples. This potential for bias was recognized as long ago as the mid-18th century when Jefferson (1788, p. 105) commented on the preservation of infant bones in an excavated mound in Virginia. There is also the excavation process itself. Some archaeological projects are more attentive to skeletons than others. Not all bones that survive to the point of discovery might have been deemed worth saving, a particular problem for old skeletal collections, and contextual documentation can be unclear or has been lost.

All burial locations do not have the same chance of being found and excavated. Much of the archaeological fieldwork over the past few decades has been in response to modern land use. Construction projects, of course, encounter archaeological sites in rural areas. But such work tends to be concentrated in and around urban settings, so cemeteries near cities have a greater chance of being excavated.

The total excavation of cemeteries can help reduce sampling problems (Dutour et al., 2003). For example, at the Danish Tirup site, a rural church, and graveyard encompassed by a ditch, burials were nonrandomly distributed by age, sex, social position, and disease experience (Boldsen, 1995, 2002, 2005a, 2005b, 2007; Kieffer-Olsen et al., 1986). Many babies were buried around the church choir, notably east of it. Early in the graveyard's history women were often interred north of the church and men south of it, although that practice was later abandoned when family or farm burial plots came into use. Graves for important parishioners were located inside the church. Finally, people with skeletal lesions consistent with leprosy were mostly buried directly west of the church. Anything less than the churchyard's complete excavation would have resulted in a biased sample of the people buried in it.

The preceding pertains to what happens after death has occurred. But what comes to a researcher's attention is a biased sample of the people who were ever alive at a particular point in time (Wood et al., 1992). This is true even in a situation that is never likely to occur: the remains of everyone who lived in a community were discovered, the bones were all well preserved, and every excavator was highly skilled. The bias comes about because death is selective with regard to conditions that increase the risk of dying, such as diseases, injuries, and occupational hazards. The individuals who die at a particular age tend to be the sickest, weakest, and most vulnerable of their birth cohort, with others living longer only to die later, often when they too are among the frailest of their age-mates. In short, mortality is selective, and it acts on a heterogeneous population with regard to individual frailty (Vaupel et al., 1979; Vaupel & Yashin, 1985; Weiss, 1990).

Furthermore, it is rarely possible to identify the full range of the once-living population that should be sampled to obtain skeletons representative of the entire biologically, socially, and economically interacting community. Like all other archaeological work, the most that can be done is to recognize that inferences drawn from skeletons must be tempered by the limits of the available sample. That involves taking advantage of what can be learned from habitation sites as well as mortuary contexts, augmented by historical sources when available.

Even when many people died at the same time for the same reason, such as in battles (Boylston et al., 2000; Holst et al., 2018; Kyle et al., 2018; Loe et al., 2014), there is still the issue of defining the social group, or groups, that contributed to the sample (Milner et al., 2019). One cannot exclude selection bias affecting the skeletons resulting from catastrophic events, as has mistakenly been done for Herculaneum's victims of Mount Vesuvius' AD 79 eruption (Capasso & Capasso, 1999). After all, in modern mass casualty incidents excess mortality is unevenly distributed by age, sex, and socioeconomic status (Myung & Jang, 2011; Santos-Burgoa et al., 2018; Zagheni et al., 2015). The same can be expected to have occurred in the past. The selective effect of mortality has even been identified in Black Death victims from a medieval London plague pit. Existing health conditions affected the risk of dying, perhaps including stature as a measure of juvenile growth deficits (DeWitte & Hughes-Morey, 2012; DeWitte & Wood, 2008). Furthermore, from documentary records it is possible that in the Netherlands somewhat more women than men died during high-mortality late medieval plague years (Curtis & Roosen, 2017). The general point is an elementary one: in mass disasters, deaths are unlikely to be entirely independent of age, sex, physical ability, or anything else affecting exposure or susceptibility.

3.2 Skeletal accumulations over time

There is another reason why collections of archaeological skeletons are not populations in the way one normally might think about them. In all but unusual situations, the deaths took place over years, if not centuries. For these accumulations of human remains, it would behoove researchers to determine if the skeletons came from people who had much the same life experiences, regardless of whether they were, or were not, part of a single multigenerational group. The individuals might have shared nothing more than the same place of burial. For example, the Read shell midden in the American midcontinent held skeletons interred over many centuries (Milner & Jefferies, 1998). These people were members of hunting-and-gathering groups that camped at the site, with some being buried there. Although they could be considered representative of a particular way of life—in this instance, there was no demonstrably meaningful change from start to finish—these individuals did not necessarily have a direct descendant relationship with one another.

For cemeteries that lasted generations, even when there was a descendant connection, the skeletons might have come from people who experienced quite different living conditions. A settlement's occupational history can also affect the age composition of the individuals buried in a cemetery. Records for an Australian graveyard show that the age-at-death distribution for the first half-century of its use, when people flocked to the community, was markedly different from what it was like long after the community was well established (Jackes, 2011). In short, one must be careful about what is represented by the skeletons being analyzed, hence what can be learned from them, including their age and sex distribution.

3.3 Sequence of deaths to survival functions

Once skeletons are arranged from young to old, there remains the issue of what to do with them. Later in this article, we address what complicates the estimation of age and generation of age distributions. Here, however, it is important to draw a distinction between different ways of arranging skeletons by age that underscore the difference between looking at them from the perspective of the dead as opposed to the living.

The simplest way of looking at deaths is to calculate the number of skeletons in each age interval relative to the entire skeletal sample. In other words, the denominator is the total number of dead people (all skeletons), and the numerator is the number of deaths in each age interval (a fraction of all skeletons). It is the proportion of the sample that corresponds to each of the age intervals.

Two other ways of plotting deaths according to age—mortality profiles and survival functions—have the same general appearance, although they highlight a distinction between approaches that tend to be descriptive and those that provide a deeper understanding of lives in the past. While paleodemographic analyses of population structure start with tallies of skeletons organized by age, the objective is to translate this information into a survival function.

A mortality profile, as the term is used here, differs from the proportion of the total sample represented in each age interval, which is described above. The mortality profile is the number of people in the skeletal sample who were alive at the beginning of each age interval relative to the number of individuals in the skeletal sample (Boldsen, 1984, 1988). It closely resembles a survival function as the resulting curve monotonically declines with advancing age. The wording here—in the sample—is important because the individuals might, or might not, have been born in the community. That is, it includes people who moved to the community, while it excludes those who moved away. The mortality profile captures the distribution of the dead as seen from the vantage point of the skeletons a researcher has on hand.

A survival function describes the chance of survival as seen from the perspective of the living. Because it is a measure of survival to a given age, it captures what took place in a once-living population. Numerators in the risk of dying are the number of people who died in each of the age intervals. Denominators for each age interval, however, are composed of the people who were alive in the community at the onset of those same intervals. Because the survival function does not have to be directly tied to individual skeletons—that is, it is a continuous function—it can be portrayed, more accurately approximated, at 1-year intervals. Here a simplifying assumption is made that the population is stationary, as is defined later.

The mortality profile and survival function distinction is perhaps best explained by looking at a decline in the number of individuals with advancing age. Even in the impossible situation where everyone's age is known without error, arranging skeletons so there are ever-decreasing numbers of individuals in sequential age intervals is not equivalent to the hazard of dying in each of those same age intervals (age-specific mortality rates). The latter requires one to know the number of people alive in each of the age intervals, as well as the number who died (Boldsen, 1988; Horowitz et al., 1988; Johansson & Horowitz, 1986). Deaths can be obtained directly from a tally of skeletons; the number alive cannot.

In modern national statistics, the survival function is based on age-specific mortality (typically in 1-year intervals) during a given period of time (usually 1 year). There might never have been a birth cohort that had the mortality experience summarized by an empirical survival function because the type and intensity of threats to survival change over time. Archaeological skeletons can likewise be regarded as cross-sectional data since people are not followed throughout their individual life courses. They are sampled at one particular time: at death.

Cemetery samples also differ from modern censuses, which generally pertain to a single year, because the deaths usually took place over much longer periods, often several centuries or more. The skeletal sample might be representative of a relatively uniform way of life, or it could be a composite of people who experienced quite different activity levels, pathogen load, dietary sufficiency, and the like. Identifying the conditions of everyday life is largely an archaeological (or historical) issue, hence the need for contextual information in paleodemographic studies.

4 SEX ESTIMATION

When estimating sex, it is commonly understood that bony features, which for any single trait can be arrayed on a spectrum from those regarded as typically male to female, only permit reliable assessments after adolescence. Sex estimates from skeletal characteristics for subadults are not sufficiently accurate for paleodemographic samples—the bones of children are often poorly preserved and size-based estimates would be affected by uncertainty about the precise age and health status of the individuals exami