Reconstructing Sample Profiles with Skeletal Populations

Reconstruction of vital statistics, such as age-at-death and sex, is essential to understanding the population of prior civilizations. However, population profiles of excavated human remains are not exact representations of the groups of people who lived and died in the study area (Milner et al. 2000; Wood et al. 1992). Choices the culture made in who was buried, differences in burial practices, as well as where people were interred can all complicate the mortuary record. Additionally, those who die and are buried must also be preserved in the local environment long enough to be found and excavated (Milner et al. 2000:473). An example of this is differences in environmental preservation, and the samples in this study can serve as one case study. The annual dry- rainy season in highland Bolivia can speed up the decomposition process, while the desert conditions in the Moquegua Valley, where rain is an infrequent occurrence, can make for near-perfect preservation of human remains, such as fully mummified

individuals. While this is one local example, environmental preservation biases affect the ease of recovery of skeletal remains throughout the world.

In addition to environmental differences, bone preservation and recovery rates may have varied due to age-at-death of the individuals buried. For example, infant bone does not often preserve as well as adult bone, due both to bone mineral composition differences, as well as the size of skeletal elements able to be recovered (i.e. multiple smaller bones in children than adults). Bone preservation and recovery also could have been dependent on bone health. An individual with fragile bones, due to a condition such as osteoporosis, does not sustain as long as an individual with good bone health does, and thus, not have been recoverable by archaeologists and bioarchaeologists.

One final idea to consider in making a population profile from prehistoric human skeletal remains is the ‘human factor’ involved in the archaeological process. Choices made by scholars about where to excavate, what to retain, and how to preserve these artifacts could change the demographic profile (Milner et al. 2000). The skeletal samples in this study can be used as an example of this process, as they represent collections from noncontiguous archaeological excavations that span three decades. The excavations were run by various archaeologists from Bolivia, Canada, Peru, and the United States, and each project had their own excavation area, research plan, and strategy. Thus, when analyzing any previously excavated archaeological skeletal collection, it is important to remember that prior decisions made by researchers may arise that could hinder a skeletal analysis.

Even considering these problems, the contextual profile of a skeletal sample is important in order to understand the past population. By focusing on individuals from a population level, many of the aforementioned challenges can be reduced, and skeletal samples used for analyses (Hoppa and Vaupel 2002; Larsen 1997; Milner et al. 2000; Wood et al. 1992). The MNI present in the sample can be clarified, along with estimated age-at-death and estimated sex of individuals present (Hoppa and Vaupel 2002; Milner et al. 2000). In addition, I realize that these Tiwanaku skeletal collections do not represent the actual population of the Titicaca Basin or the Moquegua Valley. Rather, they represent the various skeletal samples of multiple excavation projects, all with well- regarded archaeological excavation methods that made them good samples for this dissertation on labor and activity in the Tiwanaku civilization.

Calculating the Minimum Number of Individuals (MNI)

As preservation of these samples varied from fair to excellent, the majority of individuals were allotted a unique specimen number at the time of excavation, as they were discrete, single individual burials. Skeletal remains were then rechecked to

determine if there were any duplicate skeletal elements present. If I noted any duplicate skeletal elements, an additional letter was added (i.e. -a, -b, -c, and so on) to sort individuals and reach an accurate MNI. All excavation data present (e.g. site number, level, excavator) for each specimen was noted for the purposes of this study. After data were collected per each site, I established the MNI and developed further population profiles.

Age Estimation

I estimated the age-at-death of each individual using established diacritical methods: dental eruption, dental wear, cranial suture closure, epiphyseal suture closure, degeneration of the pubic symphysis, auricular surface, and sternal rib ends (Bass 1981; Brothwell 1989; Buikstra and Ubelaker 1994; Işcan and Miller-Shaivitz 1983; Krogman and Işcan 1986; Suchey and Katz 1998; Ubelaker 1999). Age estimates were first performed for dental eruption and epiphyseal closure data, which have high degrees of reliability for aging skeletons, especially when combined (Krogman and Işcan 1986; Stewart 1979; Ubelaker 1999). Secondary information consisting of dental wear, pubic symphysis surface aging, cranial suture closure, and sternal rib end data were also collected wherever possible to obtain as much age information as possible about each individual (Brothwell 1989; Krogman and Işcan 1986; Suchey and Katz 1998).

The age-at-death of individuals was first divided into two subcategories: subadult or SA (individuals under the age of 15) and adult or A (all individuals age 15 and over).

While important to understand the demographic profile of the population, subadults were not scored for osteological markers of activity and labor. As previous analyses have shown that bone remodeling in subadults is relatively rapid and does not adequately represent potential skeletal changes associated with activity (Jurmain 1999; Pearson and Buikstra 2006; Weiss and Jurmain 2007). If there was a question as to subadult or adult status of an individual near age 15, I used fusion or partial fusion of the os coxae pelvic bones to consider the individual in the adult category.

After each of the above age estimates were scored, a combined age was estimated using these multiple methods for each individual. Of the individuals determined to be in the Adult (A) category, they were categorized into five specific age ranges: 15-19, 20-29, 30-39, 40-49, and individuals over 50 years old at the time of death. It is important to note that the groups used to create these age-standards models are from outside of South America, and thus represent a potential bias. However, while these methods for

estimating age are not an exact science, they do however provide the best age estimates when working with osteological populations, as well as a standard measure comparable to other studies.

Sex Estimation

Sex estimation of adults was primarily determined through examination of the pelvis, and secondarily from the cranium. I estimated individuals, whenever possible, as female (F), male (M), possible female (PF), and possible male (PM). Seven pelvic elements (ventral arc, subpubic concavity, ischiopubic ramus/ridge, greater sciatic notch, preauricular sulcus, sacrum curvature, and the pelvic basin) were noted as male or female using standard visual methods (Bass 1981; Buikstra and Ubelaker 1994; Steele and Bramblett 1988). If sex could be estimated from the pelvic bones, no cranial analysis was

performed. However, if sex observation was conflicting, or there were no pelvic elements (i.e. os coxae and sacrum) present, then the cranium was used to estimate sex. Cranial observations were also entered as male, female, possibly male or female from six areas of the cranium and mandible – nuchal crest, mastoid process, supraorbital margin,

supraorbital ridge/glabella, mental eminence, and ascending ramus. In general, each of these cranial elements was compared to prior visual cranial morphology and scored as male if more robust (larger), and if gracile (smaller), it was scored as female (Bass 1981; Buikstra and Ubelaker 1994).