In the Spring of 2019, I conducted an internship under Dr. Kate Spradley on a bioarchaeologocial project pertaining to the preservation and analysis of historical remains. During my internship with the Forensic Anthropology Center at Texas State (FACTS) I have been tasked with cleaning and inventorying remains exhumed form a historic Austin cemetery. Along with these curatorial duties I have been learning to measure teeth for the purpose of sex and ancestry estimation. This report will detail the importance of the project as well as encompass the key elements of my internship experience; curation, inventory, data collection and analysis.
Excavation of the remains was necessitated due to a public works project geared towards improving/preserving the existing infrastructure within the historic cemetery. The graveyard itself was established in the mid 1800’s and the structure undergoing repair was erected int he early 20th century. Unmarked burials were struck in the beginning phases of construction and it became apparent that some action was necessary. The community came together and decided it best to send the remains to Texas State for analysis by the Forensic Anthropology Department. Due to the ongoing and private nature of this research project, site specific details have been omitted.
The Grady Early Building, where this internship was conducted, is a former carpet store turned skeletal lab. The open warehouse floor plan of the lab provides room for many people to work on different cases at once. Each case is laid out in anatomical position across its own table, and the remains my remain laid out and studies for several weeks. Each set of remains comes to the lab as a series of labeled tinfoil pouches in a cardboard box which represents the excavated content of each individual burial. The remains are in a generally poor state of preservation due to the factors such as soil acidity, erosional agents, type of coffin, and taphonomy (damage/alteration) associated with their excavation. Conditions range from whole bones with some cortical flaking, to largely fractured elements which can hardly be reassembled and are of no diagnostic use in building a bio Gloria life profile.
Before data collection may be performed, the remains are cleaned and inventoried by myself, grad assistant Megan Veltri, or one of the other volunteers. The initial cleaning involves removing the element from its casing and gently using a toothbrush to abrade the dry dirt off. In the case of extremely adherent dirt clods we use a wooden skewer to push the debris off the remains. In some extreme cases or in the cleaning of particularly delicate elements, like the skull, it may be necessary to first use a dropper of water to loosen debris. Special care is taken to avoid contributing to the erosion of the remains themselves. Once the initial cleaning is completed, the remains are inventoried and assigned a code based on completeness of each element. The inventory sheets, made by Megan Veltri, are adapted from Buikstra and Ubelaker’s “Standards for Data Collection from Human Skeletal Remains” to be used with the Osteoware program (Buikstra et at., 2014).
Once inventoried, the remains are subjected to non metric and morphoscopic analysis. This includes a look over to assess bone shape for any pathologies or abnormalities, bone feature morphology, suture shape/fustion, presence/absence of elements, and the prominence/protrusion of features. Using these observations, a tentative age and sex estimation can be made based on morphology of the os coxae, crania, and cranial sutures (Buikstra et al. 2014, Lovejoy et al. 1985, Katz et al. 1986). by scoring the manifestation of macromorphosopic cranial features using Ouseley and Hefner’s method, a tentative ancestry estimation can be made using Hefner’s program: Ossa. This program compares the scores entered by the observer to a data base consisting of sample scores from 164 black individuals and 116 white individuals. However useful this program is, its shortcoming is that it only represents two possible ethnicities, rendering it useless unless the individual being observed belongs to one of the two sample groups.
Following macroscopic non-metric analysis, the remains are subject to metric analysis. This means they will be measured, and the values obtained will be run through systems which use discriminant equations to test for the probable ancestry of the remains in questions. Discriminant Function Anaylsis (DFA) is a type of statistical procedures for the optimal separation of groups and classification of unknowns using measurements (Jantz & Ousley. 2005). The most commonly used DFA is the “Linear Discriminant Function” (LDF), which converts measurements into dicriminant function scores using a liner combination of the original measurements that maximizes inter-group differences (Jantz & Ousley 2005). The discriminant score of an individual of unknown group membership is then compared to the mean DFA score for each reference group; it is classified into the group with the closest mean. If there are more than two groups (g>2), more than one DFA score can be calculated, and multiple axes are used for distinguishing group differences (Jantz & Ousley 2005). FORDISC is used to analyze skeletal elements and HanihaRA is used for dental metric anaylsis.
HanihaRA is a program which uses multiple statistical treatments to estimate ancestral group membership by dental metric variation. Data is collected for this program by measuring the dentition of the left side of both Mandibular and maxillary dental arcades, if the perseveration of the left side is poor then the right-side measurements may be substituted in. Teeth are measured mesio-distally and bucco-lingual.
This program sorts the data into one of three broad geographical regions— Africa, Asia, or Europe—using multiple statistical treatments (Pilloud et al. 2014). These methods include summary statistics, Mahalanobis Distance Matrix, Posterior Probabilities, and Discriminant Function Analysis (Hannihara app).