Excavation methods
Our archaeological field and laboratory methods follow the techniques and employ the software developed by the Old Stone Age research group (http://www.oldstoneage.com). In the field, we use total stations combined with hand-held data collectors to precisely and accurately establish and maintain the exaction grid and record the provenience of all excavated materials, either individually or in aggregate. All data are maintained in a  relational database that smoothly transitions from data collected in the field to its management in the lab. 

Zooarchaeology
Comparative approach
Coming soon.

Mortality profile/age at death
Archaeological skeletal samples are formed through a variety of processes -- sometimes the processes result in a sample representative of the living population but often the sample tells us more about human behavior or taphonomy (preservation). Knowing the skeletal age-at-death of the specimens in a sample can help determine how the sample was formed and to control for variation when comparing samples. For example, age-at-death distributions of animal remains in archaeological sites can often be useful in investigating how the assemblage was accumulated (through hunting or scavenging), or if the hunters were selecting certain types of prey. If samples are believed to be representative of living populations, age-at-death is often used to reconstruct the demography of ancient populations and to understand age-related variation. 

Because of their central role in many zoo archaeological analyses, the lab maintains an interest in developing methods for estimating ages-at-death and reconstructing mortality profiles from faunal samples.  In the 1980's Dr. Richard Klein and colleagues developed the Quadratic Crown Height Method (QCHM) for estimating an animal's age-at-death from its tooth crown height. Beginning with her dissertation work, Dr. Teresa Steele expanded Klein's original sample of known-age elk to continue to refine the formula. She and Dr. Tim Weaver worked to develop a way to statistically compare mortality profiles plotted on triangular graphs, and Dr. Steele presented a comparison of the different methods used to investigate mortality profiles (the distribution of ages-at-death in an assemblage). 

Bone tool use wear
Through the zoo archaeological analysis of the faunal remains from the late Middle Paleolithic site of Abri Peyrony (France) and during the excavation, the team identified three bone tools, a very unusual finding from a Neandertal accumulated assemblage. Since the initial publication, a fourth bone tool from Abri Peyrony was found. Work on these pieces (plus one more from nearby Pech-de-l'Azé I) has been published here and here. 

To better understand the significance of these bone tools, former UC Davis graduate student Naomi Martisius focused on the production and use of similar bone tools for her PhD dissertation. She conducted experimental work while examining the original pieces and similar examples from the Upper Paleolithic.

In addition, she uses confocal disc-scanning microscopy and 3D surface texture analysis to analyze the wear traces produced on bone tools during manufacture and use. This methodology was originally developed in the field of surface metrology and has been adapted for archaeological applications in studies on teeth, lithics, ochre, and bone tools. This method allows for the quantitative analysis of changes in microtopographic bone surfaces during interaction with other materials. An important aim of this research is to develop an understanding of the basics of use-wear formation over time using experimental bone pieces prepared in a variety of ways on various materials. This experimental analysis provides a starting point for investigating use-wear in a quantitative way and will eventually be applied to archaeological bone tools. 

Statistical Analysis of the Patterning of Morphological Variation
In the paleoanthropology lab at UC-Davis, we aim to adhere to strict standards of statistical rigor and transparency in the hopes of avoiding common pitfalls lately identified in a large fraction of the recent academic literature (e.g. see the seminal paper by John Ioannidis, but also these more recent ones by Megan Head and colleagues and Timothy Parker and colleagues). In performing any sort of statistical analysis we not only strive to think carefully about what we do any why we do it, but also outsource our thinking to those more competent at it than ourselves. This has lead to much fruitful collaboration with Mark Grote – the department Statistician – as well individuals at the Center for Population Biology and the Data Science Initiative and from many departments at distant schools.

Much of our work is fundamentally grounded in quantitative genetics, phylogenetics, and population genetics, and is implemented in a variety of programming languages, including R, MATLAB, Python, C++, and RevBayes. It tends also to rely on explicit, generative models, whose statistical properties, when unknown, we seek to explore and understand. And when existing methods fail to adequately satisfy our desires, we invent new ones. Our research is also assisted and enabled by a variety of cutting edge technologies, such as 3D digitizers, printers, and scanners. We work on a variety of extinct and extant taxa, including modern humans and various other primates, such as Australopiths, Neandertals, and chimpanzees. Statistically, we are welcoming of both maximum likelihood and Bayesian frameworks, depending on the question under consideration. Those questions themselves tend to focus on the study of morphological variation, whose diversity may be a product of genetic drift, natural selection, or some other process. That’s often what we’re trying to figure out.