A New Approach to the Quantitative Analysis of Bone Surface Modifications: the Bowser Road Mastodon and Implications for the Data to Understand Human-Megafauna Interactions in North America

Otárola-Castillo ER, Melissa G Torquato, TL Keevil, AL May, SN Coon, E Stow, JB Rapes, JA Harris, CW Marean, MI Erin, and JS Shea

2022. Journal of Archaeological Method and Theory: 1-36.

Toward the end of the Pleistocene, the world experienced a mass extinction of megafauna. In North America these included its proboscideans—the mammoths and mastodons. Researchers in conservation biology, paleontology, and archaeology have debated the role played by human predation in these extinctions. They point to traces of human butchery, such as cut marks and other bone surface modifications (BSM), as evidence of human-animal interactions—including predation and scavenging, between early Americans and proboscideans. However, others have challenged the validity of the butchery evidence observed on several proboscidean assemblages, largely due to questions of qualitative determination of the agent responsible for creating BSM. This study employs a statistical technique that relies on three-dimensional (3D) imaging data and 3D geometric morphometrics to determine the origin of the BSM observed on the skeletal remains of the Bowser Road mastodon (BR mastodon), excavated in Middletown, New York. These techniques have been shown to have high accuracy in identifying and distinguishing among different types of BSM. To better characterize the BSM on the BR mastodon, we compared them quantitatively to experimental BSM resulting from a stone tool chopping experiment using “Arnold,” the force-calibrated chopper. This study suggests that BSM on the BR mastodon are not consistent with the BSM generated by the experimental chopper. Future controlled experiments will compare other types of BSM to those on BR. This research contributes to continued efforts to decrease the uncertainty surrounding human-megafauna associations at the level of the archaeological site and faunal assemblage—specifically that of the BR mastodon assemblage. Consequently, we also contribute to the dialogue surrounding the character of the human-animal interactions between early Americans and Late Pleistocene megafauna, and the role of human foraging behavior in the latter’s extinction.

Beyond Chronology, Using Bayesian Inference to Evaluate Hypotheses in Archaeology

Otárola-Castillo ER, Melissa G Torquato, J Wolfhagen, ME Hill and CE Buck

2022. Advances in Archaeological Practice: 1-17.

Archaeologists frequently use probability distributions and null hypothesis significance testing (NHST) to assess how well survey, excavation, or experimental data align with their hypotheses about the past. Bayesian inference is increasingly used as an alternative to NHST and, in archaeology, is most commonly applied to radiocarbon date estimation and chronology building. This article demonstrates that Bayesian statistics has broader applications. It begins by contrasting NHST and Bayesian statistical frameworks, before introducing and applying Bayes’s theorem. In order to guide the reader through an elementary step-by-step Bayesian analysis, this article uses a fictional archaeological faunal assemblage from a single site. The fictional example is then expanded to demonstrate how Bayesian analyses can be applied to data with a range of properties, formally incorporating expert prior knowledge into the hypothesis evaluation process.

Machine learning, bootstrapping, null models, and why we are still not 100% sure which bone surface modifications were made by crocodiles

McPherron SP, W Archer, ER Otárola-Castillo, Melissa G Torquato and TL Keevil

2022 Journal of Human Evolution 164: 103071

Keywords: crocodiles, machine learning, bone surface modifications, cut marks

Bayesian Statistics in Archaeology

Otárola-Castillo ER and Melissa G Torquato

2018 Annual Review of Anthropology 47: 435-53

Null hypothesis significance testing (NHST) is the most common statistical framework used by scientists, including archaeologists. Owing to increasing dissatisfaction, however, Bayesian inference has become an alternative to these methods. In this article, we review the application of Bayesian statistics to archaeology. We begin with a simple example to demonstrate the differences in applying NHST and Bayesian inference to an archaeological problem. Next, we formally define NHST and Bayesian inference, provide a brief historical overview of their development, and discuss the advantages and limitations of each method. A review of Bayesian inference and archaeology follows, highlighting the applications of Bayesian methods to chronological, bioarchaeological, zooarchaeological, ceramic, lithic, and spatial analyses. We close by considering the future applications of Bayesian statistics to archaeological research.

Differentiating between cutting actions on bone using 3D geometric morphometrics and Bayesian analyses with implications to human evolution

Otárola-Castillo ER, Melissa G Torquato, HC Hawkins, E James, JA Harris, CW Marean, SP McPherron, and JC Thompson

2018 Journal of Archaeological Science 89:56-67

Studies of bone surface modifications (BSMs) such as cut marks are crucial to our understanding of human and earlier hominin subsistence behavior. Over the last several decades, however, BSM identification has remained contentious, particularly in terms of identifying the earliest instances of hominin butchery; there has been a lack of consensus over how to identify or differentiate marks made by human and non-human actors and varying effectors. Most investigations have relied on morphology to identify butchery marks and their patterning. This includes cut marks, one of the most significant human marks. Attempts to discriminate cut marks from other types of marks have employed a variety of techniques, ranging from subjectively characterizing cut mark morphology using the naked eye, to using high-powered microscopy such as scanning electron microscopy (SEM) or micro-photogrammetry. More recent approaches use 3D datasets to obtain even more detailed information about mark attributes, and apply those to the fossil record. Although 3D datasets open promising new avenues for investigation, analyses of these datasets have not yet taken advantage of the full 3D surface morphology of BSM. Rather, selected cross-sectional slices of 3D scans have been used as proxies for overall shape. Here we demonstrate that 3D geometric morphometrics (GM), under the “Procrustes paradigm” and coupled with a Bayesian approach, probabilistically discriminates between marks caused by different butchery behaviors. At the same time, this approach provides a complete set of 3D morphological measurements and descriptions. Our results strengthen statistical confidence in cut mark identification and offer a novel approach that can be used to discriminate subtle differences between cut mark types in the fossil record. Furthermore, this study provides an incipient digital library with which to make future quantitative comparisons to archaeological examples, including contentious specimens that are key to understanding the earliest hominin butchery.

The Bayesian Inferential Paradigm in Archaeology

Otárola-Castillo ER, Melissa G Torquato and CE Buck

In Handbook of Archaeological Science (2nd Edition). In press.

Intensification Mechanisms Driving Dietary Change among the Great Plains Big Game Hunters of North America

Otárola-Castillo ER, Melissa G Torquato and ME Hill

In Defining and Measuring Diversity in Archaeology: Another Step Toward an Evolutionary Synthesis of Culture, ed. Metin I. Erin and Briggs Buchanan (July 2022)

Book Summary: Calculating the diversity of biological or cultural classes is a fundamental way of describing, analyzing, and understanding the world around us. Understanding archaeological diversity is key to understanding human culture in the past. Archaeologists have long experienced a tenuous relationship with statistics; however, the regular integration of diversity measures and concepts into archaeological practice is becoming increasingly important. This volume includes chapters that cover a wide range of archaeological applications of diversity measures. Featuring studies of archaeological diversity ranging from the data-driven to the theoretical, from the Paleolithic to the Historic periods, authors illustrate the range of data sets to which diversity measures can be applied, as well as offer new methods to examine archaeological diversity.