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

Abstract

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

Time wears on: Assessing how bone wears using 3D surface texture analysis

“…Research on bone surface modifications has been enhanced by the use of quantitative 3D methods [ 33 – 38 ]. These approaches and others that utilize confocal microscopy and 3D surface texture analysis [ 39 ] have appreciably reduced intra-observer error.…”

New Digs: Networks, Assemblages, and the Dissolution of Binary Categories in Anthropological Archaeology

“…Finally, they are using Bayesian modeling in free computer platforms to refine absolute chronologies to an unprecedented degree—a second radiocarbon revolution (e.g., Fitzpatrick and Jew ; Hamilton and Krus ; Lulewicz ; Miller ; Otárola‐Castillo et al. ).…”

Ultra close-range digital photogrammetry in skeletal anthropology: A systematic review

“…In fact, some methodological studies reported the best practices [6,13,14], and UCR-DP has already been widely applied in fields closely related to the anthropological research, such as archaeology and cultural heritage-for the surveying, interpretation and virtual reconstruction of excavation sites, caves, buildings, monuments [9,12,15,16], documenting statues, bas-relief and mosaics [17,18], building 3D repositories of museum collections [19]-and palaeontology, particularly for site interpretation and track site documentation [13,20,21], other than for digitising bones from mounted skeletons [22]. A number of applications have also been developed in anthropology for documenting rock art [23][24][25][26][27], artefacts [28,29], cut and percussion marks due to human activity [30][31][32][33][34], and hominin footprints [35].…”