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"Domínguez-Rodrigo, Manuel"
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Distinguishing butchery cut marks from crocodile bite marks through machine learning methods
All models of evolution of human behaviour depend on the correct identification and interpretation of bone surface modifications (BSM) on archaeofaunal assemblages. Crucial evolutionary features, such as the origin of stone tool use, meat-eating, food-sharing, cooperation and sociality can only be addressed through confident identification and interpretation of BSM, and more specifically, cut marks. Recently, it has been argued that linear marks with the same properties as cut marks can be created by crocodiles, thereby questioning whether secure cut mark identifications can be made in the Early Pleistocene fossil record. Powerful classification methods based on multivariate statistics and machine learning (ML) algorithms have previously successfully discriminated cut marks from most other potentially confounding BSM. However, crocodile-made marks were marginal to or played no role in these comparative analyses. Here, for the first time, we apply state-of-the-art ML methods on crocodile linear BSM and experimental butchery cut marks, showing that the combination of multivariate taphonomy and ML methods provides accurate identification of BSM, including cut and crocodile bite marks. This enables empirically-supported hominin behavioural modelling, provided that these methods are applied to fossil assemblages.
Journal Article
Configurational approach to identifying the earliest hominin butchers
The announcement of two approximately 3.4-million-y-old purportedly butchered fossil bones from the Dikika paleoanthropological research area (Lower Awash Valley, Ethiopia) could profoundly alter our understanding of human evolution. Butchering damage on the Dikika bones would imply that tool-assisted meat-eating began approximately 800,000 y before previously thought, based on butchered bones from 2.6- to 2.5-million-y-old sites at the Ethiopian Gona and Bouri localities. Further, the only hominin currently known from Dikika at approximately 3.4 Ma is Australopithecus afarensis , a temporally and geographically widespread species unassociated previously with any archaeological evidence of butchering. Our taphonomic configurational approach to assess the claims of A. afarensis butchery at Dikika suggests the claims of unexpectedly early butchering at the site are not warranted. The Dikika research group focused its analysis on the morphology of the marks in question but failed to demonstrate, through recovery of similarly marked in situ fossils, the exact provenience of the published fossils, and failed to note occurrences of random striae on the cortices of the published fossils (incurred through incidental movement of the defleshed specimens across and/or within their abrasive encasing sediments). The occurrence of such random striae (sometimes called collectively “trampling” damage) on the two fossils provide the configurational context for rejection of the claimed butchery marks. The earliest best evidence for hominin butchery thus remains at 2.6 to 2.5 Ma, presumably associated with more derived species than A . afarensis .
Journal Article
Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications
Bone surface modifications are foundational to the correct identification of hominin butchery traces in the archaeological record. Until present, no analytical technique existed that could provide objectivity, high accuracy, and an estimate of probability in the identification of multiple structurally-similar and dissimilar marks. Here, we present a major methodological breakthrough that incorporates these three elements using Artificial Intelligence (AI) through computer vision techniques, based on convolutional neural networks. This method, when applied to controlled experimental marks on bones, yielded the highest rate documented to date of accurate classification (92%) of cut, tooth and trampling marks. After testing this method experimentally, it was applied to published images of some important traces purportedly indicating a very ancient hominin presence in Africa, America and Europe. The preliminary results are supportive of interpretations of ancient butchery in some places, but not in others, and suggest that new analyses of these controversial marks should be done following the protocol described here to confirm or disprove these archaeological interpretations.
Journal Article
Distinguishing Discoid and Centripetal Levallois methods through machine learning
In this paper, we apply Machine Learning (ML) algorithms to study the differences between Discoid and Centripetal Levallois methods. For this purpose, we have used experimentally knapped flint flakes, measuring several parameters that have been analyzed by seven ML algorithms. From these analyses, it has been possible to demonstrate the existence of statistically significant differences between Discoid products and Centripetal Levallois products, thus contributing with new data and a new method to this traditional debate. The new approach enabled differentiating the blanks created by both knapping methods with an accuracy >80% using only ten typometric variables. The most relevant variables were maximum length, width to the 25%, 50% and 75% of the flake length, external and internal platform angles, maximum width and number of dorsal scars. This study also demonstrates the advantages of the application of multivariate ML methods to lithic studies.
Journal Article
New site at Olduvai Gorge (AGS, Bed I, 1.84 Mya) widens the range of locations where hominins engaged in butchery
Outstanding questions about human evolution include systematic connections between critical landscape resources—such as water and food—and how these shaped the competitive and biodiverse environment(s) that our ancestors inhabited. Here, we report fossil
n
-alkyl lipid biomarkers and their associated δ
13
C values across a newly discovered Olduvai Gorge site (AGS) dated to 1.84 million years ago, enabling a multiproxy analysis of the distributions of critical local landscape resources across an explicit locus of hominin activity. Our results reveal that AGS was a seasonally waterlogged, largely unvegetated lakeside site situated near an ephemeral freshwater river surrounded by arid-adapted C4 grasses. The sparse vegetation at AGS contrasts with reconstructed (micro)habitats at the other anthropogenic sites at Olduvai Gorge, suggesting that central-provisioning places depended more heavily on water access than vegetation viz. woody plants as is often observed for modern hunter-gatherers. As hominins at AGS performed similar butchering activities as at other Bed I sites, our results suggest they did not need the shelter of trees and thus occupied a competitive position within the predatory guild.
Journal Article
Lions as Bone Accumulators? Paleontological and Ecological Implications of a Modern Bone Assemblage from Olduvai Gorge
Analytic models have been developed to reconstruct early hominin behaviour, especially their subsistence patterns, revealed mainly through taphonomic analyses of archaeofaunal assemblages. Taphonomic research is used to discern which agents (carnivores, humans or both) generate the bone assemblages recovered at archaeological sites. Taphonomic frameworks developed during the last decades show that the only large-sized carnivores in African biomes able to create bone assemblages are leopards and hyenas. A carnivore-made bone assemblage located in the short-grassland ecological unit of the Serengeti (within Olduvai Gorge) was studied. Taphonomic analyses of this assemblage including skeletal part representation, bone density, breakage patterns and anatomical distribution of tooth marks, along with an ecological approach to the prey selection made by large carnivores of the Serengeti, were carried out. The results show that this bone assemblage may be the first lion-accumulated assemblage documented, although other carnivores (namely spotted hyenas) may have also intervened through postdepositional ravaging. This first faunal assemblage potentially created by lions constitutes a new framework for neotaphonomic studies. Since lions may accumulate carcasses under exceptional circumstances, such as those documented at the site reported here, this finding may have important consequences for interpretations of early archaeological and paleontological sites, which provide key information about human evolution.
Journal Article
Tracing the spatial imprint of Oldowan technological behaviors: A view from DS (Bed I, Olduvai Gorge, Tanzania)
DS (David’s site) is one of the new archaeological sites documented in the same paleolandscape in which FLK 22 was deposited at about 1.85 Ma in Olduvai Gorge. Fieldwork in DS has unearthed the largest vertically-discrete archaeological horizon in the African Pleistocene, where a multi-cluster anthropogenic accumulation of fossil bones and stone tools has been identified. In this work we present the results of the techno-economic study of the lithic assemblage recovered from DS. We also explore the spatial magnitude of the technological behaviors documented at this spot using powerful spatial statistical tools to unravel correlations between the spatial distributional patterns of lithic categories. At DS, lavas and quartzite were involved in different technological processes. Volcanic materials, probably transported to this spot from a close source, were introduced in large numbers, including unmodified materials, and used in percussion activities and in a wide variety of reduction strategies. A number of volcanic products were subject to outward fluxes to other parts of the paleolandscape. In contrast, quartzite rocks were introduced in smaller numbers and might have been subject to a significantly more intense exploitation. The intra-site spatial analysis has shown that specialized areas cannot be identified, unmodified materials are not randomly distributed, percussion and knapping categories do not spatially overlap, while bipolar specimens show some sort of spatial correlation with percussion activities.
Journal Article
Sabertooth carcass consumption behavior and the dynamics of Pleistocene large carnivoran guilds
Apex predators play an important role in the top-down regulation of ecological communities. Their hunting and feeding behaviors influence, respectively, prey demography and the availability of resources to other consumers. Among the most iconic—and enigmatic—terrestrial predators of the late Cenozoic are the Machairodontinae, a diverse group of big cats whose hypertrophied upper canines have earned them the moniker “sabertooths.” Many aspects of these animals’ paleobiology, especially their prey preferences and carcass consumption behavior, remain unsettled. While skeletal anatomy, dental morphology and wear, and isotopic profiles provide important insights, the most direct way to resolve these issues is through the fossil remains of sabertooth prey. Here, we report on a taphonomic analysis of an early Pleistocene faunal assemblage from Haile 21A (Florida, USA) that preserves feeding damage from the lion-sized sabertooth
Xenosmilus hodsonae
. Patterns of tooth-marking and bone damage indicate that
Xenosmilus
fully defleshed the carcasses of their prey and even engaged in some minor bone consumption. This has important implications for Pleistocene carnivoran guild dynamics, including the carcass foraging behavior of the first stone-tool-using hominins.
Journal Article
Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks
Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.
Journal Article