Computer vision enables taxon-specific identification of African carnivore tooth marks on bone.

Taphonomic works aim at discovering how paleontological and archaeofaunal assemblages were formed. They also aim at determining how hominin fossils were preserved or destroyed. Hominins and other mammal carnivores have been co-evolving, at least during the past two million years, and their potential interactions determined the evolution of human behavior.

Computer vision supports primary access to meat by early Homo 1.84 million years ago.

Human carnivory is atypical among primates. Unlike chimpanzees and bonobos, who are known to hunt smaller monkeys and eat them immediately, human foragers often cooperate to kill large animals and transport them to a safe location to be shared. While it is known that meat became an important part of the hominin diet around 2.

Neo-taphonomic analysis of the Misiam leopard lair from Olduvai Gorge (Tanzania): understanding leopard-hyena interactions in open settings.

Misiam is a modern wildebeest-dominated accumulation situated in a steep ravine covered with dense vegetation at Olduvai Gorge (Tanzania). It is interpreted here as a leopard lair to which carcasses have been transported for several years. Felid-specific bone damage patterns, felid-typical skeletal part profiles, taxonomic specialization and the physical presence of leopards observed by the authors show that leopards at Misiam can be specialized medium-sized carcass accumulators.

Early Pleistocene faunivorous hominins were not kleptoparasitic, and this impacted the evolution of human anatomy and socio-ecology.

Humans are unique in their diet, physiology and socio-reproductive behavior compared to other primates. They are also unique in the ubiquitous adaptation to all biomes and habitats. From an evolutionary perspective, these trends seem to have started about two million years ago, coinciding with the emergence of encephalization, the reduction of the dental apparatus, the adoption of a fully terrestrial lifestyle, resulting in the emergence of the modern anatomical bauplan, the focalization of certain activities in the landscape, the use of stone tools, and the exit from Africa.

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.

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.