Beyond Bones: Integrating Statistics and Machine Learning Tools into Archaeologi...
Beyond Bones: Integrating Statistics and Machine Learning Tools into Archaeological Evidence to Decode Neanderthal-Carnivore Scenarios
The relationship between humans and their natural environment has significantly shaped our evolutionary history. Carnivores in particular, our main competitors in many past ecosystems, likely had a profound impact on hominin behav...
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Información proyecto BITES
Duración del proyecto: 23 meses
Fecha Inicio: 2024-10-01
Fecha Fin: 2026-09-30
Líder del proyecto
UNIVERSIDADE DO ALGARVE
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The relationship between humans and their natural environment has significantly shaped our evolutionary history. Carnivores in particular, our main competitors in many past ecosystems, likely had a profound impact on hominin behaviours. Neanderthals, potentially more carnivorous than other hominin species, present an intriguing focus. Were the relationships between Neanderthals and carnivores primarily rooted in rivalry, mutual avoidance, or subtle survival adaptations? How did they impact Neanderthal niche exploitation? Can changes in these interactions shed light on Neanderthal disappearance? Archaeological sites yield thousands of animal bone fragments with traces of Neanderthal and carnivore consumption that offer valuable insights into this coexistence. Yet, these data remain underexploited, due to the challenges derived from interpreting complex mixed taphonomic processes. The BITES project aims to reconstruct Neanderthal-carnivore interactions using targeted quantitative methodologies, which are tailored to detect patterns previously unattainable with traditional approaches. Employing both multivariate statistics and machine learning, I will initially accurately quantify hominin and carnivore relative activities across four Middle Paleolithic sites in Iberia set in different environments. Subsequently, reusing literature data, the project will build statistical and machine learning models to identify the main interaction type in previously studied sites, including scavenging, avoidance, and competition for prey. Lastly, the project will explore any environmental, spatial, or temporal shifts in these interactions and their potential correlation with settlement patterns and the eventual disappearance of Neanderthals. The findings derived from bridging cutting-edge methods with ancient data promise to reshape our perspective of Neanderthal ecological behaviour and adaptive strategies, introducing fresh, integrative approaches in Paleolithic research.