Archaeological Applications of Optimal Foraging Theory: Employing Bayesian probability modeling to estimate profitability parameters for rare and extinct prey
This is an abstract from the "Novel Statistical Techniques in Archaeology II (QUANTARCH II)" session, at the 84th annual meeting of the Society for American Archaeology.
Reconstructing the subsistence strategies of past hominin populations remains one of the most important endeavors of archaeological studies. However, the presence and relative frequency of species alone, recovered as faunal material in archaeological contexts, is insufficient to reconstruct the complex foraging decisions made by hominins. Optimal foraging theory (OFT) refers to a family of formal foraging models that are designed to predict the foraging decisions of a particular organism given a suite of parameters. A key parameter is a robust estimate of profitability associated with pursued prey. However, in the case of rare and extinct prey species, measured profitability estimates do not exist. Here we describe a novel solution to this dilemma using Bayesian inference applied to modern observations of hunting behavior. We use a Bayesian probability model, populated with ecological and behavioral data collected from modern prey species collected from field research with Hadza hunter-gatherers in Tanzania, to build a predictive model of prey profitability, thus producing robust estimates of prey profitability to be used in archeological contexts.
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Archaeological Applications of Optimal Foraging Theory: Employing Bayesian probability modeling to estimate profitability parameters for rare and extinct prey. Jacob Harris, Andrew Bishop, Christopher Brooke, Kim Hill, Curtis Marean. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 452317)
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min long: -18.809; min lat: -38.823 ; max long: 53.262; max lat: 38.823 ;
Abstract Id(s): 24911