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Essay on Zooarchaeology and Optimal Foraging Theory
Animal bones unearthed at earliest human encampments can hold astounding amounts of information about our prehistoric forebears. From these remnants of a long-gone meal, specialists called zooarchaeologists can decipher not only what was on the menu but also if the animals were domesticated or wild, how they were butchered and eaten, and if they served purposes other than a meal. Zooarchaeologists analyze animal remains from archaeological sites to learn about prehistoric culture and early interactions between people and animals. Stone tool assemblage variability is considered a reliable proxy measure of adaptive variability. Raw material richness, transport distances, and the character of transported technologies are thought to signal (1) variation in raw material selectivity based on material quality and abundance, (2) optimization of time and energy costs associated with procurement of stone from spatially dispersed sources, (3) planning depth that weaves raw material procurement forays into foraging activities, and (4) risk minimization that sees materials transported in quantities and forms that are energetically economical and least likely to fail. This paper dispenses with assumptions that raw material type and abundance play any role in the organization of mobility and raw material procurement strategies. Rather, a behaviorally neutral agent-based model is developed involving a forager engaged in a random walk within a uniform environment. Raw material procurement in the model is dependent only upon random encounters with stone sources and the amount of available space in the mobile toolkit. Simulated richness-sample size relationships, frequencies of raw material transfers as a function of distance from source, and both quantity-distance and reduction intensity-distance relationships are qualitatively similar to commonly observed archaeological patterns. In some archaeological cases it may be difficult to reject the neutral model. At best, failure to reject the neutral model may mean that intervening processes (e.g., depositional time-averaging) have erased high-frequency adaptive signals in the data................
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