Integrating point process models, evolutionary ecology and traditional knowledge improves landscape archaeology—A case from southwest Madagascar

Landscape archaeology has a long history of using predictive models to improve our knowledge of extant archaeological features around the world. Important advancements in spatial statistics, however, have been slow to enter archaeological predictive modeling. Point process models (PPMs), in particular, offer a powerful solution to explicitly model both first-and second-order properties of a point pattern. Here, we use PPMs to refine a recently developed remote sensing-based predictive algorithm applied to the archaeological record of Madagascar’s southwestern coast. This initial remote sensing model resulted in an 80% true positive rate, rapidly expanding our understanding of the archaeological record of this region. Despite the model’s success rate, it yielded a substantial number (~20%) of false positive results. In this paper, we develop a series of PPMs to improve the accuracy of this model in predicting the location of archaeological deposits in southwest Madagascar. We illustrate how PPMs, traditional ecological knowledge, remote sensing, and fieldwork can be used iteratively to improve the accuracy of predictive models and enhance interpretations of the archaeological record. We use an explicit behavioral ecology theoretical framework to formulate and test hypotheses utilizing spatial modeling methods. Our modeling process can be replicated by archaeologists around the world to assist in fieldwork logistics and planning.