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Many communities in southwestern Madagascar rely on a mix of foraging, fishing, farming, and herding, with cattle central to local cultures, rituals, and intergenerational wealth transfer. Today these livelihoods are critically threatened by the intensifying effects of climate change and biodiversity loss. Improved understanding of ancient community-environment dynamics can help identify pathways to livelihood sustainability. Multidisciplinary approaches have great potential to improve our understanding of human-environment interactions across spatio-temporal scales. We combine archaeological survey data, oral history interviews, and high-resolution multispectral PlanetScope imagery to explore 400 years of human-environment interaction in the Namonte Basin. Our analysis reveals that settlement and land-use led to significant changes in the region's ecology, both during periods of occupation and after settlement abandonment. Human activity over this period may have stabilized vegetative systems, whereby seasonal changes in vegetative health were reduced compared to surrounding locations. These ecological legacies may have buffered communities against unpredictable climate challenges. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite decades of archaeological research, roughly 75% of Madagascar's land area remains archaeologically unexplored and the oldest sites on the island are difficult to locate, as they contain the ephemeral remains of mobile hunter/forager campsites. The known archaeological record is therefore biased toward later sites, especially sites dating to the second millennium AD, following the expansion of Indian Ocean trading networks. Systematic archaeological investigations are required to address these biases in the known archaeological record and clarify the island's early human history, but funding limitations, logistical and time constraints in surveying large areas and a relatively small number of active field archaeologists present substantial barriers to expansive areal survey coverage. Using theoretical models derived from human behavioral ecology (i.e., ideal free distribution, optimal foraging theory) in conjunction with freely available remote sensing data, we illustrate how archaeological survey of Madagascar's landscapes can be rapidly expanded, more effectively target early archaeological deposits, and address questions about the island's settlement. This study illustrates the potential for theoretically-driven satellite-based remote sensing analysis to improve our understanding of the archaeological record of the world's fourth largest island. • Ideal distribution models theoretically frame remote sensing analysis. • Environmental predictive model identified cultural materials in SW Madagascar. • Settlement distribution in Late Holocene appears to follow IFD predictions. • Method uses freely available data and provides cost effective prospection method. • Future surveys of identified areas may evidence earliest settlements on Madagascar. [ABSTRACT FROM AUTHOR]