Human settlement of the Caribbean represents the only example in the Americas of peoples colonizing islands that were not visible from surrounding mainland areas or other islands. Unfortunately, many interpretive models have relied on radiocarbon determinations that do not meet standard criteria for reporting because they lack critical information or sufficient provenience, often leading to specious interpretations. We have collated 2484 radiocarbon determinations, assigned them to classes based on chronometric hygiene criteria, and constructed Bayesian colonization models of the acceptable determinations to examine patterns of initial settlement. Colonization estimates for 26 islands indicate that (i) the region was settled in two major population dispersals that likely originated from South America; (ii) colonists reached islands in the northern Antilles before the southern islands; and (iii) the results support the southward route hypothesis and refute the “stepping-stone model.”

Explaining the processes underlying the emergence of monument construction is a major theme in contemporary anthropological archaeology, and recent studies have employed spatially-explicit modeling to explain these patterns. Rapa Nui (Easter Island, Chile) is famous for its elaborate ritual architecture, particularly numerous monumental platforms (ahu) and statuary (moai). To date, however, we lack explicit modeling to explain spatial and temporal aspects of monument construction. Here, we use spatially-explicit point-process modeling to explore the potential relations between ahu construction locations and subsistence resources, namely, rock mulch agricultural gardens, marine resources, and freshwater sources—the three most critical resources on Rapa Nui. Through these analyses, we demonstrate the central importance of coastal freshwater seeps for precontact populations. Our results suggest that ahu locations are most parsimoniously explained by distance from freshwater sources, in particular coastal seeps, with important implications for community formation and inter-community competition in precontact times.

Rapa Nui (Easter Island, Chile) presents a quintessential case where the tempo of investment in monumentality is central to debates regarding societal collapse, with the common narrative positing that statue platform (ahu) construction ceased sometime around AD 1600 following an ecological, cultural, and demographic catastrophe. This narrative remains especially popular in fields outside archaeology that treat collapse as historical fact and use Rapa Nui as a model for collapse more generally. Resolving the tempo of "collapse" events, however, is often fraught with ambiguity given a lack of formal modeling, uncritical use of radiocarbon estimates, and inattention to information embedded in stratigraphic features. Here, we use a Bayesian model-based approach to examine the tempo of events associated with arguments about collapse on Rapa Nui. We integrate radiocarbon dates, relative architectural stratigraphy, and ethnohistoric accounts to quantify the onset, rate, and end of monument construction as a means of testing the collapse hypothesis. We demonstrate that ahu construction began soon after colonization and increased rapidly, sometime between the early-14th and mid-15th centuries AD, with a steady rate of construction events that continued beyond European contact in 1722. Our results demonstrate a lack of evidence for a pre-contact 'collapse' and instead offer strong support for a new emerging model of resilient communities that continued their long-term traditions despite the impacts of European arrival. Methodologically, our model-based approach to testing hypotheses regarding the chronology of collapse can be extended to other case studies around the world where similar debates remain difficult to resolve.

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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.

Sources of drinking water on islands often present critical constraints to human habitation. On Rapa Nui (Easter Island, Chile), there is remarkably little surface fresh water due to the nature of the island's volcanic geology. While several lakes exist in volcanic craters, most rainwater quickly passes into the subsurface and emerges at coastal springs. Nevertheless, the island sustained a relatively large human population for hundreds of years, one that built an impressive array of monumental platforms (ahu) and statues (moai). To understand how Rapanui acquired their scarce fresh water, we review ethnohistoric data from first European arrival (1722) through the mid-twentieth century. Ethnohistoric accounts identify a diversity of freshwater sources and describe various Rapanui freshwater management strategies. Our findings highlight the importance of coastal freshwater seeps and provide much-needed insight into how Rapanui procured this vital and necessary resource.

Examining how past human populations responded to environmental and climatic changes is a central focus of the historical sciences. The use of summed probability distributions (SPD) of radiocarbon dates as a proxy for estimating relative population sizes provides a widely applicable method in this research area. Paleodemographic reconstructions and modeling with SPDs, however, are stymied by a lack of accepted methods for model fitting, tools for assessing the demographic impact of environmental or climatic variables, and a means for formal multi-model comparison. These deficiencies severely limit our ability to reliably resolve crucial questions of past human-environment interactions. We propose a solution using Approximate Bayesian Computation (ABC) to fit complex demographic models to observed SPDs. Using a case study from Rapa Nui (Easter Island), a location that has long been the focus of debate regarding the impact of environmental and climatic changes on its human population, we find that past populations were resilient to environmental and climatic challenges. Our findings support a growing body of evidence showing stable and sustainable communities on the island. The ABC framework offers a novel approach for exploring regions and time periods where questions of climate-induced demographic and cultural change remain unresolved.

Archaeologists have struggled to combine remotely sensed datasets with preexisting information for landscape-level analyses. In the American Southeast, for example, analyses of lidar data using automated feature extraction algorithms have led to the identification of over 40 potential new pre-European-contact Native American shell ring deposits in Beaufort County, South Carolina. Such datasets are vital for understanding settlement distributions, yet a comprehensive assessment requires remotely sensed and previously surveyed archaeological data. Here, we use legacy data and airborne lidar-derived information to conduct a series of point pattern analyses using spatial models that we designed to assess the factors that best explain the location of shell rings. The results reveal that ring deposit locations are highly clustered and best explained through a combination of environmental conditions such as distance to water and elevation as well as social factors.