Mesoamerican urbanism revisited: Environmental change, adaptation, resilience, persistence, and collapse.

Urban adaptation to climate change is a global challenge requiring a broad response that can be informed by how urban societies in the past responded to environmental shocks. Yet, interdisciplinary efforts to leverage insights from the urban past have been stymied by disciplinary silos and entrenched misconceptions regarding the nature and diversity of premodern human settlements and institutions, especially in the case of prehispanic Mesoamerica. Long recognized as a distinct cultural region, prehispanic Mesoamerica was the setting for one of the world's original urbanization episodes despite the impediments to communication and resource extraction due to the lack of beasts of burden and wheeled transport, and the limited and relatively late use of metal implements. Our knowledge of prehispanic urbanism in Mesoamerica has been significantly enhanced over the past two decades due to significant advances in excavating, analyzing, and contextualizing archaeological materials. We now understand that Mesoamerican urbanism was as much a story about resilience and adaptation to environmental change as it was about collapse. Here we call for a dialogue among Mesoamerican urban archaeologists, sustainability scientists, and researchers interested in urban adaptation to climate change through a synthetic perspective on the organizational diversity of urbanism. Such a dialogue, seeking insights into what facilitates and hinders urban adaptation to environmental change, can be animated by shifting the long-held emphasis on failure and collapse to a more empirically grounded account of resilience and the factors that fostered adaptation and sustainability.

Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology.

The application of light detection and ranging (LiDAR), a laser-based remote-sensing technology that is capable of penetrating overlying vegetation and forest canopies, is generating a fundamental shift in Mesoamerican archaeology and has the potential to transform research in forested areas world-wide. Much as radiocarbon dating that half a century ago moved archaeology forward by grounding archaeological remains in time, LiDAR is proving to be a catalyst for an improved spatial understanding of the past. With LiDAR, ancient societies can be contextualized within a fully defined landscape. Interpretations about the scale and organization of densely forested sites no longer are constrained by sample size, as they were when mapping required laborious on-ground survey. The ability to articulate ancient landscapes fully permits a better understanding of the complexity of ancient Mesoamerican urbanism and also aids in modern conservation efforts. The importance of this geospatial innovation is demonstrated with newly acquired LiDAR data from the archaeological sites of Caracol, Cayo, Belize and Angamuco, Michoacán, Mexico. These data illustrate the potential of technology to act as a catalytic enabler of rapid transformational change in archaeological research and interpretation and also underscore the value of on-the-ground archaeological investigation in validating and contextualizing results.

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale.

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method-combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups-to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.