Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where trueabsence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.

Significance Warfare and homicide are pervasive features of the human experience, yet scholars struggle to understand the conditions that promote violence. Climate and conflict research has revealed many linkages between climate change and human violence; however, studies often produce contrary findings, and the driving mechanisms remain difficult to identify. We suggest a solution is to identify conditions producing resource scarcity, which are necessarily a combination of climate and population dynamics. We examine patterns of lethal violence in the Prehispanic Andes and find that favorable climate conditions fostered rapid population growth within a circumscribed landscape, resulting in chronic warfare. Our work suggests that an increasingly unstable climate may promote future violence, where favorable climate regimes incentivize population growth and attendant resource strain.

Explaining how and why populations settle a new landscape is central to many questions in American archaeology. Recent advances in settlement research have adopted predictions from the Ideal Free Distribution model (IFD). Explicar cómo y por qué las poblaciones se instalan en un nuevo lugar es fundamental para muchas preguntas en la arqueología americana. Los avances recientes en la investigación de asentamientos han adoptado las predicciones del modelo de Distribución Libre Ideal (DLI). Mientras que las pruebas de las predicciones de DLI hasta la fecha se basan, ya sea en información diacrónica impresisa, que nos llega a través de los arqueólogos, o de información sincrónica precisa de etnografía. Aquí proporcionamos la primera prueba utilizando datos históricos derivados que es a la vez precisa y diacrónica. Los datos diacrónicos precisos nos permiten, evaluar las predicciones a largo plazo del modelo, en una escala temporal en línea con las decisiones de los asentamientos humanos, y también validar los proxies

Changing climates in the past affected both human and faunal population distributions, thereby structuring human diets, demography, and cultural evolution. Yet, separating the effects of climate-driven and human-induced changes in prey species abundances remains challenging, particularly during the Late Upper Paleolithic, a period marked by rapid climate change and marked ecosystem transformation. To disentangle the effects of climate and hunter-gatherer populations on animal prey species during the period, we synthesize disparate paleoclimate records, zooarchaeological data, and archaeological data using ecological methods and theory to test to what extent climate and anthropogenic impacts drove broad changes in human subsistence observed in the Late Upper Paleolithic zooarchaeological records. We find that the observed changes in faunal assemblages during the European Late Upper Paleolithic are consistent with climate-driven animal habitat shifts impacting the natural abundances of high-ranked prey species on the landscape rather than human-induced resource depression. The study has important implications for understanding how past climate change impacted and structured the diet and demography of human populations and can serve as a baseline for considerations of resilience and adaptation in the present.