To understand agricultural intensification as a form of niche construction, researchers need to be able to distinguish passive agricultural niches, where agronomic parameters were met by environmental conditions, from spaces where societies had to exert additional effort to augment the landscape to enable crop growth. The authors present an agricultural potential model that identifies passive agricultural niches and niches that required additional constructed effort and assesses the differential success of these areas for different crops by generating yield estimates. These estimates are then situated in cultural context of population, redistribution requirements, etc. to assess the sustainability of the system. The model is demonstrated through a case study in the Northern Rio Grande region of New Mexico, focusing on maize, cotton, and wheat, important crops for Ancestral Pueblo people. We use the PaleoCAR framework to assess locations where growing requirements for maize, cotton, and wheat were met, and what estimated yields (productivity) could have been in each agricultural catchment relative to the associated village population of four case study villages. The model suggests that the maize niche was relatively stable in the region while wheat production was more marginal, and cotton had a very limited niche that required additional agricultural technologies called gravel mulch fields. These results are supported by archaeobotanical data and provide an additional line of evidence to support a slower adoption of wheat relative to maize versus a quicker adoption of sheep (wool) relative to continued cotton cultivation by Ancestral Pueblo communities in the Northern Rio Grande. The model also identifies which villages may have been better positioned to meet Spanish taxation requirements.

Using a new approach to estimating its population assuming an Ideal Free Distribution, we suggest that even the highest estimates for the population served by the Chaco Regional System at its height have been somewhat too low. This complements recent findings that the US Southwest was greener and more productive for the millennium prior to about AD 1200 than afterwards, and that crude birth rates in this region, particularly in the San Juan Basin and northern San Juan regions, were high on both regional and worldwide scales from the last half of the first millennium into the early second millennium AD. We raise the possibility that the striking expansion of the Chacoan Regional System ca. A.D. 1040 should be understood through the lens of colonial systems, and demonstrate that developments in collective computation such as Chaco's roads and shrines/signaling stations are to be expected in polities of Chaco's scale. Utilizando un nuevo enfoque para estimar su poblaci & oacute;n, basado en la suposici & oacute;n de una Distribuci & oacute;n Libre Ideal, sugerimos que incluso las estimaciones m & aacute;s altas de la poblaci & oacute;n atendida por el Sistema Regional de Chaco en su apogeo han sido algo bajas. Esto complementa hallazgos recientes que indican que el suroeste de los Estados Unidos fue m & aacute;s verde y productivo durante el milenio anterior a aproximadamente el a & ntilde;o 1200 d.C. que despu & eacute;s, y que las tasas brutas de natalidad en esta regi & oacute;n -particularmente en la Cuenca del San Juan y las regiones del norte del San Juan- fueron altas tanto a nivel regional como mundial desde la segunda mitad del primer milenio hasta comienzos del segundo milenio d.C. Planteamos la posibilidad de que la notable expansi & oacute;n del Sistema Regional Chacoano hacia el a & ntilde;o 1040 d.C. deba entenderse a trav & eacute;s del lente de los sistemas coloniales, y demostramos que desarrollos en computaci & oacute;n colectiva como los caminos y los santuarios/estaciones de se & ntilde;alizaci & oacute;n de Chaco son de esperarse en entidades pol & iacute;ticas de la escala de Chaco.

Climate adaptation requires actionable scientific information about potential climate impacts. Spatial climate analogs answer the question, ‘where does the future climate of a focal location occur today?’ Analogs provide a means to develop measures of climate change exposure and can be applied to project climate change impacts. Although analogs are the basis for empirical models, recent applications of analogs have been structured as spatial models, which can contribute distinct information compared to more commonly used nonspatial approaches. Analogs may improve our ability to communicate climate change impacts for science and nonscience audiences. We review approaches for identifying analogs, summarize their applications, highlight understudied features, and examine evidence of their utility for science communication. We conclude by identifying research needs: the establishment of best practices for analog identification, the adoption of validation methods for analog impact models, and the evaluation of the utility of analogs for communication.

Drought is a period of abnormally dry weather that leads to hydrological imbalance. Drought assessments determine the characteristics, severity, and impacts of a drought. Climate change adds conceptual and quantitative challenges to traditional drought assessments. This paper highlights the challenges of assessing drought in a climate made non‐stationary by human activities or natural variability. To address these challenges, we then identify 10 key research priorities for advancing drought science and improving assessments in a changing climate. The priorities focus on improving drought indicators to account for non‐stationarity, evaluating drought impacts and their trends, addressing regional differences in non‐stationarity, determining the physical drivers of drought and how they are changing, capturing precipitation variability, and understanding the drivers of aridification. Ultimately, improved drought assessments will inform better risk management, adaptation strategies, and planning, especially in areas where climate change significantly alters drought dynamics. This perspective offers a path toward more accurate and effective drought management in a non‐stationary climate system.

Accurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in-depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long-term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were similar to 30 days for depth averaged conditions; progressively longer timescales (similar to 10-80 days) represent progressively deeper soil moisture (2-36 in.). However, soil moisture models (including Short-term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought. Optimal drought index timescales for soil moisture are relatively short (less than 100 days) and increase with increasing soil depth The Short-term Prediction Research and Transition Center, Topofire and Soil Moisture Active Passive L4 models are more accurate than timescale optimized drought indices for soil moisture anomaly prediction Drought monitoring should favor the use of soil moisture models over meteorological drought indices for characterizing soil moisture drought

Temperature variability likely played an important role in determining the spread and productive potential of North America’s key prehispanic agricultural staple, maize. The United States Southwest (SWUS) also served as the gateway for maize to reach portions of North America to the north and east. Existing temperature reconstructions for the SWUS are typically low in spatial or temporal resolution, shallow in time depth, or subject to unknown degrees of insensitivity to low-frequency variability, hindering accurate determination of temperature’s role in agricultural productivity and variability in distribution and success of prehispanic farmers. Here, we develop a model-based modern analog technique (MAT) approach applied to 29 SWUS fossil pollen sites to reconstruct July temperatures from 3000 BC to AD 2000. Temperatures were generally warmer than or similar to those of the modern (1961–1990) period until the first century AD. Our reconstruction also notes rapid warming beginning in the AD 1800s; modern conditions are unprecedented in at least the last five millennia in the SWUS. Temperature minima were reached around 1800 BC, 1000 BC, AD 400 (the global minimum in this series), the mid-to-late AD 900s, and the AD 1500s. Summer temperatures were generally depressed relative to northern hemisphere norms by a dominance of El Niño-like conditions during much of the second millenium BC and the first millenium AD, but somewhat elevated relative to those same norms in other periods, including from about AD 1300 to the present, due to the dominance of La Niña-like conditions.

Climate change is expected to increase the frequency and intensity of drought in many parts of the world, including Montana. In the face of worsening drought conditions, agricultural producers need to adapt their operations to mitigate risk. This study examined the role of local knowledge and climate information in drought-related decisions through five focus groups with Montana farmers and ranchers. We found that trust and risk perceptions mediated how producers utilized both local knowledge and climate information. More specifically, producers relied on local knowledge in drought-related decisions, regarding their own observation and past experience as trustworthy and not particularly risky. In contrast, climate information and seasonal climate forecasts in particular were regarded as risky and untrustworthy, largely due to a perceived lack of accuracy. Since producers tended to be risk averse, especially given market and climate uncertainties, they rarely relied on “risky” climate information. At the same time, producers actively managed risk and tested out new technologies and practices through processes of trial and error, what they called “experimenting,” which enabled them to build firsthand knowledge of potential adaptations. In the context of uncertainty and risk aversion, programs that reduce the financial risk of experimenting with new technologies and adaptive practices are needed to enable producers to develop direct experience with innovations designed to mitigate drought risk. Further, scientists developing climate information need to work directly with farmers and ranchers to better integrate local knowledge into climate information.

Persistence research frequently asks why some settlements endure for millennia, but long-lived settlements first require communities to be persistent places for decades and then centuries. n this paper, we present an approach to quantifying persistence on shorter timescales to understand the foundational drivers of long-term persistence. We ask: in a region where most settlements are occupied only a few decades on average, what factors encourage settlements to eventually persist for hundreds of years? We focus on the Central Mesa Verde region, where we have annual-resolution models of maize productivity, settlement occupation, and community access to civic-ceremonial structures. We constructed local subsistence catchments that vary according to annual population size and soil hand-planting suitability, and calculated annual catchment quality according to maize productivity to quantify local environmental pull for persistence. We modeled periods of persistence against catchment quality, average population, proximity of civic-ceremonial architecture, and locations of community centers–or more broadly, against demographic, geographic, and institutional drivers of persistence. We conducted a generalized linear mixed effects model (GLMM) to determine that demographic variables are key for generating persistence independent of time, but whether a settlement is a community center has a decreases persistence prior to AD 960 but increases persistence afterwards. Geographic drivers and institutional drivers have no consistent significant effect on persistence. These results aid our understanding of settlement persistence and decision-making within the upland Southwest and may have relevance for other archaeologically significant regions.

Persistent differences in wealth and power among prehispanic Pueblo societies are visible from the late AD 800s through the late 1200s, after which large portions of the northern US Southwest were depopulated. In this paper we measure these differences in wealth using Gini coefficients based on house size, and show that high Ginis (large wealth differences) are positively related to persistence in settlements and inversely related to an annual measure of the size of the unoccupied dry-farming niche. We argue that wealth inequality in this record is due first to processes inherent in village life which have internally different distributions of the most productive maize fields, exacerbated by the dynamics of systems of balanced reciprocity; and second to decreasing ability to escape village life owing to shrinking availability of unoccupied places within the maize dry-farming niche as villages get enmeshed in regional systems of tribute or taxation. We embed this analytical reconstruction in the model of an 'Abrupt imposition of Malthusian equilibrium in a natural-fertility, agrarian society' proposed by Puleston et al. (Puleston C, Tuljapurkar S, Winterhalder B. 2014 PLoS ONE 9, e87541 (doi:10.1371/journal.pone.0087541)), but show that the transition to Malthusian dynamics in this area is not abrupt but extends over centuries This article is part of the theme issue 'Evolutionary ecology of inequality'.

Archaeologists increasingly use large radiocarbon databases to model prehistoric human demography (also termed paleo-demography). Numerous independent projects, funded over the past decade, have assembled such databases from multiple regions of the world. These data provide unprecedented potential for comparative research on human population ecology and the evolution of social-ecological systems across the Earth. However, these databases have been developed using different sample selection criteria, which has resulted in interoperability issues for global-scale, comparative paleo-demographic research and integration with paleoclimate and paleoenvironmental data. We present a synthetic, global-scale archaeological radiocarbon database composed of 180,070 radiocarbon dates that have been cleaned according to a standardized sample selection criteria. This database increases the reusability of archaeological radiocarbon data and streamlines quality control assessments for various types of paleo-demographic research. As part of an assessment of data quality, we conduct two analyses of sampling bias in the global database at multiple scales. This database is ideal for paleo-demographic research focused on dates-as-data, bayesian modeling, or summed probability distribution methodologies.

Despite the acceleration of climate change, erroneous assumptions of climate stationarity are still inculcated in the management of water resources in the United States (US). The US system for drought detection, which triggers billions of dollars in emergency resources, adheres to this assumption with preference towards 60-year (or longer) record lengths for drought characterization. Using observed data from 1,934 Global Historical Climate Network (GHCN) sites across the US, we show that conclusions based on long climate records can substantially bias assessment of drought severity. Bias emerges by assuming that conditions from the early and mid 20th century are as likely to occur in today's climate. Numerical simulations reveal that drought assessment error is relatively low with limited climatology lengths (~30 year) and that error increases with longer record lengths where climate is changing rapidly. We assert that non-stationarity in climate must be accounted for in contemporary assessments to more accurately portray present drought risk.

Archaeologists and demographers increasingly employ aggregations of published radiocarbon (14C) dates as demographic proxies summarizing changes in human activity in past societies. Presently, summed probability densities (SPDs) of calibrated radiocarbon dates are the dominant method of using 14C dates to reconstruct demographic trends. Unfortunately, SPDs are incapable of converging on the distribution that generated a set of radiocarbon measurements, even when the number of observations is large. To overcome this problem, we propose a more principled alternative that combines finite mixture models and end-to-end Bayesian inference. Numerical simulations and an assessment of the statistical identifiability of our method demonstrate that it correctly converges on the generating distribution for two important models, exponentials and finite Gaussian mixtures, at least if the same statistical model is used to fit the data as was used to generate the data. To further validate this approach, we apply it to a set of radiocarbon dates from the Maya city of Tikal. We show that an end-to-end approach reconstructs with high accuracy expert demographic reconstructions based on settlement patterns and ceramics, but with more precise time-resolution and characterization of uncertainty than has heretofore been possible. Future work should consider alternatives to finite Gaussian mixtures for fitting the generating distribution.

Climate extremes are thought to have triggered large-scale transformations of various ancient societies, but they rarely seem to be the sole cause. It has been hypothesized that slow internal developments often made societies less resilient over time, setting them up for collapse. Here, we provide quantitative evidence for this idea. We use annual-resolution time series of building activity to demonstrate that repeated dramatic transformations of Pueblo cultures in the pre-Hispanic US Southwest were preceded by signals of critical slowing down, a dynamic hallmark of fragility. Declining stability of the status quo is consistent with archaeological evidence for increasing violence and in some cases, increasing wealth inequality toward the end of these periods. Our work thus supports the view that the cumulative impact of gradual processes may make societies more vulnerable through time, elevating the likelihood that a perturbation will trigger a large-scale transformation that includes radically rejecting the status quo and seeking alternative pathways.

The dispersal of rice (Oryza sativa) following domestication influenced massive social and cultural changes across South, East, and Southeast (SE) Asia. The history of dispersal across islands of SE Asia, and the role of Taiwan and the Austronesian expansion in this process remain largely unresolved. Here, we reconstructed the routes of dispersal of 0. sativa ssp.japonica rice to Taiwan and the northern Philippines using whole-genome resequencing of indigenous rice landraces coupled with archaeological and paleoclimate data. Our results indicate that japonica rice found in the northern Philippines diverged from Indonesian landraces as early as 3,500 years before present (BP). In contrast, rice cultivated by the indigenous peoples of the Taiwanese mountains has complex origins. It comprises two distinct populations, each best explained as a result of admixture between temperate japonica that presumably came from northeast Asia, and tropical japonica from the northern Philippines and mainland SE Asia, respectively. We find that the temperate japonica component of these indigenous Taiwan populations diverged from northeast Asia subpopulations at about 2,600 BP, whereas gene flow from the northern Philippines had begun before similar to 1,300 BP. This coincides with a period of intensified trade established across the South China Sea. Finally, we find evidence for positive selection acting on distinct genomic regions in different rice subpopulations, indicating local adaptation associated with the spread of japonica rice.

The northern American Southwest provides one of the most well-documented cases of human population growth and decline in the world. The geographic extent of this decline in North America is unknown owing to the lack of high-resolution palaeodemographic data from regions across and beyond the greater Southwest, where archaeological radiocarbon data are often the only available proxy for investigating these palaeodemographic processes. Radiocarbon time series across and beyond the greater Southwest suggest widespread population collapses from AD 1300 to 1600. However, radiocarbon data have potential biases caused by variable radiocarbon sample preservation, sample collection and the nonlinearity of the radiocarbon calibration curve. In order to be confident in the wider trends seen in radiocarbon time series across and beyond the greater Southwest, here we focus on regions that have multiple palaeodemographic proxies and compare those proxies to radiocarbon time series. We develop a new method for time series analysis and comparison between dendrochronological data and radiocarbon data. Results confirm a multiple proxy decline in human populations across the Upland US Southwest, Central Mesa Verde and Northern Rio Grande from AD 1300 to 1600. These results lend confidence to single proxy radiocarbon-based reconstructions of palaeodemography outside the Southwest that suggest post-AD 1300 population declines in many parts of North America. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.