As the Cordilleran and Laurentide Ice Sheets retreated, North America was colonized by human populations; however, the spatial patterns of subsequent population growth are unclear. Temporal frequency distributions of aggregated radiocarbon (14C) dates are used as a proxy of population size and can be used to track this expansion. The Canadian Archaeological Radiocarbon Database contains more than 35,000 14C dates and is used in this study to map the spatiotemporal demographic changes of Holocene populations in North America at a continental scale for the past 13,000 y. We use the kernel method, which converts the spatial distribution of 14C dates into estimates of population density at 500-y intervals. The resulting maps reveal temporally distinct, dynamic patterns associated with paleodemographic trends that correspond well to genetic, archaeological, and ethnohistoric evidence of human occupation. These results have implications for hypothesizing and testing migration routes into and across North America as well as the relative influence of North American populations on the evolution of the North American ecosystem.

Between the initial colonization of North America and the European settlement period, Indigenous American land use practices shaped North American landscapes and ecosystems, but a critical question is the extent of these impacts on the land, and how these influenced the distributions of the flora and fauna. The present study addresses this question by estimating the spatial correlation between continental-scale records of fossil pollen and archaeological radiocarbon data, and provides a detailed analysis of the spatiotemporal relationship between palaeo-populations and ten important North American pollen taxa. Maps of Indigenous American population density, based on the Canadian Archaeological Radiocarbon Database, are compared to maps of plant abundance as estimated by pollen records from the Neotoma Paleoecology Database, using nonparametric kernel estimators and cross-correlation techniques. Periods of high spatial cross-correlation (either positive or negative) between population density and plant abundance were identified, but these associations were intermittent and did not increase towards the present. In many cases, high values of population density corresponded with high values of a particular taxon in one region, but simultaneously corresponded with low values in other regions, lessening the overall correlation between the two fields. This analysis suggests that human impacts were not significant enough to be identified at a continental scale, either due to low population numbers or land use, implying significant impacts of ancient human activities on the vegetation were regional rather than continental.

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A POINT PROCESS APPROACH FOR SPATIAL STOCHASTIC MODELING OF THUNDERSTORM CELLSIn this paper we consider two different approaches for spatial stochastic modeling of thunderstorms. Thunderstorm cells are represented using germ-grain models from stochastic geometry, which are based on Coxor doubly-stochastic cluster processes. We present methods for the operationa lfitting of model parameters based on available point probabilities and thunderstorm records of past periods. Furthermore, we derive formulas forthe computation of point and area probabilities according to the proposed germ-grain models. We also introduce a conditional simulation algorithm in order to increase the model’s ability to precisely predict thunderstorm events. A systematic comparison of area probabilities, which are estimated from the proposed models, and thunderstorm records conclude the paper.

Probabilistic precipitation forecasts from numerical models are often calibrated using synoptic observations. The resulting probabilities of precipitation refer to the observation system and thus provide the likelihood that precipitation occurs exactly at the spot of the rain gauge. When probabilistic forecasts are required for larger areas, such as rural districts or catchment areas of rivers, it is not possible to interpolate the point probabilities. Instead area probabilities e.g. increase with the size of the area. In this paper we describe a general method to derive area probabilities from point forecasts based on models and methods of stochastic geometry. The method can be applied over arbitrary areas and can be used for operational applications, since it runs fully automatically without human interaction. The basic idea is to model precipitation patterns by circular precipitation cells using a germ–grain model driven by a spatial Poisson point process in a way that the point forecasts are fitted. Area probabilities can then be estimated statistically as relative frequencies based on repeated Monte Carlo simulations. As the area probabilities significantly depend on the sizes of the modelled precipitation cells, suitable cell radii are estimated based on the spatial correlation structure of given point probabilities. Verification with independent radar precipitation and comparison with area probabilities derived from the raw ensemble system COSMO‐DE‐EPS of DWD is provided and reveals essential advantages of the stochastic model in terms of bias and Brier skill score.