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Wildfires statistics for the conterminous United States (U.S.) are examined in a spatially and temporally explicit manner. We use a high-resolution data set consisting of 88,916 U.S. Department of Agriculture Forest Service wildfires over the time period 1970-2000 and consider wildfire occurrence as a function of ecoregion (land units classified by climate, vegetation, and topography), ignition source (anthropogenic vs. lightning), and decade. For the conterminous U.S., we (i) find that wildfires exhibit robust frequency-area power-law behavior in 18 different ecoregions; (ii) use normalized power-law exponents to compare the scaling of wildfire-burned areas between ecoregions, finding a systematic change from east to west; (iii) find that wildfires in the eastern third of the U.S. have higher power-law exponents for anthropogenic vs. lightning ignition sources; and (iv) calculate recurrence intervals for wildfires of a given burned area or larger for each ecoregion, allowing for the classification of wildfire regimes for probabilistic hazard estimation in the same vein as is now used for earthquakes. frequency-area statistics | power-law distribution | Bailey ecoregion divisions | U.S. Department of Agriculture Forest Service | probabilistic hazard

Invasive species have become a major stressor in many ecosystems. Therefore, public-policy decision-makers desire ecologically informed risk assessments that characterise the likelihood and severity of potential adverse effects from invasive species. These risk assessments often take the form of maps, but risk maps have generally ignored uncertainty and avoided incorporating processes such as the risk of spread. One method that has been proposed to map risk of spread is a least-cost modelling based catchment area approach. In summary, using a raster cost-surface that represents the difficulty associated with traversing different parts of a landscape, least-cost catchments are calculated for all cells in a landscape, and the catchments’ areas are then visualised as a map of intra-landscape isolation. However, there are challenges with parameterising least-cost modelling in an ecological context which means it is particularly important that any estimates of isolation are coupled with associated estimates of uncertainty. Using an example of the common brushtail possum (Trichosurus vulpecula) on the Auckland isthmus (New Zealand), we provide a demonstration of how the least-cost modelling based catchment area approach to mapping isolation can be applied to estimate isolation with uncertainty. We use a Python geoprocessing and geovisualisation framework to quantify isolation for four least-cost modelling based possum dispersal scenarios in order to produce a bivariate colour-by-alpha map that simultaneously visualises predicted isolation with associated uncertainty. The bivariate colour-by-alpha map clearly reinforces the need to consider uncertainty when producing catchment area isolation maps. While the overall pattern in isolation was a relatively simple function of the different isolation maps, the associated uncertainty had a complex spatial structure that would be difficult to understand without representing it explicitly. Providing policy decision-makers with bivariate maps that simultaneously include isolation and uncertainty will help support more robust invasive species risk assessments. [ABSTRACT FROM PUBLISHER]