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National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and south-central United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland--urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.

In the United States, housing density has substantially increased in and adjacent to forests. Our goal in this study was to identify how housing density and human populations are associated with avian diversity. We compared these associations to those between landscape pattern and avian diversity, and we examined how these associations vary across the conterminous forested United States. Using data from the North American Breeding Bird Survey, the U.S. Census, and the National Land Cover Database, we focused on forest and woodland bird communities and conducted our analysis at multiple levels of model specificity, first using a coarse-thematic resolution (basic models), then using a larger number of fine-thematic resolution variables (refined models). We found that housing development was associated with forest bird species richness in all forested ecoregions of the conterminous United States. However, there were important differences among ecoregions. In the basic models, housing density accounted for < 5% of variance in avian species richness. In refined models, 85% of models included housing density and/or residential land cover as significant variables. The strongest guild response was demonstrated in the Adirondack-New England ecoregion, where 29% of variation in richness of the permanent resident guild was associated with housing density. Model improvements due to regional stratification were most pronounced for cavity nesters and short-distance migrants, suggesting that these guilds may be especially sensitive to regional processes. The varying patterns of association between avian richness and attributes associated with landscape structure suggested that landscape context was an important mediating factor affecting how biodiversity responds to landscape changes. Our analysis suggested that simple, broadly applicable, land use recommendations cannot be derived from our results. Rather, anticipating future avian response to land use intensification (or reversion to native vegetation) has to be conditioned on the current landscape context and the species group of interest. Our results show that housing density and residential land cover were significant predictors of forest bird species richness, and their prediction strengths are likely to increase as development continues.

Rural America is witnessing widespread housing development, which is to the detriment of the environment. It has been suggested to cluster houses so that their disturbance zones overlap and thus cause less habitat loss than is the case for dispersed development. Clustering houses makes intuitive sense, but few empirical studies have quantified the spatial pattern of houses in real landscapes, assessed changes in their patterns over time, and quantified the resulting habitat loss. We addressed three basic questions: (1) What are the spatial patterns of houses and how do they change over time; (2) How much habitat is lost due to houses, and how is this affected by spatial pattern of houses; and (3) What type of habitat is most affected by housing development. We mapped 27 419 houses from aerial photos for five time periods in 17 townships in northern Wisconsin and calculated the terrestrial land area remaining after buffering each house using 100- and 500-m disturbance zones. The number of houses increased by 353% between 1937 and 1999. Ripley's K test showed that houses were significantly clustered at all time periods and at all scales. Due to the clustering, the rate at which habitat was lost (176% and 55% for 100- and 500-m buffers, respectively) was substantially lower than housing growth rates, and most land area was undisturbed (95% and 61% for 100-m and 500-m buffers, respectively). Houses were strongly clustered within 100 m of lakes. Habitat loss was lowest in wetlands but reached up to 60% in deciduous forests. Our results are encouraging in that clustered development is common in northern Wisconsin, and habitat loss is thus limited. However, the concentration of development along lakeshores causes concern, because these may be critical habitats for many species. Conservation goals can only be met if policies promote clustered development and simultaneously steer development away from sensitive ecosystems.

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.

Roads remove habitat, alter adjacent areas, and interrupt and redirect ecological flows. They subdivide wildlife populations, foster invasive species spread, change the hydrologic network, and increase human use of adjacent areas. At broad scales, these impacts cumulate and define landscape patterns. The goal of this study was to improve our understanding of the dynamics of road networks over time, and their effects on landscape patterns, and identify significant relationships between road changes and other land-use changes. We mapped roads from aerial photographs from five dates between 1937 and 1999 in 17 townships in predominantly forested landscapes in northern Wisconsin, U.S.A. Patch-level landscape metrics were calculated on terrestrial area outside of a 15-m road-effect zone. We used generalized least-squares regression models to relate changes in road density and landscape pattern to concurrent changes in housing density. Rates of change and relationships were compared among three ecological regions. Our results showed substantial increases in both road density and landscape fragmentation during the study period. Road density more than doubled, and median, mean, and largest patch size were reduced by a factor of four, while patch shape became more regular. Increases in road density varied significantly among ecological subsections and were positively related to increases in housing density. Fragmentation was largely driven by increases in road density, but housing density had a significantly positive relationship with largest patch area and patch shape. Without protection of roadless areas, our results suggest road development is likely to continue in the future, even in areas where road construction is constrained by the physical environment. Recognizing the dynamic nature of road networks is important for understanding and predicting their ecological impacts over time and understanding where other types of development are likely to occur in the future. Historical perspectives of development can provide guidance in prioritizing management efforts to defragment landscapes and mitigate the ecological impacts of past road development.