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Although the vast majority of contemporary wildfires in the Upper Midwest of the United States have a human origin, there has been no comprehensive analysis of the roles played by abiotic, biotic, and human factors in determining the spatial patterns of their origins across the region. The Upper Midwest, a 2.8 × 105 km2 area in the northern, largely forested parts of the states of Minnesota, Wisconsin, and Michigan, contains regions of varied land cover, soil type, human settlement densities, and land management strategies that may influence differences in the observed spatial distribution of wildfires. Using a wide array of satellite‐ and ground‐based data for this region, we investigated the relationship between wildfire activity and environmental and social factors for >18 000 reported fires of all sizes between 1985 and 1995. We worked at two spatial scales to address the following questions: (1) Which abiotic, biotic, and human variables best explained decade‐scale regional fire activity during the study period? (2) Did the set of factors related to large fires differ from the set influencing all fires? (3) Did varying the spatial scale of analysis dramatically change the influence of predictive variables? (4) Did the set of factors influencing the number of fires in an area differ from the set of factors influencing the probability of the occurrence of even a single fire? These data suggest that there is no simple “Lake States fire regime” for the Upper Midwest. Instead, interpretation of modern fire patterns depends on both the fire size considered and the measurement of fire activity. Spatial distributions of wildfires using two size thresholds and viewed at two spatial scales are clearly related to a combination of abiotic, biotic, and human factors: no single factor or factor type dominates. However, the significant factors for each question were readily interpretable and consistent with other analyses of natural and human influences on fire patterns in the region. Factors seen as significant at one scale were frequently also significant at the other, indicating the robustness of the analysis across the two spatial resolutions. The methods for conducting this spatially explicit analysis of modern fire patterns (generalized linear regression at multiple scales using long‐term wildfire data and a suite of environmental and social variables) should be widely applicable to other areas. Results of this study can serve as the basis for daily, seasonal, or interannual studies as well as the foundation for simulation models of future wildfire distribution.

Integration of energy crops into agricultural landscapes could promote sustainability if they are placed in ways that foster multiple ecosystem services and mitigate ecosystem disservices from existing crops. We conducted a modeling study to investigate how replacing annual energy crops with perennial energy crops along Wisconsin waterways could affect a variety of provisioning and regulating ecosystem services. We found that a switch from continuous corn production to perennial-grass production decreased annual income provisioning by 75%, although it increased annual energy provisioning by 33%, decreased annual phosphorous loading to surface water by 29%, increased below-ground carbon sequestration by 30%, decreased annual nitrous oxide emissions by 84%, increased an index of pollinator abundance by an average of 11%, and increased an index of biocontrol potential by an average of 6%. We expressed the tradeoffs between income provisioning and other ecosystem services as benefit-cost ratios. Benefit-cost ratios averaged 12.06 GJ of additional net energy, 0.84 kg of avoided phosphorus pollution, 18.97 Mg of sequestered carbon, and 1.99 kg of avoided nitrous oxide emissions for every $1,000 reduction in income. These ratios varied spatially, from 2- to 70-fold depending on the ecosystem service. Benefit-cost ratios for different ecosystem services were generally correlated within watersheds, suggesting the presence of hotspots – watersheds where increases in multiple ecosystem services would come at lower-than-average opportunity costs. When assessing the monetary value of ecosystem services relative to existing conservation programs and environmental markets, the overall value of enhanced services associated with adoption of perennial energy crops was far lower than the opportunity cost. However, when we monitized services using estimates for the social costs of pollution, the value of enhanced services far exceeded the opportunity cost. This disparity between recoverable costs and social value represents a fundamental challenge to expansion of perennial energy crops and sustainable agricultural landscapes.

Spatial aggregations of raster data based on the majority rule have been typically used in landscape ecological studies. It is generally acknowledged that (1) dominant classes increase in abundance while minor classes decrease in abundance or even disappear through aggregation processes; and (2) spatial patterns also change with aggregations. In this paper, we examined an alternative, random rule-based aggregation and its eOE ects on cover type abundance and landscape patterns, in comparison with the majority rule-based aggregation. We aggregated a classi ed TM imagery (about 1.5 million ha) from 30 m (42313 3717 pixels) incrementally to 990 m resolution (132 pixels3 116 pixels). Cover type proportion, mean patch size ratio, aggregation index (AI ), and fractal dimension (FD) were used to assess the eOE ects of aggregation. To compare landscapes under diOE erent resolutions, we assumed that the landscapes were least distorted if (1) the cover type proportions and mean patch size ratios among classes were maintained, and (2) all cover types responded in the same way for a given index as aggregation levels increased. For example, distortion is introduced by aggregation if some cover types increase their AI values with increasing aggregation levels while other cover types decrease. Our study indicated that the two spatial aggregation techniques led to diOE erent results in cover type proportions and they altered spatial pattern in opposite ways. The majority rule-based aggregations caused distortions of cover type proportions and spatial patterns. Generally, the majority rule-based aggregation ltered out minor classes and produced clumped landscapes. Such landscape representations are relatively easy for interpreting and, therefore, are suitable for land managers to conceptualize spatial patterns of a study region. By contrast, the random rule-based aggregations maintained cover type proportions accurately, but tended to make spatial patterns change toward disaggregation. Overall, the measurements of landscape indices used in this study indicated that the random rule-based aggregation maintains spatial patterning better than the majority rule-based aggregation. Random rule-based aggregations are more suitable for studies in which the accuracy of spatially explicit information is of concern. They can be very useful in scaling data of ne resolution to coarse resolution, while retaining cover type proportions.