This paper applies methods of multiple resolution map comparison to quantify characteristics for 13 applications of 9 different popular peer-reviewed land change models. Each modeling application simulates change of land categories in raster maps from an initial time to a subsequent time. For each modeling application, the statistical methods compare: (1) a reference map of the initial time, (2) ence map of the subsequent time, and (3) a prediction map of the subsequent time. The three possible two-map comparisons for each application characterize: (1) the dynamics of the landscape, (2) the behavior of the model, and (3) the accuracy of the prediction. The three-map comparison for each application specifies the amount of the prediction's accuracy that is attributable to land persistence versus land change. Results show that the amount of error is larger than the amount of correctly predicted change for 12 of the 13 applications at the resolution of the raw data. The applications are summarized and compared using two statistics: the null resolution and the figure of merit. According to the figure of merit, the more accurate applications are the 123Comparing the input, output, and validation maps for several models of land change 13 ones where the amount of observed net change in the reference maps is larger. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of models using scientifically rigorous, generally applicable, and intellectually accessible statistical techniques. JEL Classification

A central challenge in global ecology is the identification of key functional processes in ecosystems that scale, but do not require, data for individual species across landscapes. Given that nearly all tree species form symbiotic relationships with one of two types of mycorrhizal fungi - arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi - and that AM- and ECM-dominated forests often have distinct nutrient economies, the detection and mapping of mycorrhizae over large areas could provide valuable insights about fundamental ecosystem processes such as nutrient cycling, species interactions, and overall forest productivity. We explored remotely sensed tree canopy spectral properties to detect underlying mycorrhizal association across a gradient of AM- and ECM-dominated forest plots. Statistical mining of reflectance and reflectance derivatives across moderate/high-resolution Landsat data revealed distinctly unique phenological signals that differentiated AM and ECM associations. This approach was trained and validated against measurements of tree species and mycorrhizal association across ~130 000 trees throughout the temperate United States. We were able to predict 77% of the variation in mycorrhizal association distribution within the forest plots (P < 0.001). The implications for this work move us toward mapping mycorrhizal association globally and advancing our understanding of biogeochemical cycling and other ecosystem processes.

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This article examines the spatial distribution of tree-planting projects undertaken by four urban greening nonprofit organizations in the Midwest and Eastern United States. We use a unique data set of tree-planting locations, land use data, and socioeconomic information to predict whether a census block group (n = 3,771) was the location of a tree-planting project between 2009 and 2011. Regression results show tree-planting projects were significantly less likely to have occurred in block groups with higher tree canopy cover, higher median income, or greater percentages of African American or Hispanic residents, controlling for physical and socioeconomic conditions. In addition, when canopy cover or income was low, plantings were even less likely to have occurred in neighborhoods with high percentages of racial or ethnic minorities. Findings suggest nonprofit plantings might reduce existing income-based inequity in canopy cover, but risk creating or exacerbating race-based inequity and risk leaving low canopy minority neighborhoods with relatively few program benefits.

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