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[Display omitted] • Climate change is expected to alter the composition of stream fish communities. • Summer and dry season temperatures are closely linked to fish distributions. • Cool- and cold-water communities are expected to experience the greatest losses. • Portions of Iowa, Illinois, and Wisconsin were most at risk within our study area. • Coupling climate risk with threat data can help identify management opportunities. Climate change is expected to alter stream fish habitat potentially leading to changes in the composition and distribution of fish communities. In the Northeastern and Midwestern United States we identified the distribution and characteristics of those fish communities most and least at risk of experiencing changes in climate which deviate from the climate they are associated with. We classified stream fish communities based on a suite of climate and environmental variables with multivariate regression trees under both recent and future conditions based on eight climate models. Our findings showed that some areas, such as the majority of the Illinois, Wisconsin, and Iowa), have high levels of risk of change in stream class, while much of Kentucky, West Virginia, Virginia, Pennsylvania, Eastern Ohio, Southern Michigan, and the Atlantic Coast are at relatively low risk. Stream class shifts ranged from over 75% of segments lost (associated with cooler temperatures) to gains of over 40% (associated with warmer temperatures). Common warmwater species such as green sunfish (Lepomis cyanellus), bluegill (Lepomis macrochirus) and largemouth bass (Micropterus salmoides) are expected to have the largest net gains in associated stream classes, while species associated with cooler streams such as Southern redbelly dace (Chrosomus erythrogaster), slimy sculpin (Cottus cognatus), and Eastern blacknose dace (Rhinichthys atratulus) were expected to experience the largest proportional losses. By pairing our climate risk predictions with other stressors such anthropogenic land use, habitat fragmentation, and water quality impairment, we identified opportunities for preservation (low risk due to all threats), restoration (low risk due to climate, high risk due to other stressors), and adaptation (high climate risk with low risk from other stressors). Understanding which communities are at risk due to climate change will aid in developing adaptation strategies to help sustain them in the future. [ABSTRACT FROM AUTHOR]

Landsat Thematic Mapper (TM) data were the basis in production of a statewide land cover data set for Wisconsin, undertaken in partnership with U.S. Geological Survey''s (USGS) Gap Analysis Program (GAP). The data set contained seven classes comparable to Anderson Level I and 24 classes comparable to Anderson Level II/III. Twelve scenes of dual-date TM data were processed with methods that included principal components analysis, stratification into spectrally consistent units, separate classification of upland, wetland, and urban areas, and a hybrid supervised/unsupervised classification called “guided clustering.” The final data had overall accuracies of 94% for Anderson Level I upland classes, 77% for Level II/III upland classes, and 84% for Level II/III wetland classes. Classification accuracies for deciduous and coniferous forest were 95% and 93%, respectively, and forest species'' overall accuracies ranged from 70% to 84%. Limited availability of acceptable imagery necessitated use of an early May date in a majority of scene pairs, perhaps contributing to lower accuracy for upland deciduous forest species. The mixed deciduous/coniferous forest class had the lowest accuracy, most likely due to distinctly classifying a purely mixed class. Mixed forest signatures containing oak were often confused with pure oak. Guided clustering was seen as an efficient classification method, especially at the tree species level, although its success relied in part on image dates, accurate ground truth, and some analyst intervention. [Copyright &y& Elsevier]