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In most wadeable streams of the mid-Atlantic Highlands region of the eastern USA, physical habitat alteration is the primary stressor for fish. Models that predict the occurrence of stream-fish species based on habitat measures can be useful in management, and predicted probability of occurrence can be a measure of habitat suitability with which to compare alternative habitat management scenarios and assess the effectiveness of stream restoration. We developed such models for each of 13 mid-Atlantic Highlands stream-fish species and species groups by using multiple logistic regression and six instream habitat measures: depth, temperature, substrate, percent riffles, cover, and riparian vegetation. The predictive ability of the models ranged from 61% to 79% in cross-validation and from 38% to 85% on an independent data set. The models predicted well for both the original and test data sets for black bass Micropterus spp., brook trout Salvelinus fontinalis, darters Etheostoma and Percina spp., shiners Notropis spp., and suckers Hypentelium and Moxostoma spp. Suitable habitat for most of the fish species groups was characterized by intermediate depth, a high percentage of cobble and riparian vegetation, and a low percentage of instream cover. The relatively high predictive ability and reasonable responses to habitat measures indicated that these models could be useful for management. However, the models were more sensitive to depth and temperature than to measures that are more commonly affected by restoration activities, such as cover and riparian vegetation.

Input data acquisition and preprocessing is time-consuming and difficult to handle and can have major implications on environmental modeling results. US EPA’s Hydrological Micro Services Precipitation Comparison and Analysis Tool (HMS-PCAT) provides a publicly available tool to accomplish this critical task. We present HMS-PCAT’s software design and its use in gathering, preprocessing, and evaluating precipitation data through web services. This tool simplifies catchment and point-based data retrieval by automating temporal and spatial aggregations. In a demonstration of the tool, four gridded precipitation datasets (NLDAS, GLDAS, DAYMET, PRISM) and one set of gauge data (NCEI) were retrieved for 17 regions in the United States and evaluated on 1) how well each dataset captured extreme events and 2) how datasets varied by region. HMS-PCAT facilitates data visualizations, comparisons, and statistics by showing the variability between datasets and allows users to explore the data when selecting precipitation datasets for an environmental modeling application.