#XTOOLS WATERSHED SLOPES SOFTWARE#
The increased availability of digital spatial data and geographic information system (GIS) software has facilitated watershed-scale evaluations of stream health. With a growing need for stream health evaluations and ever-present logistical constraints, managers would benefit from tools that allow evaluation of streams without field measurements or observations. On-site habitat evaluations may be subject to observer biases ( Poole and others 1997 Maddock 1999). The scale of habitat assessment determines the cost and reliability of results. Indices of habitat quality are scale dependent ( Rankin 1995 Maddock 1999) and management efforts tend to be concentrated at the scale where anthropogenic activity is perceived to affect the biological system ( Poole and others 1997 Fausch and others 2002). Most habitat models use reach-scale indices relating to channel geomorphic features, water velocity, substrate, in-stream cover for fish, and condition of riparian zones ( Bain and others 1999). Efforts to predict fish assemblage attributes with reach or watershed habitat attributes have been persistent (e.g., Oswood and Barber 1982 Berkman and Rabeni 1987 Roth and others 1996 Allan and others 1997 Wang and others 2003). Physical habitat assessments are important as part of stream health evaluations for many reasons, including (i) evaluation of improvements made by fishery enhancement and stream habitat restoration (ii) identification, estimation, and prediction of alterations due to anthropogenic or natural causes (iii) identification and protection or avoidance of stream reaches or segments that are vulnerable or critical and (iv) facilitation of stream classification for management purposes ( Oswood and Barber 1982 Osborne and others 1991 Wang and others 1998 Maddock 1999). Riparian forest and length-slope (LS) factor were the most important watershed-scale variables and mostly positively correlated with IBI scores, whereas substrate and riffle-pool quality were the important reach-scale variables in the ECBP. Results should be interpreted bearing in mind that reach habitat was qualitatively measured and only fish assemblages were used to measure stream health. Variety of surficial geology contributed to decline in model predictive power. Watershed models explained about 15% more variation in IBI scores than reach models on average. Better-fitting models were associated with smaller spatial extents. Watershed models had adjusted-R 2 ranging from 0.25 to 0.93 and reach models had adjusted-R 2 ranging from 0.09 to 0.86. The importance of reach- versus watershed-scale variables was measured by multiple regression model adjusted-R 2 and best subset comparisons in the general linear statistical framework. Reach habitat was represented by metrics of a qualitative habitat evaluation index, whereas watershed variables were represented by riparian forest, geomorphology, and hydrologic indices. Watersheds hierarchically nested within the ecoregion were used to regroup sampling locations to represent varying spatial extents. Stream health was measured with scores on a fish index of biotic integrity (IBI) using data from 95 stream reaches in the Eastern Corn Belt Plain (ECBP) ecoregion of Indiana. The objective of this study was to compare the performance of reach-scale habitat and remotely assessed watershed-scale habitat as predictors of stream health over varying spatial extents. A common theme in recent landscape studies is the comparison of riparian and watershed land use as predictors of stream health.