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Network-based Prediction of Lotic Thermal Regimes Across New England
Citation:
Detenbeck, N., A. Morrison, R. Abele, D. Kopp, AND J. Morgan. Network-based Prediction of Lotic Thermal Regimes Across New England. Presented at New England Association of Environmental Biologists (NEAEB) 2013. 37th Annual Meeting, Lake Placid, NY, March 20 - 22, 2013.
Impact/Purpose:
Thermal regimes are a critical driving force for models predicting effects of watershed management activities on fish habitat suitability. Predicted thermal regime variables will be used as input to prediction models of relative abundance for selected fish species, chosen based on their sensitivity to urban development. Predicted versus observed fish community composition will be compared for watersheds in which best management practices have been applied.
Description:
Thermal regimes are a critical factor in models predicting effects of watershed management activities on fish habitat suitability. We have assembled a database of lotic temperature time series across New England (> 7000 station-year combinations) from state and Federal data sources. Using principal component analysis, we reduced 78 thermal metrics from the ThermoStat software package to four independent fish habitat predictor variables: July median temperature, Julian day of maximum daily temperature, mean daily temperature range, and maximum daily rate of temperature change. We are creating spatial statistical models for stream temperature regime metrics, using an approach developed by the U.S. Forest Service. These network-based models are unique because they account for patterns of spatial autocorrelation among locations based on both Euclidean and in-stream distances. Using a regional database of densiometer measurements, we predicted potential shading from the riparian zone as a function of percent canopy cover and channel width by land-use category. Predictor variables for stream temperature metrics include air temperature metrics, watershed area, surface water storage, drainage density, elevation, percent watershed in coarse deposits or well-drained soils, local and main channel slope, estimated stream flow, potential monthly solar radiation (corrected for topographic shading as well as riparian zone influences), and percent impervious area. Predicted thermal regime variables will be used as input to models that predict relative abundance of selected fish species, chosen based on their sensitivity to urban development. Predicted versus observed fish community composition will be compared for watersheds in which best management practices have been applied.