Modeling the effects of conservation practices on stream health

Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R2 and lowest mean squared error (MSE) for FamilyIBI (R2=0.50, MSE=29.80), HBI (R2=0.57, MSE=0.20), and EPTtaxa (R2=0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures. ► We used a watershed model to produce high-resolution flow and water quality elements. ► We develop a predictive model to explain macroinvertebrate health measures. ► Adaptive neuro-fuzzy inference system was the best predictive model. ► In general, implementation of best management practices significantly improved stream health. ► This study will aid in envir