Linking indices of biotic integrity to environmental and land use variables: multimetric clustering and predictive models.

Advanced computerized methods and models of retrieving knowledge from large multiparameter data bases were used to analyze data on fish and macroinvertebrate composition (metrics), habitat, land use and water quality. The research focused on the north central and northeastern United States and involved thousands of sites monitored by the state agencies. The techniques and methodologies included supervised and unsupervised Artificial Neural Networks (ANN) modeling, Principal Component Analysis, Canonical Component Analysis (both linear and nonlinear), Multiple Regression Analyses, and analyses of variance by ANOVA.

Extracting knowledge on the links between the water body stressors and biotic integrity.

Multi-metric indices of biological integrity (IBIs) are most frequently created by examining single biological metrics along gradients of environmental degradation, and then combining multiple metrics using "best professional judgment" to characterize and calibrate stressor-response relationships. We aim to provide an efficient data analysis and visualization tool to assess the simultaneous effects of anthropogenic stressors on the fish population through the fish metrics and the associated Index of Biotic Integrity (IBI). Kohonen's self-organizing feature maps (SOM), unsupervised neural networks, are employed to pattern the sampling sites in the state of Ohio based on similar metrics characteristics.