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

Abstract

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. Canonical correspondence analysis (CCA) allows us then to draw conclusions about the role of the environmental variables in maintaining the perfect abode for fishes. Different visualizations superimposed with SOM clustering are realized to explore the complex interrelationships in the aquatic system and aid watershed managers to comprehend the effects of the environment on the fish.