Unlocking the relationship of biotic integrity of impaired waters to anthropogenic stresses

The Clean Water Act expressed its goals in terms of restoring and preserving the physical, chemical and biological integrity of the Nation's waters. Integrity has been defined as the ability of the water body's ecological system to support and maintain a balanced integrated, adaptive community of organisms comparable to that of a natural biota of the region. Several indices of biotic integrity (IBIs) have been developed to measure quantitatively the biotic composition and, hence, the integrity. Integrity can be impaired by discharges of pollutants from point and nonpoint sources and by other pollution-related to watershed/landscape and channel stresses, including channel and riparian zone modifications and habitat impairment. Various models that link the stressors to the biotic assessment endpoints, i.e., the IBIs, have been presented and discussed. Simple models that link IBIs directly to single or multiple surrogate stressors such as percent imperviousness are inadequate because they may not represent a true cause-effect proximate relationship. Furthermore, some surrogate landscape parameters are irreversible and the relationships cannot be used for development of plans for restoration of the water body integrity. A concept of a layered hierarchical model that will link the watershed, landscape and stream morphology pollution stressors to the biotic assessment endpoints (IBIs) is described. The key groups of structural components of the model are: IBIs and their metrics in the top layer, chemical water and sediment risks and a habitat quality index in the layer below, in-stream concentrations in water and sediments and channel/habitat impairment parameters in the third layer, and watershed/landscaper pollution generating stressors, land use change rates, and hydrology in the lowest layer of stressors. A modified and expanded Maximum Species Richness concept is developed and used to reveal quantitatively the functional relationships between the top two layers of the structural components and parameters of the model.