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INVESTIGATING CAUSES OF BIOLOGICAL IMPAIRMENTS IN AQUATIC SYSTEMS: A PLAN FOR DEVELOPMENT OF THE CAUSAL ANALYSIS/DIAGNOSIS DECISION
Citation:
Norton, S B., S M. Cormier, L E. Rao, G Suter, P ShawAllen, B. Swietlik, K S. Minamyer, B Subramanian, AND B C. Autrey. INVESTIGATING CAUSES OF BIOLOGICAL IMPAIRMENTS IN AQUATIC SYSTEMS: A PLAN FOR DEVELOPMENT OF THE CAUSAL ANALYSIS/DIAGNOSIS DECISION. Presented at Society of Environmental Toxicology and Chemistry, Austin, TX, November 8-13, 2003.
Impact/Purpose:
The purpose of this research project is to provide methods, tools and guidance to Regions, States and Tribes to support the TMDL program. This research will investigate new measurement methods and models to link stressors to biological responses and will use existing data and knowledge to develop strategies to determine the causes of biological impairment in rivers and streams. Research will be performed across multiple spatial scales, site, subwatershed, watershed, basin, ecoregion and regional/state.
Description:
Increasingly, the regulatory, remedial and restoration actions taken to manage impaired environments are based on measurement and analysis of the biotic community. When an aquatic community has been identified as impaired, the cause of the impairment must be determined so that appropriate actions can be taken. The USEPA's Stressor Identification (SI) Guidance describes a methodology for identifying the causes of observed impairments in aquatic systems. Stressor identification requires extensive knowledge of the mechanism, symptoms, and stressor-response relationships for various specific stressors as well as the ability to use that knowledge in a formal method for causal analysis.
This poster describes the first version of the Causal Analysis/Diagnosis Decision Information System (CADDIS). CADDIS is envisioned as a decision support system that will help investigators in the regions, states and tribes find, access, organize, use and share information useful for causal evaluations in aquatic systems. It will include supporting case studies and causal analysis tools. Future versions will be developed incrementally and iteratively, and frequent user input and feedback will be essential to the system's success.