Model-based Clustering for Classification of Aquatic Systems and Diagnosis of Ecological StressEPA Grant Number: R831368
Title: Model-based Clustering for Classification of Aquatic Systems and Diagnosis of Ecological Stress
Investigators: Smith, Eric , Bates, Samantha , Berkson, Jim , Brannan, Kevin , Mostaghimi, Saied , Orth, Donald J. , Yagow, Gene
Institution: Virginia Polytechnic Institute and State University
EPA Project Officer: Hiscock, Michael
Project Period: November 10, 2003 through November 9, 2006
Project Amount: $843,771
RFA: Development of Watershed Classification Systems for Diagnosis of Biological Impairment in Watersheds and Their Receiving Water Bodies (2003) RFA Text | Recipients Lists
Research Category: Water and Watersheds , Water
The objectives of this research are to develop methodologies for classifying watersheds and to evaluate the ability of this classification system to delineate areas of biological stress. The novel aspect of our classification system will be in the grouping of watersheds by empirical relationships between watershed attributes and aquatic ecosystem conditions. We further propose to develop methods to assess differences in ecosystem vulnerability and to evaluate a suite of biological characteristics that are both sensitive to environmental change and applicable across different regions (transferability study).
We propose a classification system derived through model-based cluster analysis - a statistical approach that groups empirical relationships. In contrast to classification systems that group sites by similarity of attribute values, we will group sites by similarity of the empirical stressor-effect relationships. The classification procedure will be comprised of an indirect approach as well as a more direct approach based on canonical correspondence analysis to form regression relationships between abundance data and variables of interest. The classification system will be tested at local and national levels through a validation studies. We will develop a suite of biological metrics that will be transferable to different locations, and then will evaluate their ability to capture ecological effects.
The research will produce a classification system and approach to classification that may be applied at levels ranging from national to local (states, reservations). The classification system will be directly related to biological processes as well as hydrologic, landscape, habitat and chemical processes. Basing the classification system on sound ecological data will help resource managers to better diagnose ecological stress and impairment. Grouping sites by similarity of empirical stressor-effect relationships will aid in the design of monitoring strategies for stress evaluation. Because the system will be based on stressor-impairment modeling, it will aid in extrapolation of exposure-effect relationships in risk characterization. Software will be developed that is user-friendly and will be made freely available.