A geographic information systems-based, weights-of-evidence approach for diagnosing aquatic ecosystem impairment

Environmental Toxicology and Chemistry

Volumn 25, Issue 8, 2006, Pages 2237-2249

  Kapo, Katherine E ^a   Burton Jr , G Allen ^a  

^a WRIGHT STATE UNIVERSITY   (United States)

Author keywords

Bioassessment; Ecoepidemiology; Spatial analysis; Watershed

Indexed keywords

DATA REDUCTION; ECOSYSTEMS; EPIDEMIOLOGY; MARINE BIOLOGY; MATHEMATICAL MODELS; REGRESSION ANALYSIS; WATERSHEDS;

AQUATIC ECOSYSTEM; ECOEPIDEMIOLOGY; ENVIRONMENTAL STRESSORS; PREDICTIVE MODELS; SPATIAL ANALYSIS; WEIGHTED LOGISTIC REGRESSION (WLR); WEIGHTS OF EVIDENCE (WOE);

GEOGRAPHIC INFORMATION SYSTEMS;

AQUATIC ECOSYSTEM; EFFLUENT; ENVIRONMENTAL ASSESSMENT; ENVIRONMENTAL STRESS; EPIDEMIOLOGY; GIS; LOTIC ENVIRONMENT; RISK ASSESSMENT; WATER CHEMISTRY; WATERSHED;

AQUATIC ENVIRONMENT; ARTICLE; EFFLUENT TOXICITY; GEOGRAPHIC DISTRIBUTION; INFORMATION DISSEMINATION; PRIORITY JOURNAL; RISK ASSESSMENT; WASTE WATER MANAGEMENT; WATERSHED MANAGEMENT;

BAYES THEOREM; ECOSYSTEM; GEOGRAPHIC INFORMATION SYSTEMS; PROBABILITY; SPECIES SPECIFICITY;

GREAT MIAMI BASIN; LITTLE MIAMI BASIN; NORTH AMERICA; OHIO; UNITED STATES;

EID: 33751046354     PISSN: 07307268     EISSN: None     Source Type: Journal    
DOI: 10.1897/05-534R.1     Document Type: Article

References (34)

1. U.S. Environmental Protection Agency. 2000. Stressor identification guidance document. EPA-822-B-00-025. Office of Water and Office of Research and Development, Washington, DC.
2. Burton GA Jr, Chapman P, Smith E. 2002. Weight of evidence approaches for assessing ecosystem impairment. Hum Ecol Risk Assess 8:1657-1673.
3. Norton SB, Cormier SM, Smith M, Jones RC. 2000. Can biological assessments discriminate among types of stress? A case study from the Eastern Corn Belt Plains ecoregion. Environ Toxicol Chem 4:1113-1119.
4. De Zwart D, Dyer S, Posthuma L, Hawkins C. Use of predictive models to attribute potential effects of mixture toxicity and habitat alteration on the biological condition of fish assemblages. Ecol Appl (in press).
5. Dyer S, White-Hull C, Wang X, Johnson T, Carr G. 1998. Determining the influence of habitat and chemical factors on in-stream biotic integrity for a Southern Ohio watershed. J Aquat Ecosyst Stress Recovery 6:91-110.
6. Dyer S, White-Hull C, Carr G, Smith E, Wang X. 2000. Bottom-up and top-down approaches to assess multiple stressors over large geographic areas. Environ Toxicol Chem 19:1066-1075.
7. Dyer S, Wang X. 2002. A comparison of stream biological responses to discharge from wastewater treatment plants in high and low population density areas. Environ Toxicol Chem 21: 1065-1075.
8. Norton SB, Cormier SM, Smith M, Jones RC, Berigan MS. 2002. Predicting levels of stress from biological assessment data: Empirical models from the Eastern Corn Belt Plains, Ohio, USA. Environ Toxicol Chem 21:1168-1175.
9. Smith EP, Lipkovich I, Ye K. 2002. Weights-of-evidence (WOE): Quantitative estimation of probability of impairment for individual and multiple lines of evidence. Hum Ecol Risk Assess 7:1585-1596.
10. Bonham-Carter GE 1994. Geographic Information Systems for Geoscientists-Modeling in GIS. Elsevier Science, New York, NY, USA.
11. Robinson GR, Kapo KE, Raines GL. 2004. A GIS analysis to evaluate areas suitable for crushed stone aggregate quarries in New England, USA. Nat Resour Res 3:143-159.
12. Sawatzky DL, Raines GL, Bonham-Carter GF, Looney CG. 2004. ArcSDM3: ArcMAP extension for spatial data modeling using weights of evidence, logistic regression, fuzzy logic, and neural network analysis. GSC-USGS Software Project Documentation, Ottawa, ON, Canada.
13. Suter GW. 1993. Ecological Risk Assessment. Lewis, Boca Raton, FL, USA.
14. Burton GA. 1999. Realistic assessments of ecotoxicity using traditional approaches. Aquatic Ecosystem Health and Management 2:1-8.
15. Suter GW. 1996. Guide for developing conceptual models for ecological risk assessments. ES/ER/TM-186. Technical Report. Environmental Risk Assessment Program, Oakridge National Laboratory, Oak Ridge, TN, USA.
16. Agterberg FP, Bonham Carter GF, Wright DF. 1990. Statistical pattern integration for minerals exploration. In Gaal G, Merriam DF, eds, Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum. Pergamon, Oxford, UK, pp 1-21.
17. Agterberg FP, Bonham Carter GF, Cheng Q, Wright DF. 1993. Weights-of-evidence modeling and weighted logistic regression for mineral potential mapping. In Davis JC, Herzfeld UC, eds, Computers in Geology: 25 Years of Progress. Oxford University Press, Oxford, UK, pp 13-32.
18. Raines GL, Mihalasky MJ. 2002. A reconnaissance method for delineation of tracts for regional-scale mineral resource assessment based on geologic map data. Nat Resour Res 11:241-248.
19. Agterberg FP, Cheng Q. 2002. Conditional independence test for weights-of-evidence modeling. Nat Resour Res 4:249-255.
20. Maidment DR. 2002. ArcHydro: GIS for Water Resources. ESRI, Redlands, CA, USA.
21. Ohio Environmental Protection Agency. 1987. Biological Criteria for the Protection of Aquatic Life, Vol II. User's Manual for Biological Field Assessment of Ohio Surface Waters. Division of Water-Quality Monitoring and Assessment, Surface Water Section, Columbus, OH, USA.
22. Ohio Environmental Protection Agency. 1989. Addendum to Biological Criteria for the Protection of Aquatic Life, Vol II. Users Manual for Biological Field Assessment of Ohio Surface Waters. Division of Water-Quality Monitoring and Assessment, Ecological Assessment Section, Columbus, OH, USA.
23. Ohio Environmental Protection Agency. 1989. Biological Criteria for the Protection of Aquatic Life, Vol III. Standardized Biological Field Sampling and Laboratory Methods for Assessing Fish and Macroinvertebrate Communities. Division of Water-Quality Monitoring and Assessment, Ecological Assessment Section, Columbus, OH, USA.
24. DeShon JD. 1995. Development and application of the invertebrate community index (ICI). In Davis WS, Simon T, eds, Biological Assessment and Criteria: Tools for Risk-Based Planning and Decision Making. Lewis, Boca Raton, FL, USA, pp 217-243.
25. Dyer S, Caprara R. 1997. A method for evaluating consumer product ingredient contributions to surface and drinking water: Boron as a test case. Environ Toxicol Chem 16:2070-2081.
26. Larson SJ, Gilliom RJ. 2001. Regression models for estimating herbicide concentrations in U.S. streams from watershed characteristics. J Am Water Resour Assoc 5:1349-1367.
27. Franca-Rocha W, Silva AB, Bonham-Carter GF. 2003. Modeling neoproterozoic Pb-Zn mineral deposits in Brazil: GIS approach. Proceedings, International Association for Mathematical Geology Meeting, Session S10, Portsmouth, UK, September 7-12, pp 1-6.
28. Yuan L, Norton SB. 2004. Assessing the relative severity of stressors at a watershed scale. Environ Monit Assess 98:323-349.
29. Berry W, Feldman S. 1985. Multiple Regression in Practice. Sage, Beverly Hills, CA, USA.
30. Wetherill GB. 1986. Regression Analysis with Applications. Chapman & Hall, London, UK.
31. Burton GA Jr, Pitt RE. 2001. Stormwater Effects Handbook: A Toolbox for Watershed Managers, Scientists, and Engineers. CRC, Boca Raton, FL, USA.
32. Ohio Environmental Protection Agency. 1995. Biological and water-quality study of the Little Miami River and selected tributaries, Vol 1. Technical Report MAS/1994-12-11. Columbus, OH, USA.
33. Ayers M, Kennen J, Stackelberg P. 2000. Water Quality in the Long Island-New Jersey Coastal Drainages New Jersey and New York, 1996-1998. U.S. Geological Survey Circular 1201. Reston, VA.
34. U.S. Environmental Protection Agency. 2002. Current national recommended water-quality data. EPA-822-R-02-047. Washington, DC.