CWWUC Presentation April 8, 2009

1. CWWUC PresentationApril 8, 2009 Application of the Integrated Impact Analysis Tool

2. Status of Division Work Site Classes • Set up Fuzzy Set Site Classes based on elevation, slope, and precipitation. • 5 Site Classes • High-elevation, cold water sites • Mid-elevation, semi-cold, high gradient, drier • Low/mid-elevation, transitional temperature, low gradient, drier • Low-elevation, warm water, low gradient, dry • Mid-elevation, semi-cold, low gradient, moist

4. Status of Division Work Multi-Metric Index (MMI) • Based on benthic macroinvertebrates • MMI score between 0 and 100 • High index (Site Classes 1-3) • Clinger Taxa • EPT Taxa • Predator/Shredder Taxa • HBI • Total Taxa

5. Status of Division Work • Low index (Site Classes 4-5) • Insect Taxa • Non-Insect Percent of Taxa • Coleoptera Percent • Predator/Shredder Taxa • Sensitive Plains Families • Clinger/Sprawler percent • Still figuring out thresholds

6. Status of Division Work Observed over Expected Taxa (O/E) • Presented some information on the Multivariate Predictive Model that would be used to develop the Expected portion of this ratio. • April 2009 Workgroup Meeting should address this in more detail. • Basic idea – the closer the ratio is to 0, the more impaired the stream.

7. Unanswered Questions • What happens once they have the MMI and O/E? • If they find that a segment (or portion of a segment) is below the MMI threshold or the O/E is less than 1, what does that mean to a discharger? • How can a person figure out what is causing the impact to aquatic life?

8. Integrated Impact Analysis • WERF project (partly funded by EPA) to distinguish the relative impact of chemistry and habitat on aquatic life. • Teamed with GEI (Chadwick) and Risk Sciences (Tim Moore). • Has been used in Santa Ana UAA, Arid West work, and is currently being implemented by the Southeastern Wisconsin Watershed Trust.

9. Physical Chemical Biological Integrated Impact Analysis + =

10. IIA – The Gist • Uses existing statistical methods • Principle Components Analysis • All Possible Regressions • Chi-Square Automatic Interaction Detection (CHAID) • Results identify key stressors and their relative impact. • So . . . if the state identifies a stream segment or site as impacted (through O/E or MMI), IIA can help determine what is causing that impact.

11. Apply Basic Statistics Identify Key Stressor and Response Variables Rank Variables According to Relative Impact Fit Equation to Describe Interactions Between Stressors and Response MODEL Repeat Cycle if More Variables Are Needed Basic Steps of IIA

12. Step 1Apply Basic Statistics • Perform basic descriptive statistics and develop graphics • Normalize data as needed – develop new descriptive statistics and new graphics • Compile a correlation matrix

13. Water Chemistry Basic Statistics

16. Habitat Basic Statistics

18. Looking for correlations (≥0.6). • Correlated variables can act as surrogates for each other.

19. Step 2Identify Key Stressor and Response Variables • Identify key independent stressor variables and relationships between variables using: • Principle Components Analysis (PCA) • All Possible Regressions • Chi-square Automatic Interaction Detection (CHAID) • Iterative process that systematically removes variables from the larger pool of variables.

20. Principal Components Analysis • Useful in determining how variables relate to one another – how they move in space. • If variables generally move together, one variable can act as a surrogate for the other(s).

21. strongest Principal Components Analysis • Look at values with an absolute value ≥0.6. • A component in PCA is a group of variables that move in the same direction. • Generally, the variable with the highest score is identified as the surrogate. • Rerun to limit number of stressors to 6 and responses to 2 or 3.

22. Principal Components Analysis

23. All Possible Regressions • Combines one response with many stressor variables into models using all combinations of the stressor variables. • Look at all combinations to see what combinations explain the greatest amount of variance with the lowest error. • Look for lowest variable count that explains the most variance.

24. All Possible Regressions

26. All Possible Regressions

27. Chi-Squared Automatic Interaction Detection (CHAID) • Identifies both linear and non-linear relationships between variables. • Non-parametric technique, so data should not be transformed. • Robust to missing data points.

28. Macroinvertebrates P/Channel Range Macroinvertebrates NH3 range

29. Step 3Rank Variables According to Relative Impact • Develop matrix of key independent stressor variables and relationships found in Step 2 • Repeat Steps 2 and 3 until the two most influential independent stressor variables are identified for each dependent response variable

30. Rank Variables According to Relative Impact • Develop a matrix of response variables and their corresponding “important” stressor variables for each of the 3 analyses. • Look for stressors identified by multiple analyses and sort by number of analyses in common. • To help with sorting, refer back to the analyses and how strong the relationships are between the stressor and response variables.

31. Matrix of Ranked Stressors for Each Response

32. Step 4Fit Equation to Describe Interactions Between Stressors and Response • Use three-dimensional modeling program to identify specific non-linear relationship transformations

33. Fit Equation

34. Step 5Repeat Cycle if More Variables Are Needed • Enter the residuals calculated for the response variable into a new column in dataset. • The residuals are the remainder of the of the response variable after the variability caused by the 2 stressor variables is removed. • Repeat steps 2-4 to identify the next 2 important stressor variables.

35. Residuals From Equation Fitting

36. IIA Gives You . . . • An ordered list of stressors that are causing an impact on the response variables. • A model to help predict how the response variables will change based on a change in the stressor. • An understanding of whether habitat is playing a role in limiting the response variable. • A means to make sense of what the O/E and MMI metrics are showing and how that could relate to your discharge.

37. Questions