LesterLYuan11am

Using Regional Models to Assess the Relative Effects of Stressors: 

Using Regional Models to Assess the Relative Effects of Stressors Lester L. Yuan National Center for Environmental Assessment U.S. Environmental Protection Agency The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

Motivation: 

Motivation Relevant questions for an assessment: Which stressors are important? Where should we be concerned about different stressors? What is the ecological effect of a given stressor? Relative to other stressors. Within a regional context.

Which stressors are important?: 

Which stressors are important? Important stressors influence valued ecological attributes. Valued ecological attribute in small streams? Fish index of biotic integrity Macroinvertebrate index of biotic integrity Presence of certain species Biodiversity

Regional Study Area and Data: 

Regional Study Area and Data Mid-Atlantic Highlands Assessment US EPA Environmental Monitoring and Assessment Program Data collected 1st – 3rd order streams from 1993 – 1996. Biological, chemical, and physical habitat data Indices of biotic integrity (IBI) developed for fish and macroinvertebrates.

Stressor-response model: 

Stressor-response model Principle component analysis of stressor variables Generalized additive model Model each response as an arbitrary smooth curve. Allows for nonlinear relationships. Identify stressors using a stepwise modeling procedure: Total phosphorus (PTL) Nitrate (NO3) Sulfate (SO4) Physical habitat (RBP) Acidity (pH)

Model Results: 

Model Results R2 = 0.22

Where should we be concerned about different stressors?: 

Where should we be concerned about different stressors? Can we estimate stressors levels in unsampled streams? Mid-Atlantic data is not dense enough… Focus study down to a smaller area.

Study Area and Data: 

Study Area and Data Mid-Atlantic USA Western Maryland MARYLAND BIOLOGICAL STREAM SURVEY Maryland Department of Natural Resources Stratified, random sampling of 1st – 3rd order streams in Maryland. Collected biological, chemical, physical habitat data. Stressors available: NITRATE, SULFATE, and ACIDITY.

Interpolating Stream Stressors: 

Interpolating Stream Stressors Estimate stressor distributions from sampled data. Model mean stressor levels using regression models. Spatially interpolate residuals from regression.

Modeling Stressor Levels (Part I): 

Modeling Stressor Levels (Part I) Develop models to predict mean values for SO4, NO3, and PH. Explanatory variables: Percent Agriculture Percent Urban Sampling Year Catchment Size

Modeling Stressor Levels (Part II): 

Modeling Stressor Levels (Part II) Use spatial statistics to interpolate residual variation.

Spatial Distribution of NO3 Residuals: 

Spatial Distribution of NO3 Residuals

Model Performance : 

Model Performance NO3 SO4 R2 = 0.81 R2 = 0.63 Predictive power of model is reasonably high! R2 = 0.40 PH

Spatial Distributions of Stressors: 

Spatial Distributions of Stressors NITRATE SULFATE PH

How much do individual stressors affect valued ecological attributes?: 

How much do individual stressors affect valued ecological attributes? Combine regional stressor-response relationships with spatial distributions.

Combining stressor-response with stressor estimates: 

Combining stressor-response with stressor estimates

Scaled Stressor Maps: 

Scaled Stressor Maps NITRATE SULFATE PH

Issues: 

Issues Reconciling differences among studies Different measurements of qualitative habitat scores. Different analytical techniques for water chemistry. Effectiveness of spatial interpolation varies by stressor. Correlation does not imply causation. What is SO4 measuring?

Conclusions: 

Conclusions Spatial interpolation is promising approach for imputing information about unsampled streams. Scaling variables appropriately can help interpret data.