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Project Title: A pilot application of ecological risk assessment to the management of landscape change in the Interior Landscape Analysis System (INLAS) project area site, upper Grande Ronde River watershed

Status: Completed

Principal Investigators: Wayne G. Landis, Professor; April J. Markiewicz, Assistant Director; Suzanne Anderson, M.S. candidate, Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University

E-mail Contacts: Wayne Landis, wayne.landis[at]wwu.edu; Suzanne Anderson, ander323[at]cc.wwu.edu

Summary: The relative risk model (RRM) method (Landis and Wiegers 1997, 2005; Wiegers et al., 1998) is used to assess and prioritize risks by ranking and filtering the available data on the habitats, sources of stressors, and effects of stressors on the assessment endpoints in the region. It is a semi-quantitative means of combining the actions of multiple stressors on multiple assessment endpoints residing in a complex and dynamic ecosystem. In brief, sources and habitats are identified throughout the study area and grouped into risk regions or subareas. They are then assigned numerical ranks in relation to their importance in each region, and then the ranks are combined to predict relative levels of risk. The number of possible risk combinations resulting from this approach depends on the number of sources of stressors and habitats identified in each risk region. Exposure and effect filters are then used to evaluate the data and determine whether there is a low, medium, or high probability of an exposure (and therefore an effect) occurring. For example, if two source types and two habitat types are identified, then there are four possible combinations that can lead to a specific type of impact. If there are two different impacts already in evidence then eight possible combinations exist. Each identified combination therefore establishes a pathway of probable risk when exposure links the source and habitat to an effect. When a source generates stressors that affect habitats important to and utilized by the assessment endpoint(s), the ecological risk is high. When the pathway connection is indirect, resulting in minimal interaction, the risk is lower. When there is no complete pathway, no risk exists. In this project the RRM method was applied to the Interior Northwest Landscape Analysis (INLAS) study area. Of the 10 steps in the RRM method, 7 have been implemented to date, with the 8^th currently being conducted. Those steps were as follows: (1) worked with the USFS and other stakeholders to list the management goals for the study area and the location of these activities; (2) collected the data for the region and consolidated it in a GIS format; (3) made a map of the study area applicable to a regional scale ecological risk assessment; (4) divided the map into risk regions; (5) made a conceptual model to reveal pathways of potential risk, as well as identify data gaps; (6) decided on a ranking scheme based on the data obtained; and (7) Calculated the relative risks by combining rank scores and then screening the scores using exposure and effect filters to identify those ecological resources at highest risk to be impacted, their geographic location, the stressor contributing the highest risk, and identifying the impact that poses the highest risk to the valued ecological resources.

Project Goals:

1. Modify the relative risk model (RRM) for conducting regional scale ecological risk assessment to apply to the INLAS project area in the upper Grande Ronde River watershed.
2. Use the methodology of the RRM to create a data organization system to allow the estimation of the combined effects of multiple stressors within the INLAS area upon the valued ecological resources of the region.

Description: The relative risk model (RRM) method (Landis and Wiegers 1997, 2005; Wiegers et al. 1998) is used to assess and prioritize risks by ranking and filtering the available data on the habitats, sources of stressors, and effects of stressors on the assessment endpoints in the region. It is a semi-quantitative means of combining the actions of multiple stressors on multiple assessment endpoints residing in a complex and dynamic ecosystem. In brief, sources and habitats are identified throughout the study area and grouped into risk regions or subareas. They are then assigned numerical ranks in relation to their importance in each region and then the ranks are combined to predict relative levels of risk. The number of possible risk combinations resulting from this approach depends on the number of sources of stressors and habitats identified in each risk region. Exposure and effect filters are then used to evaluate the data and determine whether there is a low, medium, or high probability of an exposure (and therefore an effect) occurring. For example, if two source types and two habitat types are identified, then there are four possible combinations that can lead to a specific type of impact. If there are two different impacts already in evidence, then eight possible combinations exist. Each identified combination therefore establishes a pathway of probable risk when exposure links the source and habitat to an effect. When a source generates stressors that affect habitats important to and utilized by the assessment endpoint(s), the ecological risk is high. When the pathway connection is indirect, resulting in minimal interaction, the risk is lower. When there is no complete pathway, no risk exists.

In this project the RRM method was applied to the INLAS study area. Of the 10 steps in the RRM method, have been implemented to date, with the 8^th currently being conducted. Those steps were as follows:

1. Worked with the USFS and other stakeholders to list the management goals for the study area and the location of these activities.
2. Collected the data for the region and consolidated it in a GIS format.
3. Made a map of the study area applicable to a regional scale ecological risk assessment.
4. Divided the map into risk regions.
5. Made a conceptual model to reveal pathways of potential risk, as well as identify data gaps.
6. Decided on a ranking scheme based on the data obtained.
7. Calculated the relative risks by combining rank scores and then screening the scores using exposure and effect filters to identify those ecological resources at highest risk to be impacted, their geographic location, the stressor contributing the highest risk, and identifying the impact that poses the highest risk to the valued ecological resources.

Currently we are on step 8:

8. Evaluating the uncertainty and sensitivity associated with the risk scores using Monte Carlo analysis methods.

Status: Once we have met with the USFS again to go over the risk scores and uncertainty analyses we are currently conducting in step 8, we will proceed to steps 9 and 10 where we will:

9. Generate testable hypotheses that can be tested experimentally or by field research to confirm the relative risk scores, as well as reduce the uncertainty in the assessment process.
10. Communicate the results to the stakeholders, publish the results in a peer-reviewed scientific journal, and present the results at scientific conferences.

Deliverables: The final report with the completed conceptual model, ecosystem-based ecological risk assessment, risk calculations, and Monte Carlo analyses of uncertainty and model sensitivity will be available upon completion.

Citations:

Landis, W.G.; Wiegers, J.K. 1997. Design considerations and a suggested approach for regional and comparative ecological risk assessment. Human and Ecological Risk Assessment. 3: 287-297.

Landis W.G.; Wiegers, J.K. 2005. Chapter 2: Introduction to the regional risk assessment using the relative risk model. In: Landis, W.G., ed. Regional scale ecological risk assessment using the relative risk model. Boca Raton, FL: CRC Press: 11-36.

Wiegers, J.K.; Feder, H.M.; Mortensen, L.S.; Shaw, D.G.; Wilson, V.J.; Landis, W.G. 1998. A regional multiple stressor rank-based ecological risk assessment for the fjord of Port Valdez, AK. Human and Ecological Risk Assessment. 4: 1125-1173.

Project ID: FY06TS16