Integrated Modeling Approaches to Support Systems-Based Ecological Risk Assessment

EPA Grant Number: R835795
Title: Integrated Modeling Approaches to Support Systems-Based Ecological Risk Assessment
Investigators: von Stackelberg, Katherine Ellen , Landis, Wayne G. , Stark, John D , Sunderland, Elsie M.
Current Investigators: von Stackelberg, Katherine Ellen , Landis, Wayne G. , Stark, John D
Institution: President and Fellow of Harvard College , Washington State University , Western Washington University
Current Institution: Washington State University , Harvard University , Western Washington University
EPA Project Officer: Lasat, Mitch
Project Period: June 1, 2015 through May 31, 2017 (Extended to December 31, 2018)
Project Amount: $651,708
RFA: Systems-Based Research for Evaluating Ecological Impacts of Manufactured Chemicals (2014) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Ecosystems , Safer Chemicals


In a complex and changing environment (e.g., climate change) and with an increasing emphasis on sustainability of coupled human-environment systems, reductionist approaches to environmental management that fail to consider feedback loops, multiple stressors, and spatial and temporal characteristics of exposures and populations no longer suffice. We propose to demonstrate how existing data and models can be integrated through a BayesNet – Relative Risk Modeling (BN-RRM) framework that explicitly links molecular initiating events to regulatory outcomes of interest. The flexible approach allows multiple stressors linked to multiple outcomes. The objectives of the research are to: A. Develop quantitative adverse outcome pathways (AOPs) for at least one legacy contaminant (e.g., mercury, PCBs) and one emerging contaminant (e.g., PFCs, specific nanomaterial) and develop exposure-response profiles for use in a larger BN-RRM; B. Develop several ecological risk case studies demonstrating the integration and application of one or more underlying process models to synthesize and integrate available data across levels of biological organization, including exposure estimates, quantitative AOPs, non-chemical stressors, and population models; and, C. Apply the BN-RRM framework to demonstrate how the approach generates quantitative predictions of potential ecological risk impacts at scales relevant to policy development and regulatory decision making.

Expected Results:

The result of this effort is a generic integrated modeling framework designed to efficiently and accurately characterize the interactions between the spatial and temporal distribution of chemicals and ecological receptors, and to predict system-level consequences resulting from individual-level exposures based on the application of existing methods and models and commonly-available data.

The BN-RRM framework is flexible, visual, relatively simple to understand, and easily incorporates multiple stressors, effects, endpoints, and outcomes as represented by underlying process models and data. It is designed to incorporate new information and data as those become available. The framework provides a quantitative context for AOP development for both legacy and emerging contaminants. By working with decision makers to define the regulatory outcomes of interest, the framework provides a transparent context for productive dialogue amongst stakeholders to better support ecological risk-based decision making to ultimately improve our ability to protect the environment and public health.

Publications and Presentations:

Publications have been submitted on this project: View all 8 publications for this project

Supplemental Keywords:

BayesNet (BN), Relative Risk Model (RRM), population, bioaccumulation, FishRand, spatially-explicit, adverse outcome pathway (AOP), integrated modeling, ecological risk

Progress and Final Reports:

  • 2015 Progress Report
  • 2016 Progress Report
  • 2017