Grantee Research Project Results
2017 Progress Report: Integrated Modeling Approaches to Support Systems-Based Ecological Risk Assessment
EPA Grant Number: R835795Title: Integrated Modeling Approaches to Support Systems-Based Ecological Risk Assessment
Investigators: von Stackelberg, Katherine Ellen , Stark, John D , Landis, Wayne G. , Sunderland, Elsie M.
Institution: President and Fellow of Harvard College , Western Washington University , Washington State University
Current Institution: President and Fellow of Harvard College , Washington State University , Western Washington University
EPA Project Officer: Aja, Hayley
Project Period: June 1, 2015 through May 31, 2017 (Extended to December 31, 2018)
Project Period Covered by this Report: June 1, 2017 through May 31,2018
Project Amount: $651,708
RFA: Systems-Based Research for Evaluating Ecological Impacts of Manufactured Chemicals (2014) RFA Text | Recipients Lists
Research Category: Chemical Safety for Sustainability
Objective:
1. Develop quantitative adverse outcome pathways (AOPs) for at least one legacy contaminant (e.g., mercury, PCBs) and one emerging contaminant (e.g., PFCs) to develop exposure-response profiles for use in a larger Bayesian Network – Relative Risk Modeling (BN-RRM); 2. 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, 3. 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.
Progress Summary:
We find there is no general framework for what is necessary to estimate risk and that there are many data gaps. These data gaps exist because of the lack of a suitable framework for setting research priorities and ensuring that a cause-effect pathway is strongly documented. The classic FIFRA or TSCA tiered system of testing do not establish causality in such a manner. Efforts have focused on continued development of two case studies. The first is site-specific, focusing on organophosphate exposures to salmon populations in the Pacific Northwest, a data-rich context based on a vetted AOP from the literature. The second is more general, focused on perfluorinated compound exposures to fish and birds using a “what-if” framework to back-calculate allowable exposures given target population declines.
Organophosphate Exposures and Salmon in the Pacific Northwest
Relying on a published (but largely unquantified) adverse outcome pathway analysis for acetylcholinesterase inhibition leading to acute mortality, we have developed a relative risk network model focusing on organophosphate pesticide (OP) exposure to Pacific salmon in four rivers in the Pacific Northwest. The model is currently parameterized for exposure to a single OP (chlorpyrifos) and incorporates habitat attributes (dissolved oxygen and water temperature) that independently contribute to population viability. Results suggest that OP exposures, dissolved oxygen, and temperature contribute similarly in each of the systems. At measured concentrations in Puget Sound, the OPs do have an effect but that effect does not overwhelm the environmental pathways. The effect is greater with all three OPs rather than just chlorpyrifos and with malathion at a higher concentration because it is the synergist.
Perfluorinated Compounds
Literature review of ecological effects of perfluorinated compounds indicates a clear concentration-response relationship between concentrations of PFOA in eggs/plasma and hatching success, the outcome of interest. We are in the process of developing conditional probability tables (CPTs) for the model. This involves detailed descriptions of how we derive each node based on four methods of deriving CPTs. To the extent possible, most of the model is built on measured values from the environment, exposure-response models based on data, and case learning.
Adverse Outcome Pathway
An Adverse Outcome Pathway (AOP) is a cause-effect model that links one or more molecular initiating events (MIEs) through a series of key events (KEs) to an adverse outcome of regulatory interest at the organism level (e.g., decreases in swimming speed). The AOP provides a mechanistic basis for linking key biological events at the molecular and cellular levels to risk assessment endpoints. In principle, the AOP reflects a pathway of normal physiological function in the absence of any particular stressor exposure, in this case, the AChE pathway.
Again, in principle, developing a quantitative AOP (qAOP) requires an understanding of the mathematical relationships between each MIE and KE in the pathway, and then an understanding of how exposure to one or more environmental stressors alters those relationships. In practice, AOPs are virtually never quantitative, and there are no examples of use of the AOP framework to estimate actual ecological risks that the research team could find in the literature. Even the AOPWiki (www.aopwiki.org), which represents a formal online community of practice for AOP development, has no examples of qAOPs. They are all qualitative. Some of the key challenges that occur when quantifying AOPs includes the role of adaptation, variability in responses across individuals, variability in responses within individuals, the role of compensating mechanisms and interactions with other endogenous and exogenous constituents, feedback mechanisms, and pathway saturation. Some of these responses require a more complex conceptual model of the relationship between MIEs and KEs, and others require a better understanding of uncertainty and variability, for example, in a probabilistic framework. The research team found no examples of a quantitative treatment of any of these potential modifiers to AOPs – even in the absence of a particular stressor exposure – in the literature.
Bayesian Networks
As stated in the objectives, the goal was to use BNs as a probabilistic, causal framework for linking stressors, AOPs, and population models to inform management decision making. BNs are directed acyclic graphical models that use probabilistic relationship in the form of conditional probability tables to describe relationships between ecological variables. A BN model consists of nodes and linkages to represent cause and effect relationships, which represent the variables and the causal pathways, respectively. Each BN has parent nodes and child nodes (e.g, MIEs and KEs). Child nodes receive input from the parent nodes and parent nodes have no inputs. Conditional probability tables (CPTs) within each node describe the conditional relationship between parent and child nodes. The parent nodes in the BN developed here include the environmental concentrations of OPs in the four rivers, and the season (which impacts both concentrations as well as the environmental proxies for water quality and habitat, including dissolved oxygen and water temperature.) The model was developed in Netica, which is publicly available and free for models with 15 nodes or less.
A BN contains the following components:
Node: A variable that can be divided into a number of states; State: Conditions of the variable often depicted as numerical ranges or ranks; Parent or Input Node: A node that provides information to another node; Child or Conditional Node: The node that receives information from a parent node; Link: A graphical representation of the causal pathway between parent node(s) and child node(s); Conditional Probability Table (CPT): the conditional probabilities between the occurrence of states in the parent nodes and the resulting probabilities of states in the child nodes.
Journal Articles:
No journal articles submitted with this report: View all 13 publications for this projectProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.