You are here:
A Framework for Linking Population Model Development with Ecological Risk Assessment Objectives
Citation:
Raimondo, Sandy, M. Etterson, N. Pollesch, K. Garber, A. Kanarek, W. Lehmann, AND J. Awkerman. A Framework for Linking Population Model Development with Ecological Risk Assessment Objectives. SETAC North America 38th Annual Meeting, Minneapolis, MN, November 12 - 16, 2017.
Impact/Purpose:
The work describes a framework for development of population models in ecological risk assessment under various statutes.The work describes a framework for development of population models in ecological risk assessment under various statutes.
Description:
The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERA) has been demonstrated extensively over the past few decades. There is little debate about the utility of these models to translate multiple organism-level endpoints into a holistic interpretation of effect to the population; however, there continues to be a struggle for actual application of these models as a common practice in ERA despite the well-documented scientific basis. While general frameworks for developing models for ERA have been proposed, there is limited guidance on when models should be used, in what form, and how to interpret model output to inform the risk manager’s decision. We propose a framework for developing and applying population models in regulatory decision making that focuses on tradeoffs of generality, realism, and precision for both ERAs and models. We initiate the framework development with a conversation between regulators and modelers aimed at defining the added value of specific types of model output relative to the assessment objective. We explore why models are not widely used by comparing their requirements and limitations with the needs of regulators. Using a series of case studies under specific US regulatory frameworks (e.g., FIFRA, CERCLA, etc.), we classify ERA objectives by tradeoffs of generality, realism, and precision and demonstrate how the output of population models developed with these same tradeoffs informs the ERA objective. We discuss attributes for both ERA and models that can be used to classify each with respect to these trade-offs. The proposed framework will assist risk assessors and managers in identifying models of appropriate complexity and understanding the utility and limitations of the model’s output and associated uncertainty in the context of their assessment goals