A Framework for Linking Population Model Development with Ecological Risk Assessment Objectives

Authors

Sandy Raimondo, US Environmental Protection Agency
Matthew Etterson, US Environmental Protection Agency
Nathan Pollesch, US Environmental Protection Agency
Kristina Garber, US Environmental Protection Agency
Andrew Kanarek, US Environmental Protection Agency
Wade Lehmann, US Environmental Protection Agency
Jill Awkerman, US Environmental Protection Agency

Document Type

Article

Date of this Version

12-21-2017

Citation

Integr Environ Assess Manag 2018:369–380 DOI: 10.1002/ieam.2024

Comments

This document is a U.S. government work and is not subject to copyright in the United States.

Abstract

The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERAs) 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. Although 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 trade-offs of generality, realism, and precision for both ERAs and models. We approach the framework development from the perspective of regulators aimed at defining the needs of specific models commensurate with 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 regulatory frameworks, we classify ERA objectives by trade-offs of generality, realism, and precision and demonstrate how the output of population models developed with these same trade-offs informs the ERA objective. We examine attributes for both assessments and models that aid in the discussion of these trade-offs. The proposed framework will assist risk assessors and managers to identify models of appropriate complexity and to understand the utility and limitations of a model’s output and associated uncertainty in the context of their assessment goals.