ADVERSE OUTCOME PATHWAYS AND ECOLOGICAL RISK ASSESSMENT: BRIDGING TO POPULATION-LEVEL EFFECTS

Authors

VINCENT J. KRAMER, Dow AgroSciences
MATTHEW A. ETTERSON, U.S. Environmental Protection Agency
MARKUS HECKER, ENTRIX
CHERYL A. MURPHY, Michigan State University
GURITNO ROESIJADI, Pacific Northwest National Laboratory
DANIEL J. SPADE, University of Florida
JULANN A. SPROMBERG, National Oceanic and Atmospheric Administration Fisheries
MAGNUS WANG, RIFCON
GERALD T. ANKLEY, U.S. Environmental Protection Agency

Document Type

Article

Date of this Version

3-2-2010

Citation

Environmental Toxicology and Chemistry, Vol. 30, No. 1, pp. 64–76, 2011. DOI: 10.1002/etc.375

Comments

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

Abstract

Maintaining the viability of populations of plants and animals is a key focus for environmental regulation. Population-level responses integrate the cumulative effects of chemical stressors on individuals as those individuals interact with and are affected by their conspecifics, competitors, predators, prey, habitat, and other biotic and abiotic factors. Models of population-level effects of contaminants can integrate information from lower levels of biological organization and feed that information into higher-level community and ecosystem models. As individual-level endpoints are used to predict population responses, this requires that biological responses at lower levels of organization be translated into a form that is usable by the population modeler. In the current study, we describe how mechanistic data, as captured in adverse outcome pathways (AOPs), can be translated into modeling focused on population-level risk assessments. First, we describe the regulatory context surrounding population modeling, risk assessment and the emerging role of AOPs. Then we present a succinct overview of different approaches to population modeling and discuss the types of data needed for these models. We describe how different key biological processes measured at the level of the individual serve as the linkage, or bridge, between AOPs and predictions of population status, including consideration of community-level interactions and genetic adaptation. Several case examples illustrate the potential for use of AOPs in population modeling and predictive ecotoxicology. Finally, we make recommendations for focusing toxicity studies to produce the quantitative data needed to define AOPs and to facilitate their incorporation into population modeling.