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Moving beyond Risk Quotients: Advancing Ecological Risk Assessment to Reflect Better, More Robust and Relevant

Sandy Raimondo1, Valery E Forbes2

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Summary

Ecological Risk Assessments (ERAs) should move beyond simple estimates to use advanced mechanistic effect models. These models, guided by Pop-GUIDE, reduce uncertainty and provide more accurate chronic risk determinations for species sustainability.

Keywords:
chemical effectsmechanistic effect modelingpopulationsrisk management

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Area of Science:

  • Ecological Risk Assessment (ERA)
  • Environmental Toxicology
  • Computational Ecology

Background:

  • Current Ecological Risk Assessments (ERAs) predominantly rely on deterministic point estimates and Levels of Concern (LOCs).
  • The application of scientifically advanced mechanistic effect models in ERAs remains limited, especially in the United States.
  • Existing methods often contain substantial uncertainty, impacting the accuracy of risk evaluations.

Purpose of the Study:

  • To advocate for the adoption of mechanistic effect models in ERAs, moving beyond traditional deterministic approaches.
  • To highlight the utility and state-of-the-science in effect modeling for ERAs, particularly using the Population modeling Guidance, Use, Interpretation, and Development for ERA (Pop-GUIDE).
  • To demonstrate how mechanistic models can significantly reduce uncertainty in risk assessments.

Main Methods:

  • Review and application of mechanistic effect models, including demographic, population, and agent-based models.
  • Demonstration of the Population modeling Guidance, Use, Interpretation, and Development for ERA (Pop-GUIDE) framework.
  • Comparison of outcomes from mechanistic models versus standard deterministic methods in ERA.

Main Results:

  • Mechanistic models offer more ecologically relevant effect endpoints compared to deterministic estimates.
  • Models incorporating species life histories and environmental factors substantially reduce uncertainty in risk assessments.
  • The Pop-GUIDE framework facilitates the effective implementation of mechanistic models in ERAs.

Conclusions:

  • There is a critical need to transition from deterministic endpoints to robust mechanistic models for ERAs.
  • Mechanistic models, guided by Pop-GUIDE, provide a more accurate and ecologically relevant basis for determining chronic risks.
  • Adopting advanced modeling approaches is essential for improving the sustainability assessments of chemical exposures on diverse organisms.