Science Inventory

Endogenous Lifecycle Models for Chemical Risk Assessment.

Citation:

Etterson, M. AND G. Ankley. Endogenous Lifecycle Models for Chemical Risk Assessment. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55(23):15596-15608, (2021). https://doi.org/10.1021/acs.est.1c04791

Impact/Purpose:

This manuscript defines a new class of ecological models for translating toxicity data to ecologically relevant outcomes that can be used for risk assessment. The manuscript is intended for a wide audience, including risk assessors, quantitative ecologists, toxicologists, and modelers.

Description:

Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Six characteristics of the ELM approach are reviewed and illustrated with two ELM examples, the first for a general passerine lifecycle and the second for bald eagle (Haliaeetus leucocephalus). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a midtier risk assessment tool when lower tier analyses suggest potential risk.  

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:12/07/2021
Record Last Revised:04/13/2022
OMB Category:Other
Record ID: 354565