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Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment
Author(s) -
Murphy Cheryl A,
Nisbet Roger M,
Antczak Philipp,
GarciaReyero Natàlia,
Gergs Andre,
Lika Konstadia,
Mathews Teresa,
Muller Erik B,
Nacci Diane,
Peace Angela,
Remien Christopher H,
Schultz Irvin R,
Stevenson Louise M,
Watanabe Karen H
Publication year - 2018
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.4063
Subject(s) - stressor , adverse outcome pathway , modular design , computer science , population , risk analysis (engineering) , merge (version control) , ecology , biochemical engineering , environmental resource management , biology , engineering , environmental science , computational biology , business , demography , neuroscience , information retrieval , sociology , operating system
A working group at the National Institute for Mathematical and Biological Synthesis (NIMBioS) explored the feasibility of integrating 2 complementary approaches relevant to ecological risk assessment. Adverse outcome pathway (AOP) models provide “bottom‐up” mechanisms to predict specific toxicological effects that could affect an individual's ability to grow, reproduce, and/or survive from a molecular initiating event. Dynamic energy budget (DEB) models offer a “top‐down” approach that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources. Thus, AOP models quantify linkages between measurable molecular, cellular, or organ‐level events, but they do not offer an explicit route to integratively characterize stressor effects at higher levels of organization. While DEB models provide the inherent basis to link effects on individuals to those at the population and ecosystem levels, their use of abstract variables obscures mechanistic connections to suborganismal biology. To take advantage of both approaches, we developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage‐inducing processes affecting DEB variables and rates. We report on the type and structure of data that are generated for AOP models that may also be useful for DEB models. We also report on case studies under development that merge information collected for AOPs with DEB models and highlight some of the challenges. Finally, we discuss how the linkage of these 2 approaches can improve ecological risk assessment, with possibilities for progress in predicting population responses to toxicant exposures within realistic environments. Integr Environ Assess Manag 2018;14:615–624. © 2018 SETAC