Capturing ecology in modeling approaches applied to environmental risk assessment of endocrine active chemicals in fish

Abstract

Endocrine active chemicals (EACs) are widespread in freshwater environments and both laboratory and field based studies have shown reproductive effects in fish at environmentally relevant exposures. Environmental risk assessment (ERA) seeks to protect wildlife populations and prospective assessments rely on extrapolation from individual-level effects established for laboratory fish species to populations of wild fish using arbitrary safety factors. Population susceptibility to chemical effects, however, depends on exposure risk, physiological susceptibility, and population resilience, each of which can differ widely between fish species. Population models have significant potential to address these shortfalls and to include individual variability relating to life-history traits, demographic and density-dependent vital rates, and behaviors which arise from inter-organism and organism–environment interactions. Confidence in population models has recently resulted in the EU Commission stating that results derived from reliable models may be considered when assessing the relevance of adverse effects of EACs at the population level. This review critically assesses the potential risks posed by EACs for fish populations, considers the ecological factors influencing these risks and explores the benefits and challenges of applying population modeling (including individual-based modeling) in ERA for EACs in fish. We conclude that population modeling offers a way forward for incorporating greater environmental relevance in assessing the risks of EACs for fishes and for identifying key risk factors through sensitivity analysis. Individual-based models (IBMs) allow for the incorporation of physiological and behavioral endpoints relevant to EAC exposure effects, thus capturing both direct and indirect population-level effects.

Keywords:

• Environmental risk assessment
• endocrine active chemicals
• population sensitivity
• population resilience
• life-history strategy
• density dependence
• population models
• individual-based models

Acknowledgments

We would like to thank two anonymous referees for their valuable comments and suggestions on this manuscript.

Declaration of interest

The employment affiliation of the authors is shown on the cover page. The authors at the University of Exeter were supported by a BBSRC iCASE grant co-funded by Syngenta (BB/M503423/1). The authors employed by Syngenta prepared the review during the normal course of their employment. The paper is the exclusive professional work product of the authors. None of the authors has appeared in any litigation or regulatory proceedings during the past 5 years related to the contents of this paper.

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