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将生态学纳入应用于鱼类内分泌干扰化学物环境风险评估的建模方法中。

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

机构信息

a College of Life and Environmental Sciences , University of Exeter , Exeter , UK.

b Syngenta, Jealott's Hill International Research Centre , Bracknell , Berkshire , UK.

出版信息

Crit Rev Toxicol. 2018 Feb;48(2):109-120. doi: 10.1080/10408444.2017.1367756. Epub 2017 Sep 20.

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.

摘要

内分泌干扰化学物质(EACs)广泛存在于淡水环境中,实验室和现场研究都表明,在环境相关暴露下,鱼类的生殖系统会受到影响。环境风险评估(ERA)旨在保护野生动物种群,而前瞻性评估依赖于从实验室鱼类物种的个体水平效应推断到野生鱼类种群,使用任意的安全系数。然而,种群对化学效应的敏感性取决于暴露风险、生理敏感性和种群恢复力,这些因素在鱼类之间可能有很大差异。种群模型具有很大的潜力来解决这些不足,并包括与生活史特征、种群密度和密度依赖性关键生命率以及由生物体间和生物体-环境相互作用引起的行为相关的个体变异性。最近,种群模型的可信度使得欧盟委员会表示,在评估内分泌干扰化学物质对种群水平的不利影响的相关性时,可以考虑从可靠模型中得出的结果。 本综述批判性地评估了 EAC 对鱼类种群构成的潜在风险,考虑了影响这些风险的生态因素,并探讨了在 EAC 的 ERA 中应用种群模型(包括基于个体的模型)的益处和挑战。我们的结论是,种群模型为在评估 EAC 对鱼类的风险时纳入更大的环境相关性提供了一种方法,并通过敏感性分析确定关键风险因素。基于个体的模型(IBMs)允许纳入与 EAC 暴露效应相关的生理和行为终点,从而捕获直接和间接的种群水平效应。

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