Department of Ecology, Evolution, and Behavior, College of Biological Sciences, University of Minnesota, St Paul, Minnesota, USA.
Department of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
Integr Environ Assess Manag. 2021 May;17(3):521-540. doi: 10.1002/ieam.4362. Epub 2020 Dec 13.
Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521-540. © 2020 SETAC.
种群模型可为生态风险评估（ERA）提供有价值的工具。现在，有越来越多的关于模型开发和记录的工作可用于指导建模者和风险评估者解决不同的 ERA 问题。然而，对于 ERA 的种群模型仍然存在一些误解，并且监管机构和建模者之间的沟通仍然可能因基础形式主义、不同模型类型的实现和复杂性缺乏清晰度而受阻。特别是，对于不同类型的模型之间的差异以及包括或忽略生物体之间以及它们与环境之间的相互作用的含义存在混淆。在这篇综述中，我们提供了与 ERA 相关的种群模型中所代表的关键特征的概述，这些特征包括密度依赖性、空间异质性、外部驱动因素、随机性、生活史特征、行为、能量学，以及如何在模型中整合暴露和效应。我们区分了 3 种广义定义的种群模型类型（非结构化、结构化和基于代理），并解释了它们如何表示这些关键特征。根据 ERA 背景，某些模型特征比其他特征更为重要，这可以为模型类型选择、特征实现以及可能收集更多数据提供信息。我们表明，几乎所有特征都可以包括，而不论形式化如何，但某些特征在某些模型类型中更易于或更难纳入。我们还分析了在已发表的种群模型中，这些关键特征如何被用作非结构化、结构化和基于代理的模型。本综述的总体目标是通过模型用户和评估者在考虑种群模型在 ERA 中应用的潜力和充分性时，提高他们的信心和理解。
