A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent‐Based Population Models for Ecological Risk Assessment

Abstract

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

Pop‐guide: Population modeling guidance, use, interpretation, and development for ecological risk assessment

“…Complexity and uncertainty are central to both ERAs and model building (Brooks and Tobias 1996; Schmolke, Thorbek, Chapman et al 2010; Accolla et al 2020) and Pop‐GUIDE is developed to align these attributes so that model outputs are of a sufficient quality to serve as the basis for a decision (USEPA 1998). For example, ERAs at the screening or national level may contain low spatiotemporal resolution, be general to species or location, and can thus be relatively less complex than those used beyond the screening level.…”

“…However, the model implementation and evaluation described in Phase 5 assumes the modeler extends the conceptual model to a fully parameterized computational tool following best modeling practices. Further guidance on model type and the implementation of different key features in population models of relevance for ERA is provided by Accolla et al (2020). Following model parameterization (direct or via calibration), model analysis should be performed to evaluate the behavior and performance of the model to provide information on its uncertainty and how it can be used for regulatory purposes (EFSA 2014).…”

Validation of freshwater mussel life‐history strategies: A database and multivariate analysis of freshwater mussel life‐history traits

“…Use of population modelling to inform risk assessment has been gaining acceptance in recent years for addressing some of the limitations of traditional ecological risk assessment (European Food Safety Authority Panel on Plant Protection Products and their Residues, 2014). There is an expanding body of literature demonstrating how population models can be used to advance risk assessment (Forbes et al, 2016;Accolla et al, 2020), and there are increasing efforts to guide and standardize their development (Schmolke et al, 2017;Accolla et al, 2020;Raimondo et al, 2020).…”

Predicting Combined Effects of Chemical Stressors: Population‐Level Effects of Organic Chemical Mixtures with a Dynamic Energy Budget Individual‐Based Model

“…Out of 100 simulation runs, approximately 58 populations persisted after the acute pulse compared to one in three populations in the data. This shows the importance of including stochasticity in effect assessment, and IBMs deal particularly well with stochasticity compared to other types of population models (Acolla et al, 2020). Based on a Bernoulli trial experiment with a chance of success (=persistence) of 58%, we estimate an exact probability of 31% that one in three replicates is observed to persist for the dicofol-HCH treatment (Supporting Information, Table H8).…”