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

Chiara Accolla, Maxime Vaugeois, Volker Grimm, Adrian P Moore, Pamela Rueda-Cediel, Amelie Schmolke, Valery E. Forbes

Research output: Contribution to journalReview articlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)521-540
Number of pages20
JournalIntegrated environmental assessment and management
Issue number3
Early online dateOct 30 2020
StatePublished - May 2021

Bibliographical note

Funding Information:
The authors declare no conflicts of interest. The work is supported by a Helmholtz International Fellow Award to VE Forbes, and by the University of Minnesota. We thank the anonymous reviewers for their constructive comments and suggestions.


  • Agent-based models
  • Ecological risk assessment
  • Good modeling practice
  • Matrix models
  • ODE models
  • Risk Assessment
  • Ecology

PubMed: MeSH publication types

  • Review
  • Journal Article


Dive into the research topics of 'A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent-Based Population Models for Ecological Risk Assessment'. Together they form a unique fingerprint.

Cite this