A strategy to improve the contribution of complex simulation models to ecological theory

Research output: Contribution to journalArticleAcademicpeer-review

112 Citations (Scopus)


Large models are commonly used for simulations of past results or future scenario's of ecosystems. However, such models have been criticized, mainly because the causes of their results are hard to understand. Simple models have contributed more to the development of ecological theory. However, simple models usually neglect important aspects such as spatial heterogeneity and individual variability, and may focus too much on one of several possible causes of a phenomenon. In this paper, we present a strategy that we have found useful for improving our understanding of the way in which complex models generate their results. The strategy consists of three phases. The first phase is a thorough analysis of the model behavior with respect to a selected set of parameters ('scrutinizing'). Secondly, similar analyses are done with several simplified versions of the model ('simplifying'). In this step, relationships between state variables or species that may potentially cause incomprehensible behavior are replaced by fixed values or highly simplified relations. The last step is to explain the differences between the full and the simplified versions and to discuss the results in the light of the existing ecological theory ('synthesizing'). We argue that this way of combining analyses of simple and more elaborate models is a powerful way to gain understanding of complex systems.
Original languageEnglish
Pages (from-to)153-164
JournalEcological Modelling
Issue number2-4
Publication statusPublished - 2005


  • individual-based model
  • multiple stable states
  • oriented simulation
  • population-dynamics
  • shallow lakes
  • eutrophication
  • communities
  • macrophytes
  • competition
  • validation


Dive into the research topics of 'A strategy to improve the contribution of complex simulation models to ecological theory'. Together they form a unique fingerprint.

Cite this