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Making ecological models adequate

Research output: Contribution to journalArticle

  • Wayne M. Getz
  • Charles R. Marshall
  • Colin J. Carlson
  • Luca Giuggioli
  • Sadie J. Ryan
  • Stephanie S. Romañach
  • Carl Boettiger
  • Samuel D. Chamberlain
  • Laurel Larsen
  • Paolo D’Odorico
  • David O’Sullivan
Original languageEnglish
Pages (from-to)153-166
Number of pages14
JournalEcology Letters
Issue number2
Early online date27 Dec 2017
DateAccepted/In press - 12 Nov 2017
DateE-pub ahead of print - 27 Dec 2017
DatePublished (current) - Feb 2018


Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here we examine common issues in ecological modeling and suggest criteria for improving modeling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterization and poor model performance. Conversely, a lack of mechanistic details may limit a model’s ability to predict ecological systems’ responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and that sensitivity and validity of analyses. We also need to improve model elaboration, refinement, and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

    Research areas

  • appropriate complexity modelling, coarse graining, disease modelling, ecosystems restoration models, environmental management models, extinction risk assessment, hierarchical modelling

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