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Merging validation and evaluation of ecological models to ‘evaludation’: A review of terminology and a practical approach


  • Augusiak, Jacqueline
  • Van den Brink, Paul J.
  • Grimm, Volker


Confusion about model validation is one of the main challenges in using ecological models for decision support, such as the regulation of pesticides. Decision makers need to know whether a model is a sufficiently good representation of its real counterpart and what criteria can be used to answer this question. Unclear terminology is one of the main obstacles to a good understanding of what model validation is, how it works, and what it can deliver. Therefore, we performed a literature review and derived a standard set of terms. ‘Validation’ was identified as a catch-all term, which is thus useless for any practical purpose. We introduce the term ‘evaludation’, a fusion of ‘evaluation’ and ‘validation’, to describe the entire process of assessing a model's quality and reliability. Considering the iterative nature of model development, the modelling cycle, we identified six essential elements of evaludation: (i) ‘data evaluation’ for scrutinising the quality of numerical and qualitative data used for model development and testing; (ii) ‘conceptual model evaluation’ for examining the simplifying assumptions underlying a model's design; (iii) ‘implementation verification’ for testing the model's implementation in equations and as a computer programme; (iv) ‘model output verification’ for comparing model output to data and patterns that guided model design and were possibly used for calibration; (v) ‘model analysis’ for exploring the model's sensitivity to changes in parameters and process formulations to make sure that the mechanistic basis of main behaviours of the model has been well understood; and (vi) ‘model output corroboration’ for comparing model output to new data and patterns that were not used for model development and parameterisation. Currently, most decision makers require ‘validating’ a model by testing its predictions with new experiments or data. Despite being desirable, this is neither sufficient nor necessary for a model to be useful for decision support. We believe that the proposed set of terms and its relation to the modelling cycle can help to make quality assessments and reality checks of ecological models more comprehensive and transparent.

Suggested Citation

  • Augusiak, Jacqueline & Van den Brink, Paul J. & Grimm, Volker, 2014. "Merging validation and evaluation of ecological models to ‘evaludation’: A review of terminology and a practical approach," Ecological Modelling, Elsevier, vol. 280(C), pages 117-128.
  • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:117-128
    DOI: 10.1016/j.ecolmodel.2013.11.009

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    References listed on IDEAS

    1. James S. Hodges, 1991. "Six (Or So) Things You Can Do with a Bad Model," Operations Research, INFORMS, vol. 39(3), pages 355-365, June.
    2. Oriade, Caleb A. & Dillon, Carl R., 1997. "Developments in biophysical and bioeconomic simulation of agricultural systems: a review," Agricultural Economics, Blackwell, vol. 17(1), pages 45-58, October.
    3. Saul I. Gass, 1983. "Feature Article—Decision-Aiding Models: Validation, Assessment, and Related Issues for Policy Analysis," Operations Research, INFORMS, vol. 31(4), pages 603-631, August.
    4. Latombe, Guillaume & Parrott, Lael & Fortin, Daniel, 2011. "Levels of emergence in individual based models: Coping with scarcity of data and pattern redundancy," Ecological Modelling, Elsevier, vol. 222(9), pages 1557-1568.
    5. Landry, Maurice & Malouin, Jean-Louis & Oral, Muhittin, 1983. "Model validation in operations research," European Journal of Operational Research, Elsevier, vol. 14(3), pages 207-220, November.
    6. Aumann, Craig A., 2007. "A methodology for developing simulation models of complex systems," Ecological Modelling, Elsevier, vol. 202(3), pages 385-396.
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