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Reducing model complexity via output sensitivity

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  • Lawrie, Jock
  • Hearne, John

Abstract

Ecosystem models help us understand the mechanisms that influence ecosystem health indicators. However, if they are too complex, these mechanisms can be difficult to identify. On the other hand, if they are too simple the mechanisms may be distorted or even absent. Determining an appropriate level of model complexity is therefore desirable. This paper introduces two model simplification methods that are based on the sensitivity of performance measures to model rates and components. The first method identifies rates that have little influence on the performance measures and subsequently eliminates them. The second identifies, for a given performance measure, state variables that can be made constant. The methods can be implemented automatically, so that familiarity with the model is not required a priori. Demonstrating with a biogeochemical model of Port Phillip Bay, Australia, we find that significant reduction in model complexity is possible, including reductions in model order. Also, the process of implementing the methods reveals insights into the system that were not obvious beforehand.

Suggested Citation

  • Lawrie, Jock & Hearne, John, 2007. "Reducing model complexity via output sensitivity," Ecological Modelling, Elsevier, vol. 207(2), pages 137-144.
    • Handle: RePEc:eee:ecomod:v:207:y:2007:i:2:p:137-144
    DOI: 10.1016/j.ecolmodel.2007.04.013
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    Cited by:

    1. Gibbons, J.M. & Wood, A.T.A. & Craigon, J. & Ramsden, S.J. & Crout, N.M.J., 2010. "Semi-automatic reduction and upscaling of large models: A farm management example," Ecological Modelling, Elsevier, vol. 221(4), pages 590-598.
    2. Bar Massada, Avi & Carmel, Yohay, 2008. "Incorporating output variance in local sensitivity analysis for stochastic models," Ecological Modelling, Elsevier, vol. 213(3), pages 463-467.
    3. Piqueira, J.R.C. & de Mattos, S.H.V.L. & Vasconcelos-Neto, J., 2009. "Measuring complexity in three-trophic level systems," Ecological Modelling, Elsevier, vol. 220(3), pages 266-271.
    4. Muzy, A. & Nutaro, J.J. & Zeigler, B.P. & Coquillard, P., 2008. "Modeling and simulation of fire spreading through the activity tracking paradigm," Ecological Modelling, Elsevier, vol. 219(1), pages 212-225.
    5. Bartsev, Sergey I. & Degermendzhi, Andrey G. & Erokhin, Dmitry V., 2008. "Principle of the worst scenario in the modelling past and future of biosphere dynamics," Ecological Modelling, Elsevier, vol. 216(2), pages 160-171.

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