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Assessment of uncertainty in functional-structural plant models.
E David Ford1, Maureen C Kennedy
1School of Forest Resources, University of Washington, Seattle, WA 98195-2100, USA. edford@u.washington.edu
Annals of Botany
|May 20, 2011
Summary
This study presents a method to reduce uncertainty in functional-structural plant models (FSPMs) by using multi-criteria assessment and a Pareto Frontier. This approach helps identify effective model parameters and understand plant processes.
Area of Science:
- Plant modeling
- Computational biology
- Ecology
Background:
- Functional-structural plant models (FSPMs) integrate plant physiology and morphology.
- FSPMs face uncertainty regarding component selection, representation, output simulation, and data quality.
- A procedure is introduced to define and reduce FSPM uncertainty.
Purpose of the Study:
- To develop a method for assessing FSPM uncertainty.
- To demonstrate how uncertainty can be reduced through parameter optimization.
- To inform model development and application in plant science.
Main Methods:
- Utilizing multi-criteria assessment with numerous model-calculated variables.
- Employing evolutionary computation to search for optimal model parameters.
- Defining a Pareto Frontier to represent trade-offs in model performance.
Main Results:
- Applied multi-criteria assessment to BRANCHPRO, an FSPM for *Pseudotsuga menziesii* foliage reiteration.
- Developed a geometric probability model for bud growth explaining reiteration patterns.
- Linked reiteration patterns to species longevity.
Conclusions:
- FSPMs require simultaneous simulation of multiple criteria for robust assessment.
- A Pareto Frontier effectively visualizes model uncertainty sources.
- This method enhances the reliability and interpretability of FSPMs.
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