Using ergodic theory to assess the performance of ecosystem models

Area of Science:

• Ecological modeling
• Complex systems analysis
• Forestry science

Background:

• Ecosystem simulation models assess biogeochemical fluxes (energy, water, carbon, nitrogen).
• Model validation typically uses statistical methods focusing on static accuracy, neglecting dynamic behavior.
• Standard validation methods may fail to detect instabilities in model dynamics.

Purpose of the Study:

• Introduce ergodic theory for analyzing ecosystem model dynamics.
• Reconstruct attractor representations of model behavior from time series data.
• Assess model dynamics and identify potential instabilities missed by traditional methods.

Main Methods:

• Applied ergodic theory to analyze time series data from ecosystem model simulations.
• Reconstructed attractor representations of model behavior.
• Utilized standard statistical validation methods for comparison.

Main Results:

• Standard validation methods provided static accuracy but missed dynamic issues.
• Ergodic theory successfully reconstructed model attractors.
• Identified instability, a riddled basin configuration indicating chaotic behavior, in Cembran pine simulations using a generic parameter set.

Conclusions:

• Ergodic theory offers a powerful approach to assess ecosystem model dynamics.
• Reveals inconsistencies and potential chaotic behavior not detected by standard statistical validation.
• Enhances the reliability and understanding of ecosystem simulation models.