Confronting an individual-based simulation model with empirical community patterns of grasslands

Area of Science:

• Ecology
• Computational Biology
• Plant Science

Background:

• Grasslands are vital ecosystems influencing global biogeochemical cycles and biodiversity.
• Understanding grassland dynamics under global change requires sophisticated vegetation models.
• Model parameterization remains a significant challenge in accurately simulating grassland ecosystems.

Purpose of the Study:

• To test an individual- and process-based vegetation model for simulating grassland dynamics and structure.
• To evaluate the model's ability to reproduce empirical observations from monocultures and species mixtures.
• To identify key vegetation attributes for successful model parameterization.

Main Methods:

• An individual- and process-based model was parameterized for three grassland species.
• Simulated grassland dynamics were compared against observational data from monocultures and a two-species mixture.
• Sensitivity analysis was performed on the inverse model parameterization method.

Main Results:

• The model successfully reproduced general trends in vegetation cover, height, aboveground biomass, and leaf area index for monocultures and mixtures.
• The model accurately simulated average annual grassland cover in a species mixture (70% simulated vs. 77% observed).
• Leaf area index was identified as the most critical attribute for successful model parameterization.

Conclusions:

• Advanced vegetation models can capture general grassland dynamics, but parameterization requires careful consideration of multiple attributes.
• Improved, high-resolution grassland measurements are crucial for refining model accuracy and predictive power.
• Combining enhanced empirical data with advanced modeling approaches is essential for projecting grassland responses to global change.