Integrating Microbial Community Data Into an Ecosystem-Scale Model to Predict Litter Decomposition in the Face of...

Area of Science:

• Ecology
• Biogeochemistry
• Computational Biology

Background:

• Litter decomposition is a key ecosystem process influencing global carbon flux.
• Ecosystem models predict decomposition but often lack microbial community data.
• Integrating microbial data can enhance model accuracy for climate change predictions.

Purpose of the Study:

• To calibrate and validate the MIcrobial-MIneral Carbon Stabilization (MIMICS) model using empirical microbial community data.
• To assess the impact of incorporating microbial drivers on predicting leaf litter decomposition.
• To evaluate model performance under a climate change scenario.

Main Methods:

• Conducted a leaf litterbag experiment across 10 U.S. National Ecological Observatory Network (NEON) sites.
• Calibrated the MIMICS model using empirical decomposition rates and microbial community data (copiotroph-to-oligotroph ratio).
• Validated the calibrated model and tested it with the SSP 3-7.0 climate change scenario.

Main Results:

• Incorporating empirical microbial drivers improved or matched model predictions of leaf litter decomposition.
• Model calibration revealed different underlying ecological dynamics compared to traditional methods.
• Climate change simulations showed potential increases in litter mass loss by up to 5% at some sites.

Conclusions:

• Integrating empirical microbial data into ecosystem models enhances predictions of litter decomposition.
• This approach improves understanding of carbon cycle-climate feedbacks under climate change.
• The study provides a framework for incorporating microbial functional group data into process-based models.