BERM: a Belowground Ecosystem Resiliency Model for estimating Spartina alterniflora belowground biomass

Area of Science:

• Coastal ecology
• Salt marsh ecosystem dynamics
• Biomass estimation

Background:

• Belowground biomass (BGB) in Spartina alterniflora salt marshes is crucial for assessing ecosystem resilience.
• Accurate spatiotemporal BGB data are needed for effective marsh management.

Purpose of the Study:

• To develop and validate the Belowground Ecosystem Resiliency Model (BERM) for estimating monthly BGB.
• To provide a tool for evaluating salt marsh resiliency using freely available data.

Main Methods:

• Utilized extreme gradient boosting with field observations from four Georgia salt marshes.
• Incorporated predictors including tidal inundation, elevation, NDVI, and climate data.
• Model developed with 30-m spatial resolution using Landsat-8 and Daymet data.

Main Results:

• The final BERM model included 33 variables, with elevation, prior vapor pressure, prior NDVI, and inundation being most important.
• Achieved a root mean squared error of 313 g m⁻² (11% of range) and explained variance (R²) of 0.62-0.77.
• Testing data showed unbiased results and strong positive correlations with ground-truth data (r = 0.56-0.95).

Conclusions:

• BERM provides a robust method for estimating spatiotemporal BGB in Spartina alterniflora salt marshes.
• The model is extendable and offers a reproducible workflow for ecosystem function evaluation.
• This approach aids in understanding and managing salt marsh resiliency.