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Salt Marsh Productivity Modeling Reveals Widespread Declining Belowground Biomass and Potential for Marsh Drowning
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  • Kyle D Runion,
  • Deepak Mishra,
  • Merryl Alber,
  • Mark Lever,
  • Jessica O'Connell
Kyle D Runion
University of Texas at Austin

Corresponding Author:[email protected]

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Deepak Mishra
University of Georgia
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Merryl Alber
University of Georgia
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Mark Lever
University of Texas at Austin
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Jessica O'Connell
Colorado State University
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Abstract

Salt marshes offer important ecosystem services to coastal populations and are key organismal habitats, but are under threat as a result of drowning related to sea level rise. The extent to which any given marsh is resilient to sea level rise depends on its ability to produce vertical accretion. This is primarily driven by belowground biomass (BGB) production, which explains why BGB may serve as an early warning sign of vulnerability to marsh drowning. Declines in plant productivity may occur in BGB before aboveground biomass (AGB), indicating that BGB may serve as an early warning sign of vulnerability to marsh drowning. However, landscape assessments of BGB are rare, as BGB is difficult to measure and has high spatiotemporal variability. The Belowground Ecosystem Resiliency Model (BERM) is a geospatial informatics tool to estimate whole-plant biomass (AGB and BGB) with satellite, climate, tide, and elevation data at a 30 m spatial scale and monthly time step. BERM was built using machine learning algorithms and extensive ground-truth calibration datasets in U.S. Georgia Spartina alterniflora marshes. Here, we aimed to characterize landscape salt marsh resilience with BERM. To do this, we generated S. alterniflora AGB and BGB predictions across the Georgia coast, covering an area of 691 km2, from 2014-2023 and identified biomass trends. We found broad declines in BGB alongside gains in AGB. A total of 74% of the marsh experienced a decrease in BGB, with an average annual trend of -0.91%. Simultaneously, 88% of the marsh increased in AGB at an average rate of 0.66% per year. We classified much of the marsh (27% of area) as vulnerable to drowning (defined as a decline in BGB that exceeded model error). We also investigated biomass trends against flooding frequency, where flooding was derived via a remote sensing-based model. BGB losses were greater with increasing flooding frequencies, suggesting that accelerated SLR will further reduce productivity. Based on BERM predictions, early stage marsh drowning is likely widespread, and management actions to conserve ecosystem services are an urgent need.
21 Nov 2024Submitted to ESS Open Archive
28 Nov 2024Published in ESS Open Archive