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Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem services. Salt marsh biomass, which can strongly influence such services, varies over time in response to hydrologic conditions and other environmental drivers. We used gap-filled monthly observations ofSpartina alternifloraaboveground biomass derived from Landsat 5 and Landsat 8 satellite imagery from 1984-2018 to analyze temporal patterns in biomass in comparison to air temperature, precipitation, river discharge, nutrient input, sea level, and drought index for a southeastern US salt marsh. Wavelet analysis and ensemble empirical mode decomposition identified month to multi-year periodicities in both plant biomass and environmental drivers. Wavelet coherence detected cross-correlations between annual biomass cycles and precipitation, temperature, river discharge, nutrient concentrations (NOxand PO43–) and sea level. At longer periods we detected coherence between biomass and all variables except precipitation. Through empirical dynamic modeling we showed that temperature, river discharge, drought, sea level, and river nutrient concentrations were causally connected to salt marsh biomass and exceeded the confounding effect of seasonality. This study demonstrated the insights into biomass dynamics and causal connections that can be gained through the analysis of long-term data.
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BERM: a Belowground Ecosystem Resiliency Model for estimating Spartina alterniflora belowground biomass
Summary Spatiotemporal patterns ofSpartina alterniflorabelowground biomass (BGB) are important for evaluating salt marsh resiliency. To solve this, we created the BERM (Belowground Ecosystem Resiliency Model), which estimates monthly BGB (30‐m spatial resolution) from freely available data such as Landsat‐8 and Daymet climate summaries.Our modeling framework relied on extreme gradient boosting, and used field observations from four Georgia salt marshes as ground‐truth data. Model predictors included estimated tidal inundation, elevation, leaf area index, foliar nitrogen, chlorophyll, surface temperature, phenology, and climate data. The final model included 33 variables, and the most important variables were elevation, vapor pressure from the previous four months, Normalized Difference Vegetation Index (NDVI) from the previous five months, and inundation.Root mean squared error for BGB from testing data was 313 g m−2(11% of the field data range), explained variance (R2) was 0.62–0.77. Testing data results were unbiased across BGB values and were positively correlated with ground‐truth data across all sites and years (r = 0.56–0.82 and 0.45–0.95, respectively).BERM can estimate BGB withinSpartina alterniflorasalt marshes where environmental parameters are within the training data range, and can be readily extended through a reproducible workflow. This provides a powerful approach for evaluating spatiotemporal BGB and associated ecosystem function.
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- Award ID(s):
- 1832178
- PAR ID:
- 10447577
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 232
- Issue:
- 1
- ISSN:
- 0028-646X
- Format(s):
- Medium: X Size: p. 425-439
- Size(s):
- p. 425-439
- Sponsoring Org:
- National Science Foundation
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