Tidal wetlands produce long-term soil organic carbon (C) stocks. Thus for carbon accounting purposes, we need accurate and precise information on the magnitude and spatial distribution of those stocks. We assembled and analyzed an unprecedented soil core dataset, and tested three strategies for mapping carbon stocks: applying the average value from the synthesis to mapped tidal wetlands, applying models fit using empirical data and applied using soil, vegetation and salinity maps, and relying on independently generated soil carbon maps. Soil carbon stocks were far lower on average and varied less spatially and with depth than stocks calculated from available soils maps. Further, variation in carbon density was not well-predicted based on climate, salinity, vegetation, or soil classes. Instead, the assembled dataset showed that carbon density across the conterminous united states (CONUS) was normally distributed, with a predictable range of observations. We identified the simplest strategy, applying mean carbon density (27.0 kg C m−3), as the best performing strategy, and conservatively estimated that the top meter of CONUS tidal wetland soil contains 0.72 petagrams C. This strategy could provide standardization in CONUS tidal carbon accounting until such a time as modeling and mapping advancements can quantitatively improve accuracy and precision.
Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from freely available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error
Coastal wetland plants are adapted to varying degrees of inundation. However, functional relationships between inundation and productivity are poorly characterized for most species. Determining species-specific tolerances to inundation is necessary to evaluate sea-level rise (SLR) effects on future marsh plant community composition, quantify organic matter inputs to marsh accretion, and inform predictive modeling of tidal wetland persistence. In 2 macrotidal estuaries in the northeast Pacific we grew 5 common species in experimental mesocosms across a gradient of tidal elevations to assess effects on growth. We also tested whether species abundance distributions along elevation gradients in adjacent marshes matched productivity profiles in the mesocosms. We found parabolic relationships between inundation and total plant biomass and shoot counts in Spartina foliosa and Bolboschoenus maritimus in California, USA, and in Carex lyngbyei in Oregon, USA, with maximum total plant biomass occurring at 38, 28, and 15% time submerged, respectively. However, biomass of Salicornia pacifica and Juncus balticus declined monotonically with increasing inundation. Inundation effects on the ratio of belowground to aboveground biomass varied inconsistently among species. In comparisons of field distributions with mesocosm results, B. maritimus, C. lyngbyei and J. balticus were abundant in marshes at or above elevations corresponding with their maximum productivity; however, S. foliosa and S. pacifica were frequently abundant at lower elevations corresponding with sub-optimal productivity. Our findings show species-level differences in how marsh plant growth may respond to future SLR and highlight the sensitivity of high marsh species such as S. pacifica and J. balticus to increases in flooding.
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