Abstract
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass.
Highlights
Remote-sensing models of aboveground biomass are commonly derived from optical data [1,2], monitoring belowground biomass and root:shoot ratios to understand whole plant production patterns remains challenging
We present a hybrid modeling approach for scaling up field measured point estimates of belowground biomass and root:shoot ratios using remote-sensing and linear modeling
In addition we explored single species vs. mixed species models, and we evaluated the physical basis for spectral foliar N models by building from our earlier experimental study and examining the contribution of key wavelengths
Summary
Background and RationaleRemote-sensing models of aboveground biomass are commonly derived from optical data [1,2], monitoring belowground biomass and root:shoot ratios to understand whole plant production patterns remains challenging. Understanding dynamics in belowground biomass is important because roots and rhizomes are the precursors and dominant components of soil organic carbon [3]. Our model system was emergent vegetation within coastal freshwater marsh We selected this system because there is an urgent need to understand dynamics regulating belowground biomass within coastal wetlands. Peat up to 15 m deep, a substantial carbon pool, has been observed in some coastal freshwater wetlands [6], and is higher than in most other natural ecosystems, representing 16%–33% of the global soil carbon pool [7]. The combination of sea level rise, saltwater intrusion, peat collapse and subsidence of coastal marsh surface elevation may result in substantial global wetland losses [8,9,10,11]. Preservation of coastal marsh is an essential conservation goal, and an important first step involves understanding belowground biomass dynamics across landscapes
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