Coastal wetlands are major global carbon sinks; however, they are heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, greenhouse gas (GHG) fluxes were compared among major plant‐defined zones during growing seasons. Carbon dioxide (CO2) and methane (CH4) fluxes were compared in two mensurative experiments during summer months (2012–2014) that included low marsh (Spartina alterniflora), high marsh (Distichlis spicata and Juncus gerardii‐dominated), invasive Phragmites australis zones, and unvegetated ponds. Day‐ and nighttime fluxes were also contrasted in the native marsh zones. N2O fluxes were measured in parallel with CO2 and CH4 fluxes, but were not found to be significant. To test the relationships of CO2 and CH4 fluxes with several native plant metrics, a multivariate nonlinear model was used. Invasive P. australis zones (−7 to −15 μmol CO2·m−2·s−1) and S. alterniflora low marsh zones (up to −14 μmol CO2·m−2·s−1) displayed highest average CO2 uptake rates, while those in the native high marsh zone (less than −2 μmol CO2·m−2·s−1) were much lower. Unvegetated ponds were typically small sources of CO2 to the atmosphere (<0.5 μmol CO2·m−2·s−1). Nighttime emissions of CO2 averaged only 35% of daytime uptake in the low marsh zone, but they exceeded daytime CO2 uptake by up to threefold in the native high marsh zone. Based on modeling, belowground biomass was the plant metric most strongly correlated with CO2 fluxes in native marsh zones, while none of the plant variables correlated significantly with CH4 fluxes. Methane fluxes did not vary between day and night and did not significantly offset CO2 uptake in any vegetated marsh zones based on sustained global warming potential calculations. These findings suggest that attention to spatial zonation as well as expanded measurements and modeling of GHG emissions across greater temporal scales will help to improve accuracy of carbon accounting in coastal marshes.

Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO 2 ) and methane (CH 4 ) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO 2 and CH 4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May-October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO 2 uptake and low CH 4 emission, which did not significantly vary with the nitrogen loading gradient (5-126 kg · ha À1 · year À1 ) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4-5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law-based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash-Sutcliffe Efficiency = 0.80-0.91). The scaling models are available as a user-friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment.

We used a simple, systematic data-analytics approach to determine the relative linkages of different climate and environmental variables with the canopy-level, half-hourly CO2 fluxes of US deciduous forests. Multivariate pattern recognition techniques of principal component and factor analyses were utilized to classify and group climatic, environmental, and ecological variables based on their similarity as drivers, examining their interrelation patterns at different sites. Explanatory partial least squares regression models were developed to estimate the relative linkages of CO2 fluxes with the climatic and environmental variables. Three biophysical process components adequately described the system-data variances. The 'radiation-energy' component had the strongest linkage with CO2 fluxes, whereas the 'aerodynamic' and 'temperature-hydrology' components were low to moderately linked with the carbon fluxes. On average, the 'radiation-energy' component showed 5 and 8 times stronger carbon flux linkages than that of the 'temperature-hydrology' and 'aerodynamic' components, respectively. The similarity of observed patterns among different study sites (representing gradients in climate, canopy heights and soil-formations) indicates that the findings are potentially transferable to other deciduous forests. The similarities also highlight the scope of developing parsimonious data-driven models to predict the potential sequestration of ecosystem carbon under a changing climate and environment. The presented data-analytics provides an objective, empirical foundation to obtain crucial mechanistic insights; complementing process-based model building with a warranted complexity. Model efficiency and accuracy (R(2) = 0.55-0.81; ratio of root-mean-square error to the observed standard deviations, RSR = 0.44-0.67) reiterate the usefulness of multivariate analytics models for gap-filling of instantaneous flux data.

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