Spectral evidence for substrate availability rather than environmental control of methane emissions from a coastal forested wetland

Knowledge of the dynamics of methane (CH4) fluxes across coastal freshwater forested wetlands, such as those found in the southeastern US remains limited. In the current study, we look at the spectral properties of ecosystem net CH4 exchange (NEECH4) time series, and its cospectral behavior with key environmental conditions (temperature (T-s5), water table (WTD) and atmospheric pressure (P-a)) and physiological fluxes (photosynthesis (GPP), transpiration (LE), sap flux (J(s))) using data from a natural bottomland hardwood swamp in eastern North Carolina. NEECH4 fluxes were measured over five years (2012 - 2016) that included both wet and dry years. During the growing season, strong cospectral peaks at diurnal scale were detected between CH4 efflux and GPP, LE and J(s). This suggests that the well understood diurnal cycles in the latter processes may affect CH4 production through substrate availability (GPP) and transport (sap flow and LE). The causality between different time series was established by the magnitude and consistency of phase shifts. The causal effect of T-s5 and P-a were ruled out because despite cospectral peaks with CH4, their phase relationships were inconsistent. The effect of fluctuations in WTD on CH4 efflux at synoptic scale lacked clear indications of causality, possibly due to time lags and hysteresis. The stronger cospectral peak with ecosystem scale LE rather than J(s) suggested that the evaporative component of LE contributed equally with plant transpiration. Hence, we conclude that while the emission of dissolved gases through plants likely takes place, it may not contribute to higher CH4 emissions as has been proposed by aerenchymatous gas transport in sedge wetlands. These findings can inform future model development by (i) highlighting the coupling between vegetation processes and CH4 emissions, and (ii) identifying specific and non-overlapping timescales for different driving factors.