Spatial heterogeneity in small-scale greenhouse gas (GHG) flux measurements creates uncertainty in estimates of ecosystem-level GHG emissions. To assess how environmental heterogeneity affects ecosystem estimates of GHG emissions, we measured CO2, CH4, and N2O fluxes in a 16 hectare forest plot in northeastern Puerto Rico. We sampled gases along a topographic gradient at the base of five focal tree species to capture a range of abiotic and biotic environments in this forest: the continuous topographic gradient captured concave (valleys) and convex areas (ridges) and the focal species account for 51% of individual trees in the plot. To calculate ecosystem GHG emissions we first modeled CO2, CH4, and N2O fluxes for each tree in the plot based on a community abundance-weighted mean flux and summed individual tree GHG estimates to the plot-level. Subsequently, we modeled GHG fluxes for each tree using topography and species dependent estimates to obtain a spatially heterogeneous estimate of ecosystem GHG emissions. These two approaches were compared to understand how topography and species differences improve modeled ecosystem GHG emissions in comparison to traditional approaches. We hypothesized that topographic and species differences would improve ecosystem estimates of CO2, CH4, and N2O fluxes.
Results/Conclusions
GHG fluxes varied (p < 0.01) in magnitude and direction with topography. Compared to valleys, both CO2 production and net CH4 consumption were higher on ridges. In contrast, N2O fluxes varied from net production on ridges to net consumption in valleys. GHG fluxes also differed by species (p< 0.001); net CH4 consumption varied from 700 g CH4-C hr-1ha-1 consumption to 70 g CH4-C hr-1ha-1 production and net N2O consumption varied from 108-408 g N2O-N hr-1ha-1 production. Including species and topography in our ecosystem estimate of GHG emissions improved the model fit compared to the community mean flux estimate. Using a topography and species dependent model to calculate CO2 fluxes (R2=0.17) decreased ecosystem estimates by 10.8% to 20 kg C-CO2 hr-1ha-1. Using the topography and species dependent model similarly decreased estimates of net CH4 consumption (R2=0.23) by 34% to 2.8 g C-CH4 hr-1ha-1 and net N2O production (R2 = 0.21) by 38% to 1.5 g N-N2O hr-1ha-1. Our results suggest that the abiotic and biotic environment can help explain spatial heterogeneity in GHG fluxes. Further, these results show that accounting for the effects of environmental heterogeneity on spatial patterns of GHG production can significantly alter estimates of ecosystem GHG emissions.