GC052-0001
Understanding the response of coastal forest carbon cycling to changing salinity and moisture content: a soil transplant experiment
Understanding the response of coastal forest carbon cycling to changing salinity and moisture content: a soil transplant experiment
Thursday, 10 December 2020
Poster
Abstract:
Coastal terrestrial-aquatic interface ecosystems may exhibit particular sensitivity to changes in climate and sea level, but how changes in water availability and salinity may affect soil and ecosystem carbon cycling is poorly understood. As a part of a broader effort to understand coastal ecosystem resilience and responses to future change, this experiment took advantage of a natural salinity gradient in a tidal creek at the Smithsonian Environmental Research Center (SERC) in eastern Maryland, U.S.A., to examine how soil processes and structure may change under novel hydrologic regimes. Large (40 cm wide, 20 cm deep) soil cores were transplanted in a reciprocal design between plots varying in seawater exposure and elevation above the creek; we monitored the cores’ greenhouse gas fluxes for two years and performed chemical, structural, and biological analyses on the cores. The balance between carbon dioxide (CO2) and methane (CH4) production shifted strongly with soil drainage, with lower, water cores exhibiting higher CH4 fluxes; cores transplanted from more stressful (more saline and/or drier) conditions exhibited significantly lower fluxes relative to both undisturbed and same-plot transplant controls. Transplanted core exhibited significant changes in microbial communities that impacted key traits, with those moved to stressful conditions exhibiting reduced carbon use efficiency, turnover, and enzyme activities. We also compare these results to observations take in a west coast watershed with higher salinity and a stronger tidal cycle. In the context of ongoing climate change, manipulative transplant experiments such as this provide a crucial inferential link between purely observational experiments, data synthesis efforts, and large-scale ecosystem manipulations.