Plant functional group effects on microbial community and decomposition in peatlands
Peatlands have served as one of the most important terrestrial carbon sinks, but climate change may change peatlands from net sinks to net sources of carbon, both via direct changes in water table or via indirect mechanisms such as shifts in plant functional groups (PFGs). In order to predict decomposition processes in peatlands in response to climate change we need to understand the underlying biological mechanisms and processes. The objective of this study is to assess PFG effects on peatland microbial communities and decomposition. We established a field experiment with three PFG manipulations: ericaceous dwarf shrubs only, graminoids only, and both PFGs combined. Decomposition was measured as mass loss of cellulose strips and Sphagnum rubellum tissue at 3 depths within each plot, and by quantifying carbon compounds (e.g. tannins, organic acids, dissolved organic carbon) from pore water samples taken throughout the growing season. Microbial communities were sampled by coring, and characterized using Illumina iTag sequencing of the ITS region for fungi, and v4 region for bacteria and archaea. To understand drivers of altered decomposition we relate our findings from the decomposition assay and pore water chemistry to data on microbial community composition.
Our first round of cellulose decomposition assay reveals strong depth effects with higher decomposition in the shallow peat but no PFG effect. Preliminary results from pore water chemistry analyses show a significant vegetation and depth effect on total organic carbon. Microbial community analyses reveal substantial shifts in microbial community composition as a function of depth within the peat column as well as individual responses of some saprotroph and root-associated groups to the vegetation treatment. This project allows us to get a first insight into the makeup of the decomposer community and their associations with decomposition processes and vegetation type in peatlands. These results in turn will allow us to build more accurate models to predict global change feedbacks.