Mires, especially sedge dominated fens, are sources of the greenhouse gas CH4. Climate change scenarios predict a lowering water table (WT) in mires. To study the effect of WT drawdown on CH4 dynamics in a fen ecosystem, we took advantage of a WT drawdown gradient near a ground water extraction plant. Methane fluxes, CH4 production and oxidation potentials, were related to microbial communities responsible for the processes in four mire locations (wet, semi-wet, semi-dry and dry). Principal component analyses (PCA) performed on the vegetation, pH, CH4 and WT results clearly separated the four sampling locations in the gradient. Long-term lowering of WT was associated with decreased coverage of Sphagnum and aerenchymatic plants, decreased CH4 field emissions and CH4 production potential. Based on mcrA T-RF the methanogen community structure correlated best with the methane production and coverage of aerenchymatic plants along the gradient. Methanosarcinaceae and Methanocellales were found at the pristine wet end of the gradient, whereas the Fen cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 oxidation potential. These results advance our understanding of mire dynamics after long-term WT drawdown and of the microbiological bases of methane emissions from mires.

No abstract

No abstract

Nucleic acid-based community fingerprinting methods are valuable tools in microbial ecology, as they offer rapid and robust means to compare large series of replicates and references. To avoid the time-consuming and potentially subjective procedures of peak-based examination, we assessed the possibility to apply direct curve-based data analysis on community fingerprints produced with bacterial length heterogeneity PCR (LH-PCR). The dataset comprised 180 profiles from a 21-week rhizoremediation greenhouse experiment with three treatments and 10 sampling times. Curve-based analysis quantified the progressive effect of the plant (Galega orientalis) and the reversible effect of the contaminant (fuel oil) on bacterial succession. The major observed community shifts were assigned to changes in plant biomass and contamination level by canonical correlation analysis. A novel method to extract relative abundance data from the fingerprint curves for Shannon diversity index revealed contamination to reversibly decrease community complexity. By cloning and sequencing the fragment lengths, recognized to change in time in the averaged LH-PCR profiles, we identified Aquabacterium (Betaproteobacteria) as the putative r-strategic fuel oil degrader, and K-strategic Alphaproteobacteria growing in abundance later in succession. Curve-based community fingerprint analysis can be used for rapid data prescreening or as a robust alternative for the more heavily parameterized peak-based analysis.

No abstract