BackgroundPeatland restoration can have several objectives, for example re-establishing the natural habitat, supporting unique biodiversity attributes or re-initiating key biogeochemical processes, which can ultimately lead to a reduction in greenhouse gas (GHG) emissions. Every restoration measure, however, is itself a disturbance to the ecosystem.MethodsHere, we examine an ecosystem shift in a coastal fen at the southern Baltic Sea which was rewetted by flooding. The analyses are based on one year of bi-weekly closed chamber measurements of methane fluxes gathered at spots located in different vegetation stands. During measurement campaigns, we recorded data on water levels, peat temperatures, and chemical properties of peat water. In addition we analyzed the first 20 cm of peat before and after flooding for dry bulk density (DBD), content of organic matter and total amounts of carbon (C), nitrogen (N), sulfur (S), and other nutrients.ResultsRewetting turned the site from a summer dry fen into a shallow lake with water levels up to 0.60 m. We observed a substantial die-back of vegetation, especially in stands of sedges (Carex acutiformis Ehrh). Concentrations of total organic carbon and nitrogen in the peat water, as well as dry bulk density and concentrations of C, N and S in the peat increased. In the first year after rewetting, the average annual exchange of methane amounted to 0.26 ± 0.06 kg m-2. This is equivalent to a 190-times increase in methane compared to pre-flooding conditions. Highest methane fluxes occurred in sedge stands which suffered from the heaviest die-back. None of the recorded environmental variables showed consistent relationships with the amounts of methane exchanged.ConclusionsOur results suggest that rewetting projects should be monitored not only with regard to vegetation development but also with respect to biogeochemical conditions. Further, high methane emissions that likely occur directly after rewetting by flooding should be considered when forecasting the overall effect of rewetting on GHG exchange.
Due to the fast reconstruction of PAMoCo images and the introduction of a new motion model, we were able to re-initialize the optimization routine at pre-selected parameter sets and thereby increase the potential of the MAM algorithm. From the phantom measurement we conclude that PAMoCo performed almost equally well in all cardiac phases and suggest applying the PAMoCo algorithm for single source systems in case of patients with high or irregular heart rates.
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