Brackish Water Rewetting of a Temperate Coastal Peatland: Effects on Biogeochemistry, Microorganisms and Greenhouse Gas Emissions
Bibliographic record
Abstract
Abstract Around 4% of global greenhouse gas (GHG) emissions originate from drained peatlands. Unlike rewetting drained peatlands with freshwater, brackish water rewetting is expected to reduce CO 2 emissions, while keeping post-rewetting methane (CH 4 ) emissions low. Sulfate-containing brackish water should favor sulfate reduction and therefore limit CH 4 production and/or lead to increased CH 4 consumption. Here, we compared CO 2 and CH 4 fluxes, pore water geochemistry, and associated microbial communities of a coastal peatland along a transect one year before and after rewetting (Fig. 1) to evaluate the effect of brackish water rewetting. Brackish water rewetting increased the abundance of both CH 4 producing archaea (methanogens) as well as sulfate reducing bacteria (SRB) in most sub-sites along the transect. At the same time, the aerobic methanotroph community was overall less present after rewetting. Pore water CH 4 and CO 2 concentrations along with δ 13 C records indicated that both methanogenesis and CH 4 oxidation increased post-rewetting. Although brackish water rewetting raised average net CH 4 emissions from 2 to 25 mg CH 4 m − 2 d − 1 at previously drained locations, these fluxes were lower than CH 4 emissions reported from most freshwater peatlands. Net CO 2 emissions remained high with levels around 4 g CO 2 m − 2 d − 1 , but ecosystem respiration strongly decreased from on average 19 to 6 g CO 2 m − 2 d − 1 . The remaining net CO 2 emissions were likely associated with a lower uptake of CO 2 compared to its release after extensive vegetation die-back. Hence, the re-establishment of site-specific vegetation is important to sustain the net CO 2 uptake besides low CH 4 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".