Water Table Fluctuation in Peatlands Facilitates Fungal Proliferation, Impedes Sphagnum Growth and Accelerates Decomposition
Bibliographic record
Abstract
Northern peatlands are substantial carbon sinks because organic matter in peat is highly stable due to the low rate of decomposition. Waterlogged anaerobic conditions induce accumulation of Sphagnum-derived phenolic compounds that inhibit peat organic matter decomposition, a mechanism referred to as the “enzymic latch”. Recent studies have predicted that the water table in northern peatlands may become unstable. We observed that such unstable water table levels can impede the development of Sphagnum mosses. In this study, we determined the effects of low and high frequency water table fluctuation regimes on Sphagnum growth and peat organic matter decomposition, by conducting a year-long mesocosm experiment. In addition, we conducted a molecular analysis to examine changes in abundance of fungal community which may play a key role in the decomposition of organic matter in peatlands. We found that rapid water table fluctuation inhibited the growth of Sphagnum due to fungal infection but stimulated decomposition of organic matter that may dramatically destabilize peatland carbon storage. Increased pH, induced by the fluctuation, may contribute to the enhanced activity of hydrolases in peat. We demonstrated that the water table fluctuation in peatlands impeded Sphagnum growth and accelerates decomposition due to fungal proliferation. Thus, we suggested that understanding the microbial community in the northern peatlands is essential for elucidating the possible changes in carbon cycle of peatland under the changing world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".