Gradual drying of permafrost peat decreases carbon dioxide production in drier peat plateaus but not in wetter fens and bogs
Bibliographic record
Abstract
Abstract. Permafrost thawing of northern peatlands can cause the local collapse of peat plateaus into much wetter thermokarst bogs and fens, dominated by Sphagnum mosses and graminoids, respectively. However, permafrost thaw can also improve landscape drainage and, thus, lead to the regional drying of peatlands. How gradual drying of these thawing permafrost peatlands affects the subsequent microbial production of carbon dioxide (CO2) and nitrous oxide (N2O) is uncertain because of landscape heterogeneity in moisture, peat quality, and vegetation. Here, we collected near-surface peat samples (5–20 cm) from Alberta, Canada, across transects representing a thaw gradient from peat plateaus to a fen or bog. We incubated the samples for 2 weeks under either field moisture or under gradual drying to reduce moisture by ∼ 80 %. Only the fen sites, which had high moisture and a high percentage of total N, produced N2O (0.06–6.7 µg N2O-N per gram of dry peat), but N2O production was unaffected by the drying treatments. Peat CO2 production was greatest from the fen and the youngest stage of the thermokarst bog, despite them having the most water-saturated field conditions, likely reflecting their more labile plant inputs and, thus, more decomposable peat. We found that CO2 respiration was enhanced by drying at relatively wet sites like the fens and young bog but was suppressed by drying in relatively drier peat plateaus. Further, gradual drying increased 13C-CO2 respiration, suggesting a possible shift to more decomposed, older C being lost with peat drying. Thus, our study suggests that future peat CO2 and N2O production from peatlands will depend on whether peat plateaus thaw into fens or bogs and on their diverging responses of peat respiration to more moisture-limited conditions.
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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) | 0.001 | 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".