Fuel loads and peat smouldering carbon loss increase following peatland drainage
Bibliographic record
Abstract
Northern peatlands store ~500 Pg C and are important ecosystems for global climate regulation. Wildfire is the largest natural disturbance to peatlands within the Boreal Plains of western Canada. Historically, low-severity fires in this region release less carbon than accumulates over a fire return interval (~120 years), allowing peatlands to maintain their carbon sink function. While peat combustion (measured as the depth of burn; DOB) is typically low, ranging from 5-10 cm (representing carbon emissions of ~1 kg C m-2), during prolonged drought, or in drained peatlands, peat burn severity can reach depths >1 m (~100 kg C m-2), threatening the carbon sink function of boreal peatlands. We aimed to assess how peatland drainage altered the spatiotemporal variability in forest cover, aboveground biomass, and tree productivity and how these changes related to the spatial variability in peat burn severity from a fire 24 years post-drainage. Using remote sensing techniques, forest cover and biomass were estimated through time and with distance from the nearest ditch. Field surveys and a LiDAR-based analysis were conducted to measure the spatial variability in peat burn severity. Peatland drainage increased forest cover and aboveground biomass. Drained peatland margins had the greatest peat burn severity with a mean depth of burn of 26.9 ± 12.6 cm (34.0 ± 10.1 kg C m-2) and some locations experienced DOB >90 cm (>87 kg C m-2), where peat burn severity increased with proximity to drainage ditches and greater aboveground biomass. Peatland drainage increases both aboveground and peat fuel loads through the triggering of positive peatland drying feedbacks which increase peatland vulnerability to deep smouldering, with peatland margins experiencing the greatest peat burn severity. Drained peatlands represent a severe fire risk that can be challenging for communities and fire management agencies. Peatland restoration should be integrated into fuel management strategies to reduce the fire risk that drained peatlands pose.
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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 | 0.001 |
| 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".