Threshold peat burn severity breaks evaporation‐limiting feedback
Bibliographic record
Abstract
Abstract A suite of autogenic ecohydrological feedbacks and moss traits are important for protecting vast peatland carbon stocks following wildfire disturbance. Here, we examine how peat burn severity and water table depth (WTD) affect the strength of one such feedback—the hydrophobicity–evaporation feedback (HEF). The HEF is an evaporation‐limiting feedback known to minimize water loss following wildfire. The peatland surface becomes hydrophobic creating an evaporative cap and thereby reducing post‐fire evaporation; however, recent studies hypothesize that this is dependent on peat burn severity. To test this hypothesis, we studied plots along a peat burn severity gradient in a partially drained black spruce peatland that burned during the 2016 Fort McMurray Horse River wildfire. Evaporation rates were significantly lower in plots where hydrophobicity was present. Hydrophobicity was lowest in the severely burned area, and the average instantaneous evaporation rate (2.75 mm day−1) was significantly higher than moderately and typical‐lightly burned areas (0.82 and 1.64 mm day−1, respectively). Based on lab results, increasing WTD affected hydrophobicity within lightly burned (singed) feather moss samples but not in heavily burned feather moss, showing the importance of post‐fire ground cover and in situ WTD. Our results provide evidence of a burn severity threshold where increased depth of burn removes the feather moss evaporative cap and causes the HEF to break down. We argue that this threshold has important implications for boreal peatlands, which are predicted to undergo climate‐mediated pre‐fire drying and increasing burn severities, potentially leading to further carbon losses due to enhanced post‐fire drying and concomitant decomposition.
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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".