Ecohydrological Controls on Post‐Fire <i>Sphagnum</i> Moss Recovery in Boreal Shield Peatlands
Bibliographic record
Abstract
ABSTRACT Following wildfire, the return of boreal peatlands to a net carbon sink depends greatly on the ecohydrological recovery of Sphagnum mosses. We examined post‐fire Sphagnum moss accumulation and moss moisture stress (soil water tension, soil moisture) in triplicate burned and unburned peatlands (shallow peatlands, middle of deep peatlands and margins of deep peatlands). Shallow (< 70 cm maximum peat depth) peatlands had significantly less post‐fire moss growth than deeper peatlands, and near‐surface soil tension exceeded 100 hPa (an established ecohydrological threshold for Sphagnum moss) when the entire peat profile became desaturated, which only occurred in the shallowest peatlands. We found no significant difference in moss moisture stress between the burned and unburned landscapes 5 years following wildfire. Rather, current peat depth best explains moss moisture stress in burned and unburned landscapes, suggesting a peat depth threshold, above which Sphagnum drought resilience increases. To further examine this threshold and the ecohydrological controls on near‐surface soil tension in unburned, burned, and post‐fire moss‐recovered peatlands, we modelled the length of time until near‐surface soil tension exceeded 100 hPa under a drying scenario using HYDRUS‐1D. Burned profiles reached a near‐surface soil tension of 100 hPa faster than unburned or recovered profiles, and the greatest control on time to threshold soil tension was wildfire burn severity (depth of burn). In moss‐recovered peatlands, when the depth of recovered Sphagnum moss was greater than 4 cm, near‐surface soil tension took significantly longer to reach the ecohydrological threshold. We suggest this moss recovery depth represents an important post‐fire metric and can be used to target peatlands requiring ecohydrological adaptation strategies to enhance Sphagnum moss recovery.
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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".