Burn severity alters peatland moss water availability: implications for post‐fire recovery
Bibliographic record
Abstract
Abstract Wildfire is the largest disturbance affecting northern peatlands; however, little is known about how burn severity (organic soil depth of burn) alters post‐fire hydrological conditions that control the recovery of keystone peatland mosses (i.e. Sphagnum). For this reason, we assessed the impact of burn severity on moss water availability by measuring soil tension (Ψ) and surface volumetric moisture content (θ) in burned and unburned portions of a peatland complex 2 years after fire. We found that both high and low burn severity decreased post‐fire water availability by altering peat hydrophysical properties (moisture retention and water repellency). Locations covered by Sphagnum fuscum prior to fire exhibited a decreasing post‐fire water availability with an increasing burn severity. In contrast, the lowest water availability (Ψ > 400 cm, θ < 0·02) was observed in feather mosses that underwent low burn severity (residual branches identifiable). Deep burning (>0·20 m) in peatland margins and burn depths >0·05 m in the middle of the peatland exhibited the highest water availability (Ψ < 60 cm). Locations with low surface θ and high Ψ, notably feather mosses undergoing low burn severity, exhibited minimal moss recolonization. Such areas dominate post‐fire surface cover (~40%) within late successional (mature) peatlands or peatlands located in dry hydrological settings. We argue that such environments are under‐represented in conceptual models of post‐fire recovery. A new conceptual model is proposed in which (1) deep burning is counterbalanced by rapid recolonization and (2) pre‐fire species interact with burn severity to produce substantial lags in post‐fire moss recovery. Copyright © 2015 John Wiley & Sons, Ltd.
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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".