Low-severity wildfire prevents catastrophic impacts on fungal communities and soil carbon stability in a fire-affected Douglas-fir ecosystem
Bibliographic record
Abstract
The growing frequency, extent and severity of wildfire is destabilizing carbon sinks in western North America, underscoring an urgent need to better understand fire impacts on soil carbon stocks, carbon stability, and fungi that regulate soil carbon cycling. Here, we examined the effects of wildfire two years post-burn on soil carbon and fungal communities across a fire severity gradient in Douglas-fir forests in central British Columbia, Canada. We observed no significant differences in soil carbon or fungal community composition between low-severity and unburned stands. In contrast, high-severity wildfire resulted in a 49 % reduction in belowground carbon stocks (20.7 Mg C·ha −1 ), a 91 % decline in ectomycorrhizal fungi, 5- to 27-fold increases in pathogenic fungi, and a proliferation of pyrophilous taxa compared to unburned stands. Carbon was lost primarily as light particulate organic matter, whereas impacts to mineral-associated carbon were muted. Pyrogenic carbon preferentially associated with the mineral fraction, modestly increasing (∼0.15 Mg C·ha −1 ) the proportion of carbon resistant to decay in this stable fraction. Select helotialean (e.g. Phialocephala fortinii ) and other pyrophilous taxa were well-correlated with pyrogenic carbon, suggesting this consortium is well-adapted to decompose persistent carbon and will likely continue to mineralize soil carbon even after high severity wildfire. The markedly higher abundance of pathogenic fungi and reduced ectomycorrhizal abundance in stands affected by high-severity fires pose risks to post-fire recovery, particularly if pathogen proliferation reduces conifer fitness. These results highlight that low-severity wildfires have comparatively muted impacts on soil carbon and fungal communities relative to high-severity wildfires, underscoring the importance of management strategies such as thinning and prescribed burns to mitigate the catastrophic effects of high-severity wildfires.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".