Post-fire and harvest legacy on soil carbon and microbial communities in boreal forest soils
Bibliographic record
Abstract
The boreal forest is currently facing multiple disturbances, which are increasing in extent, frequency and severity. Of particular concern in the western boreal forest of Canada are wildfires and harvesting, which, in the short-term, may impact the forest floor, a storehouse of organic matter and site of highest soil microbial activity. However, how these disturbances may individually and cumulatively affect soil properties during ecosystem recovery is not well documented. Here we compared the separate and compound (salvage-logging) effects of wildfire and harvesting in the mixedwood boreal forest of northern Alberta. Ten years following disturbance, carbon chemical composition of the harvested forest floors was comparable to the control plots as was indicated by solid-state nuclear magnetic resonance analysis. We report a legacy effect of fire in recovering forest floors, with the continued presence of black carbon. Further, as determined by phospholipid fatty acid analysis (PLFA), both wildfire and salvage-logging disturbance resulted in distinct microbial composition from the control and harvest treatments. A shift in organic matter composition indicative of fresh plant inputs was determined to be a key driver of community differences. Despite the presence of black carbon, and shallower depth, the fire-disturbed forest floor harbored the greatest microbial biomass. However, this greater microbial biomass was not present following salvage-logging of the fire disturbed stands. Instead, the salvage-logged stands had the shallowest forest floor, low microbial biomass, and differed the most from the control forest floors based on PLFA results. Taken together, these results indicate that, ten years after disturbance, the compound disturbance of salvage-logging had a greater impact on the recovery of forest floor microbial communities than harvest or wildfire alone.
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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 | 0.001 | 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".