Effects of Wildfire and Harvest Disturbances on Forest Soil Bacterial Communities
Bibliographic record
Abstract
Wildfires and harvesting are important disturbances to forest ecosystems, but their effects on soil microbial communities are not well characterized and have not previously been compared directly. This study was conducted at sites with similar soil, climatic, and other properties in a spruce-dominated boreal forest near Chisholm, Alberta, Canada. Soil microbial communities were assessed following four treatments: control, harvest, burn, and burn plus timber salvage (burn-salvage). Burn treatments were at sites affected by a large wildfire in May 2001, and the communities were sampled 1 year after the fire. Microbial biomass carbon decreased 18%, 74%, and 53% in the harvest, burn, and burn-salvage treatments, respectively. Microbial biomass nitrogen decreased 25% in the harvest treatment, but increased in the burn treatments, probably because of microbial assimilation of the increased amounts of available NH(4)(+) and NO(3)(-) due to burning. Bacterial community composition was analyzed by nonparametric ordination of molecular fingerprint data of 119 samples from both ribosomal intergenic spacer analysis (RISA) and rRNA gene denaturing gradient gel electrophoresis. On the basis of multiresponse permutation procedures, community composition was significantly different among all treatments, with the greatest differences between the two burned treatments versus the two unburned treatments. The sequencing of DNA bands from RISA fingerprints revealed distinct distributions of bacterial divisions among the treatments. Gamma- and Alphaproteobacteria were highly characteristic of the unburned treatments, while Betaproteobacteria and members of Bacillus were highly characteristic of the burned treatments. Wildfire had distinct and more pronounced effects on the soil microbial community than did harvesting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".