Stump removal and tree species composition promote a bacterial microbiome that may be beneficial in the suppression of root disease
Bibliographic record
Abstract
Stumping is an effective forest management practice for reducing the incidence of Armillaria root-rot in regenerating trees, but its impact on the soil bacterial community has not been ascertained. This study investigated the long-term impact of stumping and tree species composition in a 48-year-old trial at Skimikin, British Columbia, on the relative abundance, diversity and taxonomic composition of bacterial communities by sequencing the v4 region of 16S rRNA gene using the Illumina Miseq platform. A total of 108 samples were collected from the forest floor (fermented (F) and humus (H) layers) and mineral soil (A (0-10 cm) and B (10-20 cm) horizons) of 36 plots (half each stumped or unstumped) that were planted with pure stands and admixtures of Douglas-fir, western redcedar and paper birch. Bacterial α-diversity in the B horizon declined with stumping whereas β-diversity was affected both by tree species and stumping treatments, with fir and birch supporting distinct bacterial communities. All horizons of stumped plots of birch and its admixtures were significantly enriched with potential plant growth-promoting bacteria. In conclusion, stumping along with planting birch alone or in admixture with other species promotes a bacterial microbiome that appears beneficial in the suppression of root disease.
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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) | 0.001 | 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".