Long-term effects of stump removal and tree species composition on the diversity and structure of soil fungal communities
Bibliographic record
Abstract
Stump removal is a common forest management practice used to reduce the mortality of trees affected by the fungal pathogen-mediated root disease, Armillaria root rot, but the impact of stumping on soil fungal community structure is not well understood. This study analyzed the long-term impact of stumping and tree species composition on the abundance, diversity and taxonomic composition of soil fungal communities using internal transcribed spacer (ITS) marker-based DNA metabarcoding in a 48-year-old trial at Skimikin, British Columbia. A total of 108 samples were collected from FH (fermented and humus layers), and soil mineral horizons (A and B) from stumped and unstumped plots of six tree species treatments (pure stands and admixtures of Douglas-fir, western red-cedar and paper birch). Fungal α-diversity in the A horizon significantly increased with stumping regardless of tree species composition, while β-diversity was significantly affected by stumping in all the horizons. We also observed that the relative abundance of the saprotrophic fungal community declined while that of the ectomycorrhizal fungal community increased with stumping. In conclusion, increase in ectomycorrhizal fungal associations, which are positively associated with tree productivity, suggests that stumping can be considered a good management practice for mitigating root disease and promoting tree regeneration.
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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".