Partial cutting reduces species richness of fungi on woody debris in oak-rich forests
Bibliographic record
Abstract
Partial cutting is increasingly applied in European temperate oak-dominated forests for biofuel harvesting, and to counteract succession in protected stands. Effects on biodiversity of these measures need to be carefully evaluated, and species-rich but neglected taxa such as fungi should be considered. We studied the effects of partial cutting on fungal fruiting bodies on woody debris. In 21 closed canopy forests rich in large oaks in Sweden, on average 25%–30% of the basal area was cut. Fruiting bodies were counted and some were collected in treated and control plots before and after treatment. We found 334 basidiomycete and 47 ascomycete species. Species richness of basidiomycetes declined significantly more in treated plots (on average 26%) than in control plots (on average 13%) between seasons. Species richness of ascomycetes increased by 17% in control plots and decreased by 2% in treated plots. Total species richness was significantly reduced on fine woody debris (1–10 cm in diameter), but not on coarse woody debris (>10 cm). Overall species composition did not change significantly as a result of partial cutting, but red-listed species tended to decrease more in treated plots. We suggest that approximately 30% of the stands should not be thinned, and dead stems and fallen branches should not be removed, to favor saproxylic fungi and their associated fauna.
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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 | 0.001 |
| 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".