The effect of buffer strip width and selective logging on streamside polypore communities
Bibliographic record
Abstract
Preserving streamside forest habitats or buffer strips is considered to reduce forestry-related biodiversity loss in commercial forest landscapes. However, it is still unclear what type of management in and near streamside forests can be undertaken without compromising their biodiversity and natural change through succession. Using a before–after, control–impact study design, we tested the impacts of forested buffer strips (15 or 30 m wide, with or without selective logging), preserved after clear-cutting, on the changes of polypore communities in streamside boreal forests in Finland. Manipulations in 28 sites produced four treatment classes, the community compositions of which were compared with seven unmanaged controls before and 12 years after the manipulations. The polypore community composition in 15 m wide buffer strips changed differently than that in controls and resembled the community composition typically found in production forests. Moreover, selective logging tended to homogenize polypore communities. These responses of polypore communities indicate that the natural biodiversity and succession of streamside forests was disturbed in both 15 m wide and selectively logged buffer strips. Streamside forests in nonlogged 30 m wide buffer strips seemed to retain the natural polypore community composition and succession, at least during the 12-year period.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".