Clear-fell harvest impacts on biodiversity: past research and the search for harvest size thresholds
Bibliographic record
Abstract
Clear-fell harvesting has large aesthetic impacts and significantly alters ecosystem attributes at multiple spatial scales. Known abiotic changes include increased microclimatic variability, changes in regional water balance, and modified hydrological patterns that influence erosion processes. Biotic changes include increased species richness immediately post-clear-felling due to shifts in species composition resulting from changes in individual species abundance and colonization by disturbance-adapted or open-habitat species. Given the large ecological changes caused by clear-fell harvesting and the negative public perception of clear-felling, it is surprising that few studies have investigated whether reducing clear-fell harvest area may be a viable strategy to mitigate ecological change within individual clearcuts. Clear-fell size studies conducted to date rarely exceed a maximum harvest area of 10 ha, and biotic communities measured exhibit mixed responses with respect to species richness and other biodiversity attributes with increasing clear-fell size. Some postharvest ecological responses are nonlinear with respect to harvest area and suggest possible threshold sizes beyond which clear-fell impacts increase disproportionately to their size. Conceptual models of potential ecological thresholds in clear-fell harvest impacts are discussed, as is the need for rigorous empirical testing to ensure a solid foundation exists for forest harvesting guidelines.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".