Trade-offs between competition and facilitation: a case study of vegetation management in the interior cedar–hemlock forests of southern British Columbia
Bibliographic record
Abstract
Vegetation-management practices are applied in temperate-zone forests on the assumption that changing the competitive environment between conifers and unwanted vegetation will improve conifer productivity. We review this assumption using research examining interactions between paper birch (Betula papyrifera Marsh.) and conifers in the highly productive Interior Cedar Hemlock zone of British Columbia. We have found that both competition and facilitation are important in young plantations, where paper birch competes for light, reducing growth of shade-intolerant conifers, but having a facilitative effect on shade-tolerant conifers. This facilitative effect may result from greater ectomycorrhizal diversity, population sizes of Armillaria ostoyae (Romagn.) Herink antagonistic bacteria, and associative nitrogen fixation in plantations where interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) is mixed with paper birch. Where paper birch is manually cut or girdled, conifers grow faster in diameter, but more die as a result of A. ostoyae root disease, and these responses increase with increasing weeding intensity. The weeding treatments do not affect plant community species richness but reduce paper birch dominants and increase understory structural diversity. British Columbia forest policy has been slow to respond to these findings, and we suggest that as a result, the forested landscape incurs substantial risk. We propose additional pathways for managing Interior Cedar Hemlock mixtures to ensure that the natural mix of forest types in the landscape is maintained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".