Diversification of tree stands as a means to manage pests and diseases in boreal forests: myth or reality?
Bibliographic record
Abstract
Pure forest stands are widely believed to be more prone to pest outbreaks and disease epidemics than mixed stands, leading to recommendations of using stand diversification as a means of controlling forest pests and pathogens. We review the existing evidence concerning the effects of stand tree-species diversity on pests and pathogens in forests of the boreal zone. Experimental data from published studies provide no overall support for the hypothesis that diversification of tree stands can prevent pest outbreaks and disease epidemics. Although beneficial effects of tree-species diversity on stand vulnerability are observed in some cases, in terms of reductions in damage, these effects are not consistent over time and space and seem to depend more on tree-species composition than on tree-species diversity per se. In addition, while mixed stands may reduce the densities of some specialized herbivores, they may be more attractive to generalist herbivores. Given that generalist mammalian herbivores cause considerable tree mortality during the early stages of stand establishment in boreal forests, the net effect of stand diversification on stand damage is unlikely to be positive.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| 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".