Aggregated retention protects trees against wind, but not against disease: a long-term study in mixed forests
Bibliographic record
Abstract
Retention forestry can help achieve multiple objectives in production forests, but its effectiveness is often low due to high mortality of the trees retained. We assessed the potential of improving tree survival in a low-level retention system in mixed forests by comparing the retention of multiple species of solitary trees (dispersed retention) and two approaches to aggregated retention. We annually monitored 58 dispersed-retention sites (since 2001) and 21 aggregated-retention sites (since 2013) in Estonia. Eight-year total mortality was 45% for solitary trees (and 1%–4% annually thereafter) but only 8% for tree groups; special planning for wind protection provided little further reduction. These estimates do not include dieback-affected Fraxinus excelsior L. that had distinct mortality dynamics independent of the retention pattern. Ulmus spp. also died frequently within the groups due to a dieback disease. Mixed-species tree groups enabled partial retention of Picea abies (L.) H. Karst that has extremely poor survival when retained solitarily. Likely, ecological costs of aggregated retention include some loss of microhabitat quality of individual trees. An optimal retention strategy could combine tree groups (maximizing wind protection and patch integrity) and individual trees (maximizing tree-scale biodiversity qualities), which collectively would also spread the risks of diverse mortality agents.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".