Long-term effects of prescribed burning, tree retention, and browsing on deciduous tree recruitment in European boreal forests
Bibliographic record
Abstract
Silvicultural practices, effective fire suppression, and increased browser densities have profoundly altered structural diversity in boreal forests. Prescribed burning and retention forestry may counteract losses in structural diversity in managed forests by maintaining a higher deciduous admixture. We constructed an experiment on 18 sites with three types of timber harvesting (uncut, cut with retention, and clearcut) and burned half of these sites. Subsequently, we established a herbivore treatment with three compartments (unfenced, fenced excluding moose (Alces alces (Linnaeus, 1758)), and fenced excluding moose and hares (Lepus spp.)). In these compartments, we planted rowan (Sorbus aucuparia L.), European aspen (Populus tremula L.), and silver birch (Betula pendula Roth) seedlings and monitored these for 17 years. Birch and rowan mortality were lower on cut and burned sites, with retention further enhancing birch survival on these sites. Retention without burning did not lower seedling mortality of any tree species. While browsing resulted in greater mortality on cut sites, burning appeared to greatly reduce browsing on birch and rowan. On mature uncut sites, seedlings of all tree species exhibited high mortality. Our findings show that deciduous tree recruitment can be improved through prescribed burning, particularly for birch and rowan, and that browsing impacts on deciduous trees depend on forest age.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".