Seventy-year history of management using low-intensity harvesting methods: weak impact on biodiversity of hemiboreal Scots pine forests
Bibliographic record
Abstract
Less intensive harvesting methods (e.g., selection cutting, shelterwood cuttings) are recommended as alternatives to clearcutting for maintaining mature forest biodiversity in the process of forest regeneration. However, the long-term impact of low-intensity harvesting methods has rarely been studied. Our aim was to clarify the long-term effects of repeated selective cutting, thinning, and shelterwood cutting on the richness, abundance, and species composition of vascular plants, bryophytes, and lichens in Scots pine forests (Pinus sylvestris L.). Data were collected from 25 mature stands located in dry Podzols in southwestern Estonia with a known management history for the last 70 years. Altogether, 35 vascular plant, 41 bryophyte, and 78 lichen species were recorded, including five species with conservational value. Generally, the management history was not related to species richness, except a negative correlation with the species number of epiphytic lichens on conifer trees. In addition, the abundance of two lichen species from the genus Chaenotheca was lower in more frequently managed stands. Species richness and composition were most strongly affected by soil pH and light conditions. We conclude that long-term, low-intensity harvesting of Scots pine forests on nutrient-poor Podzols maintains suitable habitats for vascular plants, bryophytes, and lichens, confirming its suitability for sustainable forest regeneration.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".