Effectiveness of solitary retention trees for conserving epiphytes: differential short-term responses of bryophytes and lichens
Bibliographic record
Abstract
Green-tree retention (GTR) on clearcuts is an attempt to mimic natural disturbances and provide habitat for species that are generally absent in clear-cut stands, but its efficacy for sustaining biodiversity is poorly known. We studied (i) the total cover and vitality of lichens and bryophytes on four common tree species in three locations (centre and edge of GTR cuts and adjacent forest) and (ii) the composition of and damage to various epiphytic species on European aspen (Populus tremula L.) and birches (Betula spp.) in Estonia during 2 postharvesting years. Bryophytes on all tree species throughout the GTR cuts were severely unhealthy (60% of shoots desiccated, on average); lichens were much more robust (2% of thalli bleached or broken), particularly at the edges of harvested areas and on aspen and European ash (Fraxinus excelsior L.; hereinafter referred to as ash). Most lichen damage appeared to be unrelated to logging (the damaged species were also affected in forests). Aspen hosted many more species, including those of conservation concern, than birch. If tree species, size, and bark texture are carefully considered, GTR could be a successful tool for conserving lichens, particularly many microlichens on aspen and ash. However, bryophytes on solitary trees were generally unhealthy, at least in the short term.
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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 | 0.000 |
| 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".