Influences of tree age and tree structure on the macrolichen <i>Letharia vulpina</i>: A case study in the Italian Alps
Bibliographic record
Abstract
Tree age and tree structure are important determinants for epiphytic lichen communities, affecting substrate stability, light availability, and propagule dispersal. However, little is known about the relative importance of these 2 factors on many species. This work aims to evaluate the importance of tree age and tree structure in explaining the within-stand frequency of the macrolichen Letharia vulpina. The study was carried out in 2 larch-stone pine forests in the Eastern Italian Alps. The frequency of Letharia was evaluated using a standard sampling method. To explain the within-stand frequency of Letharia, tree age and several variables related to tree structure were considered. Multiple ordinary least square regression was applied to clarify the influence of the set of variables. Several partial regressions were then computed to evaluate the relative importance of each significant predictor. The frequency of Letharia increases with increasing tree age, tree diameter, and first branch height. Tree age is the most important variable, accounting for one third of the total explained variation, although tree structure also has a significant effect on lichen frequency. Due to its dispersal limitations and old-tree dependence Letharia is proposed as a suitable indicator of forest continuity. The results may be of interest for conservation purposes, since the frequency of Letharia may be enhanced by protecting old trees.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".