Epiphytic bryophytes in Canarian subtropical montane cloud forests: the importance of the time since disturbance and host identity
Bibliographic record
Abstract
The aim of this study was to determine the short- and medium-term effects of forestry practices on epiphytic bryophyte communities growing on whole trees of three host species ( Erica arborea L., Laurus novocanariensis Rivas-Mart., Lousa, Fern. Prieto, E. Días, J.C. Costa & C. Aguilar, Myrica faya Aiton) in subtropical montane cloud forests on La Palma (Canary Islands, Spain). Specifically, we investigated differences in temporal and spatial shifts of species composition and richness of phylogenetic groupings among host species. The most common harvest method in the study area is clear-cutting. Four different postharvest successional stages (8, 15, 25, and 60 years after harvest) were studied. Temporal bryophyte species turnover varied according to host species. Most of the later-successional bryophytes with narrower ecological requirements had low abundances on L. novocanariensis; this host experienced a gradual increase of epiphytic richness along the chronosequence. Temporal changes for E. arborea and M. faya were different; they showed increasing richness during the second period (15–25 years) followed by a drop in richness during the last period (25–60 years), and early-successional species dominated throughout the chronosequence. We conclude that the protection of “old-growth stands” containing trees of selected species can contribute to the survival of epiphytic bryophytes in managed cloud-forest landscapes.
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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 | 0.001 | 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".