Importance of boreal forested wetlands for epiphytic macrolichen communities
Bibliographic record
Abstract
Forested wetlands provide ecosystem services and often support elevated levels of biodiversity and rare species. However, forested wetlands are understudied and face threats such as logging and land conversion. Epiphytic lichens are abundant in forested wetlands and may be useful to help delineate microhabitats across wetland–upland gradients. We investigated epiphytic macrolichen richness, diversity, and community composition in 15 sites in the Avalon Forest Ecoregion, Newfoundland, Canada. Within each site, we set up three parallel 40 m transects in (i) the forested wetland, (ii) the ecotone, and (iii) the upland forest. Along each transect, we selected five balsam fir (Abies balsamea (L.) Mill.) trees 10 m apart and surveyed for macrolichens on the lower bole. We collected data on tree height and tree diameter at breast height, which differed significantly among forest types. We also collected data on tree age and canopy cover, which did not differ significantly among forest types. Contrary to hypotheses suggesting that biodiversity is highest in ecotones, we found that mean macrolichen richness was significantly higher in wetlands, lower in the ecotones, and lowest in upland forests, and macrolichen diversity followed a similar pattern but with no significant difference among groups. Macrolichen community composition significantly differed among wetlands, ecotones, and upland forests. A lichen of conservation concern, Erioderma pedicellatum (Hue) P.M. Jørg., was detected primarily in forested wetlands, highlighting wetlands as key habitats for rare epiphytic macrolichens.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".