Strong influence of landscape structure on hair lichens in boreal forest canopies
Bibliographic record
Abstract
This study examines how island size, isolation, and orientation influence epiphytic hair lichens in old-growth boreal spruce forests within a naturally heterogeneous landscape with approximately 1000 forest islands distributed in open wetland matrix. Forest structure, length of Alectoria sarmentosa (Ach.) Ach., Bryoria spp., and Usnea spp., and mass of Alectoria in the lower canopy (0–5 m) of Picea abies (L.) Karst. were quantified in 30 islands (0.11–10.9 ha). Length and mass of Alectoria were also studied in 25 edges with different orientation and fetch (wind exposure). Island area had a strong positive effect on length of Alectoria but a minor effect on Bryoria and Usnea. Edge orientation influenced length and mass of Alectoria, with the strongest reduction in wind-exposed western edges, whereas fetch size had no effect. Edge influence on microclimate drives hair lichen response to landscape configuration. The gradient from Bryoria in small islands to Alectoria in large islands is caused by the same mechanisms that influence vertical canopy gradients in large homogeneous stands, with Bryoria in the upper canopy and Alectoria in the lower canopy. Genus-specific, sun-screening pigments contribute to this niche differentiation, but thallus fragmentation by wind and water storage are also important. Our findings imply that lichen conservation must consider the spatial structure of the landscape.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".