Silvicultural strategies for lichen conservation: Smaller gaps and shorter distances to edges promote recolonization
Bibliographic record
Abstract
Abstract Conservation of epiphytic lichens in managed forest landscapes relies on understanding limitations to their dispersal and establishment after harvesting. A hot spot for cephalo‐ and cyanolichens, the Kispiox inland temperate rainforest of British Columbia, Canada, is an example of where forest managers seek silviculture options that allow for forest harvesting to continue, while also conserving threatened species. To characterize colonization of these lichens (including the nationally threatened Nephroma occultum and Lobaria retigera ), we quantified thalli on 25‐year‐old regenerating spruce trees at varying distances from forest edges in logged openings of different sizes surrounded by mature (160 years) or old‐growth (≥350 years) forest. Lichen colonization 25 years after logging was influenced by distance from the forest edge, surrounding forest age, and opening size. Total mean colonization and number of species per three branches were 81 thalli and 8 species at the forest edge, declining to 18 and 4, respectively, approximately 50 m into the openings. Colonization of most species reached minimum levels within 80 m. Nationally threatened species and old‐forest associates had higher abundances and longer colonization distances in openings surrounded by old‐growth forests; whereas widespread species had greater colonization when surrounded by mature forests. Logged gaps <0.6 ha promoted higher and more diverse lichen colonization than clearcuts (>3.8 ha). This study demonstrates that forest managers should consider the spatial patterning of harvesting, with smaller openings and shorter distances between edges (likely in tandem with higher levels of retention) in forests rich in epiphytic lichens to facilitate their recolonization and conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".