Why is the rainforest lichen Methuselah’s beard (<i>Usnea longissima</i>) so rare in British Columbia’s inland temperate rainforest?
Bibliographic record
Abstract
Coastal (CTR) and inland temperate rainforests (ITR) in western North America share a rich oceanic lichen flora. The distinctive Methuselah’s beard lichen (Usnea longissima Ach.) is an exception to this pattern of shared distributions, with very few ITR locations. Does this absence reflect dispersal limitations or climatic intolerance? To answer this question, we transplanted U. longissima thalli from the CTR to three ITR locations, assessing growth rates against reciprocal CTR transplants. Canopy microclimate measurements provided concurrent data on growth conditions. Growth rate responses (length, mass, and area) were evaluated after summer and full-year transplants. Notwithstanding extended drought conditions during the summer period, annual transplants at two of the three ITR locations supported growth rates comparable to those at the CTR source U. longissima population, with summer dewfall and autumn rains being major ITR hydration sources. Thalli transplanted to a third ITR site (summer measurements only) in a location transitional to the drier interior plateau lost both mass and length. Based on these findings, we suggest that the absence of U. longissima from much of the ITR reflects the combined influence of dispersal limitations and requirements for stands with long site continuity and topographically induced summer wetting of thalli by dewfall.
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 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".