Decomposition of hair lichens (<i>Alectoria Sarmentosa</i> and <i>Bryoria</i> spp.) Under Snowpack in Montane Forest, Cariboo Mountains, British Columbia
Bibliographic record
Abstract
Abstract Montane old-growth forests on the windward slopes of interior mountain ranges in British Columbia support high loadings of arboreal lichens. These lichens represent a major source of readily labile plant material and potentially play an important role in ecosystem nutrient dynamics. Given the role of winter storms in scouring lichens from within the canopy and the extended length of winter snowpack, from November through to May or even early June, in these ecosystems, the decomposition of lichen litterfall should be heavily influenced by placement within the snowpack. We have examined this factor by placing litter bags containing samples of the hair lichens, Alectoria sarmentosa and Bryoria spp., on top of the winter snowpack in the Cariboo Mountains. Samples were set out in early- (8 Nov.) mid- (16 Jan.) and late- (22 Mar.) winter and subsequently retrieved on spring snow-melt (22 May). Lichen samples that were buried in the lower snowpack all winter long (196 days) lost two-thirds of their original mass. In contrast lichens placed on the snowpack in mid- (127 days) or late-winter (61 days) lost only 6-15% of their total mass, far less than would be predicted on the basis of time in snowpack alone. Spot measurements showed that the snowpack environment effectively buffers litter samples from extreme winter conditions. All lichen samples placed within the snowpack showed much higher C/N ratios on removal, indicating rapid leaching of readily soluble cellular constituents in the snowpack environment. These findings indicate that the snowpack environment plays a major role in decomposition processes in these high-elevation forests and reinforces our view that lichens are a readily labile nutrient source within these ecosystems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".