Regrowth of understory epiphytic bryophytes 10 years after simulated commercial moss harvest
Bibliographic record
Abstract
Commercial moss harvest is the predominant disturbance for understory epiphytic bryophyte mats in the Pacific Northwest, yet the rate and dynamics of regrowth of this nontimber forest product are unknown. The first long-term evaluation of cover and species richness regrowth following simulated commercial moss harvest from understory vine maple (Acer circinatum Pursh) shrub stems is reported. Stems harvested of moss on six sites in the Oregon Coast Range in 1994 were examined for species composition and relative abundance of regrowth over the course of a decade. Percent cover increased 5.1%/year, averaging only 51% cover in year 10. Forty percent of the total cover in year 10 was attributable to encroachment from adjacent undisturbed mats and 14% to reestablished litterfall. Shortly after harvest, many taxa established on the newly available habitat, such that species richness surpassed preharvest levels by year 3. In the absence of competitive exclusion even by year 10, species richness continued to exceed preharvest levels by two taxa. Vegetative cover regrowth may require 20 years and volume recovery even longer. Commercial moss harvest should be managed on rotations of several decades, and patchy harvest methods should be encouraged over complete strip harvesting to ensure moss regeneration and promote bryophyte diversity.
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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 | 0.000 | 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) | 0.001 | 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".