Understory plant assemblages present distinct short-term responses to the clear-cutting of an old-growth spruce forest near an alpine timberline
Bibliographic record
Abstract
Forest clear-cutting is a prominent disturbance influencing understory plant communities. We implemented a before–after, control–impact (BACI) designed experiment in a high-elevation, old-growth spruce forest in the eastern Tibetan Plateau to understand the response in cover and species richness of the understory plant community and its assemblages, as well as the driving roles of environmental alteration (e.g., radiation, temperature, humidity, and nutrients), physical disturbance (e.g., direct damage by trampling and tree-felling), and interplay effects (e.g., shading or burial from logging residue) during a 2-year period. The decrease in cover for the understory vegetation was predominantly due to a decrease in bryophytes; the grass cover, however, increased. While bryophyte species richness decreased, the total number of understory and vascular plants changed minimally. Furthermore, the environmental alteration drove the increase in grass cover, as well as the cover and species richness decline for bryophytes and its two species groups. It was also found that physical disturbances and interplay effects caused the decrease in cover for bryophytes. The total effects influenced the understory community and bryophytes in terms of cover, but not in terms of species richness. We conclude that the short-term responses of understory vegetation to clear-cutting are distinct across different assemblages due to various mechanisms. Bryophytes are more sensitive than vascular plants, and cover percentage responded more swiftly than species richness to clear-cutting.
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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".