Shrub Cover Influence on Seedling Growth and Survival Following Logging of a Tropical Forest
Bibliographic record
Abstract
Abstract Logging in tropical forests may create large canopy openings. These gaps provide suitable conditions for some opportunistic shrubs and herbs to take advantage of the surge in resources and rapidly colonize disturbed sites. This dense plant cover may limit forest regeneration by interfering with tree seedling establishment, growth, and survival by altering the light and nutrients available to seedlings, modifying herbivore behavior, or a number of other factors. In Kibale National Park (Uganda), old logging sites are mainly covered by dense stands of Acanthus pubescens Engl., which appear to inhibit tree regeneration. We wanted to identify the ecological processes underlying this regeneration collapse. To do so, we designed a factorial experiment to evaluate the influences of herbivory and vegetation cover on the growth and survival of tree seedlings. We compared the survival and growth of transplanted tree seedlings in A. pubescens stands and logged forests, in the presence or absence of the understory vegetation layer (logged forest) or vegetation cover (A. pubescens), and with or without herbivory. We found no evidence to support the hypothesis that herbivory is significantly higher under dense A. pubescens cover. Seedling survival was not influenced by the environment. Seedling growth, however, was positively influenced by the removal of A. pubescens, suggesting that changes in resource availability associated with the presence of A. pubescens, may be important for regeneration. Our results suggest that sustained cutting of A. pubescens cover could foster the growth of established seedlings and could lead to tree regeneration and habitat restoration.
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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".