Effects of dispersal, shrubs, and density-dependent mortality on seed and seedling distributions in temperate forests
Bibliographic record
Abstract
Processes limiting recruitment of trees may have large impacts on forest dynamics. In this paper, we determined the effects of dispersal, shrubs (Rhododendron maximum), and density-dependent mortality on seed and seedling distributions of Southern Appalachian trees. We quantified the spatial distribution of seed rain, seed bank densities, first-year seedlings, and older than first-year seedlings in five vegetation plots. We fit models to these data assuming effects of limited dispersal, R. maximum (an understory shrub), and (or) density-dependent mortality (as well as a null model with none of these effects) and used best-fitting models to indicate which processes affected a particular species. We found that all factors examined limit species distributions, and thus, affect seedling dynamics. Seedling densities are higher near parent trees long after dispersal occurs. This pattern is less frequently observed for animal-dispersed species than for wind-dispersed species, presumably due to secondary dispersal of seeds by animals. Seedling densities of five species are decreased beneath R. maximum. Shade tolerance does not explain which species are affected, suggesting that factors other than low light are responsible for increased seedling mortality under this shrub. Our results suggest that density-dependent mortality affects four species, decreasing seedling densities close to parent trees. Dispersal, density-dependent mortality, and R. maximum all act in ways that may promote or limit diversity, illustrating that multiple factors are likely to control species 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".