Local seed rain and seed bank in a species-rich grassland: effects of plant abundance and seed size
Bibliographic record
Abstract
In this study, we examined the relationship between seed size, seed rain, and seed bank in a species-rich perennial grassland in Sweden. The seed rain was monitored by 100 seed traps placed in a 10 m × 10 m area for 1 year. The seed bank was sampled by taking 100 soil samples, each in close vicinity to a seed trap. Abundance of reproductive ramets in the area was estimated, since this is likely to affect the proportion of hit seed traps and seed bank samples. When abundance of reproductive ramets was accounted for, we found a negative relationship between seed size and proportion of hit seed bank samples, but we found no relationship between seed size and proportion of hit seed traps. We found strong positive relationships between the abundance of reproductive ramets and proportion of hit seed traps and seed bank samples. We also found strong positive relationships between abundance of reproductive ramets and abundance of seeds in the seed rain and the seed bank, but no relationship between seed size and abundance of seeds in the seed rain or the seed bank. We discuss these results in the context of theory suggesting that large-seeded and small-seeded species may coexist because of a trade-off between colonization and competitive abilities, where smaller-seeded species are able to reach more sites than seeds of larger-seeded species, because they are more numerous and (or) better dispersed.
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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 | 0.002 |
| 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) | 0.001 | 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".