Post‐dispersal seed predation and its relations with seed traits: a thirty‐species‐comparative study
Bibliographic record
Abstract
Abstract Post‐dispersal seed predation is a key process determining the variability in seed survival in forests, where most seeds are handled by rodents. Seed predation is thought to affect seedling regeneration, colonization ability and spatial distribution of plants. Basic seed traits are the essential factors affecting rodent foraging preferences and thus seed survival and seedling recruitment. Many studies have discussed several seed traits and their effects upon seed predation by rodents. However, the results of those previous studies are usually equivocal, likely because few seed traits and/or plant species tend to be incorporated into these studies. In order to elucidate the relationships between seed predation and seed traits, we surveyed the predation of 48 600 seeds in a natural pine forest, belonging to 30 species, for three consecutive years. The results demonstrated that: (i) seed size and seed coat hardness did not significantly affect seed predation; (ii) total phenolics had a negative effect upon seed predation; (iii) positive effects of nitrogen content upon seed predation were found. From our study, it seems that the better strategy to prevent heavy predation is for plants to produce seeds with higher total phenolics content rather than physical defenses (i.e. hard seed coat) or larger seeds. Additionally, rodent foraging preference may depend more on N itrogen content than other nutrient content of seeds.
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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 | 0.001 | 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".