Recruitment advantage of large seeds is greater in shaded habitats
Why this work is in the frame
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Bibliographic record
Abstract
Large seeds are assumed to have higher probability of successful recruitment than small seeds. This is because larger seeds give rise to larger seedlings and larger seedlings better withstand environmental hazards like deep shade and drought. Biotic and abiotic limitations to seedling growth and survival, and conversely availability of safe sites for recruitment, vary along environmental gradients and between habitat types. Thus, the value to plant species of possessing large seeds may differ between plant communities. We analyzed the relationship between seed mass and per-seed recruitment success (seedlings established per number of seeds produced) along an environmental gradient from open grassland to closed-canopy forest using data collected by Uuno Perttula in southern Finland in 1934. We found that larger seeds have greater recruitment success relative to smaller seeds in all investigated communities. However, the recruitment success of large seeds relative to small seeds strongly increased from grassland and open forest to closed-canopy forest. Of the measured environmental variables, canopy closure most strongly explained this increase. This indicates a strong direct effect of deep shade on seedling survival in natural plant communities. Additional explanatory power was associated with soil moisture. Litter cover, moss cover, and soil pH did not contribute to explaining the variation in relative recruitment success of larger seeds. Thus, the advantage of large seeds in recruitment success is pronounced in deeply shaded forest but may be insignificant in open vegetation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it