Leafing intensity and the fruit size/number trade‐off in woody angiosperms
Bibliographic record
Abstract
Summary A sample of woody angiosperm species was used to test a central prediction of the ‘leafing intensity premium’ hypothesis: higher leafing intensity (number of leaves produced per unit dry mass of shoot vegetative tissue produced in the same growing season) confers a larger bud bank (i.e. number of axillary meristems per unit shoot tissue) that can be deployed for reproduction, and thus confers generally greater fruit numbers, and hence higher potential fecundity allocation (i.e. fecundity per unit size of the supporting shoot tissue that is produced in the same growing season. Current‐year shoots (i.e. bearing leaves) were collected to record: shoot dry mass, total number of leaves, total number of fruits or fruit clusters (if derived from inflorescences), mean individual leaf dry mass and mean individual fruit dry mass. Sampled individuals (shrubs and trees) were also measured for body size (main stem height and circumference). Species with larger individual fruit (or fruit cluster) mass have generally larger leaves, but they also have a negative trade‐off relationship with ‘fruiting intensity’ – that is the total number of reproductive meristems producing fruits (or fruit clusters) per unit dry mass of shoot vegetative tissue produced in the same growing season. Variation in fruiting intensity, however, is better predicted by a positive relationship with variation in bud bank size. Species with smaller leaf size (dry mass) have generally higher leafing intensity; species with higher leafing intensity in turn have generally higher fruiting intensity; and species with higher fruiting intensity in turn have generally higher potential fecundity allocation (based on the typical species maximum number of seeds per fruit, obtained from published floras). Species with smaller body size have generally higher potential fecundity allocation, but body size had no significant relationships with other measured traits when controlling for phylogeny (using phylogenetically independent contrasts). Synthesis . Our results indicate that bud bank size is an important functional trait for defining adaptive strategy in woody angiosperms. A larger bud bank is generated by higher leafing intensity, which in turn generates higher fruiting intensity, thus generating greater potential fecundity allocation. These traits will be important for maximizing reproductive economy – that is capacity to produce offspring despite growth or body size limitation (e.g. due to crowding/competition, or because of limited time available for growth, flowering, pollination or fruit/seed maturation).
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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 | 0.001 |
| 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) | 0.001 | 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".