Leafing intensity and the fruit size/number trade‐off in woody angiosperms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».