The leaf size/number trade-off within species and within plants for woody angiosperms
Notice bibliographique
Résumé
Background and aims – The leaf size/number trade-off has been recently established as a wide-spread and highly predictable relationship associated with between-species leaf size variation. In this study, we examine whether this trade-off relationship also applies at the between-plant (within-species), and at the between-shoot (within-plant) levels associated with spatial variation in incident light availability within tree canopies. Methods – Replicate current-year shoots were sampled from north-facing (shaded) and south-facing (sun-exposed) canopy sides of sixteen broadleaf tree species in eastern Ontario, Canada. For each shoot, measurements were recorded for mean individual leaf dry mass, number of leaves, number of side branches, and stem length, girth, and tissue dry mass. Leafing intensity was calculated as the number of leaves produced per unit of supporting stem tissue dry mass. Key results – All of the direct trait measurements had generally larger values for shoots collected from south-facing canopy sides (as expected). However, negative isometric relationships between leaf size and leafing intensity were found at the between-plant level (for Acer saccharum) and the between-shoot (within-tree) level for at least some individuals of most species. The predominant trend at the within-tree level, however, was allometric – i.e. north-facing (light-limited) shoots generally had lower individual leaf dry mass but disproportionately higher leafing intensity compared with south-facing shoots. Conclusions – The results confirm that there is a fundamental leaf size/number trade-off at the between-plant (within-species) level and also at the between-shoot (within-plant) level, as previously reported at the between-species level. But more specifically, the results reveal distinctly different leaf deployment strategies in response to spatial light variability within tree canopies: Under high light exposure, larger leaves are favoured (with lower leafing intensity imposed as a trade-off), but in deeply shaded portions of the canopy, smaller leaves result, we suggest, for two reasons: (i) they are favoured directly (because they minimize overlap of closely spaced adjacent leaves); (ii) they are imposed as a trade-off of selection favouring high leafing intensity, which in turn maximizes the size of the reserve bud bank (number of axillary meristems per unit of supporting stem tissue) available for initiating continued growth or reproduction in the following year.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».