Canopy and substratum heterogeneity influence recruitment of the mangrove <i>Avicennia marina</i>
Notice bibliographique
Résumé
Summary Mangrove forests are structurally complex with canopy gaps generated by physical disturbances such as storms and mounds created on the forest floor by biotic disturbances such as crabs excavating burrows. I studied how variations in canopy cover and substratum topography influence the recruitment and demography of the mangrove Avicennia marina in a temperate forest in New South Wales, Australia. Surveys showed that mounds covered up to 44% of the forest floor and were larger and covered more of the substratum under the canopy than in gaps. Propagules and new seedlings (i.e. the most recently established cohort) were more abundant under the canopy than in gaps and on flat areas than on mounds. A field experiment tested whether the observed spatial distribution of new seedlings was due to differences in the secondary dispersal of propagules or in the establishment or early survivorship of seedlings. Propagules were supplied in equal densities on mounds and flats under the canopy and in gaps and then monitored for 10 months. Four times as many propagules were dispersed away from mounds as from flats, and this difference was greater under the canopy than in gaps. In contrast, seedlings established more successfully on mounds, particularly in gaps, suggesting that this is an important microhabitat for recruitment. Seedling survival to 9 months was independent of habitat structure. The influence of mounds on dispersal was, however, much greater than on establishment, so that densities of new seedlings 9 months after establishment were greater on flats. Patterns of seedling abundance in the mangrove forest are thus primarily driven by the influence of habitat structure on the supply of propagules. In contrast, saplings were most abundant on mounds in gaps. Thus, there is a shift in the spatial distribution of the population from the early to the later stages of life history in relation to habitat structure. Long‐term survival of seedlings and their development into saplings may depend both on light availability in gaps and on sediment disturbance resulting from the creation of mounds. The causative agent, crabs, may therefore be critical for regeneration of mangrove forests.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».