Post-fire regeneration and survival of whitebark pine (Pinus albicaulis)
Notice bibliographique
Résumé
The recruitment trends of whitebark pine (Pinus albicaulis Engel.) were investigated in 18 recently burned stands in the Canadian Rockies and the North Cascades. Whitebark pine recruitment in recently burned stands (fires < 60 years prior) was compared to that on paired control stands (no recent fire) to determine if fire was necessary for successful recruitment. Recruitment in recently burned stands was compared with ecological and seed source variables. Along a chronosequence, whitebark pine recruitment was compared with precipitation and with Pacific Decadal Oscillation (PDO). Field sampling consisted of systematic plots established in sampling grids in both burned and control stands. In each plot ecological data was collected as was data regarding whitebark pine age, height, and disease status. Recent fire was not found to be a requirement for recruitment. Both distance to and size of seed source were important predictors of whitebark pine recruitment. Whitebark pine regeneration densities were low on warm steep rocky sites. High whitebark pine recruitment on cooler aspects suggested that whitebark pine could establish on cooler sites when competition was removed by fire. Most stands were composed of mixed conifers, but only lodgepole pine appeared to limit the growth of whitebark pine. Whitebark pine recruitment was episodic on all stands, and recruitment years were correlated among many stands separated by large distances. Episodic recruitment may be due to more than cone masting as recruitment in several stands was also correlated with growing season precipitation and positive PDO values, which may increase the length of growing season. A logistic regression model suggested that infection by white pine blister rust is most likely on older seedlings. The percentage of whitebark pine trees infected by white pine blister rust on a site increased with time since fire. Prescribed fires and wildfires should address retention of whitebark pine seed trees on site. Fires that remove competition from mesic-submesic stands may result in the most rapid recruitment of whitebark pine. With the exception of stands heavily stocked with lodgepole pine, all recently burned stands in this study would benefit from increased stocking of whitebark pine by planting.
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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 | 0,000 |
| 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) | 0,001 | 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 ».