Fire and whitebark pine recovery strategies: drivers of post-fire natural regeneration
Notice bibliographique
Résumé
Whitebark pine (Pinus albicaulis Engelmann), a tree species of high elevation forests in western North America, is listed as an endangered species in Canada. Prescribed burns have been employed by conservation agencies as a recovery strategy to create open habitats free of competition and to increase regeneration opportunities. However, questions remain with respect to the success of prescribed burns for the restoration of whitebark pine and best practices of this technique, as well as to what role wildfire plays in whitebark pine communities at the northern limits of its range. Understanding what drives whitebark pine post-fire regeneration and how it responds to fire severity is important for guiding future burn prescriptions and managing wildfire to effectively implement Alberta’s provincial recovery plan at a landscape scale. Therefore, this research project aimed to better understand how: (i) site, stand and plot level factors, and (ii) fire severity influences the natural regeneration occurrence and abundance of whitebark pine in post-fire environments. Five prescribed burns and four wildfires across the federal and provincial mountain parks in western Alberta were sampled and information on environmental variables and whitebark pine regeneration was collected. Generalized mixed effect models were used to test individual predictors and perform model selection. Whitebark pine post-fire regeneration was shown to be a complex process linked to a variety of biological processes at multiple spatial scales. Regeneration occurrence increased in the first 18 years after fire, mainly at stands with larger whitebark pine basal area. Seedling density increased up to 18 years on wildfires, while it declined after 10 years on prescribed burns, indicating that regeneration abundance was probably driven by the existence of favourable seedbeds and understory conditions at smaller scales. This creates a challenge in predicting regeneration abundance because of the multitude of factors that can influence post-fire conditions, such as fire severity, burning season, post-fire weather and pre-forest composition. At a plot level, decaying wood cover and litter cover up to 25 % and 9 cm depth, respectively, and medium shrub cover up to 30% were positively correlated with seedling density. Fire was not a requirement for regeneration to occur as post-fire seedling densities in the unburned plots (320.8 seedlings/ha) were higher than in the burned plots at 50 m from forest edge (288.5 seedlings/ha). We observed both beneficial and detrimental effects of fire on whitebark pine regeneration. The lower post-fire and advanced seedling densities in the burned plots may suggest that fire is not beneficial for regeneration, while the colonization of burned stands that had no mature whitebark pine trees pre-fire may suggests that fire creates new habitats for regeneration. Proximal seed sources were important as they increased the probability of regeneration occurrence. However, the current increase in tree mortality caused by white pine blister rust and mountain pine beetle threatens remaining whitebark pine stands and raises the question for how long seed sources will remain viable to sustain natural regeneration. After 18 years post-fire, regeneration densities were lower than in previous studies that looked at recent and advanced regeneration in undisturbed stands (463 – 1082 seedlings/ha) or similar to fires up to 60 years old (0 – 406 seedlings/ha). If conservation agencies are to use those densities as reference values during restoration efforts, long term post-fire occupancy surveys and artificial planting will likely be necessary to complement lack of natural regeneration in burned areas and achieve restoration goals, particularly at stands experiencing high tree mortality caused by blister rust and mountain pine beetle.
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 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».