Long-Term Effects of Forest Management on Boreal Forest Soil Organic Carbon
Notice bibliographique
Résumé
Boreal forests have historically been regarded as some of the largest terrestrial carbon (C) sinks. However, increased soil organic matter (SOM) decomposition due to forest harvesting and post-harvest silviculture (e.g., site preparation, planting, and managing for competing vegetation) may exacerbate the effects of climate warming and shift boreal forests from being C sinks to C sources. We used an established stand-scale, fully replicated, experimental study to identify how two levels of forest management (harvesting = Harvest Only, and harvesting with post-harvest silviculture = Harvest Plus) influence SOC dynamics at three boreal forest sites varying in soil texture. Each site was surveyed for forest floor (litter and F/H horizons) and mineral soils pre-harvest (0) and 5, 14, and 20 years post-harvest. We predicted that sites harvested and left to revegetate naturally would have the lowest SOC stocks after 20 years, as sites that were planted and managed for competing vegetation would recover faster and contribute to a larger nutrient pool, and that the sand-dominated site would have the largest SOC losses following harvest due to the inherently lower ability of sand soils to chemically and/or physically protect SOC from decomposition following harvest. Over a 20-year period, both forest management treatments generally resulted in reduced total (litter, F/H, and mineral horizon) SOC stocks compared with the control: the Harvest Only treatment reduced overall SOC stocks by 15% at the silt-dominated site and 31% at the clay-dominated site but increased overall SOC stocks by 4% at the sand-dominated site, whereas the Harvest Plus treatment reduced overall SOC stocks by 32% at the sand- and silt-dominated sites and 5% at the clay-dominated site. This suggests that harvesting and leaving plots to revegetate naturally on sand-dominated sites and harvesting followed by post-harvest silviculture on clay-dominated sites may minimize total SOC losses at similar sites, though a full replicated field experiment is needed to test this hypothesis. Most treatment effects in this study were observed only in the second decade post-harvest (14 and 20 years post-harvest), highlighting the importance of long-term field experiments on the effects of forest harvesting and post-harvest silviculture. This research improves our understanding of the relationship between C dynamics, forest management, and soil texture, which is integral for developing sustainable management strategies that optimize C sequestration and contribute to the resilience of boreal forest ecosystems in the face of climate change.
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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 ».