Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores
Notice bibliographique
Résumé
Wildfire is a natural disturbance in many forested biomes, with the loss of carbon to the atmosphere and mortality of trees actively sequestering carbon of global concern as a contribution to climate change. Natural regeneration is often successful at reestablishing a forest in ecosystems adapted to fire, but there is increasing concern that the changing size, frequency and severity of wildfire is causing regeneration failures or inadequate densities of trees that sequester and store carbon following these disturbances. It remains unclear whether the action of planting trees accelerates carbon storage following fire compared to forests established through natural regeneration. The central interior of British Columbia recently experienced multiple years of record-breaking fire activity. Rehabilitation planting focused on reestablishing trees in the managed forest but was also prescribed in previously unmanaged forests to initiate carbon sequestration. Planting is often accompanied by other stand treatments such as salvage harvesting or snag removal and debris clearing to ensure planter safety. Here, we determine carbon recovery and stores in 21 wildfires across a chronosequence from the early 1960s to 2015. We measured above and belowground carbon pools to determine the effect of time since fire and planting treatments on carbon. Tree planting did not increase total ecosystem carbon over time, but rather decreased carbon through the loss of dead wood from site preparation. All carbon pools were affected by time since fire except the mineral soil pool, which was best predicted by soil clay content and coarse fragments positive effects. Live tree carbon increased over time, with more stored in planted stands over 60 years compared to stands that were not planted. Projecting growth to 100 years since fire suggests we may see increasing divergence in carbon stores in planted stands over a full fire-return interval, but these differences remain relatively small [mean (sd): 140.8 (19.6) Mg⋅ha –1 in planted compared to 136.9 (27.5) Mg⋅ha –1 in not-planted stands], with 1.4 Mg⋅ha –1 year –1 sequestered in not-planted compared to 1.5 Mg⋅ha –1 year –1 in planted stands. To meet carbon objectives, replanting trees on average sites in burned forests of BC’s central interior would require preserving the carbon legacy of fire, including dead wood.
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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 ».