Effects of restoration on carbon storage in smelterimpacted industrial barrens
Notice bibliographique
Résumé
Landscape carbon (C) storage is a key component of climate change mitigation. Globally, industrial barrens cover large areas and their restoration can facilitate C storage in otherwise under-utilized sites, while concomitantly enhancing numerous other ecosystem services. I assessed how restoration of smelter impacted barren land enhanced C storage by studying a site in Sudbury, Ontario near a former Ni and Cu metal smelter that ceased operation in 1972. The site was treated by aerial liming, fertilizing, and grass and legume seeding in 1994-1997, followed by jack pine (Pinus banksiana) planting in the upland areas in 1997-2001. Forty-five 0.1 ha size plots were selected across restored and untreated adjoining areas, 32 in exposed upland industrial barrens and 13 in sheltered lowland valleys. The focus of my study was on upland industrial barrens, which exhibited severe site conditions and little natural regrowth. I measured the amount of C in coarse woody debris, fine woody debris, herbs, mineral soil, organic soil (LFH layers), shrubs, and trees in each plot. Measures of wetness index, plant species richness, soil metal concentrations, soil pH, distance from smelter, and elevation were then used to assess factors affecting total ecosystem C storage. In lowland valleys where no active tree planting occurred (only natural regeneration) the treatments with lime, fertilizer, and grass and legume seed showed a 38% increase in C storage (101.1 ± 5.5 Mg C ha-1 (mean ± S.E.)) compared to untreated lowland plots (73.3 ± 5.9 Mg C ha1 ). In upland areas where growing conditions were more severe (i.e., thin soils, low moisture), tree C increased from 0.5 ± 0.4 Mg C ha-1 in areas of natural regeneration to 19.3 ± 1.4 Mg C ha-1 following liming, fertilizing, seeding, and tree-planting. There was no significant difference in total C storage in untreated reference plots (36.1 ± 8.4 Mg C ha-1) compared to limed, fertilized, seeded, and tree-planted plots (58.2 ± 4.4 Mg C ha-1), likely due to variable site conditions across the landscape. Wetness index, plant species richness, and soil bioavailable metal concentrations were the best predictors of C storage in upland industrial barrens, with the best model explaining 64% of variance in C storage. Overall, mineral soil remained the largest C pool in both the uplands (53%) and the lowlands (40%). The forests in my study were not mature, so C storage is expected to continue to increase in the future. My findings demonstrate that soil amendments and tree planting can increase tree C storage in industrial barrens, but site characteristics, particularly wetness, are key to the rate of total C accumulation. C storage in less disturbed lowland valleys also benefitted from restoration. Well-designed restoration efforts that optimize C storage in globally extensive industrial barrens can therefore sequester C and may in turn assist in climate change mitigation.
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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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 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 ».