Effects of land reclamation on organic matter decomposition at smelter impacted hill slopes
Notice bibliographique
Résumé
Mining, smelting and forest harvest led to severe environmental disturbances across Sudbury region, resulting in extensive forest cover loss, soil erosion and contamination with acids and metals. Regreening efforts began in 1978, by liming, fertilizing and tree planting. However little work has been done to assess the influence of reclamation practices on soil processes and functioning in these industrially damaged landscapes. In particular there is a lack of such information for hillslopes and other challenging sites. In view of this, the present study examined the effects of land reclamation on key ecosystem processes such as litter decomposition and soil respiration (CO2) and CH4 and N2O fluxes and the ecological controls that regulate these processes. The study was conducted along two parallel transects, one within a barren area and the other within a regreened area at a steep hillslope within the Kelly lake watershed, a study site a few kms of the Copper Cliff smelter in Sudbury. Traditional litter bag decomposition studies are time consuming, expensive, and lack standardization of litter quality. I therefore used a new Tea Bag Index methodology to measure early stage decomposition with two contrasting standardized substrates (Green and Rooibos tea). Tea bag Index parameters were used to quantify the rate at which standardized litter is broken down (decomposition rate index (k)) and to measure the undecomposed residual substrate in the soil (stabilization factor (S)) which has a potential for sequestration. The results revealed that decomposition of Green tea (labile substrate) was twice as fast as Rooibos tea (recalcitrant substrate) in both the reclaimed and barren areas. I found that at reclaimed areas the average Green tea mass loss rates (1.37 ± 0.01 (SE) gm), CO2 production rates (98.84 ± 5.46 (SE) mg m-2 h-1), CH4 consumption rates (- 0.0104 ± 0.00 (SE) µg m-2 h-1 ) were statistically higher than at barren areas, where the average Green tea mass loss were (1.25 ± 0.01 (SE) gm), CO2 production rates were (33.79 ± 2.26 (SE) mg m-2 h-1 ) and CH4 flux rates were (0.0014 ± 0.001 (SE) µg m-2 h-1) respectively. Average Rooibos tea mass loss on the other hand showed no significant differences occurred between reclaimed areas (0.69 ± 0.02 (SE) gm) and at barren areas (0.64 ± 0.02 (SE) gm). N2O flux rates also did not differ significantly between reclaimed (-0.01048 ± 0.0006 (SE) µg m-2 h-1) and barren areas and (-0.00073 ± 0.000452 (SE) µg m-2 h-1). However, when the combined substrate measure of decomposition rate index (k) was compared, there was no significant differences between the reclaimed area soils (0.012 ± 0.001 (SE) g·g-1·day-1) and the still barren untreated soils (0.013 ± 0.001 (SE) g·g-1·day-1). Only the Stabilization factor (S) proved to significantly different with a higher value for the barren soil (0.16 ± 0.009 (SE) g g-1 ) than the reclaimed soil (0.09 ± 0.006 (SE) g g-1) suggesting that the barren areas had slower decomposition resulting in a greater potential for accumulation of undecomposed organic matter in the soil. All soils tested proved to be sources of carbon dioxide. The reclaimed areas appeared to be small sinks for methane and nitrous oxide, whereas at barren areas results were less consistent and the soils appeared as likely to emit or consume methane and nitrous oxide fluxes My main finding was that land reclamation enhanced decomposition of labile substrates CO2 production rates and CH4 consumption rates but decomposition rate index (k) and soil respiration rates (CO2) and CH4, and N2O fluxes were still very low compared to other less disturbed ecosystems. Stepwise multiple regression analysis revealed that decomposition of Green tea (labile substrate) was most likely influenced by microclimatic factors (i.e. moisture) and that decomposition and soil respiration (CO2 production rates) decreased with increasing temperature at barren areas. Topography (i.e. elevation(m)) had little or no effect on carbon turnover rates across the study sites. In conclusion, land reclamation influenced key ecosystem processes such as decomposition of labile organic matter and soil respiration, but even after 37 years post-treatment, Sudbury soils in these hillslope sites were shown to be still under severe environmental stress.
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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 | 0,000 |
| 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 ».