Effects of restoration on carbon storage in smelterimpacted industrial barrens
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".