Nutrient distribution and cycling along a forest chronosequence following the regreening of a mining and smelting degraded landscape
Bibliographic record
Abstract
Context The regreening (the one-time application of soil amendments and tree planting) of mining and smelting degraded landscapes can increase site productivity and ecosystem nutrients in the short-term, but uncertainties exist regarding long-term nutrient status. Aims This study investigated whether nutrient distribution and cycling change with stand age in regreened forests on a mining and smelting degraded landscape in the City of Greater Sudbury, Canada. Methods We measured soil and vegetation nutrient concentrations (calcium (Ca), magnesium (Mg), nitrogen (N), phosphorus (P), and potassium (K)), nutrient resorption, litter decomposition, and N mineralisation along a chronosequence of forested sites (n = 12) that were regreened 15–40 years prior to sampling. Key results As regreening stands aged, concentrations of Mg, K, and P increased in lower soil horizons, but foliar concentrations of nutrients did not change. The regreening sites were very rich in Ca and Mg but soils were poor in P, K, inorganic N, and N mineralisation rates were very low. We found few relationships between nutrient cycling and stand age. Potassium and P are thought to be the limiting nutrients in the region and while resorption efficiency of K was much higher than expected, foliar N, P and K concentrations were comparable to ‘healthy’ values. Conclusions The lack of change in foliar nutrients and nutrient cycling with stand age suggest that nutrient limitation is not inhibiting forest function 40 years following a one-time regreening treatment. Implications This study provides perspective to the long-term success of a one-time regreening on an immensely degraded industrial landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".