Long-term legacy of phytoremediation on plant succession and soil microbial communities in petroleum-contaminated sub-Arctic soils
Bibliographic record
Abstract
Abstract. Phytoremediation can be a cost-effective method of restoring contaminated soils using plants and associated microorganisms. Most studies follow the impacts of phytoremediation solely across the treatment period and have not explored long-term ecological effects. In 1995, a phytoremediation study was initiated near Fairbanks, Alaska, to determine how the introduction of annual grasses and/or fertilizer would influence degradation of petroleum hydrocarbons (PHCs). After 1 year, grass and/or fertilizer-treated soils showed greater decreases in PHC concentrations compared to untreated plots. The site was then left for 15 years with no active site management. In 2011, we re-examined the site to explore the legacy of phytoremediation on contaminant disappearance, as well as on plant and soil microbial ecology. We found that the recruited vegetation and the current bulk soil microbial community structure and functioning were all heavily influenced by initial phytoremediation treatment. The number of diesel-degrading microorganisms (DDMs) was positively correlated with the percentage cover of vegetation at the site, which was influenced by initial treatment. Even 15 years later, the initial use of fertilizer had significant effects on microbial biomass, community structure, and activity. We conclude that phytoremediation treatment has long-term, legacy effects on the plant community, which, in turn, impact microbial community structure and functioning. It is therefore important to consider phytoremediation strategies that not only influence site remediation rates in the short-term but also prime the site for the restoration of vegetation over the long-term.
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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 | 0.000 | 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) | 0.000 | 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".