Interactions between invasive plants and heavy metal stresses: a review
Bibliographic record
Abstract
Abstract Global changes have altered the distribution pattern of the plant communities, including invasive species. Anthropogenic contamination may reduce native plant resistance to the invasive species. Thus, the focus of the current review is on the contaminant biogeochemical behavior among native plants, invasive species and the soil within the plant–soil ecosystem to improve our understanding of the interactions between invasive plants and environmental stressors. Our studies together with synthesis of the literature showed that (i) the impacts of invasive species on environmental stress were heterogeneous, (ii) the size of the impact was variable and (iii) the influence types were multidirectional even within the same impact type. However, invasive plants showed self-protective mechanisms when exposed to heavy metals (HMs) and provided either positive or negative influence on the bioavailability and toxicity of HMs. On the other hand, HMs may favor plant invasion due to the widespread higher tolerance of invasive plants to HMs together with the ‘escape behavior’ of native plants when exposed to toxic HM pollution. However, there has been no consensus on whether elemental compositions of invasive plants are different from the natives in the polluted regions. A quantitative research comparing plant, litter and soil contaminant contents between native plants and the invaders in a global context is an indispensable research focus in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".