Species richness and phytoremediation potential of mine wastelands-native trees across the Zambian Copperbelt Region
Bibliographic record
Abstract
Mining activities are among the key sources of soil metal contamination in the Zambian Copperbelt, resulting in drastic landscape transformation. Plant species growing naturally on mine wastelands represent an asset for remediation on the disturbed ecosystems in the region. However, little is known about the suitability of Zambian native tree and shrub species for phytoremediation. The current study was carried to determine tree species richness and abundance on seven mine wastelands across the Zambian Copperbelt and evaluate their phytoremediation potential. Field inventory and post-hoc ecological analyses allowed identification of 32 native tree species, belonging to 13 different families, of which Fabaceae (34%) and Combretaceae (19%) predominated. Most of the identified tree species were found to be Cu, Co, Cr, Ni and Mo excluders. Among them, Rhus longipes (Anacardiaceae), Syzygium guineense (Myrtaceae), Senegalia polyacantha (Fabaceae) and Ficus craterostoma (Moraceae) were revealed as the most dominant tree species across the studied tailing dams (TDs) making them ideal candidates for metal phytostabilization. And coincidentally, their richness was positively correlated with high soil Cu concentration, a sought-after trait for phytoremediation of heavily polluted environment. Intriguingly, most identified tree species proved not suited for phytostabilization of Mn, Zn, B and Ba. On the other hand, species such as Annona senegalensis , Parinari curatellifolia , Dombeya rotundilifolia actively translocated these metals to leaves (TF > 1), indicating their potential for phytoextraction of Cu, Co, Cr, Ni, and Mo notably. Species richness and abundance significantly varied across the seven studied TDs. This was however barely influenced by soil metal contents, suggesting additional drivers dictating tree species-environment relationship in the context of studied TDs. The findings of this study provide crucial information in prospect of tree-based ecological restoration of mine wastelands, having revealed a diversified floristic composition of wastelands-native trees in the region, and clarified their respective phytoremediation attributes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".