Restoring degraded mine landscapes in Sub-Saharan Africa: Plant facilitatory mechanisms, strategies and knowledge gaps
Bibliographic record
Abstract
Facilitation is an important process for restructuring plant communities in degraded areas. However, little is known about its role in enhancing tree recruitment in mine wastelands. We reviewed and synthesised studies investigating the facilitation process in recovering degraded landscapes and critically analysed the prospects of its application in mine-generated wastelands. Our findings show that, although there are examples of application of facilitation in the restoration of tropical forest landscapes, little is known about its application in the recovery of mine wastelands. In mine wastelands, facilitation can improve vegetation recovery by driving plant structural dynamics and enhancing soil quality through litterfall and root activities, such as root interactions among leguminous trees and other plants. Facilitation also enhances nitrogen transfer, the exchange of essential nutrients and carbon through mycorrhizal fungi, and the improvement of microclimatic conditions by increasing water and nutrient availability. Furthermore, facilitation supports hydraulic lift and provides shade, reducing temperature and evapotranspiration in the understory. This, in turn, improves water status and promotes plant survival and growth under dry conditions. This review highlights the research gaps and proposes areas that require further investigation. These include studies on (1) the facilitation of dominant tree species for the long-term reclamation across different mine wastelands, (2) assisted restoration for naturally and planted dominant tree species in mine-affected areas, and (3) understanding which species best facilitates the existence of other species, their interactions during restoration, the role of functional traits in successful outcomes, and the potential impact of climate change on the suitability of dominant species for restoration efforts. Additionally, several drawbacks associated with facilitation are highlighted in this review. However, we propose suggestions that might help address these challenges and invigorate interest in this line of research. This review examines the existing gaps and summarises the fundamental mechanisms of optimizing facilitation. Furthermore, the practical potential of facilitations for future development and the application of mine wastelands restoration is emphasised, highlighting their role in accelerating ecosystem recovery.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".