Macrofauna-environment interactions and their potential in restoring degraded landscapes in the context of Sub-Saharan Africa: A review of current knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Restoring degraded landscapes, such as those induced by mining activity, is essential for recovering lost ecosystem services. This requires innovative nature-based solutions, especially in sub-Saharan Africa (SSA). This review summarizes current knowledge on soil macrofauna in degraded SSA landscapes, an otherwise overlooked component of ecological restoration. A systematic literature review was conducted, yielding 31 relevant publications that were analyzed to identify patterns in macrofauna assemblages across land-use types in SSA including agricultural, forest, bushland, grassland, savannah, dumpsite and reclaimed mine site landscapes. Bibliometric analysis showed minimal studies before 2014, with research increasing after 2017, mostly in southern and eastern Africa. West Africa remains underrepresented. We found more studies on agricultural systems type (28 of the 32 reviewed studies), reporting seven classes, while less studies were conducted on mining wasteland (3 of 31 reviewed studies) reporting only one class. This highlights the urgent need for more macrofauna research in mine wastelands to pursue restoration. Variations in macrofauna composition (at both class and order level) are also viewed in relation to their physiological and environmental plasticity adaptations. In addition, potential macrofauna functional roles, such as bioturbation, organic matter breakdown, nutrient cycling, as well as other attributes such as tolerance to harsh environments and bioindication of biodiversity recovery, that may support landscape restoration were considered as well. Macrofauna groups with potential in future bioaugmentation strategies (the deliberate introduction of beneficial soil organisms to enhance ecological functions) include earthworms (Oligochaeta), termites (Isoptera) and ants (Hymenoptera: Formicidae). Opportunities and challenges of their integration into restoration planning are also discussed, especially in the context of SSA mining landscapes, which are often characterized by severe ecological degradation such as surface water contamination and heavy metal pollution. Although there is a gradual increase in publications on macrofauna in Southern Africa, their practical inclusion in ecological restoration efforts across SSA remains limited. The lack of a better understanding of macrofauna tolerance mechanisms, particularly to environmental stressors such as temperature fluctuations, chemical pollution, and habitat alterations, and the precise nature of their interactions with both biotic and abiotic environmental factors is identified as an avenue for future investigations.
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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 it