Earthworms and soil mesofauna as early bioindicators for landfill restoration
Why this work is in the frame
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Bibliographic record
Abstract
Context Landfill activities physically disrupt soil habitats. When restoring landfill to ‘soft’ end uses such as woodland, the appropriate stockpiling and reapplication of native topsoil to provide suitable soil habitats may enhance soil fauna recolonisation and reduce restoration timeframes. Aims We studied the influence of reclamation practices on earthworm and mesofauna communities, and evaluated the role of earthworms and soil mesofauna as bioindicators of early landfill restoration success. Methods We investigated soil physico–chemical parameters, and earthworm and soil mesofauna communities at two restored landfill sites and the surrounding land uses. We also applied the QBS-collembola (QBS-c) and QBS-earthworm (QBS-e) index techniques in a reclaimed landfill setting. Results Natural colonisation of reclaimed landfill by earthworms occurred rapidly where original site topsoil was stockpiled, reapplied, and revegetated. QBS-e and QBS-c indices indicated that the most disturbed sites generally had the lowest soil biological quality. Mesofauna richness and abundance were generally higher in the low-disturbance sites. Conclusions and implications We demonstrate the value of recording a range of soil invertebrates during land reclamation, since different soil bioindicator groups respond differently to soil disturbance. QBS-c and QBS-e index techniques alongside traditional soil macro- and mesofauna assessments reinforced our observed soil fauna responses to reclamation practices. Thus, we encourage multitaxon soil monitoring during land reclamation. Where landfill restoration was carried out to a poor standard, results suggest that soil mesofauna are better indicators of soil status than earthworms. For future restoration schemes, best-practice methods are recommended to improve poor-quality reclaimed soil materials to facilitate rapid soil fauna recolonisation and soil structural development.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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