Soil fauna diversity and chemical stressors: a review of knowledge gaps and roadmap for future research
Bibliographic record
Abstract
Soils harbour highly‐diverse invertebrate communities that play important roles for ecosystem services, including the mitigation of environmental pollution. Chemical stressors, such as pesticides, pharmaceuticals and metals, are being increasingly spread into ecosystems due to human activities. While it is crucial to predict the consequences of chemical stressors for soil biodiversity, chemical toxicity is often assessed using individuals or populations in laboratory cultures. There has been no systematic evaluation of the evidence documenting the impacts of chemical stressors on diverse, natural soil communities. Here, we use a comprehensive literature review of 274 studies to evaluate the current state of knowledge about the effects of chemicals on soil fauna communities. Most research has had limited spatial scope, with noteworthy gaps in the regions that are potentially the most threatened by soil pollution (Southern Hemisphere). Furthermore, reports generally were constrained to a few emblematic soil fauna groups (nematodes, collembola and earthworms) and chemical stressors (metals). Future research should address biases in spatial distribution of studies, as well as the taxonomic groups and chemical compounds considered. Specifically, emphasis on indirect effects mediated by species interactions, ecosystem functioning and interactive effects of stressors and climate change, currently lacking in the literature, is needed to improve soil‐biodiversity conservation and restoration efforts, as well as predictions of global diversity change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".