Global change and their environmental stressors have a significant impact on soil biodiversity -- a meta-analysis
Bibliographic record
Abstract
Anthropogenic global changes are impacting biodiversity, however, many previous meta-analyses investigating the impact of different global changes on biodiversity have omitted soil fauna, or are limited in the scope of the global changes studied. Threats to soil biodiversity by global changes need to be understood to mitigate effects on ecosystem services provided by soils. We conducted a meta-analysis using 3,173 effect sizes from 627 publications focused on six global changes (climate change, land-use intensification, pollution, nutrient enrichment, invasive species, and habitat fragmentation) and their associated environmental stressors on soil fauna. We classified stressors as either pulse (short-term, acute) or press (long-term, chronic) stressors, and expected pulse stressors to have less impact on soil biodiversity due to buffering effects of the soil. Unexpectedly, pollution caused the largest loss in soil fauna communities, which is worrying due to continually increasing levels of pollution, as well as the poor mechanistic understanding of pollution impacts. There was no clear pattern of pulse stressors having a smaller impact on soil biodiversity than press stressors. Overall, this work shows the importance of including soil biodiversity in large-scale global change analyses, as soil organisms often do not show the same responses as organisms above-ground.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".