Pesticide effects on soil fauna communities—A meta‐analysis
Bibliographic record
Abstract
Abstract Soil invertebrate communities represent a significant fraction of global biodiversity and play crucial roles in ecosystems. A number of human activities threaten soil communities, in particular intensive agricultural practices such as pesticide use. However, there is currently no quantitative synthesis of the impacts of pesticides on soil fauna communities. Here, using a meta‐analysis of 54 studies and 294 observations, we quantify pesticide effects on the abundance, biomass, richness and diversity of natural soil fauna communities across a wide range of environmental contexts. We also identify scenarios with the most detrimental effects on soil fauna communities by analysing the effects of different pesticides (herbicides, fungicides, insecticides, broad‐spectrum substances and multiple substances), different application rates and temporal extents (short‐ or long‐term), as well as the response of different functional groups of soil animals (body size categories, presence of exoskeleton). Pesticides overall decreased the abundance and diversity of soil fauna communities across studies (Grand mean effect size (Hedge's g ) = −0.30 +/− 0.16) and had stronger effects on soil fauna diversity than abundance. The most detrimental scenarios involved multiple substances, broad‐spectrum substances and insecticides, which significantly decreased soil fauna diversity even at recommended rates. We found no evidence that pesticide effects dampen over time, as short‐term and long‐term studies exhibited similar mean effect sizes. Policy implications : Our study highlights that pesticide use has significant detrimental non‐target effects on soil biodiversity, eroding a substantial part of global biodiversity and threatening ecosystem health. This provides crucial evidence supporting recent policies, such as the European Green Deal, that aim to reduce pesticide use in agriculture to conserve biodiversity. The detrimental effects of multiple substances revealed here are particularly concerning because realistic pesticide use often combines several substances targeting different pests and diseases over the crop season. We suggest that future guidelines for pesticide registration, restrictions and banning should rely on data able to fully capture the long‐term consequences of multiple substances for multiple non‐target species in realistic conditions.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".