Long-term effects of fungicides on leaf-associated microorganisms and shredder populations—an artificial stream study
Bibliographic record
Abstract
Leaf litter is a major source of carbon and energy for stream food webs, while both leaf-decomposing microorganisms and macroinvertebrate leaf shredders can be affected by fungicides. Despite the potential for season-long fungicide exposure for these organisms, however, such chronic exposures have not yet been considered. Using an artificial stream facility, effects of a chronic (lasting up to 8 wk) exposure to a mixture of 5 fungicides (sum concentration 20 μg/L) on leaf-associated microorganisms and the key leaf shredder Gammarus fossarum were therefore assessed. While bacterial density and microorganism-mediated leaf decomposition remained unaltered, fungicide exposure reduced fungal biomass (≤71%) on leaves from day 28 onward. Gammarids responded to the combined stress from consumption of fungicide-affected leaves and waterborne exposure with a reduced abundance (≤18%), which triggered reductions in final population biomass (18%) and in the number of precopula pairs (≤22%) but could not fully explain the decreased leaf consumption (19%), lipid content (≤43%; going along with an altered composition of fatty acids), and juvenile production (35%). In contrast, fine particulate organic matter production and stream respiration were unaffected. Our results imply that long-term exposure of leaf-associated fungi and shredders toward fungicides may result in detrimental implications in stream food webs and impairments of detrital material fluxes. These findings render it important to understand decomposer communities' long-term adaptational capabilities to ensure that functional integrity is safeguarded. Environ Toxicol Chem 2017;36:2178-2189. © 2017 SETAC.
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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 | 0.000 | 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 | 0.001 |
| 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 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".