Effects of silver nanoparticles on bacterioplankton in a boreal lake
Bibliographic record
Abstract
Summary Silver nanoparticles (AgNPs) are widely used antimicrobial agents and a growing body of evidence suggests that their release into aquatic environments threatens natural bacterial communities and whole ecosystems. However, a knowledge gap exists between the toxic effects of AgNPs found in laboratory studies and their potential impacts in natural environments. In an enclosure experiment conducted in a boreal lake, we exposed natural bacterial communities to AgNPs with two common types of coatings (polyvinylpyrrolidone (PVP) and citrate) under two different exposure regimes, a one‐time (pulse) and a continuous (chronic) addition. AgNP additions increased Ag concentrations to nearly 50 μg L−1 in the highest treatments. We examined bacterial responses (abundance, biomass, production, chlorophyll‐a content and nutrient stoichiometry) over the course of 6 weeks in the summer of 2012. Bacterioplankton exposed to AgNPs initially accumulated Ag over the experimental period regardless of AgNP concentration or coating. After the initial period of increase, Ag in the bacterial size fraction changed largely in concert with bacterial biomass. We found no toxic effects of AgNPs on bacterioplankton abundance, biomass, production or chlorophyll‐a content throughout the experiment. Bacterial production was greater after the pulse addition of PVP‐coated AgNPs and in the chronic addition of PVP‐coated AgNPs at the highest concentrations. Furthermore, AgNPs produced no significant changes in nutrient stoichiometry of the bacterioplankton size fraction. This lack of effects of AgNPs on lake bacterioplankton observed under the natural conditions studied here differs from results of short‐term and laboratory studies of single‐species bacterial cultures. Our results thus indicate AgNP effects in lakes may be less than expected based on standard laboratory experiments, and that additional studies are needed to understand AgNP toxicity under realistic natural conditions in lakes and other ecosystems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".