Changes in the Community Structure of Under-Ice and Open-Water Microbiomes in Urban Lakes Exposed to Road Salts
Bibliographic record
Abstract
Salinization of freshwater is increasingly observed in regions where chloride de-icing salts are applied to the roads in winter, but little is known about the effects on microbial communities. In this study, we analyzed the planktonic microbiomes of four lakes that differed in degree of urbanization, eutrophication and salinization, from an oligotrophic reference lake with no surrounding roads, to a eutrophic, salinized lake receiving runoff from a highway. We tested the hypothesis that an influence of road salts would be superimposed on the effects of season and trophic status. We evaluated the microbial community structure by 16S rRNA sequencing for Bacteria, and by four methods for eukaryotes: 16S rRNA chloroplast analysis, 18S rRNA sequencing, photosynthetic pigment analysis and microscopy. Consistent with our hypothesis, chloride and total nitrogen concentrations were among the most important statistical factors explaining the differences in taxonomic composition. These factors were positively correlated with the abundance of cryptophytes, haptophytes, and cyanobacteria. Ice-cover was also a major structuring factor, with clear differences between the winter communities and those of the open-water period. Nitrifying and methane oxidizing bacteria were more abundant in winter, suggesting the importance of anaerobic sediment processes and release of reduced compounds into the ice-covered water columns. The four methods for eukaryotic analysis provided complementary information. The 18S rRNA observations were strongly influenced by the presence of ribosome-rich ciliates, but revealed a much higher degree of taxonomic richness and greater separation of lakes, seasonal changes and potential salinity effects than the other methods.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".