Rapid Changes in Microbial Community Structures along a Meandering River
Bibliographic record
Abstract
Streams and rivers convey freshwater from lands to the oceans, transporting various organic particles, minerals, and living organisms. Microbial communities are key components of freshwater food webs and take up, utilize, and transform this material. However, there are still important gaps in our understanding of the dynamic of these organisms along the river channels. Using high-throughput 16S and 18S rRNA gene sequencing and quantitative PCR on a 11-km long transect of the Saint-Charles River (Quebec, CA), starting from its main source, the Saint-Charles Lake, we show that bacterial and protist community structures in the river drifted quickly but progressively downstream of its source. The dominant Operational Taxonomic Units (OTUs) of the lake, notably related to Cyanobacteria, decreased in proportions, whereas relative proportions of other OTUs, such as a Pseudarcicella OTU, increased along the river course, becoming quickly predominant in the river system. Both prokaryotic and protist communities changed along the river transect, suggesting a strong impact of the shift from a stratified lake ecosystem to a continuously mixed river environment. This might reflect the cumulative effects of the increasing water turbulence, fluctuations of physicochemical conditions, differential predation pressure in the river, especially in the lake outlet by benthic filter feeders, or the relocation of microorganisms, through flocculation, sedimentation, resuspension, or inoculation from the watershed. Our study reveals that the transit of water in a river system can greatly impact both bacterial and micro-eukaryotic community composition, even over a short distance, and, potentially, the transformation of materials in the water column.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".