Size-Fractionated Microbiome Structure in Subarctic Rivers and a Coastal Plume Across DOC and Salinity Gradients
Bibliographic record
Abstract
Little is known about the microbial diversity of rivers that flow across the changing subarctic landscape. Using amplicon sequencing (rRNA and rRNA genes) combined with HPLC pigment analysis and physicochemical measurements, we investigated the diversity of two size fractions of planktonic Bacteria, Archaea and microbial eukaryotes along environmental gradients in the Great Whale River (GWR), Canada. This large subarctic river drains an extensive watershed that includes areas of thawing permafrost, and discharges into southeastern Hudson Bay as an extensive plume that gradually mixes with the coastal marine waters. The microbial communities differed by size-fraction (separated with a 3-μm filter), and clustered into three distinct environmental groups: (1) the GWR sites throughout a 150-km sampling transect; (2) the GWR plume in Hudson Bay; and (3) small rivers that flow through degraded permafrost landscapes. There was a downstream increase in taxonomic richness along the GWR, suggesting that sub-catchment inputs influence microbial community structure in the absence of sharp environmental gradients. Microbial community structure shifted across the salinity gradient within the plume, with changes in taxonomic composition and diversity. Rivers flowing through degraded permafrost had distinct physicochemical and microbiome characteristics, with allochthonous dissolved organic carbon explaining part of the variation in community structure. Finally, our analyses of the core microbiome indicated that while a substantial part of all communities consisted of generalists, most taxa had a more limited environmental range and may therefore be sensitive to ongoing change.
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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 | 0.000 | 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".