Contrasting dynamics and environmental controls of dispersed bacteria along a hydrologic gradient
Bibliographic record
Abstract
Freshwater bacterioplankton communities are influenced by the transport of bacteria from the surrounding terrestrial environments. It has been shown that, although most of these dispersed bacteria gradually disappear along the hydrologic continuum, some can thrive in aquatic systems and become dominant, leading to a gradual succession of communities. Here we aimed at exploring the environmental factors driving the structure of such contrasting bacterial populations as well as their functional properties. Using Illumina sequencing of the 16S rRNA gene, we characterized the taxonomic composition of bacterioplankton communities from 10 streams and rivers in Québec spanning the whole hydrologic continuum (river Strahler order 0 to 7), which were sampled in two occasions. With the aim to understand the fate and controls of the transported bacteria, among the taxa present at the origin of the hydrologic gradient (i.e., in the smallest headwater streams) we identified two types of dynamics: i) ‘Tourist’ taxa, which were those that decreased in abundance from the headwaters towards the largest rivers, and ii) ‘Seed’ taxa, those that increased their abundances along the hydrologic continuum. Communities changed gradually from the fast-flowing headwater streams dominated by ‘Tourist’ taxa (ca. 95% of the sequences) towards the largest rivers (Strahler order 4-7) where ‘Seed’ taxa comprised up to 80% of community sequences. Variation in taxonomic composition of the communities dominated by ‘Tourist’ taxa in streams seemed related to different degree of terrestrial inputs, whereas compositional changes in ‘Seed’ communities in the large rivers were linked to differences in autochthonous processes. Finally, the two types of communities differed significantly in their metabolic potential assessed through Biolog Ecoplates. All this suggests that hydrologic transport modulates the gradual replacement of two contrasting population types subjected to different environmental controls and with different metabolic potentials. Moreover, we show that the separate exploration of the two pools of taxa allows unveiling environmental drivers and processes operating on them that remain hidden if explored at the whole community level.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".