Interactions between hydrology and water chemistry shape bacterioplankton biogeography across boreal freshwater networks
Bibliographic record
Abstract
Disentangling the mechanisms shaping bacterioplankton communities across freshwater ecosystems requires considering a hydrologic dimension that can influence both dispersal and local sorting, but how the environment and hydrology interact to shape the biogeography of freshwater bacterioplankton over large spatial scales remains unexplored. Using Illumina sequencing of the 16S ribosomal RNA gene, we investigate the large-scale spatial patterns of bacterioplankton across 386 freshwater systems from seven distinct regions in boreal Québec. We show that both hydrology and local water chemistry (mostly pH) interact to shape a sequential structuring of communities from highly diverse assemblages in headwater streams toward larger rivers and lakes dominated by fewer taxa. Increases in water residence time along the hydrologic continuum were accompanied by major losses of bacterial richness and by an increased differentiation of communities driven by local conditions (pH and other related variables). This suggests that hydrology and network position modulate the relative role of environmental sorting and mass effects on community assembly by determining both the time frame for bacterial growth and the composition of the immigrant pool. The apparent low dispersal limitation (that is, the lack of influence of geographic distance on the spatial patterns observed at the taxonomic resolution used) suggests that these boreal bacterioplankton communities derive from a shared bacterial pool that enters the networks through the smallest streams, largely dominated by mass effects, and that is increasingly subjected to local sorting of species during transit along the hydrologic continuum.
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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 | 0.001 |
| 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 | 0.001 |
| 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".