Aquatic Bacterial Community Connectivity: The Effect of Hydrological Flow on Community Diversity and Composition
Bibliographic record
Abstract
Microbial communities are vital components of freshwater ecosystems due to their role in nutrient cycling and energy flow; however, the mechanisms driving their variation are still being explored. In aquatic systems, water flow (hydrology) can impact microbial community composition through community connectivity; however, the details of hydrology’s effects on microbial connectivity remain unclear. To address this question, we used 16S rRNA metabarcoding to determine bacterial community composition and connectivity across flow transects in three connected Great Lakes waterbodies with very different water-flow regimes: the Little River (high flow), the Detroit River (moderate flow), and Lake Erie (low flow). Bacterial alpha diversity (Chao1) did not differ among the three locations or sample sites along the transects. Analyses of beta diversity using community dissimilarity matrices identified significant differences among the three locations and among sample sites within locations. Bacterial community connectivity varied among the three locations, with a significant distance–decay relationship observed only in the low-flow location, which is indicative of connectivity driven by spatial proximity. Directional analyses showed that the water-flow direction affected bacterial similarity, consistent with the expected hydrological effects on community connectivity and previous published work. Our results indicate that (1) microbial community composition varies within and among even geographically close sampling locations and (2) the specific water-flow regime appears to affect bacterial community connectivity. Including hydrology in models of bacterial community composition will improve our understanding of the relative roles of selection versus stochastic effects on bacterial community diversity and composition in freshwater ecosystems.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".