Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach
Bibliographic record
Abstract
This study applied a 16S rRNA gene metabarcoding approach to characterize bacterial community compositional and functional attributes for surface water samples collected within, primarily, agriculturally dominated watersheds in Ontario and Québec, Canada. Compositional heterogeneity was best explained by stream order, season, and watercourse discharge. Generally, community diversity was higher at agriculturally dominated lower order streams, compared to larger stream order systems such as small to large rivers. However, during times of lower relative water flow and cumulative 2-day rainfall, modestly higher relative diversity was found in the larger watercourses. Bacterial community assemblages were more sensitive to environmental/land use changes in the smaller watercourses, relative to small-to-large river systems, where the proximity of the sampled water column to bacteria reservoirs in the sediments and adjacent terrestrial environment was greater. Stream discharge was the environmental variable most significantly correlated (all positive) with bacterial functional groups, such as C/N cycling and plant pathogens. Comparison of the community structural similarity via network analyses helped to discriminate sources of bacteria in freshwater derived from, for example, wastewater treatment plant effluent and intensity and type of agricultural land uses (e.g., intensive swine production vs. dairy dominated cash/livestock cropping systems). When using metabarcoding approaches, bacterial community composition and coexisting pattern rather than individual taxonomic lineages, were better indicators of environmental/land use conditions (e.g., upstream land use) and bacterial sources in watershed settings. Overall, monitoring changes and differences in aquatic microbial communities at regional and local watershed scales has promise for enhancing environmental footprinting and for better understanding nutrient cycling and ecological function of aquatic systems impacted by a multitude of stressors and land uses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".