Impact of wastewater treatment upgrade and nitrogen removal on bacterial communities and their interactions in eutrophic prairie streams
Bibliographic record
Abstract
Eutrophication can impact bacteria by altering fluxes and processing of nutrients and organic matter. However, relatively little is known of how bacterial communities, diversity, and interactions with phytoplankton might respond to nutrient management. We used 16S rRNA amplicon sequencing to compare bacterial assemblages in the water column upstream (control) and downstream (impact) of a wastewater treatment plant (WWTP) located on a eutrophic prairie stream. Sampling occurred before (2012) and after (2018) the 2016 biological nutrient removal (BNR) upgrade that removed >90% of nitrogen (N, mainly NH4+). Multivariate ordination suggested that effluent-impacted bacterial communities were associated mainly with elevated NH4+ concentrations before the upgrade, whereas those after BNR were characteristic of reference systems (low NO3-, diverse regulation). Genera such as Betaproteobacteria and Rhodocyclacea were abundant at impacted sites in 2012, whereas Flavobacterium and a potential pathogen (Legionella) were common at impacted sites in 2018. Nitrifier bacteria (Nitrospira and Nitrosomonas) were present but rare at all sites in 2012, but recorded only downstream of the WWTP in 2018. Generalized additive models showed that BNR reduced bacterial diversity, with ∼70% of the deviance in diversity explained by hydrology, pH, nutrients, and phytoplankton abundance. Overall, NH4+ removal reduced symptoms of cultural eutrophication in microbe assemblages.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".