Unexpected shift from phytoplankton to periphyton in eutrophic streams due to wastewater influx
Bibliographic record
Abstract
Abstract Pollution with nitrogen (N) and phosphorous (P) impairs streams by favoring suspended algae and cyanobacteria over diatom‐rich periphyton. Recently, wastewater treatment plants have been upgraded to biological nutrient removal to eliminate both P and N (mainly NH4+), although little is known of the effects of this effluent on flowing waters. Here, we used high performance liquid chromatography to quantify how the abundance and composition of phytoplankton and periphyton varied in response to both influx of effluent produced by biological nutrient removal and physico‐chemical conditions in small, turbid, P‐rich streams of the northern Great Plains. At the catchment scale, analysis with generalized additive models (GAMs) explained 40.5–62.6% of deviance in total phototroph abundance (as Chl a) and 72.5–82.5% of deviance in community composition (as biomarker carotenoids) in both planktonic and benthic habitats when date‐ and site‐specific physico‐chemical parameters were used as predictors. In contrast, GAMs using wastewater input (as aqueous δ15N) as a predictor explained up to 50% of deviance in Chl a, and ~60% of deviance in community composition, in both suspended (51.6% of Chl a, 67.1% of composition) and attached communities (21.5% of Chl a, 58.8% of composition). Phytoplankton was replaced by periphyton within a 60‐km wastewater‐impacted reach due to dilution of streams by transparent effluent and addition of urban NO3−, although predominance of phytoplankton was re‐established after confluence with higher‐order streams. Overall, influx of effluent shifted turbid, phytoplankton‐rich streams to clear ecosystems with abundant epilithon by improving water transparency and providing NO3− to favor benthic diatoms and chlorophytes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".