Periphyton microbial communities of artificial streams impacted similarly by distinct phosphorus enrichment patterns
Bibliographic record
Abstract
Phosphorus (P) enrichment of stream water influences the diversity and composition of periphyton assemblages. However, P enrichment can vary temporally with the source of P and there is limited knowledge of the relative impacts of different enrichment patterns on periphyton communities. To address this knowledge gap, we conducted a 25-day mesocosm experiment using nine artificial streams exposed to three P enrichment loading patterns: unenriched, continuously enriched, or event enriched. Periphyton samples were collected from each stream at six time points during the experiment and high throughput sequencing of 16S and 18S rRNA genes was performed to characterize prokaryotic and eukaryotic microbial assemblages. Microbial community compositions of stream periphyton were similar by the end of the experiment for continuously and event enriched streams, and both treatments differed from end point microbial community profiles of unenriched mesocosms. This similarity among periphyton samples from enriched stream mesocosms was primarily due to the shared dominance of green algae (Desmodesmus) and Proteobacteria (Comamonadaceae) for eukaryotic and prokaryotic assemblages, respectively. Richness of both microbial assemblages declined with enrichment, regardless of loading pattern. In contrast, evenness only differed among eukaryotic assemblages, with lower evenness in the enriched streams compared to the unenriched. Overall, our data demonstrate that the amount of P enrichment, rather than pattern of enrichment, regulates periphyton microbial community composition. Management of P enrichment may thus benefit from targeting the most efficient way to reduce P loads regardless of source.
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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 | 0.001 | 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".