Uptake rates of ammonium and nitrate by phytoplankton communities in two eutrophic tropical reservoirs
Bibliographic record
Abstract
While excess phosphorus typically results in the eutrophication of inland waters, there is growing evidence that excess nitrogen (N) and the availability of different N forms influence phytoplankton community composition, often favoring potentially toxic genera. In this study, the environmental dynamics, phytoplankton community structure, and N uptake rates were investigated in two tropical reservoirs. Phytoplankton ammonium () and nitrate () acquisition was assessed through 15N addition experiments over 2 years. We found that changes in ambient nutrient concentrations and temperature influenced different phytoplankton groups, which tended to have different N uptake strategies. The preferred N‐source by Cyanobacteria was while Dinophyceae and other groups seemed adapted to also take up , possibly due to competition. Potential uptake rates (maximum of 8.5 μM‐N hr−1 for and 1.3 μM‐N hr−1 for ) were high in comparison to previous reports from temperate freshwater or marine systems, likely due to elevated algal biomass and temperature. When normalized to biomass as chlorophyll‐a (Chl‐a), specific uptake rates varied between 0.01–3.4 μmol‐N μgChl‐a−1 day−1 for and <0.01–0.8 μmol‐N μgChl‐a−1 day−1 for and were comparable to those reported for other eutrophic and hypereutrophic aquatic systems. In addition, higher temperatures favored Cyanobacteria (e.g., Cylindrospermopsis raciborskii and Microcystis aeruginosa), while a more diverse community was found during colder months. Results highlight how high loading of reduced forms of nitrogen and high temperatures can exacerbate harmful Cyanobacteria blooms in tropical reservoirs and be a concern for drinking water quality.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".