Seasonal variation in effects of urea and phosphorus on phytoplankton abundance and community composition in a hypereutrophic hardwater lake
Bibliographic record
Abstract
Abstract Urea accounts for half of global agricultural fertiliser applications, yet little is known of its role in eutrophication of freshwater ecosystems, nor how it interacts with phosphorus (P) in regulating phytoplankton composition, especially during spring and autumn. To identify when and how urea and P inputs interact across the ice‐free period, we conducted seven monthly fertilisation experiments in 3,240‐L mesocosms from ice‐off to ice‐formation in a hypereutrophic lake. In addition, we ran bioassays with ammonium (NH4+) to compare the effects of urea with those of NH4+, the immediate product of chemical decomposition of urea. Analysis of water‐column chlorophyll a and biomarker pigments by high‐performance liquid chromatography revealed that addition of inorganic P alone (100 µg P L–1 week–1) had no significant impact on either algal abundance or community composition in hypereutrophic Wascana Lake. Instead, fertilisation with urea (4 mg N L−1 week–1) alone, or in concert with P, significantly (p < 0.05) increased algal abundance in spring and much of summer, but not prior to ice formation in October. In particular, urea amendment enhanced abundance of cryptophytes, chlorophytes, and non‐diazotrophic cyanobacteria during April and May, while fertilisation in summer and early autumn (September) increased only chlorophytes and non‐diazotrophic cyanobacteria. Comparison of urea mesocosms with NH4+ bioassays demonstrated that urea lacked the inherent toxicity of NH4+ in cool waters, but that both compounds stimulated production during summer experiments. This study showed that urea pollution can degrade water quality in P‐rich lakes across a variety of seasonal conditions, including spring, and underscores the importance of quantifying the timing and form of N inputs when managing P‐rich freshwaters.
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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) | 0.001 | 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".