Unabated Nitrogen Pollution Favors Growth of Toxic Cyanobacteria over Chlorophytes in Most Hypereutrophic Lakes
Bibliographic record
Abstract
Abstract Human release of reactive nitrogen (N) to the environment has increased 10-fold since 1860 and is expected to increase by a further ∼75% by 2050. Much of this N enters phosphorus (P)-rich, eutrophic lakes in agricultural and urbanized watersheds. While N pollution of eutrophic lakes can promote toxic cyanobacterial growth, some cases of extreme N loading have led to the dominance of chlorophytes (green algae). As N loads required to shift communities from cyanobacterial to chlorophyte dominance are unclear, we experimentally tested phytoplankton responses to a gradient of N loading in a P-rich lake. Low-to-moderate doses (1–3 mg N L–1 week–1) promoted toxic cyanobacterial dominance and elevated concentrations of the hepatotoxin microcystin. Conversely, loads characteristic of pure urban or agricultural effluents (up to 18 mg N L–1 week–1) led to the dominance of chlorophytes over cyanobacteria and lower microcystin content. This indicates that N loads needed to sustain chlorophyte dominance are uncommon, likely restricted to select shallow lakes directly exposed to urban or agricultural effluents. As most N pollution regimes in P-rich lakes will favor toxic cyanobacterial dominance, restricting future N pollution will help curb further cyanobacterial dominance in lakes both directly and by constraining the capacity for future P loading and climate warming to drive cyanobacterial growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".