Experimental iron amendment suppresses toxic cyanobacteria in a hypereutrophic lake
Bibliographic record
Abstract
Abstract The effects of reducing nutrient inputs to lakes and reservoirs are often delayed by hysteresis resulting from internal phosphorus (P) loading from sediments. Consequently, controlling harmful algal blooms ( HAB s) in many eutrophic ecosystems requires additional management to improve water quality. We manipulated iron (Fe) concentrations in a hypereutrophic lake to determine if Fe amendment would suppress HAB s by inhibiting P release from sediments. Our experiment consisted of 15 in situ mesocosms, 12 of which each received a different dose of Fe (ranging from 2 to 225 g/m 2 ); the remaining three were unmanipulated to serve as controls. Iron amendment decreased P accumulation in porewaters and the flux of P from sediments, which significantly lowered P concentrations in the water column. Iron exerted significant dose‐dependent negative effects on the biomass of phytoplankton and periphyton, and reduced the dominance of cyanobacteria. Even at the lowest doses, Fe appeared to reduce the toxicity of cyanobacterial blooms, as measured by concentrations of hepatotoxic microcystins. Overall, our findings highlight the potential for Fe treatment as an effective strategy for minimizing HAB s in eutrophic lakes and reservoirs. More broadly, our study reinforces the importance of Fe in regulating the trophic state of freshwaters, and the sensitivity of certain ecosystems to changes in Fe supply. Finally, we hypothesize that decreases in natural Fe supplies to lakes associated with anthropogenic activities may worsen outbreaks of toxic cyanobacteria.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".