Experimental culling of minnows suppresses cyanobacterial bloom under low-nutrient conditions
Bibliographic record
Abstract
Cyanobacterial blooms in lakes of low nutrient status are recent ecological surprises. Culling of planktivorous fish may help suppress phytoplankton blooms via a trophic cascade effect. To test this hypothesis, we conducted a 90-day experiment adjacent to a shallow oligomesotrophic lake increasingly beset by midsummer cyanobacterial blooms in the presence of high abundances of minnows and sparse herbivorous zooplankton. The single-factor (± three spottail shiners, Notropis hudsonius) experimental design was replicated 10 times for a total of twenty 1200 L capacity mesocosms. Contrary to the trophic cascade hypothesis, minnow removal decreased the abundance of bosminids capable of grazing cyanobacteria. Nevertheless, removal of the minnows significantly both suppressed phytoplankton biomass and offset the development of cyanobacteria, such as Gloeotrichia echinulata. Lower concentrations of phosphorus and nitrogen in the fishless relative to stocked mesocosms best explained these differences in the phytoplankton community. Our findings highlight how fisheries management practices that enhance minnow populations in lakes of low productivity may inadvertently contribute to cyanobacterial blooms through increased nutrient cycling.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".