Fish-mediated nutrient recycling and the trophic cascade in lakes
Bibliographic record
Abstract
The effects of planktivorous fish on phytoplankton through nutrient recycling and zooplankton herbivory were experimentally separated and their relative importance quantified in a eutrophic humic lake. Natural phytoplankton assemblages were incubated in nutrient-permeable chambers placed inside enclosures with or without fish. Outside these chambers, phytoplankton were exposed to zooplankton herbivory and to nutrient recycling by fish and zooplankton, whereas inside the chambers, phytoplankton were exposed only to nutrient recycling by these consumers. Our results show that fish had significant positive effects on cyanobacteria, cryptomonads, and chlorophytes inside the chambers, indicating that fish-mediated nutrient recycling had significant effects on these phytoplankton groups. However, our results also indicate that changes in zooplankton grazing induced by fish were an important mechanism by which fish affected all phytoplankton groups except cyanobacteria. Comparison of effect sizes revealed that the effects on cyanobacteria and chlorophytes through nutrient recycling were stronger than those through zooplankton grazing. Moreover, most of the fish-mediated nutrient recycling effects were due to increased nutrient recycling by zooplankton rather than direct nutrient excretion by fish. In conclusion, we provide experimental evidence supporting the hypothesis that fish-mediated nutrient recycling is an important mechanism affecting phytoplankton community structure and favoring cyanobacteria dominance in lakes.
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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 | 0.000 |
| 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".