Decrease in diatom dominance at lower Si:N ratios alters plankton food webs
Bibliographic record
Abstract
Abstract Many coastal oceans experience not only increased loads of nutrients but also changes in the stoichiometry of nutrient supply. Excess supply of nitrogen and stable or decreased supply of silicon lower silicon to nitrogen (Si:N) ratios, which may decrease diatom proportion in phytoplankton. To examine how Si:N ratios affect plankton community composition and food web structure, we performed a mesocosm experiment where we manipulated Si:N ratios and copepod abundance in a Baltic Sea plankton community. In high Si:N treatments, diatoms dominated. Some of them were likely spared from grazing unexpectedly resulting in higher diatom biomass under high copepod grazing. With declining Si:N ratios, dinoflagellates became more abundant under low and picoplankton under high copepod grazing. This altered plankton food web structure: under high Si:N ratios, edible diatoms were directly accessible food for copepods, while under low Si:N ratios, microzooplankton and phago-mixotrophs (mixoplankton) were a more important food source for mesograzers. The response of copepods to changes in the phytoplankton community was complex and copepod density-dependent. We suggest that declining Si:N ratios favor microzoo- and mixoplankton leading to increased complexity of planktonic food webs. Consequences on higher trophic levels will, however, likely be moderated by edibility, nutritional value or toxicity of dominant phytoplankton species.
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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".