Interactive effects of nitrogen and phosphorus on growth and stoichiometry of lake phytoplankton
Bibliographic record
Abstract
Abstract Phytoplankton responses to nutrient enrichment are often assumed to be universal, but in practice they can significantly vary because the effect size to an increased supply of one nutrient may depend on the availability of the other. Here, we used two complementary two‐way factorial experiments to determine how responses of lake phytoplankton to increased N and P supply vary with increasing concentrations of the other nutrient. We manipulated dissolved N and P concentrations in a 4‐d bioassay conducted with lake phytoplankton and measured/determined chlorophyllaand carbon (C)‐specific growth rates, phytoplankton size‐classes, chlorophyll‐specific N and P removal, and seston C : N : P ratios. These data were used to assess the presence and type of interactive effects between N and P on phytoplankton. For most response variables, the effects of increased N supply depended on the background concentration of P and vice versa. The specific nature of effects differed among response variables and depended on the N and P concentrations present in the bioassay. Generally, increases of N or P alone elicited few and weaker effects than increasing the supply of one element in combination with the other. Overall, our results show that phytoplankton responses to increased N or P supplies are context dependent and likely reflect multiple processes involved in nutrient uptake and use. These results also show that phytoplankton responses to N and P supply ratios can depend on the supply rate of the other nutrient, which complicates understanding how single nutrient enrichment affects lake phytoplankton producers.
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 | 0.001 |
| 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 | 0.001 | 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".