Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions
Bibliographic record
Abstract
Anthropogenic activities are altering total nutrient loads to many estuaries and freshwaters, resulting in high loads not only of total nitrogen (N), but in some cases, of chemically reduced forms, notably . Long thought to be the preferred form of N for phytoplankton uptake, may actually suppress overall growth when concentrations are sufficiently high. has been well known to be inhibitory or repressive for uptake and assimilation, but the concentrations of that promote vs. repress uptake, assimilation, and growth in different phytoplankton groups and under different growth conditions are not well understood. Here, we review N metabolism first in a “generic” eukaryotic cell, and the contrasting metabolic pathways and regulation of and when these substrates are provided individually under equivalent growth conditions. Then the metabolic interactions of these substrates are described when both are provided together, emphasizing the cellular challenge of balancing nutrient acquisition with photosynthetic energy balance in dynamic environments. Conditions under which dissipatory pathways such as dissimilatory / reduction to and photorespiration that may lead to growth suppression are highlighted. While more is known about diatoms, taxon-specific differences in and metabolism that may contribute to changes in phytoplankton community composition when the composition of the N pool changes are presented. These relationships have important implications for harmful algal blooms, development of nutrient criteria for management, and modeling of nutrient uptake by phytoplankton, particularly in conditions where eutrophication is increasing and the redox state of N loads is changing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".