Sequential nutrient uptake as a potential mechanism for phytoplankton to maintain high primary productivity and balanced nutrient stoichiometry
Bibliographic record
Abstract
Abstract. We hypothesize that phytoplankton have the sequential nutrient uptake strategy to maintain nutrient stoichiometry and high primary productivity in the water column. According to this hypothesis, phytoplankton take up the most limiting nutrient first until depletion, continue to draw down non-limiting nutrients and then take up the most limiting nutrient rapidly when it is available. These processes would result in the variation of ambient nutrient ratios in the water column around the Redfield ratio. We used high-resolution continuous vertical profiles of nutrients, nutrient ratios and on-board ship incubation experiments to test this hypothesis in the Strait of Georgia. At the surface in summer, ambient NO3− was depleted with excess PO43− and SiO4− remaining, and as a result, both N : P and N : Si ratios were low. The two ratios increased to about 10 : 1 and 0. 45 : 1, respectively, at 20 m. Time series of vertical profiles showed that the leftover PO43− continued to be removed, resulting in additional phosphorus storage by phytoplankton. The N : P ratios at the nutricline in vertical profiles responded differently to mixing events. Field incubation of seawater samples also demonstrated the sequential uptake of NO3− (the most limiting nutrient) and then PO43− and SiO4− (the non-limiting nutrients). This sequential uptake strategy allows phytoplankton to acquire additional cellular phosphorus and silicon when they are available and wait for nitrogen to become available through frequent mixing of NO3− (or pulsed regenerated NH4). Thus, phytoplankton are able to maintain high productivity and balance nutrient stoichiometry by taking advantage of vigorous mixing regimes with the capacity of the stoichiometric plasticity. To our knowledge, this is the first study to show the in situ dynamics of continuous vertical profiles of N : P and N : Si ratios, which can provide insight into the in situ dynamics of nutrient stoichiometry in the water column and the inference of the transient status of phytoplankton nutrient stoichiometry in the coastal ocean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".