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Record W2533982718 · doi:10.5194/bg-2016-426

Sequential Nutrient Uptake by Phytoplankton Maintains High Primary Productivity and Balanced Nutrient Stoichiometry

2016· article· en· W2533982718 on OpenAlexaff
Kedong Yin, Paul J. Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNutrientPhytoplanktonNitratePhosphorusPhosphateWater columnRedfield ratioSilicateSeawaterEnvironmental chemistryLimitingPhotic zoneEnvironmental scienceNitrogenProductivityAmmoniumPlanktonChemistryBiologyEcologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract. We hypothesize that phytoplankton have the sequential nutrient uptake strategy in order to maintain nutrient stoichiometry and high primary productivity in the water column. Nutrient limited phytoplankton are capable of taking up the limiting nutrient first and they take up non-limiting nutrients when the limiting nutrient debt has been overcome. 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 nitrate was depleted with excess phosphate and silicate 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 phosphate continued to be removed, resulting in additional phosphorus storage by phytoplankton. There were various shapes of vertical profiles of N : P and at the nutricline it changed quickly in response to mixing events. A field incubation of seawater also demonstrated the sequential uptake of nitrate (the most limiting nutrient) and then phosphate and silicate (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 nitrate (or pulsed regenerated ammonium). Thus, phytoplankton show variability of nutrient stoichiometry and are capable of maintaining high productivity by taking advantage of vigorous mixing regimes. To our knowledge, this is the first study to show the dynamics of continuous vertical profiles of N : P and N : Si ratios and to examine the responses of phytoplankton to nutrients supplied naturally by mixing events. The continuous nutrient profiles provided insight into the in situ dynamics of nutrient stoichiometry in the water column and the transient status of nutrient stoichiometry of phytoplankton in the field.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.183
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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