Co‐existence of distinct <i>Ostreococcus</i> ecotypes at an oceanic front
Bibliographic record
Abstract
Abstract Western boundary currents support high primary production and carbon export. Here, we performed a survey of photosynthetic picoeukaryotes in the North Pacific Ocean in four transects crossing the Kuroshio Front. Prasinophyte algae comprised 85% of 18S rRNA gene sequences for photosynthetic taxa in the <5 μm size fraction. The picoplanktonic (<2 μm) genera Micromonas and Ostreococcus comprised 30% and 51% of the total photosynthetic 18S rDNA sequences from five stations. Phylogenetic analysis showed that two Ostreococcus ecotypes, until now rarely found to co‐occur, were both present in the majority of samples. Ostreococcus ecotype OI reached 6,830 ± 343 gene copies mL−1, while Ostreococcus ecotype OII reached 50,190 ± 971 gene copies mL−1 based on qPCR analysis of the 18S rRNA gene. These values are higher than in studies of other oceanographic regions by a factor of 10 for OII. The data suggest that meso‐ and finer‐scale physical dynamics had a significant impact on the populations at the front, either by mingling ecotypes from different source regions at fine scales (∼10s km) or by stimulating their growth through vertical nutrient injections. We investigate this hypothesis with an idealized diffusion‐reaction model, and find that only a combination of mixing and positive net growth can explain the observed distributions and overlap of the two Ostreococcus ecotypes. Our field observations support larger‐scale numerical ocean simulations that predict enhanced biodiversity at western boundary current fronts, and suggest a strategy for systematically testing that hypothesis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".