Phylogenetic diversity and biogeography of the Mamiellophyceae lineage of eukaryotic phytoplankton across the oceans
Bibliographic record
Abstract
High-throughput diversity amplicon sequencing of marine microbial samples has revealed that members of the Mamiellophyceae lineage are successful phytoplankton in many oceanic habitats. Indeed, these eukaryotic green algae can dominate the picoplanktonic biomass, however, given the broad expanses of the oceans, their geographical distributions and the phylogenetic diversity of some groups remain poorly characterized. As these algae play a foundational role in marine food webs, it is crucial to assess their global distribution in order to better predict potential changes in abundance and community structure. To this end, we analyzed the V9-18S small subunit rDNA sequences deposited from the Tara Oceans expedition to evaluate the diversity and biogeography of these phytoplankton. Our results show that the phylogenetic composition of Mamiellophyceae communities is in part determined by geographical provenance, and do not appear to be influenced - in the samples recovered - by water depth, at least at the resolution possible with the V9-18S. Phylogenetic classification of Mamiellophyceae sequences revealed that the Dolichomastigales order encompasses more sequence diversity than other orders in this lineage. These results indicate that a large fraction of the Mamiellophyceae diversity has been hitherto overlooked, likely because of a combination of size fraction, sequencing and geographical limitations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".