Marine protist diversity in <scp>E</scp> uropean coastal waters and sediments as revealed by high‐throughput sequencing
Bibliographic record
Abstract
Although protists are critical components of marine ecosystems, they are still poorly characterized. Here we analysed the taxonomic diversity of planktonic and benthic protist communities collected in six distant European coastal sites. Environmental deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from three size fractions (pico-, nano- and micro/mesoplankton), as well as from dissolved DNA and surface sediments were used as templates for tag pyrosequencing of the V4 region of the 18S ribosomal DNA. Beta-diversity analyses split the protist community structure into three main clusters: picoplankton-nanoplankton-dissolved DNA, micro/mesoplankton and sediments. Within each cluster, protist communities from the same site and time clustered together, while communities from the same site but different seasons were unrelated. Both DNA and RNA-based surveys provided similar relative abundances for most class-level taxonomic groups. Yet, particular groups were overrepresented in one of the two templates, such as marine alveolates (MALV)-I and MALV-II that were much more abundant in DNA surveys. Overall, the groups displaying the highest relative contribution were Dinophyceae, Diatomea, Ciliophora and Acantharia. Also, well represented were Mamiellophyceae, Cryptomonadales, marine alveolates and marine stramenopiles in the picoplankton, and Monadofilosa and basal Fungi in sediments. Our extensive and systematic sequencing of geographically separated sites provides the most comprehensive molecular description of coastal marine protist diversity to date.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".