Environmental Barcoding Reveals Massive Dinoflagellate Diversity in Marine Environments
Bibliographic record
Abstract
BACKGROUND: Dinoflagellates are an ecologically important group of protists with important functions as primary producers, coral symbionts and in toxic red tides. Although widely studied, the natural diversity of dinoflagellates is not well known. DNA barcoding has been utilized successfully for many protist groups. We used this approach to systematically sample known "species", as a reference to measure the natural diversity in three marine environments. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we assembled a large cytochrome c oxidase 1 (COI) barcode database from 8 public algal culture collections plus 3 private collections worldwide resulting in 336 individual barcodes linked to specific cultures. We demonstrate that COI can identify to the species level in 15 dinoflagellate genera, generally in agreement with existing species names. Exceptions were found in species belonging to genera that were generally already known to be taxonomically challenging, such as Alexandrium or Symbiodinium. Using this barcode database as a baseline for cultured dinoflagellate diversity, we investigated the natural diversity in three diverse marine environments (Northeast Pacific, Northwest Atlantic, and Caribbean), including an evaluation of single-cell barcoding to identify uncultivated groups. From all three environments, the great majority of barcodes were not represented by any known cultured dinoflagellate, and we also observed an explosion in the diversity of genera that previously contained a modest number of known species, belonging to Kareniaceae. In total, 91.5% of non-identical environmental barcodes represent distinct species, but only 51 out of 603 unique environmental barcodes could be linked to cultured species using a conservative cut-off based on distances between cultured species. CONCLUSIONS/SIGNIFICANCE: COI barcoding was successful in identifying species from 70% of cultured genera. When applied to environmental samples, it revealed a massive amount of natural diversity in dinoflagellates. This highlights the extent to which we underestimate microbial diversity in the environment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".