Biodiversity and spatial distribution of potentially ciguatera-causing dinoflagellates using DNA metabarcoding
Bibliographic record
Abstract
Ciguatera poisoning (CP) is a foodborne disease caused by eating seafood containing ciguatoxins from benthic dinoflagellates of the genus Gambierdiscus. Moreover, the related Fukuyoa, which often coexists with Gambierdiscus, also produces potent marine toxins. Though CP is endemic to tropical regions, it is increasingly being reported elsewhere, including the Canary Islands (NE Atlantic). In the Balearic Islands (W Mediterranean), potentially ciguatoxin-producing species have been found frequently in recent years, but so far without documented CP. Since toxin production varies greatly among Gambierdiscus and Fukuyoa species, it is very important to be able to discriminate them and detect problematic species even when present in low densities. In this study we explored the potential of DNA metabarcoding targeting the D1-D2 LSU rDNA region to detect Gambierdiscus and Fukuyoa species alongside other dinoflagellates and microeukaryotes in the first such surveys of the studied archipelagos. The predominant species by far in the Balearics was G. australes, but in Gran Canaria, though abundant, it was often outnumbered by two more toxic species, G. excentricus and G. silvae. DNA metabarcoding recorded F. ruetzleri for the first time in the Canaries and G. carolinianus in the Balearics, both in very low relative abundance. A potential new species of Gambierdiscus ("occultus") was detected in three Balearic Islands and Gran Canaria. Extensive intragenomic variation in the D1-D2 marker was found. We could also characterize the broader dinoflagellate community, including other harmful species and also parasitoids that may affect the dynamics of the dinoflagellates present. This research shows the power of LSU rDNA-based metabarcoding for studying benthic microeukaryotic communities.
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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 | 0.004 | 0.003 |
| 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) | 0.001 | 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".