Protists Within Corals: The Hidden Diversity
Bibliographic record
Abstract
Previous observations suggested that microbial communities contribute to coral health and the ecological resilience of coral reefs. However, most studies of coral microbiology focused on prokaryotes and the endosymbiotic algae Symbiodinium. In contrast, knowledge concerning diversity of other protists is still lacking, possibly due to methodological constraints. As most eukaryotic DNA in coral samples was derived from hosts, protist diversity was missed in metagenome analyses. To tackle this issue, we designed blocking primers for Scleractinia sequences amplified with two primer sets that targeted variable loops of the 18S rRNA gene (18SV1V2 and 18SV4). These blocking primers were used on environmental colonies of Pocillopora damicornis sensu lato from two regions with contrasting thermal regimes (Djibouti and New Caledonia). In addition to Symbiodinium clades A/C/D, Licnophora and unidentified coccidia genera were found in many samples. In particular, coccidian sequences formed a robust monophyletic clade with other protists identified in Agaricia, Favia, Montastraea, Mycetophyllia, Porites, and Siderastrea coral colonies. Moreover, Licnophora and coccidians had different distributions between the two geographic regions. A similar pattern was observed between Symbiodinium clades C and A/D. Although we were unable to identify factors responsible for this pattern, nor were we able to confirm that these taxa were closely associated with corals, we believe that these primer sets and the associated blocking primers offer new possibilities to describe the hidden diversity of protists within different coral species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".