Deep COI sequencing of standardized benthic samples unveils overlooked diversity of Jordanian coral reefs in the northern Red Sea
Bibliographic record
Abstract
High-throughput sequencing (HTS) of DNA barcodes (metabarcoding), particularly when combined with standardized sampling protocols, is one of the most promising approaches for censusing overlooked cryptic invertebrate communities. We present biodiversity estimates based on sequencing of the cytochrome c oxidase subunit 1 (COI) gene for coral reefs of the Gulf of Aqaba, a semi-enclosed system in the northern Red Sea. Samples were obtained from standardized sampling devices (Autonomous Reef Monitoring Structures (ARMS)) deployed for 18 months. DNA barcoding of non-sessile specimens >2 mm revealed 83 OTUs in six phyla, of which only 25% matched a reference sequence in public databases. Metabarcoding of the 2 mm - 500 μm and sessile bulk fractions revealed 1197 OTUs in 15 animal phyla, of which only 4.9% matched reference barcodes. These results highlight the scarcity of COI data for cryptobenthic organisms of the Red Sea. Compared with data obtained using similar methods, our results suggest that Gulf of Aqaba reefs are less diverse than two Pacific coral reefs but much more diverse than an Atlantic oyster reef at a similar latitude. The standardized approaches used here show promise for establishing baseline data on biodiversity, monitoring the impacts of environmental change, and quantifying patterns of diversity at regional and global scales.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".