Priming of a DNA metabarcoding approach for species identification and inventory in marine macrobenthic communities
Bibliographic record
Abstract
In marine and estuarine benthic communities, the inventory and estimation of species richness are often hampered by the need for broad taxonomic expertise across several phyla. The use of DNA metabarcoding has emerged as a powerful tool for the fast assessment of species composition in a diversity of ecological communities. Here, we tested the amplification success of five primer sets targeting different COI-5P regions by 454 pyrosequencing to maximize the recovery of two simulated macrobenthic communities containing 21 species (SimCom1 and SimCom 2). Species identification was first performed against a compiled reference library of macrobenthic species. Reads with similarity results to reference sequences between 70% and 97% were then submitted to GenBank and BOLD to attempt the identification of concealed species in the bulk sample. The combination of at least three primer sets was able to recover more species than any primer set alone, achieving 85% of represented species in SimCom1 and 76% in SimCom2. Our approach was successful to detect low-frequency specimens, as well as concealed species, in the bulk sample, indicating the potential for the application of this approach on marine bioassessment and inventory, including the detection of "hidden" biodiversity that would hardly be possible based on morphology only.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".