DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
Bibliographic record
Abstract
Abstract Benthic communities are key components of aquatic ecosystems’ biomonitoring. However, morphology-based species identifications remain a low-throughput, and sometimes ambiguous, approach. Despite metabarcoding methodologies have been applied for above-species taxa inventories in marine meiofaunal communities, a comprehensive approach providing species-level identifications for estuarine macrobenthic communities is still lacking. Here we report a combination of experimental and field studies demonstrating the aptitude of COI metabarcoding to provide robust species-level identifications within a framework of high-throughput monitoring of estuarine macrobenthic communities. To investigate the ability to recover DNA barcodes from all species present in a bulk DNA extract, we assembled 3 phylogenetically diverse communities, using 4 different primer pairs to generate PCR products of the COI barcode region. Between 78 and 83% of the species in the tested communities were recovered through HTS. Subsequently, we compared morphology and metabarcoding-based approaches to determine the species composition from four distinct sites of an estuary. Our results indicate that the species richness would be considerably underestimated if only morphological methods were used. Although further refinement is required for improving the efficiency and output of this approach, here we show the great aptitude of COI metabarcoding to provide high quality and auditable species identifications in macrobenthos monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".