Longer amplicon metabarcoding primers enhance fish taxonomic resolution in environmental DNA samples
Bibliographic record
Abstract
Many previously designed fish eDNA metabarcoding primers amplify short regions ranging from 70 to 170 bp. However, the capacity to differentiate related taxa is positively correlated with amplicon size. We designed and/or modified existing primers to develop two novel primer pairs that target Canadian freshwater fish and which produce amplicons approx. 210 and 315 bp in size. Together, these amplicons covered ∼57% of the 12S gene and included all but a single variable region useful for differentiating taxa. Using an in silico analysis of 173 species, these primer pairs amplified more efficiently and can more readily distinguish closely related taxa relative to commonly employed shorter-amplicon primer pairs. We additionally validated their in situ sensitivity in natural ecosystems by sampling two urban pond ecosystems in Hamilton, Ontario, that were also surveyed using conventional methods, including one pond that was drained for a complete census. Both primer pairs detected all captured species, including four rare species (1–3 individuals of ∼1700). Collectively, we demonstrate that longer-read primer pairs can maximize taxonomic resolution of biodiversity surveys that employ eDNA metabarcoding.
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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) | 0.003 | 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".