Comparative analysis of fish environmental <scp>DNA</scp> reveals higher sensitivity achieved through targeted sequence‐based metabarcoding
Bibliographic record
Abstract
Environmental DNA (eDNA)-based methods of species detection are enabling various applications in ecology and conservation including large-scale biomonitoring efforts. qPCR is widely used as the standard approach for species-specific detection, often targeting a fish species of interest from aquatic eDNA. However, DNA metabarcoding has the potential to displace qPCR in certain eDNA applications. In this study, we compare the sensitivity of the latest Illumina NovaSeq 6000 NGS platform to qPCR TaqMan assays by measuring limits of detection and by analysing eDNA from water samples collected from Churchill River and Lake Melville, NL, Canada. Species-specific, targeted next generation sequencing (NGS) assays had significantly higher sensitivity than qPCR, with limits of detection 14- to 29-fold lower. For example, when analysing eDNA, qPCR detected Gadus ogac (Greenland cod) in 21% of samples, but targeted NGS detected this species in 29% of samples. General NGS assays were as sensitive as qPCR, while simultaneously detecting 15 fish species from eDNA samples. With over 34,000 fish species on the planet, parallel and sensitive methods such as NGS will be required to support effective biomonitoring at both regional and global scales.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".