Development and validation of targeted environmental <scp>DNA</scp> (<scp>eDNA</scp>) metabarcoding for early detection of 69 invasive fishes and aquatic invertebrates
Bibliographic record
Abstract
Abstract Invasive species are of concern due to their impacts on ecosystems and economies, but they pose significant control challenges. Environmental DNA (eDNA) is a powerful tool in the detection of aquatic organisms at low densities due to high detection sensitivity and relative ease of sample collection. Aquatic eDNA analyses have increased worldwide and are generally either applied to few target species (quantitative PCR), or for broad taxonomic applications (metabarcoding). Here, we describe the development and testing of a hybrid approach that utilized high‐sensitivity PCR primer sets and high‐throughput sequencing (HTS), referred to as targeted metabarcoding , to detect 69 fishes and invertebrates. We identified target species based on reports of globally important invasive species and developed two independent PCR primers for each species (CO1 and a second mtDNA region). We assessed sensitivity and eDNA interference for all 138 primers (2 per species and 69 species) using standard end‐point PCR and tested them on 10 eDNA samples spiked with various amounts of one or more of the target species DNA. The sensitivity of the 138 primer sets ranged between 1.5 × 10 −5 and 2.64 ng template DNA (mean = 0.069 ng). Primers were also tested for interference effects using plankton eDNA to simulate field conditions. The inclusion of interfering plankton DNA reduced the sensitivity for most primer sets by one or more orders of magnitude (range 0–3). Overall, our targeted metabarcoding resulted in the detection of ~98% of species in the DNA spiked samples, and perhaps more importantly, the HTS read count was positively related to the quantity of spiked DNA ( p < 0.002). We envision this technique being particularly useful for the early detection of species at low population densities; however, there are diverse applications of targeted metabarcoding for monitoring aquatic community composition and to quantify ecosystem change and health.
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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 | 0.003 | 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 | 0.000 | 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".