Targeted next‐generation sequencing of environmental DNA improves detection of invasive European green crab ( <i>Carcinus maenas</i> )
Bibliographic record
Abstract
Abstract In the northeast Pacific Ocean, there is high interest in developing eDNA‐based survey methods to aid management of invasive populations of European green crab ( Carcinus maenas ). Expected benefits are improved sensitivity for early detection of secondary spread and to assess the outcome of eradication efforts. A new eDNA‐based approach we term “targeted next‐generation sequencing (tNGS)” is introduced here and shown to improve detection relative to qPCR at sites with lower green crab CPUE values measured by trapping. DNA standards (gBlock) with starting molecule copies that were 10 to 100 times lower than the qPCR limit of detection returned significant numbers of sequencing reads, which in our field assessments translated to a 7%–10% increase in detection probability from tNGS relative to qPCR at sites with lower CPUE. We also found the number of sequencing reads from tNGS was significantly correlated with green crab CPUE whereas Ct values from qPCR were not. When sources of variation were partitioned for each assay, we found the difference between mean within‐site and mean between‐site variation was much larger and had non‐overlapping confidence intervals for tNGS relative to qPCR, suggesting the former may offer more power for detecting spatial variation in eDNA availability. Results presented here indicate this approach is suitable for species of known low abundances where a positive detection has high economic or environmental consequences, or for labs doing eDNA surveys for whom NGS‐based workflows are operationally more efficient than qPCR given its upward scalability. Any species with an existing qPCR assay can be easily tested with a tNGS assay using the approach presented here. We conclude with a discussion on the fitness for purpose of tNGS vs. qPCR for various applications and on how to best apply molecular surveys in management programs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".