Analytical validation and field testing of a specific qPCR assay for environmental DNA detection of invasive European green crab (<i>Carcinus maenas</i>)
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) methods are providing tools for detecting invasive species in aquatic environments. Targeted qPCR assays applied to eDNA samples promise to overcome limitations of traditional methods, especially for early detection. The European green crab ( Carcinus maenas ) is considered one of the most successful invasive species globally due to the large range it has invaded and negative impacts on native species, marine habitats, and shellfish industries. We developed, laboratory‐validated, and field‐tested a specific qPCR assay for the detection of green crab from eDNA samples. We also show that the assay can detect green crab in bulk DNA extracted from plankton samples. Assay design, optimization, sensitivity, and specificity testing generally followed the validation pathway recommended by the World Organization for Animal Health for assays used to manage global aquatic animal health and infectious disease. Assay specificity was verified in silico and in vitro by laboratory testing 26 nontarget species, none of which showed potential for amplification. Assay sensitivity was appropriately high, with the limit of detection approaching two gene copies/μl. The assay was field‐tested on eDNA samples collected from filtered seawater at five sites on the Pacific coast of Canada known to harbor green crab based on historical monitoring data; green crab DNA was amplified from all sites. We also present early pilot field testing of the assay done on bulk DNA extracted from plankton samples from four sites from Australia, two sites with and two sites without reported records of green crab presence. Green crab was detected at both sites with known green crab records. Significant inhibition was recorded for some plankton samples but not for eDNA samples. This is the first qPCR assay for detection of European green crab, providing researchers and managers with a valuable new tool to aid early detection and ongoing 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".