Targeted Next Generation Sequencing data for European Green Crab
Bibliographic record
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. Any species with an existing qPCR assay can be easily tested with a tNGS assay. We conclude with a discussion on the fit for purpose applications of tNGS vs. qPCR on how to best apply molecular surveys in management programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".