Targeted Next Generation Sequencing of environmental DNA improves detection and quantification 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 quantification of abundances 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 low eDNA concentrations, as is characteristic of founding or spreading populations. tNGS is based on the premise that the number of NGS reads from non-normalized (i.e. equal volumes) targeted PCR amplicons will approximate the starting DNA amount. Standard DNA concentrations 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 crab detection probability from tNGS relative to qPCR at low abundances. We also found that eDNA concentration was highly correlated with crab abundance, as measured from traditional trapping methods, for both assays; however, tNGS data had greater precision and less error than qPCR. When partitioning the sources of variation in each assay we identified greater between-site variability for tNGS relative to qPCR, suggesting the former may offer more power for detecting spatial variation in eDNA concentration. When applying this assay in management programs, we suggest including a panel of eDNA samples from sites with trapping data as standards to estimate relative abundance at sites with no a priori information. Results presented here indicate the tNGS approach has great promise for surveillance of green crab and could easily be adopted for surveillance of any species of high interest to management, including endangered species, new incursions of invasive species, and species with low eDNA shedding rates. Pros and cons of this approach compared to qPCR are discussed.
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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 | 0.002 |
| 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 | 0.001 | 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".