Conventional versus real‐time quantitative PCR for rare species detection
Bibliographic record
Abstract
Abstract Detection of species in nature at very low abundance requires innovative methods. Conventional PCR (cPCR) and real‐time quantitative PCR (qPCR) are two widely used approaches employed in environmental DNA (eDNA) detection, though lack of a comprehensive comparison of them impedes method selection. Here we test detection capacity and false negative rate of both approaches using samples with different expected complexities. We compared cPCR and qPCR to detect invasive, biofouling golden mussels ( Limnoperna fortunei ), in samples from laboratory aquaria and irrigation channels where this mussel was known to occur in central China. Where applicable, the limit of detection (LoD), limit of quantification (LoQ), detection rate, and false negative rate of each PCR method were tested. Quantitative PCR achieved a lower LoD than cPCR (1 × 10 −7 vs. 10 −6 ng/μl) and had a higher detection rate for both laboratory (100% vs. 87.9%) and field (68.6% vs. 47.1%) samples. Field water samples could only be quantified at a higher concentration than laboratory aquaria and total genomic DNA, indicating inhibition with environmental samples. The false negative rate was inversely related to the number of sample replicates. Target eDNA concentration was negatively related to distance from sampling sites to the water (and animal) source. Detection capacity difference between cPCR and qPCR for genomic DNA and laboratory aquaria can be translated to field water samples, and the latter should be prioritized in rare species detection. Field environmental samples may involve more complexities—such as inhibitors—than laboratory aquaria samples, requiring more target DNA. Extensive sampling is critical in field applications using either approach to reduce false negatives.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".