Stomach Content <scp>DNA</scp> ( <scp>scDNA</scp> ) Detection and Quantification for Predator Diet Assessment Using High‐Throughput Nanofluidic Chip Technology: Species‐Specific <scp>qPCR</scp> Assay Panel Development and Validation
Bibliographic record
Abstract
Stomach content DNA (scDNA) analyses have become the standard practice for measuring trophic interactions. scDNA metabarcoding has provided broadscale diet composition data but can potentially underestimate certain prey species, as many of the recovered sequence reads come from predator-derived DNA, potentially resulting in incomplete diet information. Targeted detection (quantitative real-time PCR-qPCR) strategies allow for single-species detection from complex multispecies scDNA mixtures. A recent advancement in qPCR technology, high-throughput qPCR (HT-qPCR), allows simultaneous multispecies targeted detection and quantification of candidate species. Here, we describe the development and validation of a panel of single-species qPCR assays targeting the CO1 region of 28 prey fishes from the Great Lakes. We performed a three-step validation procedure for all assays using high-throughput OpenArray nanofluidic technology, measuring assay sensitivity, specificity and interference. Specifically, all assays were measured against dilution series of both target and non-target species DNA with detection limits ranging from 0.00503 pg to 0.0221 ng template DNA per reaction. Assays were tested for interference (e.g., PCR inhibitor) effects by creating artificial scDNA samples spiked with serially diluted target species DNA, resulting in a range of reduction in sensitivity (range = 0.0-125x fold). We validated the OpenArray qPCR assays using individual full-reaction TaqMan qPCR for nine of the assays, finding similar sensitivity despite expectations for the loss of sensitivity in the nanoscale reactions. HT-qPCR targeted detection has the potential to revolutionise scDNA (and eDNA) monitoring by significantly reducing laboratory effort to provide sensitive, targeted and quantitative detection data for multiple species simultaneously.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".