Molecular identification of prey in the stomach contents of Harp Seals (<i>Pagophilus groenlandicus</i>) using species‐specific oligonucleotides
Bibliographic record
Abstract
All methods of diet analysis in marine mammals, including hard part analysis (HPA), have biases affecting the accuracy of prey-species identification and frequency in the estimated diet due to differential consumption, digestion and retention. Using PCR amplification of specific prey DNA with species-specific primers, we developed a DNA-based method that complements HPA and provides an alternative means to detect prey from stomach contents of Harp Seals (Pagophilus groenlandicus). The target size that could be reliably amplified was determined using a digestion time-series of Atlantic Cod (Gadus morhua) tissue in simulated seal stomachs. Various target lengths were trialed using general teleost primers; amplicons of approximately 800 bp or less were consistently obtained. Prey species-specific PCR primers for Atlantic Cod, Arctic Cod (Boreogadus saida) and Capelin (Mallotus villosus) were designed and tested with DNA from the stomach contents of 31 Harp Seals. Amplicons were obtained for all three species-specific primer sets. Amplification results compared with HPA revealed: (i) Atlantic Cod hard parts were found in five stomachs where no Atlantic Cod DNA amplified, suggesting that Atlantic Cod may be over-represented in the estimated diet, (ii) amplification of Arctic Cod DNA occurred for 17 stomachs, including all 12 stomachs with, and five stomachs without, Arctic Cod hard parts, and (iii) Capelin DNA amplified for four of five stomachs with Capelin hard parts and for one stomach without Capelin hard parts. We conclude that PCR amplification of specific prey DNA provides a viable means to complement Harp Seal diet analysis by HPA, but suggest that valuable information for quantitative diet analysis rests in a quantitative PCR approach.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".