Improving accuracy of <scp>DNA</scp> diet estimates using food tissue control materials and an evaluation of proxies for digestion bias
Bibliographic record
Abstract
Ecologists are increasingly interested in quantifying consumer diets based on food DNA in dietary samples and high-throughput sequencing of marker genes. It is tempting to assume that food DNA sequence proportions recovered from diet samples are representative of consumer's diet proportions, despite the fact that captive feeding studies do not support that assumption. Here, we examine the idea of sequencing control materials of known composition along with dietary samples in order to correct for technical biases introduced during amplicon sequencing and biological biases such as variable gene copy number. Using the Ion Torrent PGM(©) , we sequenced prey DNA amplified from scats of captive harbour seals (Phoca vitulina) fed a constant diet including three fish species in known proportions. Alongside, we sequenced a prey tissue mix matching the seals' diet to generate tissue correction factors (TCFs). TCFs improved the diet estimates (based on sequence proportions) for all species and reduced the average estimate error from 28 ± 15% (uncorrected) to 14 ± 9% (TCF-corrected). The experimental design also allowed us to infer the magnitude of prey-specific digestion biases and calculate digestion correction factors (DCFs). The DCFs were compared with possible proxies for differential digestion (e.g. fish protein%, fish lipid%) revealing a strong relationship between the DCFs and percent lipid of the fish prey, suggesting prey-specific corrections based on lipid content would produce accurate diet estimates in this study system. These findings demonstrate the value of parallel sequencing of food tissue mixtures in diet studies and offer new directions for future research in quantitative DNA diet analysis.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".