Recent Bayesian stable-isotope mixing models are highly sensitive to variation in discrimination factors
Bibliographic record
Abstract
Stable isotopes are now used widely in ecological studies, including diet reconstruction, where quantitative inferences about diet composition are derived from the use of mixing models. Recent Bayesian models (MixSIR, SIAR) allow users to incorporate variability in discrimination factors (delta13C or delta15N), or the amount of change in either delta13C or delta15N between prey and consumer, but to date there has been no systematic assessment of the effect of variation in delta13C or delta15N on model outputs. We used whole blood from Common Terns (Sterna hirundo) and muscle from their common prey items (fish and euphausiids) to build a series of mixing models in SIAR (stable isotope analysis in R) using various discrimination factors from the published literature for marine birds. The estimated proportion of each diet component was affected significantly by delta13C or delta15N. We also use recently published stable-isotope data on the reliance of critically endangered Balearic Shearwaters (Puffinus mauretanicus) on fisheries discards to show that discrimination factor choice can have profound implications for conservation and management actions. It is therefore crucial for researchers wishing to use mixing models to have an accurate estimate of delta13C and delta15N, because quantitative diet estimates can help to direct future research or prioritize conservation and management actions.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".