Influence of lipids on stable isotope ratios in mammal hair: highlighting the importance of validation
Bibliographic record
Abstract
Abstract Carbon (δ13C) and nitrogen (δ15N) stable isotope ratios are increasingly used in ecological studies to evaluate diet composition and trophic relationships. However, lipids may influence stable isotope ratios due to the depletion of 13C in adipose tissues relative to proteins and carbohydrates. δ13C values can be corrected by lipid extraction or normalization models. The aims of our study were to evaluate the effects of lipid extraction on stable isotope ratios in a terrestrial mammal, the caribou (Rangifer tarandus caribou), and to propose relevant lipid normalization models that are method‐ and tissue‐specific for δ13C values. We also evaluated whether four δ13C lipid normalization and correction models proposed in the literature were applicable to our study species. Stable isotope ratios were obtained for hair, plasma, and red blood cell samples of 44 caribou in the Gaspésie National Park (Québec, Canada). The effects of lipid extraction on stable isotope ratios were tested using a paired t‐test. A simple linear model was used to correct for the effects of lipid extraction and to assess its performance compared to that of published equations. Lipid content significantly influenced δ13C values in caribou hair. The four lipid normalization equations commonly used in the literature did not accurately predict δ13Clipid‐free values of caribou hair. Based on our results, we recommend controlling systematically for lipids in terrestrial systems and analyzing δ13C (lipid‐free) and δ15N (bulk) from two separate aliquots to reach a greater precision. We also recommend controlling for lipids in hair tissue. If not possible, we recommend using a lipid normalization model that is tissue‐, method‐, and species‐specific or applying a model that has been previously validated for the tissue and species of interest.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".