Influence of lipids on stable isotope ratios in mammal hair: highlighting the importance of validation
Bibliographic record
Abstract
Abstract Carbon (δ 13 C) and nitrogen (δ 15 N) 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 13 C in adipose tissues relative to proteins and carbohydrates. δ 13 C 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 δ 13 C values. We also evaluated whether four δ 13 C 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 δ 13 C values in caribou hair. The four lipid normalization equations commonly used in the literature did not accurately predict δ 13 C lipid‐free values of caribou hair. Based on our results, we recommend controlling systematically for lipids in terrestrial systems and analyzing δ 13 C (lipid‐free) and δ 15 N (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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".