Dealing with biases introduced by lipids in stable carbon and nitrogen isotope analyses: a solution based on 28 marine invertebrate, fish, and mammal species
Bibliographic record
Abstract
Stable carbon (δ13C) and nitrogen (δ15N) isotope ratios are widely used in marine food web and habitat use studies. However, lipids are naturally depleted in 13C relative to proteins and are variable in content, biasing δ13C of bulk samples, with consequences for the accuracy of conclusions. This issue can be resolved either by extracting lipids from samples prior to analysis, a resource-intensive process that can also alter δ15N, or by estimating lipid-free δ13C using one of several equations that differ in degree of sophistication and generalization across taxa. Here, δ13C and δ15N were measured in bulk and lipid-extracted muscle samples from over 2000 specimens of 28 species of marine invertebrates, fishes, and mammals. Our objectives were to compare the effect of lipid extraction on δ13C and δ15N across taxa and evaluate the performance of 5 normalization models, overall and using subsets of species, to propose a model to revert lipid-extracted δ15N back to their bulk values and to identify the best approach for dealing with lipid-related biases. Lipid extraction caused an uneven enrichment in δ13C and δ15N across species. Model taxonomic specificity increased estimation accuracy for both isotopes. While models from Logan et al. (2008; J Anim Ecol 77:838-846) and McConnaughey & McRoy (1979; Mar Biol 53:257-262) were the best at predicting lipid-free δ13C, a linear model reliably estimated δ15N values of lipid-free samples using δ15N values of bulk samples. This study presents a method for reliably estimating δ13C and δ15N values of muscle tissue without resorting to duplicate analyses. This represents a major step toward the harmonization of data sets generated using bulk and lipid-extracted samples.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".