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 (δ 13 C) and nitrogen (δ 15 N) isotope ratios are widely used in marine food web and habitat use studies. However, lipids are naturally depleted in 13 C relative to proteins and are variable in content, biasing δ 13 C 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 δ 15 N, or by estimating lipid-free δ 13 C using one of several equations that differ in degree of sophistication and generalization across taxa. Here, δ 13 C and δ 15 N 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 δ 13 C and δ 15 N across taxa and evaluate the performance of 5 normalization models, overall and using subsets of species, to propose a model to revert lipid-extracted δ 15 N back to their bulk values and to identify the best approach for dealing with lipid-related biases. Lipid extraction caused an uneven enrichment in δ 13 C and δ 15 N 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 δ 13 C, a linear model reliably estimated δ 15 N values of lipid-free samples using δ 15 N values of bulk samples. This study presents a method for reliably estimating δ 13 C and δ 15 N 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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".