Sensitivity of stable isotope mixing models to variation in isotopic ratios: evaluating consequences of lipid extraction
Bibliographic record
Abstract
Summary 1. Stable isotopes of carbon and nitrogen are increasingly used in studies of animal diet reconstruction via mixing models. However, isotope ratios of both consumer and source tissues can be altered by various amounts of lipids, potentially leading to biased estimates of diet composition when they are not taken into account. 2. We investigated the consequences of lipid correction on the estimation of diet composition with mixing models. Using empirical data from three northern terrestrial trophic systems, we illustrated the direct effects of lipid extraction (LE) on the δ 13 C and δ 15 N of source and consumer tissues and its ultimate effects on the reconstruction of the consumer’s diet. 3. In parallel, we developed a simulation tool in R, called fatsim , to assess sensitivity of mixing models to variation in isotopic ratios of samples from source or consumer tissues. This tool can be used to assess the effect of shifts in isotopic ratios caused by LE, or other sources of variation, in any trophic system and thus aid in decision making regarding lipid removal. 4. Using fatsim , we showed that the potential effects of LE on estimates of diet composition cannot be predicted without simulations, even in relatively simple systems. The sensitivity of a mixing model isotopic shift depends on the complexity of the system (number of sources) and on the relative positions of sources and consumers within the isotopic mixing space. 5. Our study confirms that the presence of lipids in tissues can bias the interpretation of diet reconstruction results. In a given trophic system, testing the sensitivity of a mixing model to LE can help decide whether lipid removal is required in order to avoid this bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| 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) | 0.000 | 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".