Should fatty acid signature proportions sum to 1 for diet estimation?
Bibliographic record
Abstract
Abstract Knowledge of predator diets, including how diets might change through time or differ among predators, provides essential insights into their ecology. Diet estimation therefore remains an active area of research within quantitative ecology. Quantitative fatty acid signature analysis (QFASA) is an increasingly common method of diet estimation. QFASA is based on a data library of prey signatures, which are vectors of proportions summarizing the fatty acid composition of lipids, and diet is estimated as the mixture of prey signatures that most closely approximates a predator's signature. Diets are typically estimated using proportions from a subset of all fatty acids that are known to be solely or largely influenced by diet. Given the subset of fatty acids selected, the current practice is to scale their proportions to sum to 1.0. However, scaling signature proportions has the potential to distort the structural relationships within a prey library and between predators and prey. To investigate that possibility, we compared the practice of scaling proportions with two alternatives and found that the traditional scaling can meaningfully bias diet estimators under some conditions. Two aspects of the prey types that contributed to a predator's diet influenced the magnitude of the bias: the degree to which the sums of unscaled proportions differed among prey types and the identifiability of prey types within the prey library. We caution investigators against the routine scaling of signature proportions in QFASA.
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 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 | 0.036 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".