Unlocking the power of fatty acids as dietary tracers and metabolic signals in fishes and aquatic invertebrates
Bibliographic record
Abstract
Determining the transfer and transformation of organic matter in food webs is a fundamental challenge that has implications for sustainable management of ecosystems. Fatty acids (FA) offer a potential approach for resolving complex diet mixtures of organisms because they provide a suite of molecular tracers. Yet, uncertainties in the degree of their biochemical modification by consumers, due to selective retention or metabolism, have limited their application. Here, we consolidated 316 controlled feeding studies of aquatic ectotherms (fishes and invertebrates) involving 1404 species–diet combinations to assess the degree of trophic modification of FA in muscle tissue. We found a high degree of variability within and among taxa in the %FA in consumer muscle tissue versus %FA in diet regression equations. Most saturated FA had weak relationships with the diet ( r 2 < 0.30) and shallow slopes ( m < 0.30), suggesting a lack of retention in muscle when fed in increasing amounts. Contrarily, several essential FA, including linoleic (18:2n-6) and α-linolenic acid (18:3n-3), exhibited significant relationships with the diet ( m > 0.35, r 2 > 0.50), suggesting supply limitations and selective retention in muscle by consumers. For all FA, relationships strengthened with increasing taxonomic specificity. We also demonstrated the utility of new correction equations by calculating the potential contributions of approximately 20 prey items to the diet of selected species of generalist fishes using a FA mixing model. Our analyses further reveal how a broad range of fishes and invertebrates convert or store these compounds in muscle tissue to meet physiological needs and point to their power in resolving complex diets in aquatic food webs. This article is part of the theme issue ‘The next horizons for lipids as ‘trophic biomarkers’: evidence and significance of consumer modification of dietary fatty acids’.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".