Fatty acids differentiate consumers despite variation within prey fatty acid profiles
Bibliographic record
Abstract
Abstract Techniques that biochemically trace foraging habits of predators rely on the assumption that intra‐specific variation in prey species is smaller than variation among them. At the same time, these techniques often show that diets can induce drastic changes in the biochemical profiles of prey species, especially across different ecosystems. We tested if intra‐specific variation in fatty acid profiles of prey species added enough noise to confound quantitative fatty acid signature analysis ( QFASA ) using a controlled feeding experiment. Steelhead trout ( Oncorhynchus mykiss ) were fed either alewife ( Alosa pseudoharengus ) or round goby ( Neogobius melanostomus ) from either Lake Ontario or Cayuga Lake for a period of 8 weeks. Fatty acid profiles were significantly different between prey species and between lake of origin within each species. Differences in fatty acid profiles of steelhead trout strongly reflected prey species differences, whereas differences related to prey origin (lakes) were noted at a much lesser extent. QFASA performed remarkably well given the differences observed between the lakes prey originated from. Our results indicate that QFASA models for steelhead trout are probably not specific to one lake, and could provide estimates for other freshwater systems where alewife and round goby serve as the primary forage.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".