Using bioenergetics models to predict stable isotope ratios in fishes
Bibliographic record
Abstract
To describe temporal dynamics of stable isotope ratios in fishes, we developed a bioenergetics-based model that links isotope ratios to growth, as influenced by fish size, temperature, diet, and prey quality. The model includes error terms for isotope ratios, diet proportions, and fractionation. The model accurately predicted temporal δ15N dynamics of lake trout (Salvelinus namaycush) in a diet-switch experiment but was less successful for δ13C, possibly because of variable fractionation. The model was then used in three heuristic applications. In a diet-validation scenario, a model derived from limited knowledge of rainbow smelt (Osmerus mordax) diet reasonably estimated δ13C and δ15N compared with a null model but inaccurately estimated prey consumption. In a scenario where adult lake trout briefly cannibalized stocked lake trout fingerlings, the detectability of a cannibalism-induced δ15N increase depended on predator size, duration of cannibalism, and sample size. In a scenario where seasonal isotopic variability occurred at the base of a food web, variation propagated to higher trophic levels depended on consumer size and diet. Our approach is most valuable when used to examine multiple diet combinations that produce observed stable isotope ratios; one can then identify the most reasonable diets through field tests or other observations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".