Ecological influences on the difference in δ<sup>15</sup>N and δ<sup>13</sup>C values between fish tissues: implications for studies of temporal diet variation
Bibliographic record
Abstract
Abstract Temporal diet changes can be detected by comparing δ13C and δ15N values between tissues with different isotopic turnover times. However, other factors contribute to disparities in δ13C and δ15N signatures between tissues and could confound the interpretation of stable isotope data. We examined the effects of ecological factors on differences in muscle and liver δ13C and δ15N signatures of Arctic charr [Salvelinus alpinus, (L.)] to determine whether their effects were large enough to influence studies of diet change. In addition, we examined whether differences in lipid content accounted for a significant portion of the differences in δ13C values between tissues. Results indicated that life history, diet, reproductive status and gender had significant effects on the differences in δ13C values between tissues, while gender and diet significantly affected differences in δ15N values. Differences in % lipid content between tissues also explained approximately 74% of the observed variation in the difference in δ13C values between tissues. The differences in δ13C values observed between muscle and liver tissues may be partially explained by physiological differences in lipid storage and use between tissues associated with ecological factor effects. These results demonstrate that ecological factors other than a change in diet can influence the differences in δ13C and δ15N signatures between muscle and liver tissues. However, the impact of ecological factors on studies of diet change depends on how the magnitude of the factor effects compared with a biologically meaningful change in diet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".