Using hydrogen isotopes of freshwater fish tissue as a tracer of provenance
Bibliographic record
Abstract
Abstract Hydrogen isotope (δ 2 H) measurements of consumer tissues in aquatic food webs are useful tracers of diet and provenance and may be combined with δ 13 C and δ 15 N analyses to evaluate complex trophic relationships in aquatic systems. However, δ 2 H measurements of organic tissues are complicated by analytical issues (e.g., H exchangeability, lack of matrix‐equivalent calibration standards, and lipid effects) and physiological mechanisms, such as H isotopic exchange with ambient water during protein synthesis and the influence of metabolic water. In this study, δ 2 H (and δ 15 N) values were obtained from fish muscle samples from Lake Winnipeg, Canada, 2007–2010, and were assessed for the effects of species, feeding habits, and ambient water δ 2 H values. After lipid removal, we used comparative equilibration to calibrate muscle δ 2 H values to nonexchangeable δ 2 H equivalents and controlled for H isotopic exchange between sample and laboratory ambient water vapor. We then examined the data for evidence of trophic δ 2 H enrichment by comparing δ 2 H values with δ 15 N values. Our results showed a significant logarithmic correlation between fork length and δ 2 H values, and no strong relationships between δ 15 N and δ 2 H. This suggests the so‐called apparent trophic compounding effect and the influence of metabolic water into tissue H were the potential mechanisms for δ 2 H enrichment. We evaluated the importance of water in controlling δ 2 H values of fish tissues and, consequently, the potential of H isotopes as a tracer of provenance by taking account of confounding variables such as body size and trophic effects. The δ 2 H values of fish appear to be a good tracer for tracking provenance, and we present a protocol for the use of H isotopes in aquatic ecosystems, which should be applicable to a broad range of marine and freshwater fish species. We advise assessing size effects or working with fish of relatively similar mass when inferring fish movements using δ 2 H measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".