Reconstructing the lives of fish using Sr isotopes in otoliths
Bibliographic record
Abstract
For many species, understanding the processes underlying variation in life history strategies is limited by the difficulty of tracking individuals throughout their lives. Within the rapidly expanding field of otolith microchemistry, novel approaches are being combined with state-of-the-art analytical techniques to provide new and valuable information about the environmental history of fishes. However, no approach to date allows the reconstruction of fish movements at high temporal resolution (weeks to months) over relatively small spatial scales (110 km). We used micromilling techniques to extract strontium (Sr) isotopic signatures from the otoliths of four returning Atlantic salmon (Salmo salar) adults. Distinct Sr isotopic signatures were detectable from four life cycle stages, including prefeeding hatchery development, rearing stream growth, smolt out-migration, and ocean residence. High-resolution analyses of Sr isotope records establish that natal stream signatures are recoverable and show that both site fidelity within the freshwater stage and the timing of migration vary considerably among individuals. Results made possible with this approach provide insight into a long-standing debate on the mobility of salmon during their nonmigratory stage. The ability to resolve flexible behaviors of salmon increases our understanding of their population biology and conservation needs.
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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 | 0.001 | 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) | 0.000 | 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".