Determining the Movements and Distribution of Anadromous Bering Ciscoes by Use of Otolith Strontium Isotopes
Bibliographic record
Abstract
Abstract Methods for tracking the movements and distribution of fishes have often involved expensive field logistics, which is compounded in remote regions such as Alaska. An alternative approach is to use the chemical signatures preserved in the otoliths of teleost fish to track their movement history. We used the strontium isotope signature (87Sr/86Sr) preserved in the freshwater portion of otoliths taken from Bering Ciscoes Coregonus laurettae to identify their natal river of origin and their movements. Bering Ciscoes spawn in freshwater rivers and rear in coastal marine waters. Just three spawning rivers are known for this species worldwide: the Yukon, Kuskokwim, and Susitna rivers. Rearing commonly occurs in coastal estuaries and lagoons along the Arctic coast of Alaska, the Yukon–Kuskokwim (Y–K) delta, and (rarely) the Alaska Peninsula. We compiled a set (n = 127) of Bering Cisco otoliths from fish caught in coastal marine habitats within each of these rearing areas. We measured the 87Sr/86Sr values from the freshwater portions of the otoliths and compared them to an established baseline of signatures for Bering Ciscoes sampled from the known spawning rivers. We found that 96% of the unknown‐origin specimens from the three rearing groups (Alaska Arctic coast, Y–K delta, and Alaska Peninsula) had 87Sr/86Sr values that were consistent with a Yukon River origin. The dominance of Yukon River‐origin fish in all rearing groups suggests that this population is considerably larger than the Kuskokwim River or Susitna River population. These data also indicate a widespread coastal distribution of Bering Ciscoes, with some individuals estimated to have traveled over 4,900 km between coastal rearing locations and the spawning habitat. Our approach illustrates that strontium isotopes can be used to determine the natal river and migration behavior for anadromous Bering Ciscoes of unknown origin. Received April 4, 2016; accepted August 12, 2016 Published online October 14, 2016
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".