Migration behavior of maturing sockeye (Oncorhynchus nerka) and Chinook salmon (O. tshawytscha) in Cook Inlet, Alaska, and implications for management
Bibliographic record
Abstract
Worsening recruitment of Alaskan Chinook salmon over the past decade has created major conservation problems. In Cook Inlet, lucrative Sockeye salmon fisheries are severely limited because of Chinook bycatch, restricting economic opportunity and creating political conflict between user groups. Although Chinook are thought to migrate at deeper depths than other salmon during the marine phase, an inability to quantify the depth difference has prevented regulatory changes to protect Chinook while allowing Sockeye fisheries to operate. Using a purpose-built acoustic telemetry array, we found that Chinook salmon repeatedly ‘patrolled’ back and forth in the nearshore fishing area for multiple weeks before river entry (a previously unrecognized behavior) while Sockeye salmon rapidly crossed the area to enter the river. Both species substantially increased migrations speeds at river entry. Migration speeds then progressively dropped, returning to baseline levels about 14 km upstream of the river mouth. Clear differences in the median depth of marine migration of Chinook (4.8 m) and Sockeye (1.8 m) were evident, enabling us to quantify the potential trade-off between reducing Sockeye harvest and increasing Chinook protection from using shallower gillnets in the commercial fishery. Based on the 16,608 depth measurements collected for Chinook and 3,389 measurements for Sockeye, reducing the vertical depth of surface-hung gillnets to one-half of current maximum depth would potentially reduce the Chinook interception rate by nearly two-thirds, while reducing Sockeye harvests by one-quarter. Alternatively, if commercial fishers were fully compensated for the reduced area of netting by allowing exactly compensatory increases in net length, Sockeye catches could potentially increase to 200% to 300% of current levels, but Chinook interceptions would remain similar to current levels despite reductions in net depth. Identifying an intermediate strategy between these two extremes could provide a ‘win-win’ solution rather than the current zero-sum game between deeply opposed stake-holders. Biotelemetry enabled rapid collection of very large numbers of depth measurements despite relatively few adults being tagged. The collected data have already been used to implement some of the first regulatory changes in the fishery in more than a decade and have identified a potential avenue for political accommodation between opposing user groups.
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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 | 0.000 |
| 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) | 0.001 | 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".