Migratory salmon smolts exhibit consistent interannual depensatory predator swamping: Effects on telemetry‐based survival estimates
Bibliographic record
Abstract
Abstract Migrations of juvenile salmon smolts are generally high‐risk, with predation often implicated in reduced survival. In theory, smolts can maximise survival via depensation, or synchronising movements to swamp predators. Depensation, however, is difficult to assess in the wild. Accounting for depensation could also generate more realistic telemetry‐based survival estimates for management. Here, we assess six years (2010–2014, 2016) of acoustic telemetry and outmigration density data for sockeye salmon (Oncorhynchus nerka) from Chilko Lake, British Columbia, Canada. Prevoiusly, depensation for this population wasassessed for a single year, but interannual consistency is not known. We found evidence of depensation in each year, although its strength varied. In addition, by integrating depensation with outmigration densities, annual population‐level survival estimates in this initial (14‐km) migratory segment increased by 0.02–0.24 relative to previously published estimates. However, when extending these survival rates from the first 14 km through the entire tracked migration (1,044 km), increases in estimates were small (~0.01). Potential conservation and management applications of depensation include implications for recovering imperiled populations and informing hatchery release strategies.
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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 | 0.005 |
| 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 | 0.000 |
| 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".