Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—Selective fishing targets potential breeders with particular characteristics, hence, it can change a population in ways that affect its abundance and productivity. Chinook salmon Oncorhynchus tshawytscha show a wide range of sizes and ages at adulthood and are exposed to fishing during much of their lives. Size-selective fishing can remove the largest and oldest individuals from a population. What is the role of fishing as a factor affecting size, and what are the genetic consequences of change in size for life history and viability? To address these questions for Chinook salmon in the Arctic-Yukon-Kuskokwim region of Alaska, evolutionary and demographic models of long-lived, large-bodied Chinook salmon are linked to assess the effects of two idealized fishing regimes on age-specific length, spawner abundance, and yield to the fishery. The lengths for fish of each age are treated as distinct but correlated traits. The models showed that a constant exploitation rate above a minimum fish size reduces abundance and yield within 100 years unless genetic variation for, and stabilizing natural selection on, length are sufficient to permit adaptation. Because lengths at age were correlated, fish in all age groups, including those under weak selection, responded to selection by declining in length, and abundance and yield both decreased. When fishing removed fish between a minimum and maximum size limit, fish increased in length during adaptation to fishing, and the population could achieve higher abundance after 100 years than that predicted by a non-genetic model. Under both fishing regimes, the population showed evidence of adaptation to fishing if length was heritable and natural selection on length was evident. Management intervention through aggressive reduction of exploitation rate allowed the population to eventually achieve or exceed pre-fishing abundances and stable catches in both regimes. When sufficiently strong and selective, fishing can cause fish size to evolve rapidly, with potential consequences for viability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".