Size‐selective fishing and the potential for fisheries‐induced evolution in lake whitefish
Bibliographic record
Abstract
Abstract The long‐term evolutionary effects of fishing on maturation schedules can depend on gear type, the shape of the gear type's size‐selectivity function, and the size and age structure of a population. Our goal was to better understand how environmentally induced differences in somatic growth influence the evolutionary effects of size‐selective fisheries, using lake whitefish ( Coregonus clupeaformis ) in Lake Huron as a case study. Using a state‐dependent optimization model of energy allocation parameterized for lake whitefish, we show that fishing with gill nets (bell‐shaped selectivity) and trap nets (sigmoid‐shaped selectivity) can be potent agents of selection on size thresholds for maturity. Compared to trap nets and large mesh (114 mm) gill nets, small mesh (89 mm) gill nets are better able to buffer populations from fishing‐induced evolution by safeguarding large, fecund fish, but only when overall fishing mortality is low and growth rates sufficiently fast such that fish can outgrow vulnerable size classes. Regardless of gear type, and all else being equal, high fishing mortality in combination with low growth rates is expected to intensify the long‐term evolutionary effects of fishing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".