Comparing rates of contemporary evolution in life-history traits for exploited fish stocks
Bibliographic record
Abstract
Trait evolution over time periods spanning generations, not millennia, is increasingly observed to be above the natural baseline in populations experiencing human-induced perturbations. We investigated the relative speed of trait change by comparing rates of evolution in haldanes and darwins for size at maturation as measured by probabilistic maturation reaction norm midpoints for fish stocks from the Pacific Ocean, North Atlantic, Barents Sea, eastern Baltic Sea, and the North Sea. Rates in haldanes for 23 stocks ranged from –2.2 to 0.9 and from 0.5 to 153 in kilodarwins for 26 stocks. The highest rates of evolution corresponded to the most heavily exploited stocks; rates slowed after moratoria were introduced. The estimated rates in fish life-history characteristics were comparable to other examples of human-induced evolution and faster than naturally induced rates. Stocks with high growth showed slower evolutionary change, even under high mortality, suggesting that compensatory somatic growth can slow the rate of trait evolution. Regardless of whether trait changes are due to exploitation or environmental factors, the costs of ignoring trait evolution are high. Management strategies should be based upon precautionary principles; therefore, the effect of changing traits must be integrated into the fisheries assessment process.
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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 | 0.004 |
| 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 | 0.000 | 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".