Consequences of fisheries-induced evolution for population productivity and recovery potential
Bibliographic record
Abstract
Fisheries-induced evolution has become a major branch of the research on anthropogenic and contemporary evolution. Within the conservation context, fisheries-induced evolution has been hypothesized to negatively affect the persistence and recovery potential of depleted populations, but this has not been explicitly investigated. Here, we investigate how fisheries-induced evolution of Atlantic cod (Gadus morhua L.) life histories affects per capita population growth rate, a parameter negatively correlated with extinction risk. We simulate the evolutionary and ecological dynamics of a cod population for a 100 year period of size-selective harvesting, followed thereafter by 300 years of recovery. To evaluate the relative importance of harvest-induced evolution, we either allowed life histories to evolve during and after the fishing period, or we assumed that fisheries-induced evolution was absent. Population growth rates did not differ appreciably between the evolutionary and non-evolutionary simulation scenarios, despite the emergence of rather pronounced differences in life histories. The underlying reason was that in the absence of fishing the cumulative lifetime reproductive outputs were very similar among differing life histories. The results suggest that fisheries-induced evolution might not always have as clear-cut an effect on population growth rate as previously anticipated.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".