Density-dependent consequences of size-selective induced life-history changes to population fitness in medaka (<i>Oryzias latipes</i>)
Bibliographic record
Abstract
There is an increasing concern about the potential for size-selective harvest to impair population persistence. Yet little is known about the relative contribution of the evolutionary (shifts in life history) and demographic effects (decreased population density and size truncation) of harvesting to changes in fitness. Using medaka (Oryzias latipes), we experimentally investigated the fitness consequences of antagonistic size-dependent selection under contrasted levels of density (low versus high) and size structure (uniform versus truncated). The size-dependent selection generated large- and small-breeder lines with fast growth and late maturity versus slow growth and early maturity life histories, respectively. A decrease in density had a positive effect on almost all fitness components, while size truncation only had a positive effect on fish growth. Small-breeder fish grew slower and had a greater probability of reproducing, at least when considering small-sized females. The number of larvae and juveniles did not differ between the two lines. Finally, the positive effect of decreased density on population asymptotic growth rate was less pronounced in small-breeders than in large-breeders of medaka. These findings stress the importance of considering the ramifications of fishing-adapted life history to population persistence in the light of density-dependent population dynamics.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".