Age is not just a number—Mathematical model suggests senescence affects how fish populations respond to different fishing regimes
Bibliographic record
Abstract
Abstract Senescence is often described as an age‐dependent increase in natural mortality (known as actuarial senescence) and an age‐dependent decrease in fecundity (known as reproductive senescence), and its role in nature is still poorly understood. Based on empirical estimates of reproductive and actuarial senescence, we used mathematical simulations to explore how senescence affects the population dynamics ofCoregonus albula, a small, schooling salmonid fish. Using an empirically based eco‐evolutionary model, we investigated how the presence or absence of senescence affects the eco‐evolutionary dynamics of a fish population during pristine, intensive harvest, and recovery phases. Our simulation results showed that the presence or absence of senescence affected how the population responded to the selection regime. At an individual level, gillnetting caused a larger decline in asymptotic length when senescence was present, compared to the nonsenescent population, and the opposite occurred when fishing was done by trawling. This change was accompanied by evolution toward younger age at maturity. At the population level, the change in biomass and number of fish in response to different fishery size‐selection patterns depended on the presence or absence of senescence. Since most life‐history and fisheries models ignore senescence, they may be over‐estimating reproductive capacity and under‐estimating natural mortality. Our results highlight the need to understand the combined effects of life‐history characters such as senescence and fisheries selection regime to ensure the successful management of our natural resources.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".