Reproductive resilience: a paradigm shift in understanding spawner‐recruit systems in exploited marine fish
Bibliographic record
Abstract
Abstract A close relationship between adult abundance and stock productivity may not exist for many marine fish stocks, resulting in concern that the management goal of maximum sustainable yield is either inefficient or risky. Although reproductive success is tightly coupled with adult abundance and fecundity in many terrestrial animals, in exploited marine fish where and when fish spawn and consequent dispersal dynamics may have a greater impact. Here, we propose an eco‐evolutionary perspective, reproductive resilience, to understand connectivity and productivity in marine fish. Reproductive resilience is the capacity of a population to maintain the reproductive success needed to result in long‐term population stability despite disturbances. A stock's reproductive resilience is driven by the underlying traits in its spawner‐recruit system, selected for over evolutionary timescales, and the ecological context within which it is operating. Spawner‐recruit systems are species specific, have both density‐dependent and fitness feedback loops and are made up of fixed, behavioural and ecologically variable traits. They operate over multiple temporal, spatial and biological scales, with trait diversity affecting reproductive resilience at both the population and individual (i.e. portfolio) scales. Models of spawner‐recruit systems fall within three categories: (i) two‐dimensional models (i.e. spawner and recruit); (ii) process‐based biophysical dispersal models which integrate physical and environmental processes into understanding recruitment; and (iii) complex spatially explicit integrated life cycle models. We review these models and their underlying assumptions about reproductive success vs. our emerging mechanistic understanding. We conclude with practical guidelines for integrating reproductive resilience into assessments of population connectivity and stock productivity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".