A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks
Bibliographic record
Abstract
Selectivity in fish is often size-dependent, which results in differential fishing mortality rates across fish of the same age, an effect known as “Lee’s Phenomenon”. We extend previous work on using length composition to estimate the spawning potential ratio (SPR) for data-limited stocks by developing a computationally efficient length-structured per-recruit model that splits the population into a number of subcohorts, or growth-type-groups, to account for size-dependent fishing mortality rates. Two simple recursive equations, using the life history ratio of the natural mortality rate to the von Bertalanffy growth parameter (M/K), were developed to generate length composition data, reducing the complexity of the previous approach. Using simulated and empirical data, we demonstrate that ignoring Lee’s Phenomenon results in overestimates of fishing mortality and negatively biased estimates of SPR. We also explored the behaviour of the model under various scenarios, including alternative life history strategies and the presence of size-dependent natural mortality. The model developed in this paper may be a useful tool to estimate the SPR for data-limited stock where it is not possible to apply more conventional methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".