Estimating and testing non-additivity in fishing mortality: implications for detecting a fisheries collapse
Bibliographic record
Abstract
Common in many fisheries collapses is a disproportionate increase in fishing mortality at younger ages. One mechanism by which this increase could occur is sufficient depletion of the population at older ages due to strong overfishing, which leads to targeting of younger fish. Thus, it is essential for assessments to estimate and test for a change in selectivity in the fishery. We introduce a simple and powerful approach based upon Tukey's one degree of freedom test for non-additivity. This approach can be applied within any statistical age-structured population model that estimates selectivity. We illustrate the approach with data from Atlantic cod (Gadus morhua) from St. Pierre Bank, Canada. The results show significant non-additivity in fishing mortality that translates into an increase in selectivity on younger ages when fishing mortality is high. This approach also can be applied to the output of an age-structured model that assumes catch-at-age is known without error or to any survey or catch-per-unit-effort data for which estimates of abundance are made by year and age. We believe that this approach should be routinely applied in assessments, particularly when overfishing has led to depletion of the overall population or to truncation of the age structure.
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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 | 0.062 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".