A heuristic model of socially learned migration behaviour exhibits distinctive spatial and reproductive dynamics
Bibliographic record
Abstract
Abstract We explore a “Go With the Older Fish” (GWOF) mechanism of learned migration behaviour for exploited fish populations, where recruits learn a viable migration path by randomly joining a school of older fish. We develop a non-age-structured biomass model of spatially independent spawning sites with local density dependence, based on Pacific herring (Clupea pallasii). We compare a diffusion (DIFF) strategy, where recruits adopt spawning sites near their natal site without regard to older fish, with GWOF, where recruits adopt the same spawning sites, but in proportion to the abundance of adults using those sites. In both models, older individuals return to their previous spawning site. The GWOF model leads to higher spatial variance in biomass. As total mortality increases, the DIFF strategy results in an approximately proportional decrease in biomass among spawning sites, whereas the GWOF strategy results in abandonment of less productive sites and maintenance of high biomass at more productive sites. A DIFF strategy leads to dynamics comparable to non-spatially structured populations. While the aggregate response of the GWOF strategy is distorted, non-stationary and slow to equilibrate, with a production curve that is distinctly flattened and relatively unproductive. These results indicate that fishing will disproportionately affect populations with GWOF behaviour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".