Collapse, Tipping Points, and Spatial Demographic Structure Arising from the Adopted Migrant Life History
Bibliographic record
Abstract
The roles of dispersal and recruitment have long been a focal point in ecology and conservation. The adopted migrant hypothesis proposes a life history in which social learning transmits migratory knowledge between generations of iteroparous fish. Specifically, juveniles disperse from the parental spawning site, encounter and recruit to a local adult population, and learn migration routes between spawning and foraging habitats by following older, experienced fish. Although the adopted migrant life history may apply to many species of pelagic marine fishes, there is scant theoretical or empirical work on the consequent population dynamics. We developed and analyzed a mathematical model of this life history in which the recruitment of juveniles depends on the relative abundance of the local populations and recruitment overlap, which measures the ease with which juveniles are recruited by a nonparental population. We demonstrate that the adopted migrant life history can maintain spatial demographic structure among local populations, that it can also predispose local populations to collapse when a tipping point is crossed, and that recovery after collapse is impaired by reduced recruitment at small local population sizes.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".