Philopatry as a Tool to Define Tentative Closed Migration Cycles and Conservation Areas for Large Pelagic Fishes in the Pacific
Bibliographic record
Abstract
Migrations of large pelagic fishes across the Pacific are usually inferred from tagging or genetic studies. Even though these techniques have improved over time, they still fail to demonstrate large transoceanic migrations, usually proposing ‘routes’ that do not cycle seasonally. The current study uses the concept of ‘philopatry’ in 11 large pelagic fish species, i.e., the tendency for animals to return to their natal site to reproduce. Tentative migration routes and maps emerge by applying this concept to the movements extracted through a comprehensive review of the literature on satellite and conventional tagging, and population and subpopulation linkages inferred from genetic and/or genomic studies. Moreover, when comparing these proposed migration routes and the mapped reconstructed catch (1950–2016, Sea Around Us) of each species in the Pacific, similarities emerge, reinforcing the accuracy of these migration cycles informed by philopatry. Finally, by superposing the migration routes of our 11 species, we identified areas of the Pacific that are part of the inferred migration routes of multiple species, leading to a discussion of possible ‘blue corridors’ that would protect the studied species’ key migration routes and stocks, which are important for the fisheries, culture and nutrition of Pacific islanders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".