The West Pacific diversity hotspot as a source or sink for new species? Population genetic insights from the Indo-Pacific parrotfish Scarus rubroviolaceus
Bibliographic record
Abstract
We used a population genetic approach to quantify major population subdivisions and patterns of migration within a broadly distributed Indo-Pacific parrotfish. We genotyped 15 microsatellite loci in Scarus rubroviolaceus collected from 20 localities between Africa and the Americas. A STRUCTURE model indicates the presence of four major populations: Eastern Pacific, Hawaii, Central-West Pacific and a less well-differentiated Indian Ocean. We used the isolation and migration model to estimate splitting times, population sizes and migration patterns between sister population pairs. To eliminate loci under selection, we used BayeScan to select loci for three isolation and migration models: Eastern Pacific and Central-West Pacific, Hawaii and the Central-West Pacific, and Indian Ocean and the Central-West Pacific. To test the assumption of a stepwise mutation model (SMM), we used likelihood to test the SMM against a two-phase model that allowed mutational complexity. A posteriori, minor departures from SMM were estimated to affect ≤2% of the alleles in the data. The data were informative about the contemporary and ancestral population sizes, migration rates and the splitting time in the eastern Pacific/Central-West Pacific comparison. The model revealed a splitting time ∼17,000 BP, a larger contemporary N(e) in the Central-West Pacific than in the eastern Pacific and a strong bias of east to west migration. These characteristics support the Center of Accumulation model of peripatric diversification in low-diversity peripheral sites and perhaps migration from those sites to the western Pacific diversity hotspot.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".