Mechanisms of global diversification in the brown booby (Sula leucogaster) revealed by uniting statistical phylogeographic and multilocus phylogenetic methods
Bibliographic record
Abstract
Recent theoretical and empirical research suggests that statistical models based on coalescent theory can improve both phylogeographic and phylogenetic inference. An approach that involves elements of both statistical phylogeography (e.g. Isolation with Migration analyses) and multilocus phylogenetic inference (e.g. *beast) may be particularly useful when applied to populations with relatively old divergence times. Here, we use such an approach in the globally distributed brown booby (Sula leucogaster). We sampled 215 individuals from all major breeding areas and genotyped them at eight microsatellite and three nuclear intron loci. We found that brown booby populations were highly differentiated and that colonies can be grouped into four major genetic populations (Caribbean Sea, Central Atlantic Ocean, Indo-Central Pacific and Eastern Pacific). These populations apparently diverged in the absence of gene flow and, with one exception, currently exchange few to no migrants. The Eastern Pacific population diverged from all other populations approximately one million years ago [90% highest posterior density: 330,000-2,000,000 years ago] and exhibits a distinct male plumage, relative to other populations. However, recent gene flow from the Indo-Central Pacific into the Eastern Pacific appears to have occurred, suggesting that approximately one million years of genetic isolation and divergence in male plumage colour are not sufficient to prevent interbreeding. Gene flow following secondary contact of the Indo-Central Pacific and Eastern Pacific populations was not detected in previous mitochondrial DNA (mtDNA) studies, and the contrast between the mtDNA results and our current results highlights the advantage of a multilocus phylogeographic approach.
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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 | 0.002 |
| 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".