Historical fragmentation and stepping‐stone gene flow led to population genetic differentiation in a coastal seabird
Bibliographic record
Abstract
Abstract Understanding the forces that shape population genetic structure is fundamental both for understanding evolutionary trajectories and for conservation. Many factors can influence the geographic distribution of genetic variation, and the extent to which local populations differ can be especially difficult to predict in highly mobile organisms. For example, many species of seabirds are essentially panmictic, but some show strong structure. Pigeon Guillemots (Cepphus columba; Charadriiformes: Alcidae) breed in small colonies scattered along the North Pacific coastline and feed in shallow nearshore waters year‐round. Given their distribution, gene flow is potentially lower and population genetic structure is stronger than in most other high‐latitude Northern Hemisphere seabirds. We screened variation in the mitochondrial control region, four microsatellite loci, and two nuclear introns in 202 Pigeon Guillemots representing three of five subspecies. Mitochondrial sequences and nuclear loci both showed significant population differences, although structure was weaker for the nuclear loci. Genetic differentiation was correlated with geographic distance between sampling locations for both the mitochondrial and nuclear loci. Mitochondrial gene trees and demographic modeling both provided strong evidence for two refugial populations during the Pleistocene glaciations: one in the Aleutian Islands and one farther east and south. We conclude that historical fragmentation combined with a stepping‐stone model of gene flow led to the relatively strong population differentiation in Pigeon Guillemots compared to other high‐latitude Northern Hemisphere seabird species. Our study adds to growing evidence that Pleistocene glaciation events affected population genetic structure not only in terrestrial species but also in coastal marine animals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".