GENOMIC VARIATION REVEALS PLEISTOCENE-DRIVEN GENETIC DIVERGENCE AND CONTEMPORARY TRANS-OCEANIC GENE FLOW IN A MIGRATORY BIRD
Bibliographic record
Abstract
The relative contributions of past and present evolutionary processes in shaping population genetic differentiation can be difficult to ascertain, especially in highly mobile animals. The Northern Gannet (Morus bassanus; hereafter, gannet) is a migratory colonial seabird that is widely distributed across the North Atlantic Ocean. Despite strong dispersal abilities, decades of banding and tracking studies indicate that the North Atlantic is a barrier for seasonal migration of gannets: gannets breeding in North America winter along the southeastern coast of USA and Mexico, while European breeders winter along the western European and African coasts. However, telemetry recently revealed that some gannets migrate across the ocean, suggesting that trans-Atlantic gene flow is possible. We investigated patterns of genomic variation among gannets from 12 colonies across the species’ range using double digest restriction-site associated sequencing (ddRADseq). Indices of genetic differentiation, principal component analysis, a Bayesian clustering method, and discriminant analysis of principal components all indicated that gannets breeding in North America versus Europe differ genetically, in accordance with segregation at both breeding and non-breeding areas. However, Bayesian assignment methods indicated that low, unidirectional introgression occurs from Europe into North America, suggesting that the North Atlantic is a semi-permeable barrier to gene flow in gannets. Evolutionary modeling suggested that the two genetic populations originated in separate refugia during the Pleistocene and underwent secondary contact during the Holocene. These results are consistent with results of previous studies and provide direct evidence that seasonal migratory behaviour can influence population genetic structure in a highly mobile organism.
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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 | 0.001 | 0.001 |
| 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".