A review of historical and contemporary processes affecting population genetic structure of Southern Ocean seabirds
Bibliographic record
Abstract
Genetic signatures of historical, behavioural and environmental processes are evident in contemporary seabird populations. Molecular markers have allowed us to determine historical patterns of gene flow, relationships among taxa, and contemporary dispersal barriers. The Southern Ocean contains a number of small, isolated islands that are home to four families of seabirds: albatrosses, petrels, penguins and skuas, which have been the focus of a number of population genetic studies. While capable of travelling large distances, many seabirds have restricted dispersal and exhibit high levels of population structure; typically in northern areas and areas with high endemism (e.g. New Zealand). We reviewed 29 studies of 25 Southern Ocean seabird species comparing biogeographic patterns, glacial history and barriers to gene flow, especially at-sea distribution and ocean currents. Despite diversity in behaviour and life history, our review demonstrates that population genetic structure of the seabirds corresponds to the same barriers. For penguins, currents are the major impediment to dispersal whereas at-sea distribution and island location influence population structure for many seabirds with genetically distinct populations on islands at the periphery of their range. As environmental conditions change, it will become more important to assess how seabirds respond and how these changes influence both dispersal and population structure. It is particularly important as a disproportionately high number of Southern Ocean seabirds are threatened or near threatened. Future studies need to focus on adaptive genetic markers, range-wide comprehensive sampling, influence of behaviour on genetic structure and lesser studied seabirds such as terns and cormorants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".