Genotyping-by-sequencing reveals genomic homogeneity among overwintering Pacific Dunlin (Calidris alpina pacifica) aggregations along the Pacific coast of North America
Bibliographic record
Abstract
Abstract Information on how migratory populations are genetically structured during the overwintering season of the annual cycle can improve our understanding of the strength of migratory connectivity and help identify populations as units for management. Here, we use a genotype-by-sequencing approach to investigate whether population genetic structure exists among overwintering aggregations of the Pacific Dunlin subspecies (Calidris alpina pacifica) sampled at 2 spatial scales (within and among overwintering sites) in the eastern Pacific Flyway. Genome-wide analyses of 874 single nucleotide polymorphisms across 80 sampled individuals revealed no evidence for genetic differentiation among aggregations overwintering at 3 locations within the Fraser River Estuary (FRE) of British Columbia. Similarly, comparisons of aggregations in the FRE and those overwintering in southern sites in California and Mexico indicated no genetic segregation between northern and southern overwintering areas. These results suggest that Pacific Dunlin within the FRE, Sacramento Valley (California), and Guerrero Negro (Mexico) are genetically homogeneous, with no evident genetic structure between sampled sites or regions across the overwintering range. Despite no evidence for differentiation among aggregations, we identified a significant effect of geographical distance between sites on the distribution of individual genotypes in a redundancy analysis. A small proportion of the total genotypic variance (R2 =0.036, P = 0.011) was explained by the combined effect of latitude and longitude, suggesting weak genomic patterns of isolation-by-distance that are consistent with chain-like migratory connectivity between breeding and overwintering areas. Our study represents the first genome-scale investigation of population structure for a Dunlin subspecies and provides essential baseline estimates of genomic diversity and differentiation within the Pacific Dunlin.
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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) | 0.000 | 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".