Spatial Genetic Structure of Long-tailed Ducks (<i>Clangula hyemalis</i>) among Alaskan, Canadian, and Russian Breeding Populations + Supplementary Appendix Tables S1 and S2 (See Article Tools)
Bibliographic record
Abstract
Arctic ecosystems are changing at an unprecedented rate. How Arctic species are able to respond to such environmental change is partially dependent on the connections between local and broadly distributed populations. For species like the Long-tailed Duck (Clangula hyemalis), we have limited telemetry and band-recovery information from which to infer population structure and migratory connectivity; however, genetic analyses can offer additional insights. To examine population structure in the Long-tailed Duck, we characterized variation at mtDNA control region and microsatellite loci among four breeding areas in Alaska, Canada, and Russia. We observed significant differences in the variance of mtDNA haplotype frequencies between the Yukon-Kuskokwim Delta (YKD) and the three Arctic locations (Arctic Coastal Plain in Alaska, eastern Siberia, and central Canadian Arctic). However, like most sea duck genetic assessments, our study found no evidence of population structure based on autosomal microsatellite loci. Long-tailed Ducks use multiple wintering areas where pair formation occurs with some populations using both the Pacific and Atlantic Oceans. This situation provides a greater opportunity for admixture across breeding locales, which would likely homogenize the nuclear genome even in the presence of female philopatry. The observed mtDNA differentiation was largely due to the presence of two divergent clades: (A) a clade showing signs of admixture among all breeding locales and (B) a clade primarily composed of YKD samples. We hypothesize that the pattern of mtDNA differentiation reflects some degree of philopatry to the YKD and isolation of two refugial populations with subsequent expansion and admixture. We recommend additional genetic assessments throughout the circumpolar range of Long-tailed Ducks to further quantify aspects of genetic diversity and migratory connectivity in this species.
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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) | 0.005 | 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".