Population genetic differentiation and hybridization in the Glaucous Gull (Larus hyperboreus)
Bibliographic record
Abstract
Climate change poses a significant threat to the future of Arctic ecosystems. To effectively conserve Arctic species, genetically differentiated populations must be defined for adaptive and neutral genetic variation to be appropriately managed. This project examines population genetic differentiation in the Glaucous Gull (Larus hyperboreus) – a circumpolar Arctic species – and assesses hybridization between Glaucous Gulls and three closely-related species. The Glaucous Gull is a valuable species, biologically and culturally. As apex predators, Glaucous Gulls develop high levels of toxins in their tissue and are, therefore, excellent bioindicators of the long-range transport of contaminants in the Arctic. Glaucous Gulls also hybridize with other white-headed gull species where breeding ranges overlap. Although the International Union for Conservation of Nature currently lists Glaucous Gull as Least Concern, declines have been reported across their range in Arctic Canada. Currently no finescale population genetic information exists for this species, and management units have not been delineated. Double digest restriction-site associated DNA sequencing was conducted on 62 Glaucous Gulls, 18 American Herring Gulls (L. smithsonianus), 6 European Herring Gulls (L. argentatus), and 15 Glaucous-winged Gulls (L. glaucescens) sampled across the North American and European Arctic. Despite the geographic distance between sampling locations, molecular assignments and principal coordinates analysis suggest only weak population differentiation between sampled European and Canadian colonies of Glaucous Gull. Interspecific analyses using 2145 loci show that Glaucous Gull and Glaucous-winged Gull are genetically distinct species but that Glaucous Gull and the two species of Herring Gull are only weakly differentiated. Several sampled individuals may represent hybrids between Glaucous Gulls and American Herring Gulls. Detailed information on population genetic structure and hybridization will help conservation practitioners effectively manage the long-term persistence of Glaucous Gull populations. Proactive management strategies for this species will benefit both Glaucous Gull and the entire Arctic ecosystem.
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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 | 0.000 |
| 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) | 0.000 | 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".