Long-term population decline of a genetically homogenous continental-wide top Arctic predator
Bibliographic record
Abstract
ABSTRACT Genetic analysis can provide valuable information for conservation programs by unraveling the demographic trajectory of populations, by estimating effective population size, or by inferring genetic differentiation between populations. Here, we investigated the genetic differentiation within the Snowy Owl ( Bubo scandiacus ), a species identified as vulnerable by the IUCN, to (i) quantify connectivity among wintering areas, (ii) to evaluate current genetic diversity and effective population size and (iii) to infer changes in the historical effective population size changes from the last millennia to the recent past. The Snowy Owl, a highly mobile top predator, breeds across the Arctic tundra which is a region especially sensitive to current climate change. Using SNP-based analyses on Snowy Owls sampled across the North American nonbreeding range, we found an absence of genetic differentiation among individuals located up to 4,650 km apart. Our results suggest high genetic intermixing and effective dispersal at the continental scale despite documented philopatry to nonbreeding sites in winter. Reconstructing the population demographic indicated that North American Snowy Owls have been steadily declining since the Last Glacial Maximum ca 20,000 years ago and concurrently with global increases in temperature. Conservation programs should now consider North American Snowy Owls as a single, genetically homogenous continental-wide population which is most likely sensitive to the long-term global warming occurring since the Last Glacial Maximum.
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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 | 0.000 | 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) | 0.001 | 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".