Data from: Influence of ecological and geological features on rangewide patterns of genetic structure in a widespread passerine.
Bibliographic record
Abstract
Abstract Geological and ecological features restrict dispersal and gene flow, leading to isolated populations. Dispersal barriers can be obvious physical structures in the landscape; however microgeographic differences can also lead to genetic isolation. Our study examined dispersal barriers at both macro- and micro-geographical scales in the black-capped chickadee, a resident North American songbird. Although birds have high dispersal potential, evidence suggests dispersal is restricted by barriers. The chickadee’s range encompasses a number of physiological features which may impede movement and lead to divergence. Analyses of 913 individuals from 34 sampling sites across the entire range using 11 microsatellite loci revealed as many as 13 genetic clusters. Populations in the east were largely panmictic whereas populations in the western portion of the range showed significant genetic structure, which often coincided with large mountain ranges, such as the Cascade and Rocky Mountains, as well as areas of unsuitable habitat. Unlike populations in the central and southern Rockies, populations on either side of the northern Rockies were not genetically distinct. Furthermore, Northeast Oregon represents a forested island within the Great Basin; genetically isolated from all other populations. Substructuring at the microgeographical scale was also evident within the Fraser Plateau of central British Columbia, and in the southeast Rockies where no obvious physical barriers are present, suggesting additional factors may be impeding dispersal and gene flow. Dispersal barriers are therefore not restricted to large physical structures, although mountain ranges and large water bodies do play a large role in structuring populations in this study.
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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 | 0.001 |
| 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) | 0.008 | 0.001 |
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".