Genetic and ecological data provide incongruent interpretations of population structure and dispersal in naturally subdivided populations of white‐tailed ptarmigan (<i>Lagopus leucura</i>)
Bibliographic record
Abstract
The dispersal of individuals among populations affects the demographic and adaptive trajectories of animal populations and is fundamental to understanding population dynamics. White-tailed ptarmigan (Lagopus leucura) are a high elevation grouse species that live year-round in patchily distributed alpine areas in western North America. We investigated the patterns of dispersal and identified barriers to gene flow for a threatened subspecies (L. l. saxatilis) endemic to Vancouver Island, Canada. Connectivity among seven sites was examined using nine microsatellite loci (n = 133 individuals, H(O) = 0.62, mean number of alleles = 10) and direct movement observations using radio-telemetry (n = 118 individuals). Average movement distances of individuals measured by radio-telemetry were 0.63-3.23 km and considerably less than the shortest distance between sampling sites (18 km). Furthermore, despite extensive radio-telemetry data, movement was never observed between any of the seven sampling sites. In contrast, genetic results (STRUCTURE, TESS) showed connectivity among most of the seven sampling sites and suggested that genetic variation is best explained by two clusters of individuals which separated the South sampling site from all other areas of Vancouver Island. Analysis of molecular data also showed a generally consistent pattern of isolation by distance (Mantel test r = 0.11, P < 0.01) with large areas of unsuitable low elevation habitat possibly acting as barriers to gene flow. Despite the naturally subdivided distribution of populations, white-tailed ptarmigan do not fit well into any common definition of a metapopulation. We conclude the incongruities between the genetic and radio-telemetry data are best explained by episodic dispersal patterns. In this study, we demonstrated the importance of combining genetic and ecological data in understanding patterns of dispersal and population structure.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".