Genetic differentiation and gene flow among populations of the alpine butterfly, <i>Parnassius smintheus</i>, vary with landscape connectivity
Bibliographic record
Abstract
Levels of gene flow among populations vary both inter- and intraspecifically, and understanding the ecological bases of variation in levels of gene flow represents an important link between the ecological and evolutionary dynamics of populations. The effects of habitat spatial structure on gene flow have received considerable attention; however, most studies have been conducted at a single spatial scale and without background data on how individual movement is affected by landscape features. We examined the influence of habitat connectivity on inferred levels of gene flow in a high-altitude, meadow-dwelling butterfly, Parnassius smintheus. For this species, we had background data on the effects of landscape structure on both individual movement and on small-scale population genetic differentiation. We compared genetic differentiation and patterns of isolation by distance, based on variation at seven microsatellite loci, among three regions representing two levels of connectivity of high-altitude, nonforested habitats. We found that reduced connectivity of habitats, resulting from more forest cover at high altitudes, was associated with greater genetic differentiation among populations (higher estimated FST), a breakdown of isolation by distance, and overall lower levels of inferred gene flow. These observed differences were consistent with expectations based on our knowledge of the movement behaviour of this species and on previous population genetic analyses conducted at the smaller spatial scale. Our results indicate that the role of gene flow may vary among groups of populations depending on the interplay between individual movement and the structure of the surrounding landscape.
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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 | 0.001 | 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) | 0.001 | 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".