Parnassius smintheus SNP and associated weather and landscape variables
Bibliographic record
Abstract
Weather is an important short-term, local driver of population size and dispersal, which in turn contribute to patterns of genetic diversity and differentiation within species. Climate change is leading to greater weather variability and more frequent extreme weather events. While the effects of long-term and broad-scale mean climate conditions on genetic variation are well studied, our understanding of the effects of weather variability and extreme conditions on genetic variation is less developed. We assessed the influence of temperature and snow depth on genetic diversity and differentiation of populations of the alpine butterfly, Parnassius smintheus. We examined the relationships between a suite of variables, including those representing extreme conditions, and population-level genetic diversity and differentiation across 1453 single nucleotide polymorphisms, using both linear and gravity models. We additionally examined effects of land cover variables known to influence dispersal and gene flow in this species. We found that extreme low temperature events and the lowest recorded mean snow depth were significant predictors of genetic diversity. Extreme low temperature events, mean snow depth, and land cover resistance were significant predictors of genetic differentiation. These results are congruent with known effects of early winter weather on population size and habitat connectivity on dispersal in P. smintheus. Our results demonstrate the potential for changes in the frequency or magnitude of extreme weather events to alter patterns of genetic diversity and differentiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".