Common factors drive adaptive genetic variation at different spatial scales in <i>Arabis alpina</i>
Bibliographic record
Abstract
A major challenges facing landscape geneticists studying adaptive variation is to include all the environmental variables that might be correlated with allele frequencies across the genome. One way of identifying loci that are possibly under selection is to see which ones are associated with environmental gradient or heterogeneity. Since it is difficult to measure all environmental variables, one may take advantage of the spatial nature of environmental filters to incorporate the effect of unaccounted environmental variables in the analysis. Assuming that the spatial signature of these variables is broad-scaled, broad-scale Moran's eigenvector maps (MEM) can be included as explanatory variables in the analysis as proxies for unmeasured environmental variables. We applied this approach to two data sets of the alpine plant Arabis alpina. The first consisted of 140 AFLP loci sampled at 130 sites across the European Alps (large scale). The second one consisted of 712 AFLP loci sampled at 93 sites (regional scale) in three mountain massifs (local scale) of the French Alps. For each scale, we regressed the frequencies of each AFLP allele on a set of eco-climatic and MEM variables as predictors. Twelve (large scale) and 11% (regional scale) of all loci were detected as significantly correlated to at least one of the predictors ( > 0.5), and, except for one massif, 17% at the local scale. After accounting for spatial effects, temperature and precipitation were the two major determinants of allele distributions. Our study shows how MEM models can account for unmeasured environmental variation in landscape genetics models.
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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 | 0.001 |
| 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".