Geographical restructuring of <i>Arabidopsis thaliana</i> ’s genetic makeup in the Iberian Peninsula due to climate change based on genetic cluster membership
Bibliographic record
Abstract
ABSTRACT Aim To assess the effects of climate change on genetic lineages of Arabidopsis thaliana at the admixed population level by directly modelling genetic cluster membership values to predict potential genetic cluster memberships across the Iberian Peninsula. Location Iberian Peninsula Methods We used a dataset of 274 accessions structured in four genetic clusters as inferred from 250 nuclear single-nucleotide polymorphisms with Bayesian clustering methods. We predicted the change in percentages of genetic cluster membership at a population level and the changes in potential suitability across the study area by combining parametric (Beta regression) and non-parametric (Recursive trees) methods. Results Climate change will affect genetic lineages of Arabidopsis thaliana differently. Genetic clusters GC1 and GC2 will suffer a substantive reduction of their respective suitable areas while GC3 and GC4 will expand northward. At the population level, except for GC4, the rest of the lineages will undergo a genetic turnover for many of their populations. Main conclusions A. thaliana in the Iberian Peninsula will undergo a major internal genetic restructuring and range change due to climate change. Genetic lineages of Arabidopsis thaliana in the Iberian Peninsula will be affected differently which reinforce the need for taking into account intraspecific genetic variation when modelling species distribution. Despite limited predictive power of individual statistical models, the combination of distinct models can compensate this shortcoming.
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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 | 0.000 |
| 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".