Genome‐wide epigenetic isolation by environment in a widespread <i>Anolis</i> lizard
Bibliographic record
Abstract
Epigenetic changes can provide a pathway for organisms to respond to local environmental conditions by influencing gene expression. However, we still know little about the spatial distribution of epigenetic variation in natural systems, how it relates to the distribution of genetic variation and the environmental structure of the landscape, and the processes that generate and maintain it. Studies examining spatial patterns of genetic and epigenetic variation can provide valuable insights into how ecological and population processes contribute to epigenetic divergence across heterogeneous landscapes. Here, we perform a comparative analysis of spatial genetic and epigenetic variation based on 8,459 single nucleotide polymorphisms (SNPs) and 8,580 single methylation variants (SMVs) from eight populations of the Puerto Rican crested anole, Anolis cristatellus, an abundant lizard in the adaptive radiations of anoles on the Greater Antilles that occupies a diverse range of habitats. Using generalized dissimilarity modelling and multiple matrix regression, we found that genome-wide epigenetic differentiation is strongly correlated with environmental divergence, even after controlling for the underlying genetic structure. We also detected significant associations between key environmental variables and 96 SMVs, including 42 located in promoter regions or gene bodies. Our results suggest an environmental basis for population-level epigenetic differentiation in this system and contribute to better understanding how environmental gradients structure epigenetic variation in nature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".