The Epigenetic Signature of Colonizing New Environments in Anolis Lizards
Bibliographic record
Abstract
Founder populations often show rapid divergence from source populations after colonizing new environments. Epigenetic modifications can mediate phenotypic responses to environmental change and may be an important mechanism promoting rapid differentiation in founder populations. Whereas many long-term studies have explored the extent to which divergence between source and founder populations is genetically heritable versus plastic, the role of epigenetic processes during colonization remains unclear. To investigate epigenetic modifications in founding populations, we experimentally colonized eight small Caribbean islands with brown anole lizards (Anolis sagrei) from a common source population. We then quantitatively measured genome-wide DNA methylation in liver tissue using reduced representation bisulfite sequencing of individuals transplanted onto islands with high- versus low-habitat quality. We found that lizard sex and habitat quality explained a significant proportion of epigenetic variation. Differentially methylated cytosines mapped to genes that encode proteins with functions likely to be relevant to habitat change (e.g., signal transduction, immune response, circadian rhythm). This study provides experimental evidence of a relationship between epigenetic responses and the earliest stages of colonization of novel environments in nature and suggests that habitat quality influences the nature of these epigenetic modifications.
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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".