Complementary genomic and epigenomic adaptation to environmental heterogeneity
Bibliographic record
Abstract
While adaptation is commonly thought to result from selection on DNA sequence-based variation, recent studies have highlighted an analogous epigenetic component as well. However, the relative roles of these mechanisms in facilitating population persistence under environmental heterogeneity remain unclear. To address the underlying genetic and epigenetic mechanisms and their relationship during environmental adaptation, we screened the genomes and epigenomes of nine global populations of a predominately sessile marine invasive tunicate, Botryllus schlosseri. We detected clear population differentiation at the genetic and epigenetic levels. Patterns of genetic and epigenetic structure were significantly influenced by local environmental variables. Among these variables, minimum annual sea surface temperature was identified as the top explanatory variable for both genetic and epigenetic variation. However, patterns of population structure driven by genetic and epigenetic variation were somewhat distinct, suggesting possible autonomy of epigenetic variation. We found both shared and specific genes and biological pathways among genetic and epigenetic loci associated with environmental factors, consistent with complementary and independent contributions of genetic and epigenetic variation to environmental adaptation in this system. Collectively, these mechanisms may facilitate population persistence under environmental change and sustain successful invasions across novel environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".