Quasi epigenetic equilibrium: the implications of plasticity and variability on evolution and extinction
Bibliographic record
Abstract
Abstract The phenotypic effects of epigenetic modifications, and thus their evolutionary consequences, depend on how the modifications interact with the underlying genetics and the surrounding environment. These interactions lead to a complex model that has so far prevented general analytical progress, and thus limited our understanding. Here, we show that the timescale difference between epigenetic and genetic changes create a quasi-epigenetic equilibrium (QEE). The QEE allows us to tackle the complexity of population epigenetic models by reducing them to their underlying population genetic models with effective parameters. Using this technique, we show how epigenetics modifies key evolutionary parameters, such as the strength of selection and dominance, which can have drastic evolutionary consequences on mutation-selection balance. Further, we show how the QEE allows us to analytically investigate the effect of epigenetics on the probability of a population escaping extinction in a harsh environment via adaptation – evolutionary rescue – by altering the number of potential rescue lineages and their probability of establishing. These calculations show that whether epigenetics helps or hurts population persistence depends non-trivially on the frequency and stability of 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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".