Patterns of Genetic And Epigenetic Diversity Across A Range Expansion in The White-Footed Mouse (<i>Peromyscus Leucopus</i>)
Bibliographic record
Abstract
Synopsis Populations at the leading front of a range expansion must rapidly adapt to novel conditions. Increased epigenetic diversity has been hypothesized to facilitate adaptation and population persistence via non-genetic phenotypic variation, especially if there is reduced genetic diversity when populations expand (i.e., epigenetic diversity compensates for low genetic diversity). In this study, we use the spatial distribution of genetic and epigenetic diversity to test this hypothesis in populations of the white-footed mouse (Peromyscus leucopus) sampled across a purported recent range expansion gradient. We found mixed support for the epigenetic compensation hypothesis and a lack of support for expectations for expansion populations of mice at the range edge, which likely reflects a complex history of expansion in white-footed mice in the Upper Peninsula of Michigan. Specifically, epigenetic diversity was not increased in the population at the purported edge of the range expansion in comparison to the other expansion populations. However, input from an additional ancestral source populations may have increased genetic diversity at this range edge population, counteracting the expected genetic consequences of expansion, as well as reducing the benefit of increased epigenetic diversity at the range edge. Future work will expand the focal populations to include expansion areas with a single founding lineage to test for the robustness of a general trend that supports the hypothesized compensation of reduced genetic diversity by epigenetic variation observed in the expansion population that was founded from a single historical source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".