Data from: Disentangling the genetic effects of refugial isolation and range expansion in a trans-continentally distributed species
Bibliographic record
Abstract
AbstractIn wide-ranging taxa with historically dynamic ranges, past allopatric isolation and range expansion can both influence the current structure of genetic diversity. Considering alternate historical scenarios involving expansion from either a single refugium or from multiple refugia can be useful in differentiating the effects of isolation and expansion. Here, we examined patterns of genetic variability in the trans-continentally distributed painted turtle (Chrysemys picta). We utilized an existing phylogeographic dataset for the mitochondrial control region and generated additional data from nine populations for the mitochondrial control region (n = 302) and for eleven nuclear microsatellite loci (n = 247). We created a present-day ecological niche model (ENM) for C. picta and hindcast this model to three reconstructions of historical climate to define three potential scenarios with one, two, or three refugia. Finally, we employed spatially-explicit coalescent simulations and an approximate Bayesian computation (ABC) framework to test which scenario best fit the observed genetic data. Simulations indicated that phylogeographic and multilocus population-level sampling both could differentiate among refugial scenarios, although inferences made using mitochondrial data were less accurate when a longer coalescence time was assumed. Furthermore, all empirical genetic datasets were most consistent with expansion from a single refugium based on ABC. Our results indicate a stronger role for post-glacial range expansion, rather than isolation in allopatric refugia followed by range expansion, in structuring diversity in this species. To distinguish among complex historical scenarios, we recommend explicitly modeling the effects of range expansion and evaluating alternate refugial scenarios for wide-ranging taxa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".