Controlling for the Effects of History and Nonequilibrium Conditions in Gene Flow Estimates in Northern Bullfrog (Rana catesbeiana) Populations
Bibliographic record
Abstract
Nonequilibrium conditions due to either allopatry followed by secondary contact or recent range expansion can confound measurements of gene flow among populations in previously glaciated regions. We determined the scale at which gene flow can be estimated among breeding aggregations of bullfrogs (Rana catesbeiana) at the northern limit of their range in Ontario, Canada, using seven highly polymorphic DNA microsatellite loci. We first identified breeding aggregations that likely share a common history, determined from the pattern of allelic richness, factorial correspondence analysis, and a previously published mtDNA phylogeography, and then tested for regional equilibrium by evaluating the association between pairwise F(ST) and geographic distance. Regional breeding aggregations in eastern Ontario separated by <100 km were determined to be at or near equilibrium. High levels of gene flow were measured using traditional F-statistics and likelihood estimates of Nm. Similarly high levels of recent migration (past one to three generations) were estimated among the breeding aggregations using nonequilibrium methods. We also show that, in many cases, breeding aggregations separated by up to tens of kilometers are not genetically distinct enough to be considered separate genetic populations. These results have important implications both for the identification of independent "populations" and in assessing the effect of scale in detecting patterns of genetic equilibrium and gene flow.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".