Interpreting the estimated timing of migration events between hybridizing species
Bibliographic record
Abstract
The question of whether speciation can occur in the presence of gene flow has long been a contentious one. However, measuring the amount and timing of gene flow remains challenging. The computer program IMa2 allows researchers to estimate the timing of migration events for each locus during analyses, and these estimates have been used to infer the timing of introgression and mode of speciation. We use simulated data sets to examine the degree to which gene-flow timing estimates can be used for these purposes, and what demographic conditions and data sets may be most amenable to gene-flow timing estimation. We find that the 90% highest posterior density (HPD) interval of gene-flow timing is almost always substantially wider than the actual window of gene flow, and increasing the information content of the data set in terms of number of loci, number of sequences sampled or locus length (and thus number of variable sites) has little impact on the posterior distribution over the range of values we tested. Even when simulated gene flow only occurred over the most recent 0.01% of the species' history, the HPD interval usually encompasses the inferred divergence time. Our results indicate that gene-flow timing estimates made using the method currently implemented in IMa2 cannot reliably be used to make inferences about the timing of introgression between diverged species or to distinguish between speciation with gene flow and allopatric speciation followed by one or more episodes of 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".