Genomic data improve coalescent inference across a range of demographic parameters and life-histories
Bibliographic record
Abstract
Understanding the demographic context for population divergence and speciation in the sea often requires distinguishing the contributions of mutation, isolation, and gene flow on temporal or geographical scales where those diverse processes may not achieve equilibrium conditions. Coalescent isolation-with-migration (IM) models can meet this need for non-equilibrium modelling of genetic variation, but the quality of IM model parameter estimation depends on the breadth of genome sampling. Here, we describe three improvements in IM parameter estimates based on hundreds of loci from RNA-seq assemblies relative to previously published results based on few loci in two sea star study systems that differ in the tempo of population divergence. (1) Precision of all model parameter value estimates (with narrow posterior distributions) was vastly better in both study systems and resolved uncertainty around one key parameter in each. (2) Maximum likelihood estimates of some model parameters were broadly similar to previously published estimates, but with greater precision we obtained more realistic values for some parameters that were consistent with expectations based on the biogeography of the organisms. (3) We found non-zero but demographically trivial gene flow in one study system where we previously estimated gene flow to be zero, and modest symmetrical gene flow (2Nm<1) in a second study system where we previously estimated gene flow to be massive (2Nm~10) and asymmetrical. Improved understanding through judicious application of genome-wide sampling in replication studies as shown here may improve the information needed for biodiversity management and conservation.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".