Influence of finite‐sites mutation, population subdivision and sampling schemes on patterns of nucleotide polymorphism for species with molecular hyperdiversity
Bibliographic record
Abstract
Molecular hyperdiversity has been documented in viruses, prokaryotes and eukaryotes. Such organisms undermine the assumptions of the infinite-sites mutational model, because multiple mutational events at a site comprise a non-negligible portion of polymorphisms. Moreover, different sampling schemes of individuals from species with subdivided populations can profoundly influence resulting patterns and interpretations of molecular variation. Inspired by molecular hyperdiversity in the nematode Caenorhabditis sp. 5, which exhibits average pairwise differences among synonymous sites of >5% as well as modest population structure, we investigated via coalescent simulation the joint effects of a finite-sites mutation (FSM) process and population subdivision on the variant frequency spectrum. From many demes interconnected through a stepping-stone migration model, we constructed local samples from a single deme, pooled samples from several demes and scattered samples of a single individual from numerous demes. Compared with a single panmictic population at equilibrium, we find that high population mutation rates induce a deficit of rare variants (positive Tajima's D) under a FSM model. Population structure also induces such a skew for local samples when migration is high and for pooled samples when migration is low. Contrasts of sampling schemes for C. sp. 5 imply high mutational input coupled with high migration. We propose that joint analysis of local, pooled and scattered samples for species with subdivided populations provides a means of improving inference of demographic history, by virtue of the partially distinct patterns of polymorphism that manifest when sequences are analyzed according to differing sampling schemes.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".