Twigs on the tree of life? Neutral and selective models for integrating macroevolutionary patterns with microevolutionary processes in the analysis of asexuality
Bibliographic record
Abstract
Neutral models characterize evolutionary or ecological patterns expected in the absence of specific causal processes, such as natural selection or ecological interactions. In this study, we describe and evaluate three neutral models that can, in principle, help to explain the apparent 'twigginess' of asexual lineages on phylogenetic trees without involving the negative consequences predicted for the absence of recombination and genetic exchange between individuals. Previously, such phylogenetic twiggyness of asexual lineages has been uncritically interpreted as evidence that asexuality is associated with elevated extinction rates and thus represents an evolutionary dead end. Our first model uses simple phylogenetic simulations to illustrate that, with sexual reproduction as the ancestral state, low transition rates to stable asexuality, or low rates of ascertained 'speciation' in asexuals, can generate twiggy distributions of asexuality, in the absence of high extinction rates for asexual lineages. The second model, developed by Janko et al. (2008), shows that a dynamic equilibrium between origins and neutral losses of asexuals can, under some conditions, generate a relatively low mean age of asexual lineages. The third model posits that the risk of extinction for asexual lineages may be higher than that of sexuals simply because asexuals inhabit higher latitudes or altitudes, and not due to effects of their reproductive systems. Such neutral models are useful in that they allow quantitative evaluation of whether empirical data, such as phylogenetic and phylogeographic patterns of sex and asexuality, indeed support the idea that asexually reproducing lineages persist over shorter evolutionary periods than sexual lineages, due to such processes as mutation accumulation, slower rates of adaptive evolution, or relatively lower levels of genetic variability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".