Phylogenetic relationships between parthenogens and their sexual relatives: the possible routes to parthenogenesis in animals
Bibliographic record
Abstract
In theory, parthenogenetic lineages have low evolutionary potential because they inexorably accumulate deleterious mutations and do not generate much genotypic diversity. As a result, most parthenogenetic taxa occupy the terminal nodes of phylogenetic trees. The rate and mode of development of parthenogenesis are important factors to consider when assessing its costs and benefits since they determine both the level of genetic diversity and the ecological adaptability of the resulting lineages. The origin of parthenogenesis is polyphyletic in many taxa, suggesting that genetic systems maintaining sexuality are often labile. In addition, the loss of sex may be achieved in several ways, leading to parthenogenetic lineages with distinct genetic profiles. This could then influence not only the fate of such lineages in the long term, but also the outcome of competition with their sexual counterparts in the short term. In this paper, we review the possible evolutionary routes to parthenogenesis based on a survey of the phylogenetic relationships between sexual and parthenogenetic lineages in a broad range of animals. We also examine the different mechanisms by which parthenogenetic lineages could arise, and discuss the influence of these mechanisms on both the genetic properties and the ecological life styles of the resulting lineages.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".