Genome architecture and speciation in plants and animals
Bibliographic record
Abstract
This review asks how variation in genome architecture impacts speciation across the plant and animal kingdoms. First, we briefly summarize what is known about speciation in these groups; importantly, the diversification rate of plants is about twice that of animals, and species barriers in plants appear to arise at an earlier stage of divergence. Next, we discuss several of the major differences in how plant and animal genomes evolve, and how they may impact the evolution of reproductive barriers and potentially speciation rates. Key differences include (1) the higher frequency of whole genome duplications and more rapid loss of synteny in plants; (2) the higher incidence and greater divergence of sex chromosomes in animals; (3) higher rates of sequence change, but slower rates of structural evolution, in animal relative to plant mitochondrial genomes; and (4) the higher abundance of transposable elements in plant genomes. Overall, we find the genomes of plants typically diverge much more rapidly in structure than those of animals (although there are many exceptions), which likely contributes to the more rapid emergence reproductive barriers in plants. However, we also found that comparisons of genome evolution between the kingdoms are hampered by inconsistency in the methods employed, and in the metrics used to report on rates of structural evolution. Another theme from our review is the huge variation in genome architecture within each kingdom. While this variation complicates broad generalizations, it also enables powerful comparative analyses that link differences in genome architecture to patterns and processes of speciation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".