Genomics of plant speciation
Bibliographic record
Abstract
Studies of plants have been instrumental for revealing how new species originate. For several decades, botanical research has complemented and, in some cases, challenged concepts on speciation developed via the study of other organisms while also revealing additional ways in which species can form. Now, the ability to sequence genomes at an unprecedented pace and scale has allowed biologists to settle decades-long debates and tackle other emerging challenges in speciation research. Here, we review these recent genome-enabled developments in plant speciation. We discuss complications related to identification of reproductive isolation (RI) loci using analyses of the landscape of genomic divergence and highlight the important role that structural variants have in speciation, as increasingly revealed by new sequencing technologies. Further, we review how genomics has advanced what we know of some routes to new species formation, like hybridization or whole-genome duplication, while casting doubt on others, like population bottlenecks and genetic drift. While genomics can fast-track identification of genes and mutations that confer RI, we emphasize that follow-up molecular and field experiments remain critical. Nonetheless, genomics has clarified the outsized role of ancient variants rather than new mutations, particularly early during speciation. We conclude by highlighting promising avenues of future study. These include expanding what we know so far about the role of epigenetic and structural changes during speciation, broadening the scope and taxonomic breadth of plant speciation genomics studies, and synthesizing information from extensive genomic data that have already been generated by the plant speciation community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".