Genome sequencing and population genetics provide insights into local adaptation of <i>Opisthopappus</i> species on cliff environments of Taihang Mountains
Bibliographic record
Abstract
Local adaptation represents a pivotal theme in evolutionary biology. The Opisthopappus genus, comprising Opisthopappus longilobus and O. taihangensis, thrives on the cliffs of the Taihang Mountains. During their evolutionary history, two species are hypothesized to have locally adapted to their cliff habitats. In the present study, we employed a combined approach of whole-genome sequencing of O. taihangensis and population genomic analysis from both species to gain deeper insights into their patterns of local adaptation. Our results revealed that the expansive genome of O. taihangensis (3010.18 Mb), a consequence of a whole-genome duplication (WGD) event, coupled with a high proportion of repetitive sequences (82.70%), was postulated as one of its adaptive strategies. A clear differentiation between O. taihangensis and O. longilobus was observed, with the two species diverging approximately 17.57 million years ago (Mya), with O. longilobus serving as the ancestor. Since their divergence, limited gene flow was observed between the two species. Post-divergence, the effective population sizes of both species expanded, yet underwent a dramatic reduction at approximately 0.07 Mya. Furthermore, a total of 798 adaptive genes were identified, of which 207 overlapped with expanded genes, and eight genes were found to be under positive selection. These genes primarily regulated the growth and development of both species via pathways such as oxidation-reduction and ubiquitin-proteasome, enabling them to withstand climate changes. These findings provide profound insights into the local adaptation of Opisthopappus species to the cliff environments and offer valuable clues for further exploring the local adaptation among various cliff-dwelling organisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".