Ecophysiological transition mediated by hybridization in a hybrid pine species complex
Bibliographic record
Abstract
Hybridization is a driving force in ecological transitions and speciation, yet direct evidence linking it to adaptive differentiation in natural systems remains limited. This study evaluates the role of hybridization in the speciation of Pinus densata , a keystone forest species on the southeastern Tibetan Plateau. By creating artificial interspecific F1s and a long-term common garden experiment on the plateau, we provide in situ assessments on 44 growth and physiological traits across four seasons, along with RNA sequencing. We found significant phenotypic divergence between P. densata and its putative parental species P. tabuliformis and P. yunnanensis , with P. densata demonstrating superior growth and dynamic balance between photosynthesis and photoprotection. The F1s closely resembled P. densata in most traits. Gene expression revealed 19% - 10% of 34,000 examined genes as differentially expressed in P. densata and F1s relative to mid-parent expression values. Both additive (4%) and non-additive gene actions (5 – 6% in F1s, 10 – 12% in P. densata ) were common, while transgressive expression occurred more frequently in the stabilized natural hybrids, illustrating transcriptomic reprogramming brought by hybridization and further divergence by natural selection. We provide compelling evidence for hybridization-derived phenotypic divergence at both physiological and gene expression levels that could have contributed to the adaptation of P. densata to high plateau habitat where both parental species have low fitness. The altered physiology and gene expression in hybrids serve both as a substrate for novel ecological adaptation and as a mechanism for the initiation of reproductive isolation.
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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 | 0.000 | 0.000 |
| 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".