Contrasting levels of transcriptome-wide SNP diversity and adaptive molecular variation among conifers
Bibliographic record
Abstract
Adaptive convergence can arise when response to natural selection involves shared molecular or functional mechanisms among multiple taxa. Conifers are archaic species of ancient origin with delayed sexual maturity related to their woody perennial nature. Thus, they represent a relevant plant group to assess if convergence from selection may have become disconnected between molecular and functional levels. In this purpose, transcriptome-wide SNP diversity was assessed in seven partially sympatric and reproductively isolated conifer species (118 individuals from 67 populations) populating the temperate and boreal forests of northeastern North America. SNP diversity was found highly heterogeneous among species, which would relate to variation in species-specific demography and history. Rapidly evolving genes with signatures of positive selection were identified, and their relative abundance among species reflected differences in transcriptome-wide SNP diversity. The analysis of sequence homology also revealed very limited convergence among taxa in spite of sampling same tissues at same age. However, convergence increased gradually at the levels of gene families and biological processes, which were largely related to stress response and regulatory mechanisms in all species. Given their multiple small to large gene families and long time since inception, conifers may have had sufficient gene network flexibility and gene functional redundancy for evolving alternative adaptive genes for similar metabolic responses to environmental selection pressures. Despite a long divergence time of ~350 Mya between conifers and Angiosperms, we also uncovered a set of 17 key genes presumably under positive selection in both 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 | 0.000 | 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".