Evolutionary Genomics Unravels the Responses and Adaptation to Climate Change in a Key Alpine Forest Tree Species
Bibliographic record
Abstract
Despite widespread biodiversity loss, our understanding of how species and populations will respond to accelerated climate change remains limited. In this study, we integrate population genomics, experimental evolution, and environmental modeling to elucidate the evolutionary responses to climate change in Populus lasiocarpa, a key alpine forest tree species primarily distributed in the mountainous regions of a global biodiversity hotspot. Over historical timescales, our findings demonstrate that demographic dynamics, divergent selection, and long-term balancing selection have shaped and maintained genetic variation within and between populations. In examining genomic signatures of contemporary climate adaptation, we found that haplotype blocks, potentially caused by inversion polymorphisms that suppress recombination, are linked to enriched combinations of locally adaptive environmental variations. We further assessed the relative contributions of environmentally induced plastic responses, constitutive expression divergence between genetic clusters, and their interactions in driving gene expression variation and divergence. Notably, we observed a strong correlation between sequence divergence and constitutive differential expression among genetic clusters. Finally, by incorporating genetic adaptation, migration, and genetic load into our predictions of population-level climate change risks, we identified western populations-primarily distributed in the Hengduan Mountains, a region known for its environmental heterogeneity and significant biodiversity-as the most vulnerable to climate change. These populations should be prioritized for conservation and management. Overall, our study advances the understanding of the relative roles of long-term natural selection, local environmental adaptation, and immediate plastic expression changes in shaping the responses of natural populations of keystone species to climate change.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".