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Record W4410729068 · doi:10.1093/molbev/msaf116

Evolutionary Genomics Unravels the Responses and Adaptation to Climate Change in a Key Alpine Forest Tree Species

2025· article· en· W4410729068 on OpenAlexaff
Zhiqin Long, Yupeng Sang, Jiajun Feng, Lushui Zhang, Loren H. Rieseberg, Jianquan Liu, Jing Wang

Bibliographic record

VenueMolecular Biology and Evolution · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of ChinaFundamental Research Funds for the Central UniversitiesSichuan UniversityNational Natural Science Foundation of China
KeywordsBiologyLocal adaptationBiodiversityClimate changeEcologyEnvironmental changeAdaptation (eye)PopulationEvolutionary biologyNatural selectionPopulation genomicsGenetic variationGenetic diversityGenomicsGeneticsGeneGenome

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.258
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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