Patterns of genotype-environment association in the eastern North American yellow birch (<em>Betula alleghaniensis</em> Britt.)
Bibliographic record
Abstract
Understanding how genomic adaptation shapes species’ responses to climate change is essential for developing climate-resilient forests, as shifting conditions increasingly drive range shifts and maladaptation. This study investigates adaptive genomic variation in Betula alleghaniensis (yellow birch), a widely distributed hardwood of eastern North America. Genome-wide SNP variation from 27 populations was analyzed using 3D-genotype-by-sequencing and two genotype–environment association methods: redundancy analysis and Gradient Forests. A total of 124 putatively adaptive loci were identified, linked to extreme minimum temperature, degree-days below 0°C, winter precipitation, and snowfall. Functional annotation revealed roles in stress response and transcriptional regulation. Patterns of adaptive variation showed a latitudinal gradient tied to winter severity and spatially heterogeneous responses to snowfall. Two distinct clusters of adaptive loci were identified along climate gradients, suggesting winter climate plays a dominant role in shaping local adaptation. Future climate projections (SSP5-8.5, 2041–2070) predict substantial shifts in adaptive alleles in the Northeastern Appalachians, Maritimes, and St. Lawrence River regions. Nevertheless, genetic offset across the range was relatively low, suggesting genomic resilience potentially supported by yellow birch’s autohexaploid genome and extensive gene flow, including adaptive introgression from hybridization with other Betula species. These findings support integrating genomic data into forest management.
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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".