Exploring genomic variation associated with drought stress in <i>Picea mariana</i> populations
Bibliographic record
Abstract
Abstract Predicted increases in drought and heat stress will likely induce shifts in species bioclimatic envelopes. Genetic variants adapted to water limitation may prove pivotal for species response under scenarios of increasing drought. In this study, we aimed to explore this hypothesis by investigating genetic variation in 16 populations of black spruce (Picea mariana) in relation to climate variables in Alaska. A total of 520 single nucleotide polymorphisms (SNPs) were genotyped for 158 trees sampled from areas of contrasting climate regimes. We used multivariate and univariate genotype‐by‐environment approaches along with available gene annotations to investigate the relationship between climate and genetic variation among sampled populations. Nine SNPs were identified as having a significant association with climate, of which five were related to drought stress response. Outlier SNPs with respect to the overall environment were significantly overrepresented for several biological functions relevant for coping with variable hydric regimes, including osmotic stress response. This genomic imprint is consistent with local adaptation of black spruce to drought stress. These results suggest that natural selection acting on standing variation prompts local adaptation in forest stands facing water limitation. Improved understanding of possible adaptive responses could inform our projections about future forest dynamics and help prioritize populations that harbor valuable genetic diversity for conservation.
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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 | 0.000 |
| 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".