Exome Sequencing and Allele Dosage Analysis of Coast Redwood, a Hexaploid Conifer, Reveals a Major Population Break South of San Francisco Bay
Bibliographic record
Abstract
1 Abstract The coast redwood ( Sequoia sempervirens ) is a long-lived, hexaploid conifer of high ecological, cultural, and economic value whose range has been greatly reduced by historical logging [1]. Effective restoration and conservation depend on understanding patterns of genetic differentiation across the redwood range to delineate populations for management prioritization. Yet, past range-wide studies provided only a partial picture of population structure in coast redwood as they relied on a limited set of genetic markers [2, 3] or limited sampling, as sequencing was done on the same range-wide provenance collection [4, 5]. Here, we analyze 334,029 SNPs from a new range-wide set of 224 individuals using a dosage-based approach that accounts for polyploidy. Principal coordinates and clustering analyses reveal clear latitudinal structure, with a steeper break south of San Francisco Bay. Outlier SNP analysis identified new candidate loci involved in salinity tolerance, climate stress response, and nutrient uptake, suggesting potential local adaptation. These results point to the central role of geography in shaping genetic variation in coast redwood and give scientific basis for designing new conservation and management strategies aimed at preserving the species’ genetic diversity into the future.
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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".