The Range‐Wide Genetic Structure of a High‐Elevation Conifer Species, Subalpine Larch
Bibliographic record
Abstract
ABSTRACT Aim Subalpine larch is a long‐lived conifer with a restricted distribution at treeline in the mountains of western North America. Climate change is reducing the availability of this habitat and subalpine larch is unlikely to adapt to its changing environment due to its long generation time, relatively low levels of standing genetic variation and the high degree of habitat fragmentation across the species' range. This study seeks to elucidate the genetic structure of subalpine larch to inform future conservation and management efforts. Location Western North America. Taxon Subalpine larch (Larix lyallii Parl.). Methods Tissue was collected from high‐elevation populations across the entire species range. Restriction site associated DNA sequencing was used to generate single nucleotide polymorphism (SNP) data. Population genetic analyses identified genetic differentiation. Results Clustering analyses performed using 751 neutral SNPs identified three genetically differentiated regions: the Cascade Range, the southern Rocky Mountains and the northern Rocky Mountains. AMOVA confirmed significant genetic differentiation among regions. A discriminant analysis of principal components and a dendrogram of Provesti's genetic distance both supported the hypothesis that genetically distinct lineages arose after dispersal from a single Pleistocene refugium. Significant isolation by distance (IBD) supported the key role of dispersal in shaping modern patterns of genetic variation in subalpine larch. Main Conclusions Post‐Pleistocene dispersal resulted in genetic differentiation among groups of subalpine larch in the Cascade Range, the southern Rocky Mountains and the northern Rocky Mountains. These three regions should be prioritised for future 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 | 0.001 | 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".