Long‐distance pollen dispersal during recent colonization favors a rapid but partial recovery of genetic diversity in <i>Picea sitchensis</i>
Bibliographic record
Abstract
Tree species in the northern hemisphere are currently subject to rapid anthropogenic climate change and are shifting their ranges in response. This prompts questions about the mechanisms allowing tree populations to respond quickly to selection pressures when establishing into new areas. Focusing on the northern expanding range edge of Picea sitchensis, a widespread conifer of western North America, we ask how genetic structure and diversity develop during colonization, and assess the role of demographic history in shaping the evolutionary trajectory of an establishing population. We combined 500 yr of tree-ring and genotyping-by-sequencing data in 639 trees at the expansion front on the Kodiak Archipelago. We show that alleles accumulated rapidly during an increase in recruitment rate in the early 1700s. A shift from foreign to local pollen flow subsequently homogenized genetic structure at the expansion front. Taking advantage of the exceptional longevity of conifers, we highlight the major role of long-distance pollen dispersal in the rapid but incomplete recovery of genetic diversity during the initial stages of colonization. We also warn that slow initial population growth as well as long-lasting dominance of local gene flow by early founders could increase evolutionary load under a rapidly changing climate.
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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".