The interplay between demography and neutral evolution at the expansion front of a widespread conifer, <i>Picea sitchensis</i>
Bibliographic record
Abstract
Abstract Tree species in the northern hemisphere have advanced and retreated with interglacial and glacial periods, and are currently subject to rapid anthropogenic climate change. These observations prompt 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 a colonizing population. By combining nearly 500 years of tree-ring and genetic data at the expansion front on the Kodiak Archipelago, we show that allelic richness - but not expected heterozygosity - increased rapidly during early stages of establishment in the 1600s, while genetic differentiation from populations further from the front decreased. This trend ended in the 1700s, after an increase in population growth rate. These findings highlight the major role of long-distance pollen dispersal in the recovery of genetic diversity during initial stages of colonization, and suggest that demographic dynamics including an initial lag in population growth are likely limiting factors in the adaptation of tree populations tracking their niche in a 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".