Effects of Wild Forest Fires on Genetic Diversity and Population Structure of a Boreal Conifer, White Spruce (Picea glauca (Moench) Voss): Implications for Genetic Resource Management and Adaptive Potential under Climate Change
Bibliographic record
Abstract
Climate change is predicted to increase forest fires in boreal forests, which can threaten the sustainability of forest genetic resources. Wildfires can potentially impact genetic diversity and population structure in forest trees by creating population bottlenecks, and influencing demography, effective population size (Ne) and various evolutionary processes. We have investigated this critical issue in a widely-distributed, transcontinental, ecologically and economically important and fire-intolerant boreal conifer, white spruce (Picea glauca (Moench) Voss). We tested the hypothesis that in a predominantly outcrossing species with long distance gene flow, such as white spruce, located in primary undisturbed forests, normal forest fires do not adversely affect genetic diversity and population structure. We used 10 nuclear genic and genomic microsatellite loci to examine genetic diversity and population structure of post-fire pristine old-growth (PF-OG) and adjacent post-fire naturally regenerated young (PF-YR) stands. The genetic diversity, inbreeding and genetic differentiation levels, Bayesian population structure, Ne and latent genetic potential were statistically similar between the PF-OG and PF-YR populations. None of the microsatellites showed any signature of selection. Our study demonstrates that normal wild forest fires do not adversely affect genetic diversity, differentiation, and population genetic structure in white spruce. The results should have wide significance for sustainable forest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".