Signs of rapid evolution in an invasive forest species: Drivers of the incipient neutral, adaptive and phenotypic divergence
Bibliographic record
Abstract
Evolutionary forces such as genetic drift and natural selection operate during the process of biological invasion. More specifically, nonnative populations suffer genetic and demographic bottlenecks, and are exposed to new environmental, climatic and biotic filters. Thus, neutral and/or adaptive drivers may contribute to genetic differentiation of the introduced germplasm. We hypothesize that the gene pool of fire-adapted species that become invasive in fire-prone ecosystems may differ from that of originally planted and native one because of novel selective forces and founder effects. Genetic impoverishment due to genetic drift could also be buffered by demographic processes as the production of a large aerial seed bank by serotiny. We conducted genomic analyses of the world’s most planted tree Pinus radiata using Single Nucleotide Polymorphisms, comparing introduced cohorts with and without fire exposure, and also with one population of the native range. Phenotypic traits of introduced cohorts were assessed for quantitative characters. In the introduced range we found lower expected heterozygosity and nucleotide diversity due to bottlenecks during introduction and commercial selection in orchards. We also detected decreased inbreeding, related to a serotinous seed bank that buffers bottlenecks. We found genomic regions under selection, both in the comparison between native and introduced range, and between introduced cohorts with and without exposure to fire. Within the introduced area, we observed possible fire-mediated diversifying selection for five quantitative traits. Our results show that both neutral and adaptive forces promote early divergence of the introduced species Pinus radiata . Also, fire acts as a rapid selection factor, prompting the incipient adaptive divergence of cohorts recruited after fires, facilitating invasion.
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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 | 0.001 |
| 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".