Genetic signatures of natural selection in response to air pollution in red spruce (<i><scp>P</scp>icea rubens</i>, Pinaceae)
Bibliographic record
Abstract
One of the most important drivers of local adaptation for forest trees is climate. Coupled to these patterns, however, are human-induced disturbances through habitat modification and pollution. The confounded effects of climate and disturbance have rarely been investigated with regard to selective pressure on forest trees. Here, we have developed and used a population genetic approach to search for signals of selection within a set of 36 candidate genes chosen for their putative effects on adaptation to climate and human-induced air pollution within five populations of red spruce (Picea rubens Sarg.), distributed across its natural range and air pollution gradient in eastern North America. Specifically, we used FST outlier and environmental correlation analyses to highlight a set of seven single nucleotide polymorphisms (SNPs) that were overly correlated with climate and levels of sulphate pollution after correcting for the confounding effects of population history. Use of three age cohorts within each population allowed the effects of climate and pollution to be separated temporally, as climate-related SNPs (n = 7) showed the strongest signals in the oldest cohort, while pollution-related SNPs (n = 3) showed the strongest signals in the youngest cohorts. These results highlight the usefulness of population genetic scans for the identification of putatively nonneutral evolution within genomes of nonmodel forest tree species, but also highlight the need for the development and application of robust methodologies to deal with the inherent multivariate nature of the genetic and ecological data used in these types of analyses.
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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 | 0.001 | 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) | 0.000 | 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 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".