Genomic signatures of selection in response to sulfate air pollution in natural populations of red spruce
Bibliographic record
Abstract
Rapid environmental changes, such as anthropogenic air pollution, can create significant evolutionary pressures on populations, species and ecosystems. Evolutionary processes occurring in natural populations at very short time scales, especially in response to human-induced environmental changes, are not well understood. Confounding effects of geographic variation and demography cannot be easily separated from signatures of recent selection in natural populations. We investigated the genetic response of declining red spruce (Picea rubens) populations at high elevation sites in Southern Appalachians to anthropogenic sulfate depositions. Red spruce seedlings are more sensitive to drought and cold stresses elicited by exposure to anthropogenic sulphate air pollution, than old trees. Genetic variation in seedlings and young trees was significantly reduced in heavily polluted stands. Several candidate genes involved in cold acclimation and calcium metabolism demonstrated signatures of selection corresponding with sulfate pollution levels. SNP allele frequencies at one gene involved in calcium metabolism demonstrated directional selection in response to anthropogenic sulfate deposition in red spruce growing at severely polluted high elevation sites, which corresponds well with the putative role of this gene in adaptation to acidification stress. Unlike range-wide experimental designs (e.g. the popular FST outlier test) and nucleotide diversity-based association studies, our within-population testing approach disentangled the confounding effects of geographic variation and demography from the genetic effects of recent selection.
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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 | 0.001 |
| 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".