Predicting Adaptive Genetic Variation of Loblolly Pine (Pinus taeda L.) Populations Under Projected Future Climates Based on Multivariate Models
Bibliographic record
Abstract
Greenhouse gas emission and global warming are likely to cause rapid climate change within the natural range of loblolly pine over the next few decades, thus bringing uncertainty to their adaptation to the environment. Here, we studied adaptive genetic variation of loblolly pine and correlated genetic variation with bioclimatic variables using multivariate modeling methods-Redundancy Analysis, Generalized Dissimilarity Modeling, and Gradient Forests. Studied trees (N = 299) were originally sampled from their native range across eight states on the east side of the Mississippi River. Genetic variation was calculated using a total of 44,317 single-nucleotide polymorphisms acquired by exome target sequencing. The fitted models were used to predict the adaptive genetic variation on a large spatial and temporal scale. We observed east-to-west spatial genetic variation across the range, which presented evidence of isolation by distance. Different key factors drive adaptation of loblolly pine from different geographical regions. Trees residing near the northeastern edge of the range, spanning across Delaware and Maryland and mountainous areas of Virginia, North Carolina, South Carolina, and northern Georgia, were identified to be most likely impacted by climate change based on the large difference in genetic composition under current and future climate conditions. This study provides new perspectives on adaptive genetic variation of loblolly pine in response to different climate scenarios, and the results can be used to target particular populations while developing adaptive forest management guidelines.
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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 | 0.000 |
| 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 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".