Climatic niche predicts the landscape structure of locally adaptive standing genetic variation
Bibliographic record
Abstract
Abstract Within a species’ range, intraspecific diversity in the form of adaptive standing genetic variation (SGV) may be non-randomly clustered into different geographic regions, reflecting the combined effects of historical range movements and spatially-varying natural selection. As a consequence of a patchy distribution of adaptive SGV, populations in different parts of the range are likely to vary in their capacity to respond to changing selection pressures, especially long-lived sessile organisms like forest trees. However, the spatial distribution of adaptive SGV across the landscape is rarely considered when predicting species responses to environmental change. Here, we use a landscape genomics approach to estimate the distribution of adaptive SGV along spatial gradients reflecting the expansion history and contemporary climatic niche of balsam poplar, Populus balsamifera (Salicaceae), a widely distributed forest tree with a transcontinental distribution in North America. By scanning the genome for signatures of spatially varying local adaptation, we estimated how adaptive SGV has been shaped by geographic distance from the rear range edge (expansion history) versus proximity to the current center of the climatic niche (environmental selection). We found that adaptive SGV was strongly structured by the current climatic niche, with surprisingly little importance attributable to historical effects such as migration out of southern refugia. As expected, the effect of the climatic niche on SGV was strong for genes whose expression is responsive to abiotic stress (drought), although genes upregulated under biotic (wounding) stress also contained SGV that followed climatic and latitudinal gradients. The latter result could reflect parallel selection pressures, or co-regulation of functional pathways involved in both abiotic and biotic stress responses. Our study in balsam poplar suggests that clustering of locally adaptive SGV within ranges primarily reflects spatial proximity within the contemporary climatic niche – an important consideration for the design of effective strategies for biodiversity conservation and avoidance of maladaptation under climate change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".