Adaptive divergence along environmental gradients in a climate‐change‐sensitive mammal
Bibliographic record
Abstract
In the face of predicted climate change, a broader understanding of biotic responses to varying environments has become increasingly important within the context of biodiversity conservation. Local adaptation is one potential option, yet remarkably few studies have harnessed genomic tools to evaluate the efficacy of this response within natural populations. Here, we show evidence of selection driving divergence of a climate-change-sensitive mammal, the American pika (Ochotona princeps), distributed along elevation gradients at its northern range margin in the Coast Mountains of British Columbia (BC), Canada. We employed amplified-fragment-length-polymorphism-based genomic scans to conduct genomewide searches for candidate loci among populations inhabiting varying environments from sea level to 1500 m. Using several independent approaches to outlier locus detection, we identified 68 candidate loci putatively under selection (out of a total 1509 screened), 15 of which displayed significant associations with environmental variables including annual precipitation and maximum summer temperature. These candidate loci may represent important targets for predicting pika responses to climate change and informing novel approaches to wildlife conservation in a changing world.
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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 | 0.001 | 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) | 0.001 | 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".