Data from: Genomic vulnerability of a sentinel mammal under climate change
Bibliographic record
Abstract
Climate change poses a significant threat to biodiversity, particularly in alpine ecosystems where species have already undergone elevational range shifts. Genomics can be used to estimate the adaptive potential of species, as well as the shift in adaptive genomic composition necessary for populations to adjust to climate change (e.g. genomic offset). Here, we investigated patterns of climate-mediated adaptive genetic variation and predicted the degree of genomic offset under multiple climate change scenarios for a sentinel alpine mammal, the American pika (Ochotona princeps). We collected genome-wide data (29,709 SNPs) from 363 individuals spanning the entire range in western North America and employed genotype-environment association analyses to identify 938 robust outlier SNPs, several of which were linked to genes previously associated with high elevation and hypoxia responses in various pika species (Ochotonidae). Adaptive genomic variation was most strongly influenced by mean warmest month temperature, followed by precipitation of the coldest quarter. Spatial patterns of genomic offset were heterogeneous, significantly predicted by levels of adaptive genetic variation, elevation and latitude. Sites within the Northern Rocky Mountains exhibited the highest genomic offset under projected climate change despite possessing high levels of adaptive genetic variation. As such, while our study provides an example of how genomic data can be used to explore the potential consequences of climate change, it further highlights the need for careful consideration of genomic offset values within their proper ecological context.
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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) | 0.001 | 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 | 0.001 |
| 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".