Microclimate predicts frost-hardiness of alpine <i>Arabidopsis thaliana</i> populations better than altitude because the microclimate effect increases with altitude
Bibliographic record
Abstract
Abstract In mountain regions average temperatures decrease at higher altitudes. In addition, microenvironmental conditions can strongly affect microclimate and may counteract average effects of altitude. We investigated winter frost hardiness of Arabidopsis thaliana accessions originating from 13 sites along altitudinal gradients in the Southern Alps during three winters on an experimental field station on the Swabian Jura and compared levels of frost damage with the observed number of frost days (<1°C) in eight collection sites. We found that frost-hardiness increased with altitude in a log-linear fashion. This is consistent with adaptation to higher frequency of frost conditions, but also indicates a decreasing rate of change in frost hardiness with increasing altitude. Moreover, the number of frost days measured with temperature loggers at the original collection sites correlated much better with frost-hardiness than the altitude of collection sites, suggesting that populations were adapted to their local microclimate. Notably, the variance in frost days across sites increased exponentially with altitude. Together, our results suggest that strong microclimate heterogeneity of high alpine environments may preserve functional genetic diversity in small populations. This challenges the suitability of habitat predictions based on large scale climatic variables (or proxies, such as altitude) for topographically complex areas.
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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 | 0.000 | 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 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".