Experimental migration upward in elevation is associated with strong selection on life history traits
Bibliographic record
Abstract
Abstract One of the strongest biological impacts of climate change has been the movement of species poleward and upward in elevation. Yet, what is not clear is the extent to which the spatial distribution of locally adapted lineages and ecologically important traits may also shift with continued climate change. Here, we take advantage of a transplant experiment mimicking up‐slope seed dispersal for a suite of ecologically diverse populations of yellow monkeyflower ( Mimulus guttatus sensu lato) into a high‐elevation common garden during an extreme drought period in the Sierra Nevada mountains, California, USA. We use a demographic approach to quantify fitness and test for selection on life history traits in local versus lower‐elevation populations and in normal versus drought years to test the potential for up‐slope migration and phenotypic selection to alter the distribution of key life history traits in montane environments. We find that lower‐elevation populations tend to outperform local populations, confirming the potential for up‐slope migration. Although selection generally favored some local montane traits, including larger flowers and larger stem size at flowering, drought conditions tended to select for earlier flowering typical of lower‐elevation genotypes. Taken together, this suggests that monkeyflower lineages moving upward in elevation could experience selection for novel trait combinations, particularly under warmer and drier conditions that are predicted to occur with continued climate change.
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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) | 0.010 | 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".