Population climatic history predicts phenotypic responses in novel environments for <i>Arabidopsis thaliana</i> in North America
Bibliographic record
Abstract
PREMISE: Determining how species perform in novel climatic environments is essential for understanding (1) responses to climate change and (2) evolutionary consequences of biological invasions. For the vast majority of species, the number of population characteristics that will predict performance and patterns of natural selection in novel locations in the wild remains limited. METHODS: We evaluated phenological, vegetative, architectural, and fitness-related traits in experimental gardens in contrasting climates (Ontario, Canada, and South Carolina, USA) in the North American non-native distribution of Arabidopsis thaliana. We assessed the effects of climatic distance, geographic distance, and genetic features of history on performance and patterns of natural selection in the novel garden settings. RESULTS: We found that plants had greater survivorship, flowered earlier, were larger, and produced more fruit in the south, and that genotype-by-environment interactions were significant between gardens. However, our analyses revealed similar patterns of natural selection between gardens in distinct climate zones. After accounting for genetic ancestry, we also detected that population climatic distance best predicted performance within gardens. CONCLUSIONS: These data suggest that colonization success in novel, non-native environments is determined by a combination of climate and genetic history. When performance at novel sites was assessed with seed sources from geographically and genetically disparate, established non-native populations, proximity to the garden alone was insufficient to predict performance. Our study highlights the need to evaluate seed sources from diverse origins to describe comprehensively phenotypic responses to novel environments, particularly for taxa in which many source populations may contribute to colonization.
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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) | 0.004 | 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".