Clinal variation in drought response is consistent across life stages but not between native and non-native ranges
Bibliographic record
Abstract
Summary Clinal variation, i.e., intraspecific variation that corresponds to environmental gradients, is common in widely distributed species. Studies on clinal variation across multiple ranges and life stages are lacking, but can enhance our understanding of specieś adaptive potential to abiotic environments and may aid in predicting future species distributions. This study examined clinal variation in drought responses of 59 Conyza canadensis populations across large aridity gradients from the native and non-native ranges in three greenhouse studies. Experimental drought was applied to recruitment, juveniles, and adult stages. Drought reduced growth at all three life stages. However, contrasting patterns of clinal variation emerged between the two ranges. Native populations from xeric habitats were less inhibited by drought than mesic populations, but such clinal variation was not apparent for non-native populations. These range-specific patterns of clinal variation were consistent across the life stages. The experiments suggest that invaders may succeed without complete local adaptation to their new abiotic environments, and that long-established invaders may still be evolving to the abiotic environment. These findings may explain lag times in some invasions and raise concern about future expansions.
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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".