Natural Selection and Genetic Constraints on Flowering Phenology in an Invasive Plant
Bibliographic record
Abstract
Theory suggests that the contemporary evolution of local adaptation may increase the rate of biological invasion, yet natural selection has rarely been measured in invasive species. A recently published model predicted that latitudinal variation in the strength of stabilizing selection on two correlated traits—flowering time and size—can result in local adaptation during plant invasion but implicitly assumed that population × environment interactions are weak. We tested for stabilizing selection and the effect of growing environment on 13 populations of the invasive plant Lythrum salicaria sampled along a latitudinal gradient. Plants were grown under uniform glasshouse conditions and in a 3-year common garden field experiment at midlatitude. Consistent with model assumptions, the ranking of population means was highly correlated across the 3 years, and with glasshouse measurements, despite significant interannual differences in flowering time and vegetative size. As predicted, the first principal component (PC1) of flowering time and size was under stabilizing selection, and directional selection was detected on PC2, thus favoring plants that flowered earlier at a larger size. Our results demonstrate the value of using a priori models of natural selection to guide hypothesis testing and support the prediction that fitness trade-offs can constrain adaptive evolution in invasive species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".