Growth variation and life history trade-offs of the invasive plant Lythrum salicaria
Bibliographic record
Abstract
Trade-offs are useful for understanding patterns of variation among life history traits because they can explain why fitness cannot be maximized for all traits. Among these traits is flowering phenology, which is of ecological importance in Lythrum salicaria (purple loosestrife), an invasive plant in North America that has spread across a wide latitudinal range. Selection on flowering time is affected by differences in season length among environments, with early flowering strongly favoured in the north. However, due to a positive correlation between size at flowering and time to flower, late-flowering genotypes are favoured in the south. I test the hypothesis that developmental constraints produce the trade-off between size at flowering and time to flowering by quantifying developmental variation of L. salicaria with the aid of logistic growth models. In these models, I analyzed the growth of 2278 individuals from 221 maternal half-sibs families that I raised in a common garden at the Queen's University Biological Station. These families were collected from a total of 20 populations of L. salicaria across a 10° latitudinal gradient of eastern North America. I use nonlinear mixed-effects models to estimate the contribution of family and population effects to overall variation. I found strong positive correlations between two traits measured in the common garden, time to flower and size at flower, and two components of the growth function, asymptotic size and time to inflection. I characterized individual variation among these parameters using a principal components analysis, identifying three axes of variation. The primary axis, capturing 53% of total variation, was associated with asymptotic size and time to inflection. These findings demonstrate the importance of heterochrony, a mode of developmental variation with an implicit trade-off, in the evolution of invasive populations of L. salicaria. By linking the divergence of development among populations with the divergence of flowering time, my study deepens our knowledge of the mechanistic basis of this variation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".