Environmental heterogeneity generates intrapopulation variation in life‐history traits in an annual plant
Bibliographic record
Abstract
Environmental variation affects a plant's life cycle by influencing the timing of germination and flowering, and the duration of the growing season. Yet we know little information about how environmental heterogeneity generates variation in germination schedules and the consequences for growth and fecundity through genetic and plastic responses. We use an annual population of Mimulus guttatus in which, in nature, seeds germinate in both fall and spring. We investigate whether there is a genetic basis to the timing of germination, the effect of germination timing on fecundity, and if growth and flowering respond plastically to compensate for different season lengths. Using sibling families grown in simulated seasonal conditions, we find that families do not differ in their propensity to germinate between seasons. However, the germination season affects subsequent growth and flowering time, with significant genotype-by-environment interactions (G × E). Most G × E is due to unequal variance between seasons, because the spring cohort harbours little genetic variance. Despite their different season lengths, the cohorts do not differ in flower number (fecundity). Heterogeneous environments with unpredictable risks may maintain promiscuous germination, which then affects flowering time. Therefore, if selection at particular life stages changes with climate change, there may be consequences for the entire life cycle.
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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 | 0.001 |
| 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".