Growth and fecundity of colonizing hybrid <i>Raphanus</i> populations are environmentally dependent
Bibliographic record
Abstract
PREMISE: Hybrid gene pools harbor more genetic variation than progenitor populations. Thus, we expect hybrid populations to exhibit more dynamic evolutionary responses to environmental variation. We ask how environmental variation experienced by adapted and transplanted populations influence the success of late-generation hybrid populations during invasion. METHODS: For four generations, 20 wild (Raphanus raphanistrum) and 20 hybrid radish (R. sativus × R. raphanistrum) plant populations evolved under experimentally manipulated moisture conditions (dry, wet, control-sheltered, or control-unsheltered plots; i.e., evolutionary environment) in old fields near Toronto, Canada. We planted advanced-generation wild and hybrid radishes in sheltered plots and exposed them to either an evolutionary or a novel watering environment. To determine how soil moisture would influence invasion success, we compared the phenotype and fecundity of plants grown in these various environments. RESULTS: Hybridization produced larger plants. In wet environments, hybrid seedlings emerged more frequently and expressed higher photosynthetic activity. Low-moisture, novel conditions delayed and reduced seedling emergence frequency. Hybrid plants and those that evolved under relatively wet environments exhibited higher aboveground biomass. Hybrid plants from control-sheltered plots colonizing novel moisture environments were more fecund than comparable wild plants. CONCLUSIONS: Dry environments are less likely than other evolutionary environments to contribute colonists. However, relatively wet locations support the evolution of relatively fecund plants, especially crop-wild hybrid populations. Thus, our results provide a strong mechanistic explanation for variation in the relative success of crop-wild hybrids among study locations and a new standard for studies that assess the risk of crop-wild hybridization events.
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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".