Assessing the effects of hybridization and precipitation on invasive weed demography using strength of selection on vital rates
Bibliographic record
Abstract
BACKGROUND: As global climate change transforms average temperature and rainfall, species distributions may meet, increasing the potential for hybridization and altering individual fitness and population growth. Altered rainfall specifically may shift the strength and direction of selection, also manipulating population trajectories. Here, we investigated the role of interspecific hybridization and selection imposed by rainfall on the evolution of weedy life-history in non-hybrid (Raphanus raphanistrum) and hybrid (R. raphanistrum x R. sativus) populations using a life table response experiment. RESULTS: In documenting long-term population dynamics, we determined intrinsic (r) and asymptotic (λ) population growth rates and sensitivities, a measure of selection imposed on demographic rates. Hybrid populations experienced 8.7-10.3 times stronger selection than wild populations for increased seedling survival. Whereas crop populations generally exhibit little dormancy and wild populations often exhibit dormancy, non-hybrid populations experienced 10% stronger selection than hybrid populations for exhibiting seed dormancy. Selection on survival-to-flowering in wild, not hybrid, populations declined marginally with increasing soil moisture. Hybrid populations exhibited greater r, but not λ, than wild populations regardless of moisture environment. In general, fecundity contributed most to differences in λ but fecundity only contributed positively to hybrid λ relative to wild λ when precipitation was altered (either higher or lower than control) and not under control watering conditions. CONCLUSIONS: Selection on key demographic traits may not change dramatically in response to rainfall, and hybridization may more strongly influence the demography of these weedy species than rainfall. If hybrid populations can respond to selection for increased dormancy, this may make it more difficult to deplete weed seed banks and increase the persistence of crop genes in weed populations.
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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 | 0.000 | 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".