Fungal infection and soybean competition induce plastic responses in velvetleaf (<i>Abutilon theophrasti</i>) growth and reproductive output
Bibliographic record
Abstract
The historic maternal environment; the identity of competing, neighboring plants; and biotic stresses, such as fungal disease and herbivory, interact to influence the competitive ability, reproductive output, and plasticity of weed populations. A weed capable of altering its phenotype in response to environmental factors is better able to compete for resources in agroecosystems, thereby reducing crop yields and contributing more seeds to the seed bank. Velvetleaf is a highly competitive annual weed in many North American cropping systems, exhibiting a high degree of phenotypic plasticity in response to biotic and abiotic factors. We examined the effects of seed size (small and large), competitive environment (with and without soybean), and stress level (none = no treatment, moderate = pathogen inoculation, high = pathogen plus herbicide) on velvetleaf allometry and reproductive output during three field seasons. Only under the high-stress treatment was velvetleaf biomass, height, and reproductive output compromised (> 15% each), and these traits were further reduced with soybean competition. Soybean competition alone reduced velvetleaf biomass (> 50%), height (25%), and seed output (50%), but effects of competition varied with stress level. Velvetleaf plants that originated from large seeds (≥ 10 mg) generally outperformed plants originating from small seeds (< 10 mg) except under the high-stress treatment. These findings suggest that velvetleaf life-history traits and reproductive output in the current generation are a function of both present and historic factors. Management strategies for this species should involve tactics that not only reduce its competitive ability (e.g., biomass, height) but also decrease its seed production via synergistic stressors such as increased crop competitiveness, reduced-rate herbicide applications, and pathogen infection.
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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 | 0.001 |
| 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".