Increased growth in sunflower correlates with reduced defences and altered gene expression in response to biotic and abiotic stress
Bibliographic record
Abstract
Cultivated plants have been selected by humans for increased yield in a relatively benign environment, where nutrient and water resources are often supplemented, and biotic enemy loads are kept artificially low. Agricultural weeds have adapted to this same benign environment as crops and often have high growth and reproductive rates, even though they have not been specifically selected for yield. Considering the competing demands for resources in any plant, a key question is whether adaptation to agricultural environments has been accompanied by life history trade-offs, in which resistance to (largely absent) stress has been lost in favour of growth and reproduction. The experiments reported here were designed to test for growth-defence trade-offs in agricultural weeds, crops and native varieties of common sunflower (Helianthus annuus L., Asteraceae) by comparing their performance in the presence or absence of abiotic (drought and crowding) or biotic (simulated herbivory, insect herbivory and fungal) stress. We found that growth, as well as viability of crops and weeds, was reduced by abiotic drought stress. The weakened defence in the agricultural genotypes was further evident as increased susceptibility to fungal infection and higher level of insect palatability. To uncover molecular mechanisms underlying these trade-offs, we monitored gene expression kinetics in drought-stressed plants. By correlating phenotypic observations with molecular analyses, we report the identification of several genes, including a protein phosphatase 2C and the HD-Zip transcription factor Athb-8, whose expression is associated with the observed phenotypic variation in common sunflower.
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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".