The molecular basis of invasiveness: differences in gene expression of native and introduced common ragweed (<i><scp>A</scp>mbrosia artemisiifolia</i>) in stressful and benign environments
Bibliographic record
Abstract
Although the evolutionary and ecological processes that contribute to plant invasion have been the focus of much research, investigation into the molecular basis of invasion is just beginning. Common ragweed (Ambrosia artemisiifolia) is an annual weed native to North America and has been introduced to Europe where it has become invasive. Using a custom-designed NimbleGen oligoarray, we examined differences in gene expression between five native and six introduced populations of common ragweed in three different environments (control, light stress and nutrient stress), as well as two different time points. We identified candidate genes that may contribute to invasiveness in common ragweed based on differences in expression between native and introduced populations from Europe. Specifically, we found 180 genes where range explained a significant proportion of the variation in gene expression and a further 103 genes with a significant range by treatment interaction. Several of these genes are potentially involved in the metabolism of secondary compounds, stress response and the detoxification of xenobiotics. Previously, we found more rapid growth and greater reproductive success in introduced populations, particularly in benign and competitive (light stress) environments, and many of these candidate genes potentially underlie these growth differences. We also found expression differences among populations within each range, reflecting either local adaptation or neutral processes, although no associations with climate or latitude were identified. These data provide a first step in identifying genes that are involved with introduction success in an aggressive annual weed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".