Data from: Gene expression and drought response in an invasive thistle
Bibliographic record
Abstract
Though rapid phenotypic evolution has been observed in many invasive plant species, less is known about the associated genetic mechanisms. Some hypotheses invoke the evolution of trade-offs in resource allocation to explain phenotypic differences between the native and invaded ranges of a species. Alternately, invasive species may benefit from a generalist strategy and perform well in many environments. Identification of the molecular changes associated with successful invasions can offer clues regarding the mechanistic basis of such hypotheses, even in non-model organisms. To complement studies of phenotypic variation, we investigate gene expression during drought response that might underlie variation in drought tolerance between native and introduced populations of diffuse knapweed (Centaurea diffusa), and possibly contribute to invasion success. Using species-specific microarrays and tissue sampled under drought and control conditions at three time points, we identified genes whose expression either varied constitutively or responded to drought stress differently between ranges. Further, we functionally investigate these genes. Based on these data, invasive populations have constitutively higher levels of expression relating to energy production and lower levels of signal transduction expression relative to native populations. Under drought conditions, invasive populations may maintain energy production and react less strongly to drought than native populations, which may allow them to maintain fitness across moderate environmental variation. This supports the expectation of invasive populations consisting of generalist genotypes capable of fitness homeostasis, which may have facilitated the successful invasion of many environments in North America.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".