Selective ancestral sorting and de novo evolution in the agricultural invasion of <i>Amaranthus tuberculatus</i>
Bibliographic record
Abstract
The relative role of hybridization, de novo evolution, and standing variation in weed adaptation to agricultural environments is largely unknown. In Amaranthus tuberculatus, a widespread North American agricultural weed, adaptation is likely influenced by recent secondary contact and admixture of two previously isolated lineages. We characterized the extent of adaptation and phenotypic differentiation accompanying the spread of A. tuberculatus into agricultural environments and the contribution of ancestral divergence. We generated phenotypic and whole-genome sequence data from a manipulative common garden experiment, using paired samples from natural and agricultural populations. We found strong latitudinal, longitudinal, and sex differentiation in phenotypes, and subtle differences among agricultural and natural environments that were further resolved with ancestry inference. The transition into agricultural environments has favored southwestern var. rudis ancestry that leads to higher biomass and treatment-specific phenotypes: increased biomass and earlier flowering under reduced water availability, and reduced plasticity in fitness-related traits. We also detected de novo adaptation in individuals from agricultural habitats independent of ancestry effects, including marginally higher biomass, later flowering, and treatment-dependent divergence in time to germination. Therefore, the invasion of A. tuberculatus into agricultural environments has drawn on adaptive variation across multiple timescales-through both preadaptation via the preferential sorting of var. rudis ancestry and de novo local adaptation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".