Phenotypic plasticity, population structure and adaptation in a young weed species with a worldwide distribution
Bibliographic record
Abstract
Summary The colonization success of a species depends on phenotypic plasticity, adaptive potential and population structure. Assessing their relative contributions during a colonization process is challenging, and a large-scale experiment had yet to be done. In this study, we attempted to tease apart their effects on the fitness of one of the most common plant on Earth, the shepherd’s purse ( Capsella bursa-pastoris ), a self-fertilizing and allopolyploid weed, with a worldwide distribution. The overarching goal is to eventually understand how the shepherd’s purse extensive distribution range was established so rapidly. To do so, we carried out three common gardens, located in Europe, Asia and North America, and measured several life-history traits on field-collected accessions belonging to three distinct genetic clusters (Middle East, Europe, and Asia). Our experiment showed that (i) the success of C. bursa-pastoris is mainly due to its high degree of phenotypic plasticity; and (ii), genetic cluster effect reflected a classic pattern observed in core vs marginal populations, with the Middle Eastern cluster (putative core population) outperforming the European and Asian clusters. This study therefore revealed, in a model species, different relative contributions of plasticity and adaptation to fitness, depending on the population and the time since colonization occurred.
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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 | 0.001 | 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".