Native seeds of Solidago gigantea exhibit equal or superior performance compared to non-native seeds
Bibliographic record
Abstract
Germination and early seedling establishment are critical bottlenecks in a plant’s life cycle and can strongly influence adult performance. While many studies showed that performance of adult plants can differ substantially between native and non-native populations, it remains unclear whether such differences are already evident during the earliest life stages. We compared germination success and early seedling growth of Solidago gigantea sourced from 28 native and 33 non-native populations. As adult plants from the non-native range typically grow much larger than their native counterparts, range-dependent eco-evolutionary dynamics likely contribute to the invasion success of this species. Seeds from native populations germinated equally well or better than those from non-native populations. Similar patterns were observed for seedling growth. Moreover, plants from plain habitats outperformed those from mountainous habitats, but this difference was only apparent within the non-native range. Other habitat variables showed no effect on germination or seedling performance. Although previous work indicates strong selection for increased growth rates in adult S. gigantea individuals from the non-native range, such selection seems not to act on germination or seedling establishment. Our findings imply that recruitment traits are probably not key targets of selection in the context of habitat differentiation or invasion dynamics.
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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".