Sex morphs and invasiveness of a fleshy-fruited tree in natural grasslands from Argentina
Bibliographic record
Abstract
Invasiveness has usually been studied as a species-level attribute; nevertheless, phenotypic differences between individuals in a population can lead to significant variations in colonization ability. In this paper, we analyse the potential effects of sex morphs of Prunus mahaleb L., a gynodioecius fleshy-fruited tree, on its invasiveness in natural grasslands in the southern Argentine Pampas. We assessed the abundance of both hermaphrodite and female plants, and compared their fecundity, propagule size, and germination response. We found that the females were less abundant in the invasive populations studied, apparently since the beginning of the colonization. However, our results demonstrated that at the present time, females do not show any fecundity reduction, which clearly shows that P. mahaleb has established an effective interaction with generalist pollinators that compensates for the apparently disadvantaged females. Fruit set showed a wider range of variability over time in the females than in the hermaphrodites, which could be the consequence of greater susceptibility to changes in the activity of pollinators. We found no evidence of a female benefit due to reallocation of resources or better outcrossed progeny when considering propagule size and germination. We discuss the relative importance of sex morphs and interactions at different stages of the invasion process.
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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".