Environmental and genetic effects on the early survival and growth of the invasive grass <i>Phalaris arundinacea</i>
Bibliographic record
Abstract
Both genetic and environmental factors can determine whether an invasive plant species will establish. To determine how these factors influence the establishment of the invasive grass Phalaris arundinacea L. (reed canary grass), we grew three genotypes in 10 artificial environments and asked how a genotype's growth and survivorship was affected by environmental conditions. We found that genotype strongly influenced survivorship, but there was no significant effect of environment on survivorship. However, environmental conditions did significantly affect growth. Individual plants produced more aboveground and belowground biomass when grown singly than they did when grown with competitors. The identity of neighboring competitors had no effect. Although genotype had no significant efffect, there was a significant genotype × environment interaction. Clone 1 produced more root biomass than either clone 2 or clone 3 when there was no or little competition, but in competitive neighborhoods, clone 3 allocated relatively more biomass to roots than to shoots. Under ideal conditions in the greenhouse, clone 2 produced more tillers than the other two clones. Based on these results, it appears that clone 1 may have an advantage under sparse vegetative cover, but clone 3 may have a competitive advantage in highly competitive neighborhoods because it could preferentially allocate more biomass to roots. The presence of three growth patterns from such a small, localized sample suggests that different growth strategies are probably common within populations of reed canary grass and may be important for allowing reed canary grass to successfully invade new habitats.
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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".