Explaining naturalization and invasiveness: new insights from historical ornamental plant catalogs
Bibliographic record
Abstract
We identified plant attributes associated with naturalization and invasiveness using century-old ornamental plant catalogs from Québec (Canada). We tested the hypothesis that naturalization is determined by fewer factors than invasiveness, as the latter also requires dispersal, which introduces additional complexity. The approach we used took into account not only plant attributes as explanatory factors, but also propagule pressure, while accounting for phylogenetic relationships among species. Museum collections were used, in combination with scientific journal databases, to assess invasiveness. Particular attention was given to species that never escaped from gardens and thus represent cases of "failed" invasions. Naturalization in cold-temperate environments is determined by fewer factors than invasion, but only if phylogenetic links between species are taken into account, highlighting the importance of phylogenetic tools for analyzing species pools not resulting from a random selection of taxa. Hardiness is the main factor explaining naturalization in Québec. Invasion requires dispersal, as shown by three significant variables associated with the spread of diaspores in the invasiveness model (seed weight, hydrochory, number of seed dispersal modes). Plants that are not cold-hardy are likely to disappear from the market or nature, but the disappearance phenomenon is more complex, involving also seed dispersal abilities and propagule pressure. Factors contributing to naturalization or invasiveness may differ greatly between regions. Differences are due in part to the plant traits used in the models and the methodology. However, this study, conducted in a cold-temperate region, sheds new light on what is likely a context (climatic)-dependant phenomenon.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".