Explaining invasiveness from the extent of native range: new insights from plant atlases and herbarium specimens
Bibliographic record
Abstract
Abstract Aim We tested the relationship between the extent of the native range and the success (number of occurrences) in the introduced range of European vascular plant species naturalized in the province of Québec (Canada). We hypothesized that the performance of models linking native range size and species invasiveness can be improved if residence time and climate tolerance are taken into account. Methods The extent of the native range (Europe, Asia) was estimated using plant atlases. The number of occurrences in the introduced range (Québec) was estimated using the number of herbarium specimens stored in herbaria. Herbarium specimens were also used to obtain residence time. Plant hardiness was used as an indicator of the suitability of a species to the climate of the introduced range. Multiple linear regression models, corrected to take into account phylogenetic biases, were used to calculate correlations between the extent of the native range and the number of occurrences in the introduced range. Results The larger the native distribution area in Eurasia, the greater the number of occurrences (herbarium specimens) in Québec. The shorter the residence time and the less hardy the plant, the fewer the number of occurrences. In all models tested, the phylogenetic structure explained a significant proportion of the variance, but its influence decreased as the number of species or area studied (Europe versus Eurasia) increased. Main conclusions The extent of the native range is a good explanatory variable for the invasion success of vascular plants, especially once other factors (residence time, climate tolerance, phylogeny) are taken into account. Thus, a model using these variables could be used by environmental managers to flag species warranting further investigation. With the emergence of online databases, gathering the required information is becoming easier and cheaper. As online databases continue to improve and new analytical tools are developed, this approach will become even more powerful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".