Integrating climate and trait models to predict the invasiveness of exotic plants in Canada’s Riding Mountain National Park
Bibliographic record
Abstract
Invasive exotic species threaten the biodiversity and function of native ecosystems. Existing models, attempting to predict successful invaders, often emphasize isolated stages of biological invasions and fail to formalize interactions between exotic species and recipient environments. By integrating the native climatic range and biological traits of exotic vascular plants reported inside and outside Riding Mountain National Park, Manitoba, Canada, we present a model where invasion risk is predicted using the likelihood of establishment and proliferation of exotic species. Exotic vascular plants constituted 11.5% (77/669) of the total vascular flora of Riding Mountain National Park and approximately 14.2% (202/1418) of the flora of Manitoba. Based on their climatic range in Europe, 155 among 174 exotic species absent from the Park, including those found in Manitoba and other natural areas in Canada, were predicted to establish within its boundaries. Among the biological traits of exotic plants, perenniality and vegetative reproduction correctly identified 61% of documented invaders of natural areas in Canada and helped to further define a subset of 40 exotic plants with the highest potential to proliferate within the Park’s natural areas. Among these, 11 are still absent from inside its boundaries and 17 are not yet reported in Manitoba. Our results demonstrate that Riding Mountain National Park and natural areas in western Canada will continue to be impacted by exotic vascular plants. By integrating establishment and proliferation, key stages in the progress of biological invasions, our model offers a synthetic approach to the prediction and management of biological invasions inside natural areas; such integration is critical to the protection of Canada’s endemic biodiversity. Key words: Biological invasions, invasive alien plants, climate-matching model, life history, natural areas, risk assessment
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".