Alien flora of mountains: global comparisons for the development of local preventive measures against plant invasions
Bibliographic record
Abstract
Aim We use data from 13 mountain regions and surrounding lowland areas to identify (1) the origins, traits and cultural uses of alien plant species that establish in mountains, (2) the alien species that are most likely to be a threat and (3) how managers might use this information to prevent further invasions. Location Australia, Canada, Chile, India, New Zealand, South Africa, Spain, Switzerland, USA. Methods Lists of alien species were compiled for mountains and their surrounding or nearby lowlands. Principal co-ordinates analysis was performed on a matrix of similarities created using presence/absence data for alien species. The significance of differences between means for (1) similarity metrics of lowland and mountain groups and (2) species traits of lowland and mountain alien floras was determined using t-tests. In seven of the 13 mountain regions, lists of alien species undergoing management were compiled. The significance of differences between proportions of traits for species requiring and not requiring management input was determined with chi-square tests. Results We found that the proximal lowland alien flora is the main determinant of a mountain region’s alien species composition. The highest similarities between mountain floras were in the Americas/Pacific Region. The majority of alien species commonly found in mountains have agricultural origins and are of little concern to land managers. Woody species and those used for ornamental purposes will often pose the greatest threat. Main conclusions Given the documented potential threat of alien species invading mountains, we advise natural resource managers to take preventive measures against the risk of alien plant invasion in mountains. A strategy for prevention should extend to the surrounding lowland areas and in particular regulate the introduction of species that are already of management concern in other mountains as well as climatically pre-adapted alien mountain plants. These may well become more problematic than the majority of alien plants currently in mountains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".