Here, There and Everywhere: Widespread Non‐Native Plants in the World's Urban Ecosystems
Bibliographic record
Abstract
ABSTRACT Aim To (a) produce a list of the most widespread naturalised non‐native plant species across cities of the world; (b) explore whether cities on different continents are invaded by the same group of widespread naturalised species; and (c) elucidate the origins of the most widespread naturalised urban species. Location Global. Time Period No specific period. Major Taxa Studied Vascular plants. Method Using the most comprehensive and up‐to‐date dataset of non‐native urban floras yet assembled (GUBIC; Global Urban Biological Invasions Compendium), we identified the most widespread naturalised plant species (the global urban florome) by filtering for taxa present across all continents (except Antarctica) and their frequency in urban areas. To assess global patterns of urban plant naturalisation, we conducted ordination analyses and visualised species co‐occurrence. We also examined species origins and their environmental impact. Results Among the 7792 naturalised plant species recorded in 553 urban centres, 302 species (4%) were found on all six continents. Of these, 96 species, considered the most widespread species, were present in more than half of urban centres in Oceania, North America and South America; this proportion was higher than in Africa, Asia and Europe. Cities outside Europe and Asia are more homogeneous in terms of the species composition of the most widespread invaders. An analysis of species origins showed that temperate Asia contributed the most species globally, while intercontinental exchange patterns varied, with a notable one‐directional flow from North to South America. Main Conclusions Our results suggest that urban ecosystems outside Europe and Asia are more susceptible to recent invasions than those within these two continents. Understanding the composition and origins of these widespread species is crucial for developing coordinated management strategies and improving the resilience of urban biodiversity.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".