Botanic gardens play key roles in the regional distribution of first records of alien plants in China
Bibliographic record
Abstract
Abstract Aim Plant invasions pose a serious risk to biodiversity, and living collections in botanic gardens are recognized as a potentially important source of alien plant introductions. However, it is not yet known how the risks from botanic gardens compare with other socioeconomic and environmental factors in influencing the regional distribution of alien plant introductions. Location China. Time period 1840–2018. Major taxa studied All vascular plants. Method We compiled a dataset of the locations of first records and introduction pathways for 454 plant species naturalized in China and used boosted regression trees to evaluate the roles of the number, size and age of botanic gardens, local climate, native species richness and trade on the total number of first records of alien plants at both city and province level. Results Botanic gardens with large living collections played the most important role in influencing the total number of first records of alien plant species at both city and province scale. However, the importance of botanic gardens depended on the introduction pathway of naturalized species. The first records of alien species introduced for horticulture were more influenced by the attributes of botanic gardens. Moreover, the first records of alien species introduced for agriculture were associated with climatic variables, and the first records of alien species introduced accidentally were strongly shaped by trade. Main conclusions Our results highlight the importance of botanic gardens in facilitating alien plant introductions across a continent. Given the rapid rise in the number of botanic gardens in Asia since 1950, our results point to these as potential hotspots for future plant invasions. Botanic gardens should implement screening procedures to assess the risk of invasion arising from species in their living collections and undertake systematic surveys of their plantings for evidence of plant naturalization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".