Temporal trends in the accumulation of alien vascular plant species through intentional and unintentional introductions in Japan
Bibliographic record
Abstract
Clarifying the temporal trends of alien plant accumulation is increasingly important for informing global and national management efforts to decelerate biological invasions, following the adoption of Target 6 of the Kunming-Montreal Global Biodiversity Framework. However, such trends have not yet been analysed in many countries including Japan, which has the highest number of naturalised alien plant species among islands. To clarify the past and recent trends in the accumulation of alien plant species in Japan, we compiled a dataset of the year of first record for 1,463 alien vascular plant species deliberately and accidentally introduced and analysed the changes in the annual number of first records over time for each overall, intentional and unintentional introductions. We found that, overall, the annual number of first records of alien plant species in Japan began to increase in the late 1800s, and the increase continued until the late 1950s, with an estimated maximum of 15.7 new species per year. The increase then halted by 1960 and began a slow decline; the estimated average records per year between 1991 and 2000 dropped to 13.3 species. Since 1900, the annual number of first records associated with intentional introductions has more than doubled the number linked to unintentional introductions. Additionally, the proportion of invasive species brought through intentional introductions was larger than that brought through unintentional introductions. We highlight that while Japan experienced a rapid accumulation of alien plant species, including invasive species, by the 1950s, the rate of accumulation showed signs of saturation by 1960 and has since been slowly declining. Further deceleration and prevention of the introduction of invasive alien species, as targeted in the Kunming-Montreal Global Biodiversity Framework, may be achieved through increased investment in pathway management, especially management of intentional pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".