Predicting current and future distribution of Hovenia dulcis Thunb. (Rhamnaceae) worldwide
Bibliographic record
Abstract
Abstract Biological invasions are increasingly recognized as one of the major threats to biodiversity. The Japanese raisin tree (Hovenia dulcis) is native to East Asia, however, in southeastern South America this species has become one of the most pervasive invaders. Hovenia dulcis has many biological characteristics that favor the process of invasion and few studies have indicated changes in the structure and composition of native plant communities where this species has become invader. Given the invasiveness shown in southeastern South America, our main goal was to identify the potentially suitable habitats for this invasive species at a global scale. In this sense, we modeled the potential distribution of H. dulcis along the terrestrial areas worldwide using an ensemble forecasting approach. Additionally, the percentage of overlapping biodiversity hotspot areas with the currently suitable areas for this species was calculated. Our results revealed that the current potential H. dulcis range is equivalent to 7.88% (12,719,365 km2) of the terrestrial area worldwide. For the future scenarios of climate change, the potential distribution area tends to have a small reduction. However, significant suitable areas were identified for H. dulcis range in the northern limits of the boreal distribution. Currently, around 17% of biodiversity hotspot areas overlap with the suitable areas for H. dulcis occurrence. In summary, given that the prevention is well-recognized as a more effective management action against invasive alien species, it is essential to implement policies to prevent H. dulcis introduction in suitable areas worldwide, as well as local population control, especially in biodiversity hotspots.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".