Predicting the potential distribution of the invasive weed Mikania micrantha and its biological control agent Puccinia spegazzinii under climate change scenarios in China
Bibliographic record
Abstract
Research on the potential distribution of invasive plants and their biological control agents under climate change is critical for informing strategies in invasive species management. The rust fungus Puccinia spegazzinii shows significant potential as a biological control agent for the invasive weed Mikania micrantha . The MaxEnt (Maximum Entropy) model was used to simulate the distribution of M. micrantha and P. spegazzinii under current and future climate scenarios. The models achieved excellent prediction performance, with M. micrantha and P. spegazzinii having area under the curve values of 0.921 and 0.978 respectively, and true skill statistics values of 0.886 and 0.902 respectively. Precipitation is the primary factor influencing the distributions of M. micrantha , while P. spegazzinii is determined by both temperature and precipitation. The suitable areas for the two species are concentrated in southern China, with M. micrantha exhibiting broader adaptability compared to P. spegazzinii . Under future climate scenarios, the suitable areas for M. micrantha in China will expand northward, with a maximum projected growth rate of 84.6 % in the 2070 s, whereas P. spegazzinii exhibits a contracting trend (with a projected reduction of 40.8 % in the 2050 s). Under the current climate scenario, the overlapping suitable areas between the two species account for 25.2 % of the total suitable area for M. micrantha and 100 % of that for P. spegazzinii and both remain relatively stable under future climate scenarios. This work can provide guidance for the application of biological control, and serves as a valuable reference for developing early warning and management response strategies for invasive species in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".