Climate-induced shifts in the spatial distribution of invasive weed Sungrass (Imperata cylindrica) in the Asia-Pacific
Bibliographic record
Abstract
Invasive alien plant species pose significant threats to biodiversity, ecosystem services, and regional economies. Imperata cylindrica (commonly known as Sungrass or Cogongrass) ranks among the most invasive weeds in the Asia-Pacific region, causing substantial damage to forest and agricultural landscapes. Effective control and monitoring of this species necessitate a thorough understanding of its distribution patterns and the environmental factors influencing its survival and proliferation. Thus, this study investigates the current and future distribution of Sungrass under climate change scenarios using ensemble species distribution modeling. By integrating multiple machine learning algorithms with climatic and topographic variables, this study estimated spatially suitable habitats across the region and assessed how these areas may shift under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for the years 2050 and 2070. The findings indicate Sungrass currently occupies a broad climatic niche, with habitat suitability most strongly influenced by precipitation patterns and elevation. Tropical regions with relatively stable climates were identified as the most favorable. Projections indicate a moderate expansion of suitable habitat by mid-century, with an expected increase of approximately 1.25 % by 2050 and 1.18 % by 2070 under both climate scenarios. Among the models used, Random Forest and Support Vector Machine showed the highest accuracy (∼98 %), supporting the suitability of the ensemble approach. While the models highlight key environmental drivers, the distribution data were predominantly sourced from Bangladesh, which may limit the spatial generalizability of the findings. Nonetheless, this study provides critical spatial understandings to guide early warning, habitat monitoring, and regional invasive species management. The results emphasize the need for proactive, climate-informed strategies to mitigate Sungrass invasion risk under evolving environmental conditions. • Due to climate change, Sungrass habitat in the Asia-Pacific is likely to expand by ∼1.25 % by 2050, then decrease by ∼0.89 % by 2070 under RCP 8.5 scenarios. • Suitability peaks in warm, wet lowlands, shaped by elevation and moderate seasonal precipitation. • Tropical regions in Asia-Pacific are at high-risk zones for future invasion. • Increased forest cover and effective forest management could reduce expansion by ∼0.85 %. • Ensemble models offer robust predictions but require caution in ecologically complex areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".