Climate-induced shifts in the spatial distribution of invasive weed Sungrass (Imperata cylindrica) in the Asia-Pacific

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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Publication Details

Journal
GEOMATICA
Published
2025-08-28
DOI
https://doi.org/10.1016/j.geomat.2025.100068
Citations
1
Primary Topic
Biological Control of Invasive Species
Type
article
Field-Weighted Citation Impact
1.67
Controls
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article

Climate-induced shifts in the spatial distribution of invasive weed Sungrass (Imperata cylindrica) in the Asia-Pacific

Mohammad Redowan, Biplob Dey, Michael Hewson, Kazi Al Muqtadir Abir et al.
1 citations
GEOMATICA
Biological Control of Invasive Species
1.67
article

Climate-induced shifts in the spatial distribution of invasive weed Sungrass (Imperata cylindrica) in the Asia-Pacific

Mohammad Redowan, Biplob Dey, Michael Hewson, Kazi Al Muqtadir Abir, Akbar Hossain Kanan, Faria Erfana Hasan
article en
1 citations

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.

GEOMATICAVol. 77(2)
Shahjalal University of Science and Technology (BD), University of Padua (IT), Institute of Forest Ecology of the Slovak Academy of Sciences (SK), Fondazione Eni Enrico Mattei (IT), Nordwestdeutsche Forstliche Versuchsanstalt (DE), University of Göttingen (DE), Central Queensland University (AU)
Climate action
Openalex Percentile: Top 15%
Biological Control of Invasive Species
1.67
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