Background: Invasive plant species are considered a major threat to biodiversity, ecosystem functioning, and human wellbeing worldwide. Climatically suitable ranges for invasive plant species are expected to expand due to future climate change. The identification of current invasions and potential range expansion of invasive plant species is required to plan for the management of these species. Here, we predicted climatically suitable habitats for 11 invasive plant species and calculated the potential species richness and their range expansions in different provinces of the Republic of Korea (ROK) under current and future climate change scenarios (RCP 4.5 and RCP 8.5) using the maximum entropy (MaxEnt) modeling approach. Results: Based on the model predictions, areas of climatically suitable habitats for 90.9% of the invasive plant species are expected to retain current ecological niches and expand to include additional climatically suitable areas under future climate change scenarios. Species richness is predicted to be relatively high in the provinces of the western and southern regions (e.g., Jeollanam, Jeollabuk, and Chungcheongnam) under current climatic conditions. However, under future climates, richness in the provinces of the northern, eastern, and southeastern regions (e.g., Seoul, Incheon, Gyeonggi, Gyeongsangnam, Degue, Busan, and Ulsan) is estimated to increase up to 292%, 390.75%, and 468.06% by 2030, 2050, and 2080, respectively, compared with the current richness. Conclusions: Our study revealed that the rates of introduction and dispersion of invasive plant species from the western and southern coasts are relatively high and are expanding across the ROK through different modes of dispersion. The negative impacts on biodiversity, ecosystem dynamics, and economy caused by invasive plant species will be high if preventive and eradication measures are not employed immediately. Thus, this study will be helpful to policymakers for the management of invasive plant species and the conservation of biodiversity.

Acacia mearnsii, among the 100 worst invasive weeds worldwide, negatively impacts native biodiversity, agriculture, and natural ecosystems. Global climate change, characterized by rising temperatures and altered precipitation patterns, enhances the risk of A. mearnsii invasion in Asia, making it crucial to identify high-risk areas for effective management. This study performed species distribution modeling using the maximum entropy (MaxEnt) algorithm to predict the potential introduction and spread of A. mearnsii under various climate scenarios based on shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). Currently, only 4.35% of Asia is invaded, with a high invasion risk identified in six countries, including Bhutan, Lebanon, and Taiwan, where more than 75% of their areas are threatened. Under future climate scenarios, 21 countries face invasion risk, among which 14 countries, such as Georgia, Laos, Republic of Korea, and Turkey, are at moderate to very high risk, potentially encompassing up to 87.89% of their territories. Conversely, Northern Asian countries exhibit minimal changes in invasion risk and are considered relatively safe from invasion. These findings underscore that climate change will exacerbate invasion risks across Asia, emphasizing the urgent need for robust management strategies, including stringent quarantine measures and control efforts, to mitigate the threat of A. mearnsii expansion.

Aquatic ecosystems worldwide are increasingly threatened by invasive species, with water hyacinth [Eichhornia crassipes (Mart.) Solms] being among the most destructive aquatic weeds. Despite numerous regional studies, a global assessment integrating climatic and hydrological drivers remains lacking. Here, we assessed current and future invasion risks across 55,945 freshwater lakes using the maximum entropy (MaxEnt) model. Climatic variables and key aquatic parameters, including biological oxygen demand (BOD), water depth, and discharge, were incorporated under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Annual mean temperature, annual precipitation, and BOD were the strongest predictors of habitat suitability. Under current conditions, 5524 lakes, primarily in tropical and subtropical regions, were identified as being suitable habitats, with medium-sized lakes exhibiting the highest proportional suitability (16.54%). Although small lakes were most frequently classified as suitable due to their abundance, larger lakes showed higher suitability intensity. Future projections indicated marked habitat expansion, especially under SSP5-8.5, with suitable lake surface area increasing to 18.12% by 2061–2080. Moreover, 543 currently unsuitable lakes, including Lake Erie, Lake Huron, and Lake Ontario, were projected to face elevated invasion risk, particularly in Africa, South Asia, Southeast Asia, and North America. This global, lake-specific assessment supports early warning, targeted management, and climate-responsive policy planning.

Opuntia stricta is a highly invasive cactus species that threatens arid and semiarid ecosystems worldwide. We identified the optimal species distribution model (SDM) to predict its global habitat suitability using three hierarchical frameworks: climate only, climate with soil, and climate with soil and human influences. Among five modeling algorithms, Maximum entropy (MaxEnt) showed the highest predictive performance, supporting its use as the primary habitat-suitability model. Habitat suitability was driven mainly by climatic variables, with precipitation of the driest month, isothermality, and potential evapotranspiration contributing 30.7%, 26.7%, and 18.3%, respectively, whereas soil and human factors made smaller contributions. Biome-level analysis revealed that Mediterranean ecosystems exhibited the highest proportional habitat suitability (48.5%). The climate-only model predicted 7.35% of global land as suitable, which decreased by 34% after soil and human variables were incorporated. Native-introduced niche-overlap analysis further showed that additional environmental predictors refined environmental niche characterization across modeling scenarios. These findings highlight the importance of integrating multiple environmental predictors to improve habitat-suitability predictions and provide a robust framework for supporting the management of O. stricta and other invasive plant species worldwide.