Ensemble modelling to assess the dynamics of invasive plant species under changing climate in Bagmati Province, Nepal - MANISH SHRESTHA; SONY BARAL; UTTAM BABU SHRESTHA; RIPU MARDHAN KUNWAR (Society for Indonesian Biodiversity, 2025)
Abstract. Shrestha M, Baral S, Shrestha UB, Kunwar RM. 2025. Ensemble modelling to assess the dynamics of invasive plant species under changing climate in Bagmati Province, Nepal. Biodiversitas 26: 2589-2600. Invasive Alien Plant species (IAPs) present a significant threat to biodiversity, particularly under changing climatic conditions. This study investigates the distribution patterns of three IAPs, viz. Ageratina adenophora, Chromolaena odorata, and Lantana camara, within Bagmati Province, Nepal, under both current and projected climate scenarios (SSP245 and SSP585). Using species occurrence data from field surveys, available secondary data, and eight bioclimatic variables, we applied an ensemble modelling approach across twelve Global Circulation Models (GCMs). The findings reveal that, under present conditions, A. adenophora occupies the most extensive suitable area (4,121.6 km²), whereas C. odorata occupies the least (1,964.07 km²). Future projections indicate an increase in habitat suitability for all three IAPs in future climates. Specifically, under SSP245, L. camara shows a 12.4% rise in suitable habitat area during the period 2021-2040, with C. odorata showing a 15.6% increase during 2041-2060. Under SSP585, the suitability of L. camara is projected to increase substantially by 37.2% and 40.7% in the periods 2021-2040 and 2041-2060, respectively. This highlights the more conducive environment created by climate change for IAPs in the future. The models demonstrate higher suitability in the middle mountain regions and a decline in suitability with increasing elevation. Specifically, C. odorata favors tropical zones, and A. adenophora and L. camara thrive in subtropical regions. These IAPs pose the greatest risks to cropland and forest ecosystems, adversely affecting local economies and biodiversity. The high-performance metrics of the models (AUC: 0.85-0.92, TSS: 0.51-0.67) confirm their reliability, with Precipitation of Driest Month (BIO14) being the most significant variable under current climate conditions and Mean Diurnal Range (BIO2) emerging as critical for future climate scenarios. These results emphasize the need for proactive site-specific management strategies to mitigate the impacts of IAPs under climate change.