"Probability distributions of three invasive woody species in India under current and future conditions using ecological niche modeling"

Non-native plant species have emerged as critical drivers of ecological change with significant implications for biodiversity and ecosystem services. Certain non-native plants contribute to climate change mitigation and help maintain vegetation cover, while invasive plants often disrupt ecological equilibrium thereby adversely affecting native flora and fauna. This master’s thesis focuses on assessing the spatial distribution and potential future expansion of selected invasive plant species in India, using a combination of data analysis and advanced ecological modeling techniques. Through a comprehensive review of literature, non-native plant species were identified in India from which three invasive species Lantana camara L., Muntingia calabura L. and Prosopis juliflora (Sw.) DC. were selected as case studies based on their ecological significance and availability of distributional data from Global Biodiversity Information Facility (GBIF). Possible spatial distribution for these three invasive species were analysed using the SPOCC library which yielded a foundational dataset of 8,070 occurrence points: 5,052 for Lantana camara L., 826 for Muntingia calabura L. and 2,192 for Prosopis juliflora (Sw.) DC. Ecological niche modeling was used to analyze the current and projected probability distributions in which elevation and 19 bioclimatic variables sourced from WorldClim were incorporated. Future spatial distributions were simulated for 2040 under four Shared Socioeconomic Pathways (SSPs) representing diverse climate and socioeconomic trajectories: SSP126, SSP245, SSP370 and SSP585. These projections were generated using machine learning algorithms from the Stacked Species Distribution Modelling (SSDM) framework which integrates multiple distribution models for improved predictive accuracy. Our findings reveal the potential for substantial shifts in the spatial distribution of these invasive species under different climate scenarios, underscoring the need for region-specific management interventions. Through this study we are trying to provide a replicable methodology for leveraging biodiversity databases and modeling tools to assess and predict the ecological impacts of invasive plant species. The framework presented here offers a scalable approach to inform evidence-based strategies for managing non-native species in forest ecosystems, particularly under changing climatic conditions.