Machine learning for biosecurity: A probabilistic framework for invasive species management

Abstract Preventing the spread of non‐native invasive species into new habitats is crucial to mitigate biosecurity risks that threaten food security, the conservation of natural ecosystems and the delivery of ecosystem services. We present a novel probabilistic machine learning (ML) framework to quantify species progression along the introduction–naturalization–invasion continuum, identify key traits driving invasion transitions and quantify the probability of invasion prior to introduction into new habitats, thereby enabling proactive interventions. We illustrate this approach using a dataset comprising 1032 non‐native plant species across 15 Caribbean islands. We demonstrate that multiple ML methods achieve >90% accuracy in predicting invasion success. Feature importance analysis identified habitat affiliation and invasion history traits as key predictors of invasion success, emphasizing the role of ecological adaptability and historical context in species establishment and expansion. Critically, our ML models can accurately predict invasion risk before species enter new habitats, identifying high‐risk invaders not yet present in our study sites and quantifying prediction uncertainties to inform biosecurity decisions. Synthesis and applications . By using pre‐introduction traits and leveraging ML for early detection, this study presents a scalable, data‐driven framework for invasion risk assessment and conservation planning. Our approach enables targeted monitoring of species with a high risk of invasion and the development of regulatory actions to mitigate biosecurity risks.