Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change

Abstract

Biological invasions pose significant threats to ecosystem structure and function, disrupt ecosystem services, cause high economic losses, and negatively impact human well-being. However, accurate prediction of species distribution is a critical challenge in ecological and biodiversity conservation. This study compares the predictive performance of 10 machine learning algorithms, including random forests, maximum entropy, support vector machines, and others, by integrating global occurrence records with climatic, edaphic, and human activity variables to identify the most robust model for predicting the global distribution of the invasive weed, Conyza sumatrensis (Retz.) E.Walker. Different algorithms yielded large variations in the predicted area of C. sumatrensis. Among these, random forests had the highest performance accuracy metrics and high agreement of predictions, aligning well with global occurrence records, and are used to explain and predict the potential distribution of C. sumatrensis. Distributions of C. sumatrensis are mainly influenced by temperature variables, adapted to a wide range of precipitation and various soil conditions, and facilitated by human activities. Currently, C. sumatrensis is distributed widely across all continents (6.20 Mkm²). The suitable habitat for C. sumatrensis is projected to have an increase of 8.03–8.78 % by 2041–2060 and 0.84–3.29 % by 2081–2100. In addition, the global extent of suitable environmental conditions for the establishment and spread of C. sumatrensis was anticipated to expand in urban and farmland by 2081–2100. The results provide an early warning of specific land-use types at higher risk of C. sumatrensis extent, offering valuable insights for managers to develop targeted prevention and control strategies. Additionally, to enhance predictive accuracy, our study underscores the critical role of selecting suitable algorithms and integrating human activity factors into invasive species distribution models.