Challenges in Designing Workflows for Establishing Species Distribution Models of Invasive Alien Plant

Development of such invasive alien species (IAS) researches is challenged by grasping location intelligence (LI) of IAS in the large-scale environmental. In this study, multiple approaches were adopted to collect in-situ data, and four machine learning (ML), logistic multiple regression (LMR), support vector machine (SVM), maximum entropy (MAXENT), and random forest (RF), were used to develop SDMs for predicting IAS's patterns. In order to evaluate the key factors that affect the distribution of IAS, the study uses environmental variables including terrain-related and human-disturbance variables. The results show that the workflow for establishing SDM can overcome limitations in the natural environment with diverse survey approaches to find environment preferences for IAS. Moreover, we plot the prediction distribution with the best performing model, such as RF in this study and use this map to effectively limit the investigation range of IAS and improve the investigation efficiency.