| Citation: |
Fei T, Ye J X, Wang J W. Risk Assessment of Solenopsis invicta Invasion in Yiliang County Based on Machine Learning[J]. Journal of Southwest Forestry University, 2025, 45(2): 183–191. DOI: 10.11929/j.swfu.202402014
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Using Yiliang County of Central Yunnan, China as the study area, the invasion risk of the Solenopsis invicta was assessed based on a series of possible drivers, aiming to explore its spatial dispersal mechanisms and patterns. Firsly, various environmental variables such as meteorology, vegetation, watershed, topography and geomorphology, and anthropogenic activities were quantified and raster mapped based on field work by remote sensing inversion and geographic information system(GIS) analysis. Three maching learning of MaxEnt, random forest and Logistic prediction models were constructed, also the accuracy was assessed, and a factor analysis was conducted on driving factors, respectively, so as to visualize the risk patterns of S. invicta invasion. The results show that the AUC values of MaxEnt, random forest and Logistic models are 0.926, 0.944 and 0.950 respectively, and the AUC values of the models are greater than 0.9, which indicates that the models are reliable; factor analysis shows that the main driving factors of the model are multifaceted, and the dominant independent variables of all models are different. The driving factors of the optimal Logistic model are surface humidity, food, altitude and aspect; the invasion risk model shows strong spatial heterogeneity; the high-risk areas for S. invicta in the study area are concentrated in the surrounding water bodies, barren hills and wastelands and newly forested land, which are less likely to invade the natrual vegetation areas at high altitude; special attention should be paid to the quarantine work and preventive disinfection and sterilisation of seedlings, and to strengthening the management of forest operations to promote the improvement of forest quality and the construction of natural barriers. The study complements the invasion pattern and potential risk of S. invicta in central Yunnan, providing a scientific basis for effective prevention and control, as well as a reference meaning for the risk assessment of other invasive species.
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