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Published on: October 11, 2016
Identification of suitable habitats for the six-spotted mite on rubber trees using multi-source data and MaxEnt
Yanan You1,2,3, Huichun Ye4,5,6, Donghua Wang1,2,3
1College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Experimental & Applied Acarology
|July 9, 2026
Summary
The six-spotted mite (SSM) poses a significant threat to rubber plantations. This study maps SSM suitable regions in Hainan Island using MaxEnt, identifying key environmental factors and predicting an expanding suitable area.
Area of Science:
- Agricultural Entomology
- Pest Management
- Ecological Modeling
Background:
- The six-spotted mite (Eotetranychus sexmaculatus) is a major pest in rubber plantations.
- Accurate identification of pest-suitable regions is crucial for effective management and early warning systems.
Purpose of the Study:
- To develop a high-precision method for extracting SSM suitable regions in Hainan Island.
- To identify the primary environmental drivers of SSM distribution.
- To assess the spatial and temporal dynamics of SSM suitable areas.
Main Methods:
- Integration of multi-source data: field survey points, topography, meteorology, and remote sensing vegetation indices.
- Application of the maximum entropy algorithm (MaxEnt) for species distribution modeling.
- Validation using Area Under Curve (AUC) and True Skill Statistic (TSS).
Main Results:
- The MaxEnt model demonstrated excellent predictive performance (AUC=0.907, TSS=0.803).
- Key factors influencing SSM distribution include elevation, slope, annual precipitation, and temperature/vegetation indices during May-July.
- Suitable areas cover 92% of Hainan's rubber plantations, exhibiting a 'core-edge' pattern, with high suitability in central and northwestern regions.
- The total suitable area has expanded by 5.8% in the last decade.
Conclusions:
- This study provides a robust methodology for large-scale dynamic monitoring and precision control of SSM.
- Findings offer critical decision support for agricultural biosecurity and pest management strategies.
- The predicted expansion highlights the need for adaptive pest management in response to changing environmental conditions.
