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<p>The six–spotted mite (SSM), <i>Eotetranychus sexmaculatus</i> (Acari: Tetranychidae), is one of the most destructive pests threatening rubber tree plantations. Accurately identifying its suitable regions is essential for implementing targeted pest management and establishing early warning systems. This study, focusing on Hainan Island, China, innovatively integrates multi-source data, including field survey points, topography, meteorology, and remote sensing vegetation indices, to develop a high-precision method for extracting SSM suitable regions based on the maximum entropy algorithm implemented in the MaxEnt. The results demonstrate that the model exhibits excellent performance (Area Under Curve [AUC] = 0.907, True Skill Statistic [TSS] = 0.803), with 93.9% of pest occurrence points accurately located within the predicted suitable regions, and all cross-replicate prediction standard deviations are lower than 0.3. Elevation, slope, annual precipitation, minimum land surface temperature from May to July, and average renormalized difference vegetation index from May to July were identified as the primary driving factors influencing SSM distribution. The spatial distribution of SSM suitable regions in Hainan Island revealed a ‘core–edge’ pattern, with the total suitable area covering approximately 92% of the island’s rubber plantations. Of this, the high suitable region accounts for 27.9%, and is predominantly located in the central and northwestern regions of the island. Furthermore, the total suitable area has expanded by approximately 5.8% over the past decade. This research not only provides a scientific paradigm for the large-scale dynamic monitoring and regionalized precision control of SSM but also offers forward-looking decision support for addressing future agricultural biosecurity challenges.</p>

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Identification of suitable habitats for the six-spotted mite on rubber trees using multi-source data and MaxEnt modeling

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Yanan You,
  • Huichun Ye,
  • Donghua Wang,
  • Chaojia Nie,
  • Jingjing Wang,
  • Bingsun Wu,
  • Fengzheng Cai,
  • Jiajian Deng,
  • Lixia Shen,
  • Fu Wen

摘要

The six–spotted mite (SSM), Eotetranychus sexmaculatus (Acari: Tetranychidae), is one of the most destructive pests threatening rubber tree plantations. Accurately identifying its suitable regions is essential for implementing targeted pest management and establishing early warning systems. This study, focusing on Hainan Island, China, innovatively integrates multi-source data, including field survey points, topography, meteorology, and remote sensing vegetation indices, to develop a high-precision method for extracting SSM suitable regions based on the maximum entropy algorithm implemented in the MaxEnt. The results demonstrate that the model exhibits excellent performance (Area Under Curve [AUC] = 0.907, True Skill Statistic [TSS] = 0.803), with 93.9% of pest occurrence points accurately located within the predicted suitable regions, and all cross-replicate prediction standard deviations are lower than 0.3. Elevation, slope, annual precipitation, minimum land surface temperature from May to July, and average renormalized difference vegetation index from May to July were identified as the primary driving factors influencing SSM distribution. The spatial distribution of SSM suitable regions in Hainan Island revealed a ‘core–edge’ pattern, with the total suitable area covering approximately 92% of the island’s rubber plantations. Of this, the high suitable region accounts for 27.9%, and is predominantly located in the central and northwestern regions of the island. Furthermore, the total suitable area has expanded by approximately 5.8% over the past decade. This research not only provides a scientific paradigm for the large-scale dynamic monitoring and regionalized precision control of SSM but also offers forward-looking decision support for addressing future agricultural biosecurity challenges.