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<p>The giant borer (<i>Telchin licus</i>) is considered a key pest of sugarcane, causing significant productivity losses in Central and South American countries. This study used the Maximum Entropy algorithm (MaxEnt) to model the current and future potential distribution of <i>T. licus</i> under the SSP1-2.6 and SSP5-8.5 climate scenarios, based on the MIROC6 model (CMIP6) for the periods of 2030 and 2050. The model showed high accuracy (AUC = 0.979), with Temperature Seasonality (Bio4, 43.6%) and Annual Precipitation (Bio12, 30.2%) as the most influential variables. Projections indicate an increase in suitable areas for the pest by 2050, with increases of up to 20.88% in high-suitability zones. Additionally, there is a risk of expansion into Africa, Asia, and Oceania, regions where the pest is not yet present but exhibits climatic conditions similar to its native areas, which may favor its establishment. The results highlight the importance of preventive phytosanitary measures and integrated pest management strategies in sugarcane-producing regions, especially in Brazil, the world’s largest producer. This study provides scientific support for control and monitoring policies, although it presents limitations inherent to models based solely on climatic data. Other factors, such as soil characteristics, host presence, and agricultural practices, should be considered in future analyses.</p>

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Predicting the current and future distribution of Telchin licus (Lepidoptera: Castniidae) based on the maximum entropy model

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Marinaldo Loures-Ferreira,
  • Gildriano Soares de Oliveira,
  • André Medeiros de Andrade,
  • Jackson Paulo Silva Souza,
  • Ricardo Siqueira da Silva

摘要

The giant borer (Telchin licus) is considered a key pest of sugarcane, causing significant productivity losses in Central and South American countries. This study used the Maximum Entropy algorithm (MaxEnt) to model the current and future potential distribution of T. licus under the SSP1-2.6 and SSP5-8.5 climate scenarios, based on the MIROC6 model (CMIP6) for the periods of 2030 and 2050. The model showed high accuracy (AUC = 0.979), with Temperature Seasonality (Bio4, 43.6%) and Annual Precipitation (Bio12, 30.2%) as the most influential variables. Projections indicate an increase in suitable areas for the pest by 2050, with increases of up to 20.88% in high-suitability zones. Additionally, there is a risk of expansion into Africa, Asia, and Oceania, regions where the pest is not yet present but exhibits climatic conditions similar to its native areas, which may favor its establishment. The results highlight the importance of preventive phytosanitary measures and integrated pest management strategies in sugarcane-producing regions, especially in Brazil, the world’s largest producer. This study provides scientific support for control and monitoring policies, although it presents limitations inherent to models based solely on climatic data. Other factors, such as soil characteristics, host presence, and agricultural practices, should be considered in future analyses.