Baloch Muhammad N, Fan Jingyu, Haseeb Muhammad, Zhang Runzhi
Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Insects. 2020 Mar 9;11(3):172. doi: 10.3390/insects11030172.
is a serious agricultural pest native to tropical and subtropical areas of the Americas. It has a broad host suitability range, disperses rapidly, and has now invaded nearly 100 countries around the world by quickly establishing in the novel ecologies. Based on the native occurrence records and environmental variables, we predicted the potential geographic distribution of in Central Asia using the MaxEnt model and the ArcGIS. Irrigation is considered to be the main factor for the maize crop production in the Central Asia; therefore, we sought to map the potential spread of using two modeling approaches together with adjusted rainfall indices and environmental data from this region. The results showed that both approaches (MCP and Obs) could predict the potential distribution of . The Observation points (Obs) approach gave predicted more conservative projections compared with the Minimum Convex Polygon (MCP) approach. Areas of potential distribution that were consistently identified by the two modeling approaches included Western Afghanistan, Southern Kazakhstan and Southern Turkmenistan. The Receiver Operating Characteristic (ROC) curve test presented herein provided reliable evidence that the MaxEnt model has a high degree of accuracy in predicting the invasion of in Central Asia.
是一种原产于美洲热带和亚热带地区的严重农业害虫。它具有广泛的寄主适应性范围，扩散迅速，现已通过在新生态环境中迅速定殖而入侵了全球近100个国家。基于本地发生记录和环境变量，我们使用MaxEnt模型和ArcGIS预测了其在中亚的潜在地理分布。灌溉被认为是中亚玉米作物生产的主要因素；因此，我们试图通过两种建模方法结合该地区调整后的降雨指数和环境数据来绘制其潜在扩散图。结果表明，两种方法（MCP和Obs）都可以预测其潜在分布。与最小凸多边形（MCP）方法相比，观测点（Obs）方法给出的预测更为保守。两种建模方法一致确定的潜在分布区域包括阿富汗西部、哈萨克斯坦南部和土库曼斯坦南部。本文给出的受试者工作特征（ROC）曲线检验提供了可靠证据，证明MaxEnt模型在预测其在中亚的入侵方面具有高度准确性。
