基于MaxEnt的西藏飞蝗在中国的适生区预测
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四川省教育厅资助项目(07ZA066)


Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling
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    摘要:

    西藏飞蝗是青藏高原本地特有物种,为青稞和牧草的重要害虫,近年来危害范围有扩大蔓延趋势。研究并明确西藏飞蝗在中国的适生区域,对制定该虫的早期监测、预警及控制措施意义重大。近年来最大熵理论在物种适生研究领域得到广泛应用,基于西藏飞蝗的分布信息和环境变量,采用MaxEnt生态位模型和ArcGIS对其在中国的潜在分布区进行预测,用ROC曲线检测模型精度、刀切法(jackknife test)筛选主导环境变量及其适宜值。两次模拟的AUC值分别为0.996和0.993,预测结果与实际拟合度很高。西藏飞蝗在中国的高适生区主要位于四川的甘孜州,西藏的昌都地区、林芝地区、山南地区及拉萨市,中适生区则以高适生区为中心向外扩散,集中在青藏高原东部地区。海拔、8月份平均雨量、1月份平均雨量、等温性、12-2月份的平均温度是影响西藏飞蝗潜在分布的主要环境变量。

    Abstract:

    Locusta migratoria tibetensis Chen, a unique subspecies in the Tibetan Plateau, is a destructive pest of highland barley and herbage. The methods of Maximum Entropy have been deployed for some years to address the problem of species abundance distributions. To monitor and control L. migratoria tibetensis, it is necessary to investigate the potential distribution area of this pest. In this approach, ecological niche modeling software MaxEnt (the maximum entropy model), combined with ArcGIS (Geographic information System), was applied to predict the potential geographic distribution of L. migratoria tibetensis in China. Bioclimatic dominant factors and the appropriate ranges of their values were also investigated. The results showed that training data AUC were 0.996 and 0.993 in the two simulations, which indicated a better forecast. The highly suitable area for L. migratoria tibetensis was Ganzi in Sichuan Province, and Changdu, Linzhi, Shanan, and Lasa of the Tibet Autonomous Region, whereas the moderately suitable area was in the west of Sichuan, east of Xizang, and north of Yunnan. The important environmental factors affecting the distribution of L. migratoria tibetensis were altitude, average precipitation in August, average precipitation in January, isothermality, average mean temperature in January, average mean temperature in December, and average mean temperature in February.

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王茹琳,李庆,封传红,石朝鹏.基于MaxEnt的西藏飞蝗在中国的适生区预测.生态学报,2017,37(24):8556~8566

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