基于MaxEnt模型预测黄脊竹蝗在中国的适生区

为探明黄脊竹蝗Ceracris kiangsu在我国的潜在适生区,做好早期虫情监测.本研究根据267个黄脊竹蝗物种分布点,结合气候数据,采用最大熵(Maxent)模型和ArcGIS预测黄脊竹蝗在我国的适生区分布.结果表明:影响黄脊竹蝗适生区分布的主要环境变量为最干月降水量和最冷月最低温,次要环境变量为湿季降水量、最热月最高温、降水量季节性变异系数和温度年较差.预测的黄脊竹蝗高适生区、中适生区、低适生区分别占全国陆地总面积的3.0％、5.6％和10.3％,适生区主要分布在江淮流域、长江中下游地区、华南及西南等地.模型预测结果与实际调查一致性较高,能够反映真实分布情况,对科学开展黄脊竹蝗防控具有较高参考价值.

Potential distribution prediction of yellow spined bamboo locust (Ceracris kiangsu) in China based on maxent model

In order to find out the potential suitable areas of yellow spined bamboo locust (Ceracris kiangsu) in China, and do a good job in early monitoring. Based on 267 species distribution sites of C. kiangsu were obtained. Combined with climate data, the MaxEnt model and ArcGIS were used to predict the suitable habitat distribution of the C. kiangsu in China. The results showed that the main environmental variables affecting the distribution of C. kiangsu were the driest monthly precipitation and the coldest month, while the secondary environmental variables were the wet season precipitation, the hottest month maximum temperature, the seasonal variation coefficient of precipitation and the annual temperature range. The predicted high, medium and low suitable areas of the C. kiangsu, respectively, account for 3.0%, 5.6% and 10.3% of the total land area of China. The suitable areas were mainly distributed in the Yangtze Huaihe River Basin, the middle and lower reaches of the Yangtze River, South China and Southwest China. The predicted results of the model were consistent with the actual survey, which could reflect the real distribution, and had a high reference value for the scientific prevention and control of C. kiangsu.