摘要
为减少外来入侵物种菜豆象Acanthoscelides obtectus和蚕豆象Bruchus rufimanus对中国造成的潜在威胁,收集这2种豆象的全球地理分布数据,采用Pearson相关性分析和主成分分析分别从19个环境变量中筛选关键环境变量,采用MaxEnt模型对历史气候条件下和未来气候情景下这2种豆象在中国的适生区进行预测,并对预测结果进行分析。结果显示,经Pearson相关性分析共筛选出4个关键环境变量用于菜豆象适生性区的模型构建,分别为最暖季度平均温度、最干月份降水量、年气温变化范围及最湿季度降水量,其对MaxEnt模型的累积贡献率分别为31.6%、28.4%、26.3%和13.7%;经Pearson相关性分析共筛选出4个主要关键环境变量用于蚕豆象适生性区的模型构建,分别为最冷季度平均温度、最干月份降水量、最热月份最高温度和最湿月份降水量,其对MaxEnt模型的累积贡献率分别为48.5%、39.5%、7.8%和4.2%。MaxEnt模型重复运行10次后,菜豆象训练数据的平均AUC值为0.938,蚕豆象训练数据的平均AUC值为0.963,均显著高于随机模型的AUC值,表明基于MaxEnt模型的菜豆象和蚕豆象在中国适生区的预测结果准确。未来气候情景下,这2种豆象在中国的适生区均呈现向北扩张的趋势,需加强对这2种豆象的检疫与防治,严防发生区域进一步扩大。
To reduce the damage caused by bean weevil Acanthoscelides obtectus and broad bean weevil Bruchus rufimanus in China,the data on the potential geographical distribution of these two insects were collected.Key environmental variables were selected from 19 variables through Pearson correlation analysis and principal component analysis.Subsequently,the potential suitable areas for these two bean weevils under current climate condition and future climate scenarios in China were analyzed using the MaxEnt model.As a result of the analysis,four key environmental variables were selected by Pearson correlation analysis and principal component analysis for modeling the suitable area of A.obtectus,including the average temperature in the warmest quarter,the driest monthly precipitation,annual temperature range,and precipitation in the wettest quarter.The MaxEnt model yielded cumulative contribution rates of 31.6%,28.4%,26.3%,and 13.7%for each variable,respectively.Applying a similar analysis method,four key environmental variables were chosen for constructing the model of the natural habitat of fava bean weevils.These variables consist of the average temperature in the coldest quarter,precipitation in the driest month,maximum temperature in the hottest month,and precipitation in the wettest month.Their cumulative contribution rates to the MaxEnt model were 48.5%,39.5%,7.8%,and 4.2%,respectively.After conducting ten repeats of the MaxEnt model,the average area under the curve(AUC)value for the fava bean weevils training data was 0.938,and for the broad bean weevils training data,it was 0.963.Both values were significantly higher than the AUC value of the random model.These findings indicate the accuracy of the MaxEnt model in predicting suitable areas for Chinese bean weevils and broad bean weevils.Under future climate scenarios,the suitable habitats of these two species of soybean weevils were projected to expand northward.It is recommended to enhance quarantine and control measures for these species to further expand areas
作者
易山青
彭硕
贾涛
王雁楠
黄宏坤
赵紫华
Yi Shanqing;Peng Shuo;Jia Tao;Wang Yannan;Huang Hongkun;Zhao Zihua(Key Laboratory of Surveillance and Management for Plant Quarantine Pests of Ministry of Agriculture and Rural Affairs,College of Plant Protection,China Agricultural University,Beijing 100193,China;Agency of Agricultural Ecology and Resource Protection,Ministry of Agriculture and Rural Affairs,Beijing 100125,China)
出处
《植物保护学报》
CAS
CSCD
北大核心
2023年第6期1480-1490,共11页
Journal of Plant Protection
基金
国家重点研发计划(2021YFC2600400)。
关键词
菜豆象
蚕豆象
MaxEnt模型
生物入侵
适生区
生物气候因素
Acanthoscelides obtectus
Bruchus rufimanus
MaxEnt model
biological invasion
suitable area
bioclimate factor
