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基于MaxEnt模型预测梨火疫病菌的潜在地理分布
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引用本文:吴卓瑾,梁特,石娟.基于MaxEnt模型预测梨火疫病菌的潜在地理分布.植物保护学报,2023,50(6):1518-1527
DOI:10.13802/j.cnki.zwbhxb.2023.2023828
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作者单位E-mail
吴卓瑾 北京林业大学林学院, 林木有害生物防治北京市重点实验室, 中法欧亚森林入侵生物联合实验室, 北京 100083  
梁特 北京林业大学林学院, 林木有害生物防治北京市重点实验室, 中法欧亚森林入侵生物联合实验室, 北京 100083  
石娟 北京林业大学林学院, 林木有害生物防治北京市重点实验室, 中法欧亚森林入侵生物联合实验室, 北京 100083 BJshijuan@bjfu.edu.cn 
中文摘要:为探究梨火疫病菌解淀粉欧文氏菌Erwinia amylovora在全球的潜在地理分布,基于其全球分布数据和筛选得到的环境变量,利用MaxEnt模型对其在当前气候和未来气候条件下的潜在地理分布进行预测,并利用刀切法和皮尔逊相关性分析法筛选对梨火疫病菌分布有重要影响的环境变量。结果显示,对梨火疫病菌分布有重要影响的环境变量包括2月平均最高温度、1月平均降水量、7月平均最低温度、温度变化方差、昼夜温差月均值和7月平均降水量,表明春季和夏季的温度和降水对梨火疫病菌的分布有较大影响。在当前气候条件下,梨火疫病菌在全球的适生区分布较广,适生区总面积达到5.58×107 km2,且高适生区主要分布在北美洲沿海地区、地中海沿岸和亚洲中部及东部的部分地区;梨火疫病菌在我国的适生区总面积为7.36×106 km2,占全国陆地总面积的76.70%;在未来气候SSP126和SSP585情景下,梨火疫病菌在全球的适生区总面积分别为5.52×107 km2和5.24×107 km2。表明梨火疫病菌对我国大部分地区有潜在威胁,应加强监测与防控。
中文关键词:梨火疫病菌  MaxEnt模型  潜在地理分布  气候变化  环境变量
 
Prediction of the global potential geographical distribution of fire blight pathogen Erwinia amylovora by using the MaxEnt model
Author NameAffiliationE-mail
Wu Zhuojin Sino-France Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Key Laboratory for Forest Pest Control, College of Forestry, Beijing Forestry University, Beijing 100083, China  
Liang Te Sino-France Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Key Laboratory for Forest Pest Control, College of Forestry, Beijing Forestry University, Beijing 100083, China  
Shi Juan Sino-France Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Key Laboratory for Forest Pest Control, College of Forestry, Beijing Forestry University, Beijing 100083, China BJshijuan@bjfu.edu.cn 
Abstract:In order to explore the potential global geographical distribution of fire blight pathogen Erwinia amylovora, MaxEnt model was used for prediction under near current and future climate conditions based on the global distribution data and selected environmental variables. Jackknife analysis and Pearson correlation analysis were used to screen the environmental variables that had an important effect on the distribution of E. amylovora. The results showed that the maximum temperature in February, precipitation in January, minimum temperature in July, temperature seasonality, mean diurnal range and precipitation in July were important environmental variables, which indicated that temperature and precipitation in spring and summer had a significant influence on the distribution of E. amylovora. Under near current climate conditions, E. amylovora had a wide potential distribution, the total area of suitable areas reached 5.58×107 km2, and the highly suitable areas were mainly distributed in the coastal areas of North America, the Mediterranean coast and parts of central and eastern Asia. The total suitable area of E. amylovora in China was 7.36×106 km2, accounting for 76.70% of the total land area. Under SSP126 and SSP585 climate conditions in the future, the total area of the global potential distribution is 5.52×107 km2 and 5.24×107 km2, respectively. To sum up, E. amylovora poses a threat to most areas in China, and surveillance and prevention should be strengthened
keywords:Erwinia amylovora  MaxEnt model  potential geographical distribution  climate change  environmental variable
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