| 摘要: |
| 【目的】用已有的采集标本分布记录，模拟三叶海棠的地理分布。【方法】从中国4个标本馆获取三叶海棠的291个分布数据，从WorldClim网站下载1950－2000年生物气候数据，用最大熵模型(MaxEnt)模拟其地理分布。【结果】①三叶海棠的潜在分布地区有：巴基斯坦东北部；不丹中部、东北部；印度东北部；中国西藏东南部、河北省东北部和北京西部山区。②依据模拟分布值0.5～0.7标定的三叶海棠分布区域，排除其潜在分布区域，再结合这些区域的地理间隔性，从宏观景观上将三叶海棠的地理分布格局划分为4个区块：中国四川、甘肃、陕西三省交界地区；中国重庆大部分地区，贵州大部分地区，湖北西南部，湖南大部分地区，广西与贵州、湖南交界地区，江西，广东与湖南、江西交界地区，福建东北部，浙江大部分地区，安徽南部；韩国南部沿海地区；日本岛大部分地区。③刀切法检测表明，温暖季节平均降雨量对三叶海棠的分布增益贡献最大，三叶海棠喜生于温暖季节平均降雨量在450～800 mm的地区。【结论】用MaxEnt模拟三叶海棠的地理分布有一定的准确性，反映出了三叶海棠基本的地理分布格局和潜在分布区域，并阐明了主导其地理分布的生物气候因子。 |
| 关键词: 三叶海棠 MaxEnt模型 地理分布 |
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| 基金项目:国家自然科学基金项目（31070588）；安徽省自然科学基金项目（10040606Q18）；安徽农业大学人才引进基金项目（2008010） |
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| MaxEnt based geographic distribution pattern of Malus toringo |
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| Abstract: |
| 【Objective】Geographic distribution pattern of Malus toringo was simulated using distribution records of collected specimen.【Method】 Geographic distribution of M.toringo was modeled using MaxEnt based on 291 distribution records from four herbariums in China and bioclimatic data (1950－2000) from WorldClim.【Result】① Potential distribution areas of M.toringo included the northeast of Pakistan,the central and northeast of Bhutan,the northeast of India,the southeast of Xizang,the northeast of Hebei,and the western mountains of Beijing.②The geographic distribution pattern of M.toringo were divided into four distribution blocks based on distribution values of 0.5-0.7:the border area of Sichuan,Gansu and Shaanxi in China;Chongqing,the most area of Guizhou,the southwest of Hubei,the most area of Hunan,the border area of Guangxi,Guizhou and Hunan,the most area of Jiangxi,the border area of Guangdong,Hunan and Jiangxi,the northeast Fujian,the most area of Zhejiang,the south of Anhui;the southern coastal areas of Korea;and the most area of Japanese islands.③Jackknife Test showed that average precipitation in warm seasons had the greatest contribution to the distribution gain of M.toringo and it naturally distributed over areas with average precipitation of 450－800 mm.【Conclusion】In general,MaxEnt accurately simulated the geographical distribution of M.toringo.It showed the basic pattern of geographic distribution and the potential distribution areas,and clarified the dominant bioclimatic factors to geographic distribution of M.toringo. |
| Key words: Malus toringo Siebold ex De Vriese maximum entropy modelling(MaxEnt) geographic distribution |
