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    刘佳琪, 魏广阔, 史常青, 赵廷宁, 钱云楷. 基于MaxEnt模型的北方抗旱造林树种适宜区分布[J]. 北京林业大学学报, 2022, 44(7): 63-77. DOI: 10.12171/j.1000-1522.20210527
    引用本文: 刘佳琪, 魏广阔, 史常青, 赵廷宁, 钱云楷. 基于MaxEnt模型的北方抗旱造林树种适宜区分布[J]. 北京林业大学学报, 2022, 44(7): 63-77. DOI: 10.12171/j.1000-1522.20210527
    Liu Jiaqi, Wei Guangkuo, Shi Changqing, Zhao Tingning, Qian Yunkai. Suitable distribution area of drought-resistant afforestation tree species in north China based on MaxEnt model[J]. Journal of Beijing Forestry University, 2022, 44(7): 63-77. DOI: 10.12171/j.1000-1522.20210527
    Citation: Liu Jiaqi, Wei Guangkuo, Shi Changqing, Zhao Tingning, Qian Yunkai. Suitable distribution area of drought-resistant afforestation tree species in north China based on MaxEnt model[J]. Journal of Beijing Forestry University, 2022, 44(7): 63-77. DOI: 10.12171/j.1000-1522.20210527

    基于MaxEnt模型的北方抗旱造林树种适宜区分布

    Suitable distribution area of drought-resistant afforestation tree species in north China based on MaxEnt model

    • 摘要:
        目的  樟子松、油松、山桃和山杏作为中国北方半干旱半湿润气候区的常用造林树种,具备抗旱耐寒特性和保持水土的功能,研究其适宜空间分布对中国北方植被恢复具有指导作用。
        方法  以半干旱半湿润气候区的樟子松、油松、山桃和山杏为研究对象,获取树种地理分布点位数据和与树种生态学相关的24个环境因子(地形、土壤和气象),基于协同克里金插值法,将限制因子叠加法与最大熵模型(MaxEnt)相结合,研究4类树种适宜区分布。
        结果  (1)4类树种MaxEnt模型预测精度达到准确水平(AUC > 0.90)。(2)影响樟子松分布的主导因子依次为土壤类型、最冷月均温和最冷月平均风速;油松的主导因子依次为高程、年均气温标准差、土壤类型、年降水量;山桃的主导因子依次为最暖月均温、高程、年极端最低气温、年均降水量标准差、坡度、土壤类型;山杏的主导因子依次为高程、土壤类型、最暖月平均降水量、湿润系数、最暖月均温。(3)樟子松中高适宜区主要分布于内蒙古、黑龙江、吉林等地,油松、山桃和山杏主要分布在山西、陕西、甘肃、河北、内蒙古等地。
        结论  MaxEnt模型模拟结果,可准确反映4类树种的适宜区分布情况,结果可为我国半干旱半湿润区绿化造林提供适地适树的科学指导。

       

      Abstract:
        Objective  Pinus sylvestris var. mongolica, Pinus tabuliformis, Amygdalus davidiana and Armeniaca sibirica are commonly used afforestation tree species in semi-arid and semi-humid areas, which have drought-resistant and cold-resistant characteristics and the function of soil and water conservation. Studying their suitable spatial distribution can guide the vegetation restoration in northern China.
        Method  Based on the ecological characteristics of tree species, with the data of tree species distribution and 24 environmental variables (topography, soil and meteorology), based on the Co-Kriging method, the limiting factor superposition method and the maximum entropy model (MaxEnt) were combined to study the distribution of the suitable areas of 4 tree species.
        Result  (1) The prediction accuracy of MaxEnt model of four tree species reached the accurate level (AUC > 0.90). (2)The dominant factors affecting the distribution of Pinus sylvestris var. mongolica were ordered as soil type, average temperature in the coldest month and average wind speed in the coldest month. The dominant factors of Pinus tabuliformis were ordered as elevation, standard deviation of annual average temperature, soil type and annual precipitation; the dominant factors of Amygdalus davidiana were ordered as the average temperature of the warmest month, elevation, annual extreme minimum temperature, standard deviation of annual precipitation, slope and soil type; the dominant factors of Prunus armeniaca were elevation, soil type, average precipitation in the warmest month, wetting coefficient and average temperature in the warmest month in turn. (3) The middle and high suitable areas of Pinus sylvestris var. mongolica were mainly distributed in Inner Mongolia of northern China, Heilongjiang and Jilin provinces of northeastern China, Pinus tabuliformis, Amygdalus davidiana and Armeniaca sibirica were mainly distributed in Shanxi, Hebei provinces and Inner Mongolia of northern China, Shaanxi, Gansu provinces of northwestern China,
        Conclusion  In this study, MaxEnt model can accurately reflect the distribution of four tree species, and the results can provide scientific guidance for the afforestation in the semi-arid and semi-humid climate regions of China.

       

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