何兰君, 李林霞, 欧光龙. 基于标志种分布预测的哀牢山植被潜在分布及气候解释研究[J]. 西南林业大学学报(自然科学), 2024, 44(3): 52–60 . DOI: 10.11929/j.swfu.202303072
引用本文: 何兰君, 李林霞, 欧光龙. 基于标志种分布预测的哀牢山植被潜在分布及气候解释研究[J]. 西南林业大学学报(自然科学), 2024, 44(3): 52–60 . DOI: 10.11929/j.swfu.202303072
He Lanjun, Li Linxia, Ou Guanglong. Prediction on the Potential Distribution of Vegetation and Climate Interpretation Based on the Distribution of Indicative Species in Ailao Mountain[J]. Journal of Southwest Forestry University, 2024, 44(3): 52-60. DOI: 10.11929/j.swfu.202303072
Citation: He Lanjun, Li Linxia, Ou Guanglong. Prediction on the Potential Distribution of Vegetation and Climate Interpretation Based on the Distribution of Indicative Species in Ailao Mountain[J]. Journal of Southwest Forestry University, 2024, 44(3): 52-60. DOI: 10.11929/j.swfu.202303072

基于标志种分布预测的哀牢山植被潜在分布及气候解释研究

Prediction on the Potential Distribution of Vegetation and Climate Interpretation Based on the Distribution of Indicative Species in Ailao Mountain

  • 摘要: 基于哀牢山植被12个标志种的地理分布信息和19个生物气候因子数据,采用MaxEnt模型分析和预测物种在当前气候背景下在中国和哀牢山的潜在适生分布区。结果表明:5类植被的MaxEnt模型预测精度达到非常好水平(AUC>0.90)。影响暖温性针叶林的主要因子为年气温变化范围(bio7)和最冷季度平均气温(bio11),影响暖热性针叶林的主导因子为最暖季度平均降水量(bio18)、年平均温度(bio1)、昼夜温差与年温度比值(bio3)和最干季度降水量(bio17);影响半湿润常绿阔叶林的主导因子为最冷月份最低温度(bio6)、温度变化方差(bio4)、年气温变化范围(bio7)、最冷季度平均温度(bio11)、年平均温度(bio1),影响中山湿性常绿阔叶林的主导因子为最冷月份最低温度(bio6)、年气温变化(bio7)、温度变化方差(bio4)、最暖季度平均降水量(bio18)和最冷季度平均降水量(bio19),影响干热河谷稀疏灌木草丛的主导因子为温度变化方差(bio4)和最冷季度平均温度(bio11)、最冷月份最低温度(bio6)、昼夜温差月均值(bio2)、年气温变化范围(bio7)、最暖季度平均降水量(bio18)、最干月份降水量(bio14)。暖温性针叶林和半湿润常绿阔叶林中高适生区主要分布于中国西南地区和哀牢山中北部地区,暖热性针叶林主要分布在中国云南和哀牢山的西南地区,中山湿性常绿阔叶林主要分布在中国的西南地区、台湾等地和哀牢山全域,干热河谷稀疏灌木草丛主要分布在中国的西南地区、广东、广西、福建、台湾等地和哀牢山的中南部及北部地区。MaxEnt模型所模拟的结果,可以准确反映5类植被的适生区分布情况,揭示生物气候因子对其影响,可以为哀牢山植被的保护管理提供参考。

     

    Abstract: Based on the 12 indicative species Ailao Mountain vegetation geographical information and 19 biological climate factor data as the research object. Using ArcGIS software research and MaxEnt model predicting species in the current climate background in China and the Ailao Mountain potential suitable area. The results show that the 5 types of vegetation MaxEnt model prediction accuracy is a very good level (AUC > 0.90). The main factors affecting the warm coniferous forest are mainly temperature annum range (bio7) and mean temperature of coldest quarter (bio11); warm coniferous forest of the dominant factors precipitation of warmest quarter (bio18), annual mean temperature (bio1), isothermality (bio3) and temperature annum range (bio7); the dominant factor of semi-humid evergreen broad-leaved forest is min temperature of coldest month (bio6), temperature seasonality (bio4), temperature annum range (bio7), mean temperature of coldest quarter (bio11)and annual mean temperature (bio1); the dominant factor of montane mois evergreen broad-leaved forest are mainly min temperature of coldest month (bio6), temperature annum range (bio7), temperature seasonality (bio4), precipitation of warmest quarter (bio18)and precipitation of coldest quarter (bio19); the dominant factor of the dry hot valleys sparse shrub grass is temperature seasonality (bio4), mean temperature of coldest quarter (bio11), min temperature of coldest month (bio6), mean diurnal range (bio2), temperature annum range (bio7), precipitation of warmest quarter (bio18) and precipitation of driest month (bio14). Warm temperate coniferous forests and semi humid evergreen broad-leaved forests are mainly distributed in the high suitability areas of southwestern China and the central northern part of the Ailao Mountains. Warm temperate coniferous forests are mainly distributed in Yunnan and the southwestern part of the Ailao Mountains in China. Wet evergreen broad-leaved forests in the middle mountains are mainly distributed in southwestern China, Taiwan, and the entire Ailao Mountains. Sparse shrubs and grass in dry and hot river valleys are mainly distributed in southwestern China, such as Guangdong, Guangxi, Fujian, Taiwan, and the central, southern, and northern parts of the Ailao Mountains. The simulation results of MaxEnt model can accurately reflect 5 types of vegetation, and provide reference for the protection and management of vegetation in Ailao Mountains.

     

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