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

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

  • 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.
  • loading

Catalog

    Turn off MathJax
    Article Contents

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return