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云南省思茅松林群落数量分类及物种多样性与自然环境的关系

李帅锋1,2,苏建荣1,2**,刘万德1,2,郎学东1,2,张志钧1,2,苏磊1,2,杨华景3   

  1. 1中国林业科学研究院资源昆虫研究所, 昆明 650224; 2国家林业局普洱森林生态系统定位研究站, 昆明 650224; 3云南省-普洱市林业科学研究所, 云南普洱 665000)
     
  • 出版日期:2013-12-10 发布日期:2013-12-10

Quantitative classification of Pinus kesiya var. langbianensis communities and their species richness in relation to the environmental factors in Yunnan Province of Southwest China.

LI Shuai-feng1,2, SU Jian-rong1,2**, LIU Wan-de1,2, LANG Xue-dong1,2, ZHANG Zhi-jun1,2, SU Lei1,2, YANG Hua-jing3   

  1. (1Research Institute of Resource Insects, Chinese Academy of Forestry, Kunming 650224, China; 2The Pu’er Forest Ecosystem Research Station, State Forestry Bureau, Kunming 650224, China; 3Forestry Research Institute of Pu’er Municipality, Pu’er 665000, Yunnan, China)
  • Online:2013-12-10 Published:2013-12-10

摘要:

通过对云南省中南部和西南部9个县天然分布的思茅松林群落的调查,应用双向指示种分析(TWINSPAN)对群落进行数量分类,采用主成分分析(PCA)、冗余分析(RDA)和广义可加模型(GAM)等方法对群落类型进行排序,分析群落类型、物种分布及物种多样性与环境因子的关系。结果表明:(1)研究区域的思茅松林可分为8个主要群落。(2)对群落和物种分布产生显著影响的主导因子是海拔,其次是年均气温、年均降雨量和坡度。RDA的排序轴反映思茅松林群落物种尤其是优势物种分布随海拔、年均气温、年均降雨量和坡度的变化而变化,思茅松的分布与海拔和年均降雨量有显著的负相关,与年均气温和坡度有显著正相关。(3)GAM拟合结果显示,海拔和年均降雨量对物种丰富度的影响达极显著水平(P<0.001)。
 

Abstract: A field survey was conducted on the naturally distributed Pinus kesiya var. langbianensis communities in nine counties of southcentral and southwest Yunnan Province. The twoway indicators species analysis (TWINSPAN) was applied to quantitatively classify the communities, and the principal component analysis (PCA) and redundancy analysis (RDA) were adopted to coordinate the communities, with the relationships between the community type, species distribution, species diversity, and environmental factors analyzed. In the study area, the P. kesiya var. langbianensis communities could be classified into eight major communities. The dominant environmental factors affecting the communities and species distribution were in the order of altitude, mean annual air temperature, mean annual precipitation, and slope. The RDA ordination axes indicated that the distribution of the species, especially of the dominant species, varied with the variations of altitude, mean annual air temperature, mean annual precipitation, and slope. The distribution of P. kesiya var. langbianensis had significant negative correlations with altitude and mean annual precipitation, and significant positive correlations with mean annual temperature and slope. The generalized additive model (GAM) fitted the responses of species richness to various environmental factors, with the effects of altitude and mean annual precipitation on the species richness being significant (P<0.001).