秦岭南坡中段主要森林群落类型划分及环境梯度解释

The Quantitative Classification and Environmental Interpretation of Forest Communities in the Middle Area of South Slope of Qinling Mountains

该研究旨在揭示秦岭大熊猫主要栖息地植物群落分布规律及其与环境的对应关系。在研究区设置2条样带,每条样带各设置40-50个样地,共计93个。通过样地调查,利用双向聚类法（Two-way Clustering Method）、DCA、DCCA对秦岭大熊猫（Ailuropoda melanoleuca）栖息地森林群落进行数量分类和排序。（1）TWINSPAN将研究区93个样地分成太白红杉（Larix chinensis）、巴山冷杉（Abies fargesii）、铁杉（Tsuga chinensis）、华山松（Pinus armandii）、油松（Pinus tabulaeformi）、锐齿槲栎（Quercus aliena var.acuteserrata）等34个群落类型,并通过去趋势对应分析（DCA）对93个样地进行排序,结果表明不同类型样地呈现聚集分布,充分验证了TWINSPAN对群落类型划分的科学性。（2）通过对93个样地除趋势典范对应分析（DCCA）和Monte Carlo显著性检验表明,在分析12个环境因子中,制约秦岭南坡大熊猫栖息地群落类型、植物种分布格局的主要因素是海拔、坡度、速效磷与全磷。海拔、坡度、速效磷和全磷能够很好地解释群落样地及优势种在环境梯度上的分布,同时表现出样方间在种类和环境因子组成上的相似性。

In order to revealed the plant community distribution pattern and the corresponding relation with environment.Tow line transects were set between the altitude of 900-3 071 m in the field,and 93 standard plots（25 m×20 m） were investigated on transects.Vegetation community,growth status and distribution environment were recorded and soil samples were collected in the standard plots.Based on analysis of plots and soil data,the main forests in giant panda habitat were classified and ordering by using the two-way indicator species analysis（TWINSPAN）,Detrended Corresponding Analysis（DCA） and Detrended Canonical Correspondence Analysis（DCCA）.The results showed that：（1） 93 standard plots were classified into 34 plant communities by Two-way Indicators Species Analysis（TWINSPAN）.The result of ordering by DCA showed that the standard plots fit to aggregation distribution and verified the scientificity of TWINSPAN.（2） The restrictive factors of community type and species distribution were altitude,slope,rapidly-available phosphorus and total phosphorus at the landscape level by the test of significance between DCCA and Monte Carlo.Factors of altitude,slope,rapidly-available phosphorus and total phosphorus can explain distribution of communities and dominant species at the landscape level,and the species and environmental factors show great similarities between different plots.