四川盆地路域边坡植被的数量分类与排序

Quantitative Classification and Ordination of Roadside Slope Vegetation in Sichuan Basin

对四川盆地内14条道路的路域边坡植被进行调查,其中包括铁路、高速公路和普通公路3种类型。用TWINSPAN方法对其进行数量分类,并用CCA排序对路龄、海拔、坡度、坡向、土壤厚度和基岩风化程度等影响植被组成和结构的环境因子进行评估。结果表明:四川盆地路域边坡植被分为8个群丛,各群丛在排序图中的位置能反映其在特定环境条件下的适应性;蒙氏置换检验表明物种与环境因子整体效果间存在极显著的相关性,排序结果较优;定量型环境因子中,路龄对植被组成和结构的解释占17.45%,海拔占14.62%,风化程度占14.53%,坡度占13.11%,坡向占11.78%,土壤厚度占11.60%,前四者受随机因素的影响较小,显著性水平达到极显著(P<0.01),坡向的显著性水平为显著(P<0.05),而土壤厚度解释能力受随机影响较大,显著性水平为不显著。

In order to understand the composition and structure of the roadside slope vegetation in Sichuan Basin, we use TWINSPAN, which is a wildly used approach of quantitative classification, to analyze the data from the survey of the slope vegetation along 14 roads, which include railways, express ways and highways. We also use canonical cori＇espondence analysis （CCA） to assess the impact of environment variables on the species composition and structure of the vegetation, such as the road age, the altitude, the gradient, the aspect, the soil thickness and the weathering degree. The results show that the roadside slope vegetation of Sichuan Basin is divided into 8 associations. The ordination diagram can well reflect the adaptability of these associations to specific environmental conditions. Monte Carlo permutation test shows that the correlation between species＇ and environmental variables is extremely significant. In quantitative environmental variables, explanatory contribution of road age, altitude, degree of weathering, gradient, aspect and soil thickness are 17.45%, 14.62%, 14.53%, 13.11%, 11.78% andll. 60%, respectively, The former four are rarely affected by random error, and the significance level is 1% （P 〈0.01）. In.terms of aspect, the significance level is 5% （P 〈0.05）. Explanatory ability of soil thickness is largely affected by random error, and it Showed no significant difference （P 〉 0.05）.