Welcome to visit Scientia Silvae Sinicae,Today is

Scientia Silvae Sinicae ›› 2008, Vol. 44 ›› Issue (8): 1-8.doi: 10.11707/j.1001-7488.20080801

    Next Articles

Spatial Distribution of Tree Species and Environmental Interpretations of Secondary Forest in Changbai Mountains

Zhang Chunyu1,Zhao Xiuhai1,Xia Fucai 1.2   

  1. (1.The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University Beijing 100083;2.Forestry College of Beihua University Jilin 132013)
  • Received:2007-07-04 Revised:1900-01-01 Online:2008-08-25 Published:2008-08-25

Abstract:

Spatial pattern of 12 tree species in a secondary forest was investigated, and the relationship between spatial distribution of trees and environment factors was analyzed. The results indicated: 1) Most of environment factors and tree species showed significant spatial autocorrelation, that is, there was a spatially clumped structure;2) Variation partition analysis of the space indicated that environment factors regulated mainly the spatial pattern of saplings and young trees, however there was little effect of the environment factors on the distribution of big trees;3) Soil moisture and soil pH value significantly influence on the distribution pattern of most tree species,and soil nutrition (including total N,total P,total K) showed slight effect on the distribution pattern; however, soil organic matter,leaf area index and PPFD had no significant effect on spatial variation of 12 tree species;4) Environment factors hardly interpreted spatial variation of tree species (including variation interpreted by environment alone and variation interpreted by environment and space in common). In contrast, pure spatial variables exhibited stronger interpretation power,which suggested that some unknown spatial process or processes independent of environment factors might play an important role in the formation of spatial structure of tree species.

Key words: local Moran s I, variation partition, environmental interpretations, autocorrelation