Community structure in different successional stages in north temperate forests of Changbai Mountains, China.

Abstract

Aims The Changbai Mountains had a large area of undisturbed temperate old-growth forest that was partly destroyed by large-scale industrial logging at the turn of the 20th century and replaced by secondary forests and plantations. Our objective was to further understand ecosystem devel-opment,community composition and structure,and spatial distribution of major tree populations. Methods Three long-term 5.2 hm2 forest plots were established in secondary Populus davidi-ana-Betula platyphylla forest (PBF),secondary conifer and broadleaved mixed forest (SCBMF) and Tilia amurensis-Pinus koraiensis mixed forest (TPMF) in the Changbai Mountains following the proto-cols of the Center for Tropical Forest Science forest dynamic plot. We tagged and recorded the species,diameter at breast height (DBH),height and crown diameter of all live and dead trees with DBH 1 cm. We determined the spatial distribution of five dominant canopy tree species by pair-correlation g(r) function and performed variation partitioning analysis to estimate the environmental effect on spatial distribution. Important findings We recorded 20 949,14 725 and 12 062 live individuals and basal areas of 24.74,32.07 and 56.64 m2.hm–2 in PBF,SCBMF and TPMF,respectively. Tilia amurensis,with the overall highest importance value and basal area,was the major dominant tree species in SCBMF,P. davidianaand B. platyphylla dominated PBF and P. koraiensis dominated TPMF. In the secondary forests BPF and SCBMF,the size-class distribution of Pinus koraiensis,Acer mono,Abies nephrolepis,Picea jezoensis and Tilia amurensis showed the reverse J-shape form,whereas P. koraiensis and T. amurensis showed normal distributions in TPMF. Paired-correlation g(r) function showed that the main tree species had aggregated distributions,and the aggregation intensity decreased with the increasing distances. Environment variables had a strong effect on tree distribution,but the interpretation power of environment variables was very low in TPMF.