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Biomass allocation characteristics of the main forest ecosystems in the Great Xing’an Mountains, Heilongjiang Province.

WANG Xiao-li1,2,3, CHANG Yu1**, CHEN Hong-wei1, HU Yuan-man1, FENG Yu-ting4, WU Wen1,3, JIAO Lin-lin1,3   

  1. (1State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; 2Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 3University of Chinese Academy of Sciences, Beijing 100049, China; 4Liaoning Academy of Environmental Sciences, Shenyang 110031, China)
  • Online:2014-06-10 Published:2014-06-10

Abstract: The Great Xing’an Mountains is one of the most important regions with abundant forest resources. Its large amounts of forest biomass and carbon stocks have significant implications for maintaining the carbon balance of Northeast China. Based on field investigation data, the forest biomass and its allocation characteristics as well as the quantitative relationships among biomass of each component of forest ecosystems at the stand level in the Great Xing’an Mountains were analyzed. Furthermore, the plots surveyed were divided into seven community types by ordination axes clustering analysis with the software of CANOCO 4.5 on species composition, and the biomass allocation characteristics of each of the community types and the relationship with environmental factors were elucidated. The results showed that the total forest biomass in the Great Xing’an Mountains ranged from 17.83 to 283.93 t·hm-2, and the average biomass density of each vegetation type was in the order of Quercus mongolica forest > larch forest > Pinus sylvestris var. mongolica forest. Compared with Q. mongolica forest, the percentage of underground biomass or litter biomass in coniferous forests was larger. Meantime, the differences of geographical distribution of plant community as well as biomass allocation characteristics were obvious along the elevation gradient.

Key words: soil phosphorus, semi-variance function, spatial variation., flooded area of the Yellow River, Kriging interpolation