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北京松山天然油松林土壤有机碳分布及其影响因素

高杰,郭子健,刘艳红*   

  1. (北京林业大学森林培育与保护省部共建教育部重点实验室, 北京 100083)
  • 出版日期:2016-10-10 发布日期:2016-10-10

Soil organic carbon distribution and its influencing factors of Beijing Songshan natural Chinese pine forests.

GAO Jie, GUO Zi-jian, LIU Yan-hong*#br#   

  1. (The Key Laboratory for Silviculture and Conversation of Ministry of Education, Beijing Forestry University, Beijing 100083, China).
  • Online:2016-10-10 Published:2016-10-10

摘要: 随着全球气候的变化,森林土壤有机碳作为碳库的重要组成部分,成为森林碳循环研究的重点之一。以北京松山8块不同林龄天然油松林样地为研究对象,通过方差分析及方差分解的方法分析不同林龄土壤有机碳、碳密度的分布特征及影响因素,结果表明:(1)该地区油松林土壤碳含量平均值为20.61 g·kg-1;土壤碳密度为153.67 t·hm-2,低于中国森林生态系统平均值(193.55 t·hm-2)。同一林龄土壤有机碳含量随土壤深度的增加而显著降低(P<0.05)。(2)在0~50 cm土层,不同林龄土壤有机碳含量普遍存在显著差异性(P<0.05),在50~100 cm层差异不显著。随着林龄增大,土壤碳密度显著增加(P<0.05)。各土层土壤碳密度与土壤碳含量随林龄变化趋势并不一致。中龄林、近熟林、成熟林、过熟林土壤有机碳均集中分布在较浅表层(0~30 cm),分别占总土层有机碳含量的81.1%、83.6%、82.5%、81.7%。(3)土壤各层碳含量、碳密度与土壤含水量呈显著相关(P<0.001,P<0.05),各层土壤碳含量与土壤容重呈显著负相关(P<0.05)。各样地土壤平均碳含量(ACC)、碳密度(ACD)与地形因子、林分特征因子以及土壤因子之间普遍存在显著关联。地形模型、林分特征模型、土壤模型对ACC、ACD方差的解释程度具有一定的差异性。总体而言,林分特征模型能较好地解释ACD方差,地形因子模型、土壤因子模型对结果解释程度相对偏低。林分特征模型和土壤模型结合起来能较好地解释ACC方差,地形因子模型对结果解释程度不高。

关键词: 生态系统服务, 生态系统服务流, 成本效应, 载体

Abstract: With the change of global climate, forest soil organic carbon, as an important part of the carbon pool, has become one of the key research topics of forest carbon cycle. We studied soil organic carbon, carbon density and their influencing factors of eight natural Chinese pine forests with different ages in Songshan, Beijing using the variance analysis and variance decomposition method. We found that (1) the average soil carbon content of the pine forests in the studied area was 20.61 g·kg-1. The soil carbon density was 153.67 t·hm-2, which was lower than the average value of Chinese forest ecosystems (193.55 t·hm-2). At the same aged stands, the soil organic carbon content decreased with the increase of soil depth significantly (P<0.05). (2) In the 0-50 cm soil layers, there existed significant differences in soil organic carbon content among different aged stands (P<0.05). However, in the 50-100 cm soil layer, the difference was not significant (P>0.05). As the stand age increased, soil carbon density increased significantly (P<0.05). Soil organic carbon in the shallow surface layers (0-30 cm) accounted for 81.1%, 83.6%, 82.5%, and 81.7% of total soil organic carbon in middleaged forest, nearmature forest, mature forest, and overmature forest, respectively. (3) The carbon content and carbon density of each layer were significantly positively correlated with soil water content (P<0.001, P<0.05), and the carbon content of each layer was significantly negatively correlated with soil bulk density (P<0.05). The average carbon content (ACC), and average carbon density (ACD) of various soil layers in each plot were significantly correlated with topographic factors, stand characteristic factors and soil factors. Terrain model, stand characteristic model, and soil model explained the degree of variance of ACD and ACC differently. Overall, the stand characteristic model can explain the ACD variance well, while the terrain factor model and the soil factor model can not. The stand characteristic model and the soil model together can explain the variance of ACC well, while the terrain factor model can not.

Key words: ecosystem services, carrier, ecosystem services flow, cost-effective.