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石灰岩山地优势种淡竹生物量分配的影响主因研究
引用本文:樊 燕,郭春兰,方 楷,黎祖尧,施建敏.石灰岩山地优势种淡竹生物量分配的影响主因研究[J].广西植物,2016,36(10):1172-1178.
作者姓名:樊 燕  郭春兰  方 楷  黎祖尧  施建敏
作者单位:江西农业大学 林学院,南昌330045; 江西省竹子种质资源与利用重点实验室,南昌330045; 江西特色林木资源培育与利用2011协同创新中心,南昌330045
基金项目:国家自然科学基金(31260174); 教育部科学技术研究重点项目(211089); 江西农业大学研究生创新专项(NDYC2014-02)[Supported by the National Natural Science Foundation of China(31260174); the Key Program of Chinese Ministry of Education(211089); the Innovation Fund for Graduate Students of Jiangxi Agricultural University(NDYC2014-02)]。
摘    要:该研究在江西省瑞昌市设置9个淡竹林样地,调查和测定了淡竹林密度、淡竹各构件的生物量和总生物量,以及土壤含水率、土层厚度、林下裸岩率、pH、电导率、全氮和全磷等7个土壤环境因子,并对竹林密度、土壤环境因子和淡竹生物量分配指标进行了相关分析和回归分析。结果表明:(1)密度与淡竹蔸比重相关系数r达0.66( P=0.02<0.05),而与叶比重、枝比重、秆比重、鞭比重、根比重及根冠比均无显著相关关系;土壤环境因子与生物量分配指标有密切相关,环境主成分Z1与叶比重、秆比重及蔸比重均显著相关(P<0.05),Z2与鞭比重显著相关(P=0.034<0.05)。(2)密度与土壤环境因子密切相关(P<0.05),控制土壤环境因子的偏相关分析显示密度与淡竹生物量分配不显著相关( P>0.05),而控制密度时,土壤环境因子与淡竹生物量分配仍有显著相关关系(P<0.05);逐步回归分析也验证了偏相关分析的结果,密度被排除出回归方程。分析认为,土壤含水率、土层厚度及土壤养分等环境因子是影响石灰岩山地优势种淡竹生物量分配的主因,密度对生物量分配的影响实为土壤环境因子的间接作用。该研究结果为石灰岩地区植被恢复提供了理论支撑。

关 键 词:淡竹  石灰岩山地  生物量分配  密度  土壤因子  主成分分析
收稿时间:2015/12/18 0:00:00
修稿时间:2016/3/15 0:00:00

Principal factors influencing the biomass allocation of Phyllostachys glauca, a dominant species in the limestone mountain
FAN Yan,GUO Chun-Lan,FANG Kai,LI Zu-Yao,SHI Jian-Min.Principal factors influencing the biomass allocation of Phyllostachys glauca, a dominant species in the limestone mountain[J].Guihaia,2016,36(10):1172-1178.
Authors:FAN Yan  GUO Chun-Lan  FANG Kai  LI Zu-Yao  SHI Jian-Min
Institution:1. Forestry College, Jiangxi Agricultural University, Nanchang 330045, China; 2. Jiangxi Provincial Key Laboratory for Bamboo Germplasm Resources and Utilization, Nanchang 330045, China; 3. Collaborative Innovation Center of Jiangxi Typical Trees Cultivation and Utilization, Nanchang 330000, China
Abstract:To clarify the effects of density and soil environmental factors on the biomass allocation of Phyllostachys glauca in limestone mountain, nine plots were set in Ruichang City, Jiangxi Province, China. The structural component biomass and the ramet biomass as well as the density of P. glauca forest were measured. Meanwhile, soil environmental factors including water content, soil depth, rock coverage, pH, electrical conductivity, total nitrogen and total phosphorus were determined. The results of correlation and regression analysis were as follows:(1)The density of P. glauca forest was significantly associated with the stump mass fraction(SuMF)and the correlation coefficient was 0.66(P<0.05), while it had no relevant relation with other indexes of biomass allocation, such as leaf mass fraction(LMF), branch mass fraction(BMF), stem mass fraction(SeMF), rhizome mass fraction(RhMF), root mass fraction(SoMF)and root shoot ratio(RSR). The soil environmental factors were also significantly correlated with the biomass allocation indexes. Two principal components extracted from the seven soil environment factors were transformed to principal component variables Z1 and Z2. Z1 was significantly correlated with LMF, SeMF and SuMF(P<0.05), and Z2 was significantly correlated with RhMF(P<0.05).(2)Considering the close correlation between the density and the soil environmental factors(P<0.05), the partial correlation analysis was employed. The results showed that the density and biomass allocation were not significantly correlated(P>0.05)when the soil environmental factors were controlled; while the soil environmental factors and the biomass allocation were significantly correlated(P<0.05)when the density was controlled. Moreover, stepwise regression analysis also showed the similar result that the density was excluded from the regression equation. It indicated that soil environmental factors including water content, soil depth and soil nutrient were the principal factors influencing the biomass allocation of P. glauca, while the influence of density on the biomass allocation was the indirect influence of soil environmental factors.
Keywords:Phyllostachys glauca  limestone mountain  biomass allocation  density  soil environment factor  principal component analysis
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