基于结构方程的云冷杉阔叶混交林土壤有机碳影响因子  

Influencing factors of soil organic carbon in mixed spruce-fir-broadleaved forest based on structural equation

作　　者：赵晗 王海燕 胡兴国 雷相东[3] 杜雪 邹佳何 崔雪 ZHAO Han;WANG Haiyan;HU Xingguo;LEI Xiangdong;DU Xue;ZOU Jiahe;CUI Xue(College of Forestry,Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100083,China;Wangqing Forestry Bureau of Jilin Province,Wangqing 133200,Jilin,China;Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China)

机构地区：[1]北京林业大学林学院,森林培育与保护教育部重点实验室,北京100083 [2]吉林省汪清林业局,吉林汪清133200 [3]中国林业科学研究院资源信息研究所,北京100091

出　　处：《生态学杂志》2024年第1期264-272,共9页Chinese Journal of Ecology

基　　金：长白山次生林抚育更新技术研究与示范(2017YFC0504101);国家重点研发计划重点专项(2017YFC0504002)资助。

摘　　要：植被、凋落物、地形以及土壤属性都是土壤有机碳(SOC)变化的驱动因素,而多个驱动因素如何同时作用于SOC的研究较少。本文以云冷杉阔叶混交林为对象,通过样地调查、采样以及实验测定获得200组数据,包括不同土层土壤、植被以及不同分解层凋落物的数据,并通过遥感技术获得了地形数据。采用相关分析以及结构方程模型量化这些因子对SOC的影响。基于相关分析结果构建了5个潜变量,包含植被、地形、凋落物归还特征、土壤属性以及SOC含量和土壤有机碳密度(SOCD),选取了15个观测变量进行建模。结构方程模型的各项检验系数基本通过,且多元平方系数值为0.94,表明方程构建合理且效果较好,模型的收敛效度达标。4个预测潜变量对SOC含量和SOCD均有显著正效应,总效应从大到小表现为土壤属性(0.938)>地形(0.383)>植被(0.131)>凋落物归还特征(0.099),可解释部分效应贡献占比分别为60.5%、24.7%、8.4%和6.4%,其中高程、20~40 cm土壤全磷、20~40 cm土壤全氮、0~20 cm土壤全氮、20~40 cm土壤含水率为效应贡献前五位,对SOC含量和SOCD有较大的正效应。地形(高程与坡向)与植被(Shannon多样性指数、物种数、郁闭度)对SOC含量和SOCD具有间接正效应,地形通过土壤属性和凋落物归还特征两个路径影响SOC,且均达到极显著水平(P<0.001);植被仅通过土壤属性路径在0.1水平达到显著。综上所述,SOC驱动因素的关系非常复杂,植被通过影响氮磷以及深层土壤属性影响SOC,凋落物通过直接输入对SOC产生正效应,地形通过水热分配影响其他土壤属性间接影响SOC。本研究构建的结构方程阐明了云冷杉阔叶混交林各类驱动因素的作用关系,为SOC保护以及天然林土壤养分管理提供理论依据。Vegetation,litter,topography,and soil attributes are driving factors of soil organic carbon(SOC)variations.However,few studies have examined those multiple driving factors simultaneously.The mixed spruce-fir-broadleaved forest was selected with 200 groups of data,including soil data of different soil layers,vegetation data,and data of different litter decomposition layers.Topographic data were obtained with remote sensing technology.To quantify the effects of each driving factor,correlation analysis and structural equation model were used to establish the relationship between observation variables and latent variables.According to the results of correlation analysis,five latent variables including vegetation,topography,litter return characteristics,soil properties,SOC content and SOC density(SOCD)were constructed,and 15 observation variables were selected.The test coefficients of the structural equation model basically passed with a multivariate square coefficient of 0.94,indicating that the equation construction was reasonable and that the convergence validity of the model met the standard.The four predicted latent variables had significant positive effects on SOC content and SOCD.The total effects were in order of soil attribute(0.938)>topography(0.383)>vegetation(0.131)>litter return characteristics(0.099).The contributions of interpretable partial effects were 60.5%,24.7%,8.4%,and 6.4%,respectively,with elevation,soil total phosphorus at depth of 20-40 cm,soil total nitrogen at 20-40 cm,soil total nitrogen at 0-20 cm and soil moisture content at 20-40 cm as the top five contributors to SOC content and SOCD.Topography(elevation and aspect)and vegetation(Shannon diversity index,number of species,canopy density)had indirect positive effects on SOC content and SOCD.Topography reached a very significant level through two paths:soil attribute and litter return characteristics(P<0.001),while vegetation only reached a significant level of 0.1 through soil attribute path.The relationship among driving factors was very

关 键 词：凋落物 环境因子 土壤有机碳 结构方程模型 

分 类 号：S714.2[农业科学—林学]