
Application of structural equation model in growth of Larix gmelinii stand
GAO Yu, LI Jing, LIU Yang, WU Yahan, GONG Jiaxing, XIN Qirui
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (1) : 38-46.
Application of structural equation model in growth of Larix gmelinii stand
【Objective】 Structural equation modelling (SEM) was used to determine the effects of climate, soil, and altitude on growth indicators and pathway relationships in Xing’an larch (Larix gmelinii) forests. 【Method】 The annual mean temperature, annual mean precipitation, solar radiation, soil total nitrogen content, soil organic carbon density, and altitude were selected as influencing factors to explore the relationships between aboveground biomass, underground biomass, and tree height and these underlying factors. A structural equation model of climate, soil, and altitude was constructed using AMOS 21.0 software to measure the growth of Larix gmelinii stand. 【Result】 The aboveground and underground biomass of Larix gmelinii first increased and then decreased with an increase in altitude and annual mean precipitation, and the tree height increased with increasing altitude. The aboveground and underground biomass increased with an increase in soil organic carbon density. The total effect coefficient of altitude on the growth of Larix gmelinii was positive (0.200), and the direct effect (0.224) of altitude on the growth of Larix gmelinii was greater than the indirect effect (-0.024). The total effect coefficient of the climatic factors on the growth of Larix gmelinii was negative, at -0.771. The total influence coefficient of soil factors on the growth of Larix gmelinii was -0.216, which means these factors can slightly inhibit the growth of Larix gmelinii. 【Conclusion】 According to the path coefficient of the structural equation model, the absolute value of the total influence coefficient of climate factors was the largest, followed by that of soil and altitude. The static growth of Larix gmelinii forest is mainly restricted by climatic factors, which has guiding significance for predicting and evaluating changes in forest growth at high latitudes under the condition of global climate change.
Larix gmelinii / influence coefficient / climate factor / soil factor / elevation / aboveground and belowground biomass / tree height / structural equation model
[1] |
程开明. 结构方程模型的特点及应用[J]. 统计与决策, 2006(10):22-25.
|
[2] |
张娅, 孙舒蕊, 张文会, 等. 基于结构方程模型的居住区停车泊位共享意愿研究[J]. 森林工程, 2021, 37(6): 143-150, 158.
|
[3] |
舒树淼, 赵洋毅, 段旭, 等. 基于结构方程模型的云南松次生林林木多样性影响因子[J]. 东北林业大学学报, 2015, 43(10):63-67.
|
[4] |
楚春晖, 佘济云, 陈冬洋, 等. 大围山杉木林林分生长与影响因子耦合分析[J]. 西南林业大学学报, 2016, 36(2):108-112.
|
[5] |
黄兴召, 许崇华, 徐俊, 等. 利用结构方程解析杉木林生产力与环境因子及林分因子的关系[J]. 生态学报, 2017, 37(7):2274-2281.
|
[6] |
王冬至, 张志东, 牟洪香, 等. 结构方程模型在落叶松林经营中的应用[J]. 北京林业大学学报, 2015, 37(3):69-75.
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
靳天恩, 马彦红, 李善文. 影响人工油松林生长的相关因子的研究[J]. 防护林科技, 1999(3):12-14,59.
|
[15] |
张长现. 不同生态条件下五脉绿绒蒿生物碱与黄酮成分研究[D]. 兰州: 中国科学院西北高原生物研究所, 2009.
|
[16] |
王文杰, 孙伟, 邱岭, 等. 不同时间尺度下兴安落叶松树干液流密度与环境因子的关系[J]. 林业科学, 2012, 48(1):77-85.
|
[17] |
台秉洋. 大兴安岭北部高山兴安落叶松树木生长与气候变化的关系[D]. 哈尔滨: 东北林业大学, 2012.
|
[18] |
孙振静, 赵慧颖, 朱良军, 等. 大兴安岭北部不同降水梯度下兴安落叶松生长对升温的响应差异[J]. 北京林业大学学报, 2019, 41(6):1-14.
|
[19] |
高涛. 大气环流和海温变化对兴安落叶松生长的气候影响:以根河地区为例[D]. 呼和浩特: 内蒙古农业大学, 2013.
|
[20] |
|
[21] |
|
[22] |
罗云建, 王效科, 逯非. 中国主要林木生物量模型手册[M]. 北京: 中国林业出版社, 2015.
|
[23] |
吴明隆. 结构方程模型:AMOS的操作与应用万卷方法统计分析方法丛书[M]. 重庆: 重庆大学出版社, 2009.
|
[24] |
何晓群. 多元统计分析[M]. 北京: 中国人民大学出版社, 2004.
|
[25] |
侯杰泰. 结构方程模型及其应用[M]. 北京: 教育科学出版社, 2004.
|
[26] |
周健平. 基于结构方程模型的林分特征因子间耦合关系分析[D]. 哈尔滨: 东北林业大学, 2015.
|
[27] |
|
[28] |
孟军贵, 铁牛. 气温、降水对兴安落叶松生长的影响研究[J]. 科学技术创新, 2019(18):143-144.
|
[29] |
白学平. 海拔对大兴安岭落叶松径向生长与气候响应的影响研究[D]. 沈阳: 沈阳农业大学, 2019.
|
[30] |
杨志香, 周广胜, 殷晓洁, 等. 中国兴安落叶松天然林地理分布及其气候适宜性[J]. 生态学杂志, 2014, 33(6):1429-1436.
|
/
〈 |
|
〉 |