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黄兴召,许崇华,徐俊,陶晓,徐小牛.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.生态学报,2017,37(7):2274~2281 本文二维码信息
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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
Structural equation model analysis of the relationship between environmental and stand factors and net primary productivity in Cunninghamia lanceolata forests
投稿时间:2015-12-13  最后修改时间:2016-07-11
DOI: 10.5846/stxb201512132482
关键词杉木林  净初级生产力  结构方程  通经系数
Key WordsCunninghamia lanceolata forests  net primary productivity  structural equation model  path coefficient
基金项目国家"973"计划项目(2012CB416905),中国科学院战略先导性科技专项(2011XDA05050204);安徽农业大学青年项目(2014zr013)
作者单位E-mail
黄兴召 安徽农业大学林学与园林学院, 合肥 230036;大别山区农林特色产业协同创新中心, 合肥 230036  
许崇华 安徽农业大学林学与园林学院, 合肥 230036  
徐俊 安徽农业大学林学与园林学院, 合肥 230036  
陶晓 安徽农业大学林学与园林学院, 合肥 230036;大别山区农林特色产业协同创新中心, 合肥 230036  
徐小牛 安徽农业大学林学与园林学院, 合肥 230036;大别山区农林特色产业协同创新中心, 合肥 230036 xnxu2007@ahau.edu.cn 
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摘要:
通过收集155篇644条杉木林生产力数据,利用结构方程模型,分析杉木林净初级生产力与年均降雨量、年均温度、林分密度和林龄之间的关系。结果表明:杉木林净生产力与年均降水量和年均温度呈显著正相关,相关系数分别为0.63和0.378。杉木林净生产力与林龄和林分密度呈显著负相关,相关系数分别为-0.332和-0.408。结构方程模型较好的解析了杉木净初级生产力与环境因子和林分因子之间的关系。杉木林净生产力与年均降水量、年均温度、林龄、林分密度都有影响,其总通径系数分别为0.398(P < 0.01)、0.746(P < 0.01)、-0.321(P < 0.01)和-0.738(P < 0.01)。年均温度和林龄不仅直接影响杉木林净生产力,还通过影响年均降水量和林分密度间接影响林分净生产力。年均温度和林龄的直接通径系数分别为0.494(P < 0.01)和-0.700(P < 0.01);年均温度和林龄的间接通径系数分别为0.252(P < 0.05)和0.379(P < 0.05)。结构方程作为大尺度分析净初级生产力的方法,杉木林净初级生产力影响因素的62%来自年均降水量、年均温度、林龄和林分密度。
Abstract:
We used a structural equation model to analyze the relationship between environmental and forest stand factors and net primary productivity in Cunninghamia lanceolata forests. We collected 644 data points from 155 published studies on net primary productivity (NPP) measurements of Cunninghamia lanceolata forests. The environmental factors included mean annual precipitation (MAP) and mean annual temperature (MAT). The stand factors included age and density of trees. The correlations between NPP and environmental and stand factors were different. NPP was significantly positively correlated with both MAP and MAT, with correlation coefficients of 0.630 and 0.378 respectively. Conversely, NPP was significantly negatively correlated with both age and density, with correlation coefficients of -0.332 and -0.408 respectively. Each variable fitted a normal distribution after natural logarithmic transformation. We used a structural equation model to explore the relationship between NPP and MAP, MAT, age, and density. The results showed that the structural equation model was an excellent method to explain the relationship between environmental and stand factors, and NPP. MAP, MAT, age and density, all had an effect on NPP, with total path coefficients of 0.398 (P < 0.01), 0.746 (P < 0.01), -0.321 (P < 0.01) and -0.738 (P < 0.01), respectively. MAT and age had both direct and indirect effects on NPP, as MAT had a direct effect on MAP, and age had a direct effect on density. MAT and age directly affected NPP as well, and were therefore included as direct and indirect path coefficients in the structural equation model. The direct path coefficients of MAT and age were 0.494 (P < 0.01) and -0.700 (P < 0.01) respectively. The indirect path coefficients of MAT and age were 0.252 (P < 0.05) and 0.379 (P < 0.05) respectively. The structural equation model analysis indicated that MAP and MAT were the strongest positive drivers of NPP, whereas age and density were the strongest negative drivers of NPP. The structural equation model analysis also indicated that MAP, MAT, age, and density explained 62% of the variation in NPP of Cunninghamia lanceolata forests. We conclude that the structural equation model is the most appropriate approach to understand and predict ecosystem functioning, as understanding NPP requires an accurate assessment of large-scale patterns in NPP distribution and partitioning in relation to environmental and stand factors.
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