Agents Affecting the Productivity of Pine Plantations on the Loess Plateau in China: A Study Based on Structural Equation Modeling

Abstract

Stability and productivity are important indicators used to measure the state of forest ecosystems. Artificial forests populations with reasonable structures and strong stability are critical for ecosystem productivity. Previous studies have focused on individual factors, while the mechanisms of how multiple factors affect population productivity remain unknown. We used 57 plots in a Chinese pine (Pinus tabuliformis) plantation to investigate 23 stand factors and analyzed the relationships among site factors, population structure, population stability, and population productivity using partial least square-structural equation modeling (PLS-SEM). The results showed that the population productivity of the plantation was directly affected by the population stability latent variable but indirectly affected by the site conditions latent variables (indirect effect path coefficient = 0.249) and forest structure (indirect effect path coefficient = 0.222). However, the site conditions latent variable was the main factor directly affecting the population stability latent variables; the total effect was 0.511 (direct effect path coefficient = 0.307, indirect effect path coefficient = 0.204), and the influence of forest structure on population stability was lower than that of the site conditions latent variable (direct effect path coefficient = 0.454). The factor with the greatest weight among the site conditions latent variable was slope (0.747), indicating that slope contributes the most to latent variables related to forest population stability. Among all variables affecting the forest stability latent variables, forest density had the highest weight value (0.803), and the weight value of forest mortality was lower than that of forest density. The weights of the latent variables associated with population structure from high to low were canopy density, the uniform angle index, and the spatial competition index, indicating that competition for space had the lowest influence on the population stability latent variables. The results provide new insights and ideas for quantifying relationships among different driving factors and a basis for scientific and rational plantation management.

Spatial autocorrelation signatures of ecological determinants on plant community characteristics in high Andean wetlands

“…We investigated the influence of each ecological factor (wetland connectivity and size, species co-occurrence, and environmental variability) on community indices by carrying out PLS-SEM 85 . This is a comprehensive technique based on the use of a variable covariance matrix that identifies relationships and causality between variables 86 with minimum requirements regarding measurement scales, sample sizes, and residual distributions 85 . The measurement model (i.e.…”

Assessment of the effects of multigenerational succession on soil chemical environment, fertility and enzyme activity in Eucalyptus plantations using structural equation modeling

“…Structural equation modeling (SEM) Structural equation modeling is an extension of regression and path analysis and can be used to model multivariate relationships and evaluate multivariate hypotheses, the ability to analyze structural relationships between variables that cannot be measured directly (latent variables), and the ability to discover indirect relationships between latent and explicit variables [38][39].The latent variables in this study included organic fraction, enzyme activity, fertility, and biomass. The explicit variables for fertility included pH, SOM, TN, TP, TK, AN, AP, and AK; the explicit variables for organic composition included aliphatic , phenolic, polysaccharide, and aromatics; the explicit variables for enzyme activity included SC, NAG, CEL, AMY, URE, and ACP; and the explicit variables for biomass included Height and DBH.Fertility, enzyme activity, and organic composition data were analyzed using IBM SPSS Statistics (Version 24.0) software (IBM, Armonk, NY, USA).…”

“…Multi-generational succession is not conducive to the activation of organic carbon in the soil of Eucalyptus plantations, and also leads to a homogenization of the main functional groups in the soil organic fraction, with a significant increase in the content of aromatic (1631 cm-1) and phenolic alcohol functional groups (3620 cm-1), which are more stable sources of carbon and difficult to be used by soil microorganisms and affects the nutrient conversion rate [49]. Meanwhile, new unsaturated (C=C, C=O, 2361 cm1) production was found in the high-generation T3 soils, indicating that successive plantings change their native soil organic fraction and structure [50][51].…”

Novel Weighting Method for Evaluating Forest Soil Fertility Index: A Structural Equation Model

“…Confirmatory factor analysis (CFA) uses the degree of fit test to determine the degree of fitness of the model and to conduct a comprehensive evaluation of the model avoiding the evaluation of direct relationships between individual variables [ 16 ]. Exploratory factor analysis (EFA) can simultaneously explore the relationships between multiple variables in the system and determine the impact intensity for comprehensively reflecting the overall state of soil nutrients and the complex influence relationships and mechanisms among various indicators [ 17 , 18 , 19 ]. The SEM can analyze the relationships between various factors in the system based on the existing theoretical knowledge to evaluate soil nutrient status.…”