2.4.1 Identify the main influencing factors for AGB temporal stability and productivity in planted and natural forests
We used generalized least squares models (GLS) and LME to assess the joint effects of climatic, forest structure, and environmental factors on stability and productivity. Based on the AIC (Akaike’s information criterion) for GLS and LME models (Zuur et al., 2009), we fit the joint effects using GLS models with a Gaussian error structure with the “nlme ” (v3.1-150) software package (Pinheiro, 2020). All environmental and forest structure variables and climatic variables were z-transformed to facilitate interpretation of parameter estimates. We also tested for multi-collinearity; predictors with sufficiently low variance inflation factors (VIF<5) were included in the model.
We carried out a model-averaging procedure on the basis of AIC (ΔAIC<4) to decide parameter standard coefficients (Grueber et al., 2011) for the main influencing factors of stability and productivity for planted and natural forests using a dredge function in the “MuMIn” package (Barton, 2015). We considered spatial autocorrelation (SCV) based on latitude and longitude in the GLS model (Rousset et al. 2018). All response and prediction variables were calculated as averages in the continuous investigation cohort. The structure of our GLS model is as follows:
Stability/Productivity~θ clim+θ fore+θ envi+SCV +e ,
where θ clim are the climatic variables (annual precipitation, MAT), θ fore are the mean values of variables describing forest structure (canopy cover, stand age, abundance, richness, DBH, tree height, and tree density), andθ envi are environmental context variables (latitude, altitude, aspect, slope, and soil depth); SCV refers to the spatial correlation variance structure. e represents the residual. All terms were modeled as additive effects, and no interactions were calculated in this top model.
Considering potential nonlinear responses of AGB stability and productivity to different factors, we used the “mgcv” R-package to fit the effect of forest structure in generalized additive mixed models (GAMM) (Fig. S3) (Wood, 2017). The effects of different factors in the GAMM and GLS were then compared.