2.3 Statistical analysis
Dry mass content (DMC) of each sample was calculated as:
DMC=\(\frac{W_{\text{FS}}-W_{\text{DS}}}{W_{\text{FS}}}\),
where WFS and WDS are fresh weight and dry weight, respectively. We multiplied the total fresh weight of each tissue by the corresponding DMC to obtain the dry biomass of every individual tree. A power-law function was used to describe the scaling relationship between trunk D and H or branch diameter (d ) and length (L ):
y = β x α ,
where y represents H or L , x representsD or d , β is the normalization constant, andα is the scaling exponent (Niklas 1994). To stabilize the variance, data were log-transformed as log(y)=log(β )+a log(x) (Niklas 1994). Similar to previous statistical analyses (Zhang et al. 2016; Sun et al. 2019), standard major axis (SMA) regression of the log-transformed data was used to determine the numerical value of scaling exponents (a ) and normalization constants (β ) (Smith 2009). The heterogeneity of scaling exponent was significantly, when the 95 % confidence intervals did not overlap. One-way analyses of variance (ANOVA) with least significant difference (LSD) fisher multiple comparisons was used to test differences in biomass, growth rates, and leaf mass per branch. In addition, the Benjamini-Hochberg method was used for p -value correction. All statistical analyses were performed using the statistical software environment R (version 3.6.0) (R Core Team 2019); SMA regression was performed using the packagesmatr (version 3.4-8) (Warton et al. 2012). All tests for heterogeneity were based on p < 0.05.