Factors Controlling Productivity: The Role of Canopy Structure

Growth rate of individual trees

The regression analysis showed that the effects of canopy structure differed for tall trees (≥ 66 %) of dominant height in the plot) and smaller understory trees (Table 3.1), which contributed on average with about 88 and 12 % to totalANPP_wood. Although the wood production of both tree groups was significantly influenced by tree biomass (that is, size) and slope and aspect, canopy structure played different roles for the two groups. For tall trees, the model showed a significant influence of the density of the lower canopy (LAIe-low), whereas small trees were dependent on theLAI_eof the upper canopy and its spatial heterogeneity (LAI_e-up and IQR(LAI_e-up)).

Stand-LevelANPP_wood

According to the base model (Table 3.2 top) without canopy structure parameters (r²=0.56), ANPP_wooddepended significantly on the slope and aspect of the primeval forest plots (p = 0.03).

Sloping terrain with plot exposition to the east influenced tree growth positively, while western exposition had a negative effect (Fig. 3.3). In plots with low inclination (slope<10°), microto-pography lost its influence on wood production. Basal area and stem density tended to influence wood production positively (β= 1.1 and 0.4, respectively), but only the effect of basal area was

Figure 3.3:Predicted relationship between stand density (basal area—dotted; stem density—dashed) and interaction of slope and aspect onANPP_woodaccording to the regression models (AandB, base model, r²=0.56, Table 3.2).

Additional covariates quantifying canopy structure were added to the base model: the spatial heterogeneity ofLAI_e of the upper canopy (C, IQR(LAIe-up), r²=0.64), orLAI_eof the lower canopy (D,LAI_e-low, r²=0.73). Model predictions were calculated for the range of all observed values of the respective focal covariate (quantiles, x-axis).

For all other covariates than the focal one, their medians were used in the model runs. Shaded areas show the standard error of the predictions. A scatterplot matrix of the used variables is presented in supplemental Figure 3.A1.

Table 3.1:Influence of stand density (stem density, basal area), region, the three transformed microtopography parameters north, east and slope (see methods section), and canopy structure as reflected inLAI_eand

LAI_e-variability IQR(LAIe)) on the growth of small and large trees (woody biomass increment) as derived from linear models.

Model covariates

F(dfm/dfe) p β

Small-tree model (trees<2/3 of dominant height)

Region 4.26 (2, 343) 0.015 H: 0, K: -0.0009, S: -0.0008 Biomass 235.18 (1, 343) <0.001 0.61

Microtopography 5.02 (3, 343) 0.002 north: 0.01, east: 0.09, slope: 0.01 IQR(LAIe-up) 15.85 (1, 343) <0.001 0.09

LAI_e-up 6.57 (1, 343) 0.011 -0.06

Large-tree model (trees = 2/3 of dominant height)

Region 7.73 (2, 228) <0.001 H: 0, K: -0.014, S: -0.008 Biomass 161.01 (1, 228) <0.001 0.71

Microtopography 3.54 (3, 228) 0.015 north: 0.06, east: 0.11, slope: -0.03

LAI_e-low 6.07 (1, 228) 0.015 0.1

Only

covariates with a positive influence on the AIC were considered in the models. Factor levels of the regions are abbreviated by the regions’ first letters (Haveˇsov´a,Kyjov,Stuˇzica). Degrees of freedom are denoted by dfm (model) and dfe (error). F and p values and standardized regression

coefficients (b) of the covariates are given. Significant relationships are displayed in bold.

significant (p<0.001).

TheLAI_eof the upper canopy layer had no influence onANPP_woodaccording to the extended model. Instead, the spatial heterogeneity ofLAIein the upper canopy (expressed by IQR(LAIe-up)) exerted a significant positive effect on stand-level wood production; including this variable increased the model r²from 0.56 to 0.65 (effect significant at p = 0.02; Table 3.2: centre). The model assigned an increase inANPP_woodof approximately 1 Mg ha⁻¹yr⁻¹to the effect of a high spatial canopy heterogeneity (large IQR(LAIe-up)-value; Figure 3.3C). In relation to the size of overallANPP, this effect stands for an increase of approximately 10 %.

Table 3.2:Influence of stand density (stem density, basal area), region, the three transformed microtopography parameters north, east and slope (see methods section), and canopy structure as reflected inLAI_eand LAI_e-variability (IQR(LAIe)) at plot-LevelANPP_woodas derived from linear models.

Model fit Model covariates

F(dfm/dfe) p β

Base model

r² 0.56 Basal area 16.04 (1, 28) <0.001 1.05 AIC 141.64 Stem density 2.42 (1, 28) 0.131 0.41

Region 3.89 (2, 28) 0.032 H: 0, K: -2.10, S: -1.15 Microtopography 3.57 (3, 28) 0.026 north: -0.06; east: 1.00 ;

slope: -0.04 Upper canopy model

r² 0.65 Basal area 23.15 (1, 27) <0.001 1.18 AIC 136.13 Stem density 4.91 (1, 27) 0.035 0.55

Region 4.22 (2, 27) 0.025 H: 0, K: -2.19, S: -0.78 Microtopography 4.17 (3, 27) 0.015 north: 0.09; east: 0.94 ;

slope: -0.18 IQR(LAI_e) 6.26 (1, 27) 0.019 0.64

Lower canopy model

r² 0.73 Basal area 27.50 (1, 27) <0.001 1.11 AIC 127.05 Stem density 7.91 (1, 27) 0.009 0.62

Region 7.46 (2, 27) 0.003 H: 0, K: -2.46, S: -1.16 Microtopography 6.04 (3, 27) 0.003 north: 0.28; east: 0.93 ;

slope: -0.3

LAI_e-low 15.81 (1, 27) <0.001 0.9

LAI_edata of the upper canopy were included in the upper canopy model and those of the lower canopy in the lower canopy model. Only covariates with a positive influence on the AIC were considered in the models. Factor levels of the regions are abbreviated by the regions’ first letters (Haveˇsov´a,Kyjov,Stuˇzica). Degrees of freedom are denoted by dfm (model) and dfe (error). The raw data of the used variables are presented with histograms and scatter plots in supplemental Figure 3.A1. F and p values and standardized regression coefficients (b) of the covariates are given. Significant relationships are displayed in bold.

The effect of canopy structure on productivity became more apparent, when parameters characterizing the structure of the lower canopy were added to the base model. Here, the effect of spatialLAIvariation (IQR(LAI_e-low)) was not significant, butLAIe-lowitself influenced stand productivity strongly in a positive way (β= 0.9, p < 0.001; Table 3.2: bottom). The inclusion of this structural parameter improved the base model considerably (r²=0.73). The predicted increase inANPP_wooddue to a higherLAI_eof the lower canopy layer was similar to the productivity-promoting effect of upper canopy heterogeneity (IQR(LAI_e-up)) (Figure 3.3D).

Due to the key role played by leaf area for light interception, canopy carbon gain and produc-tivity, we chose opticalLAI_emeasurements to characterize forest structural diversity. To analyse model sensitivity towards the selection of different indices characterizing canopy structural diversity, we additionally used the standard deviation and the Gini–Simpson coefficient of tree height (h_sd andhgs), which are often used to describe the vertical layering of the canopy at the plot level. Since both parameters were negatively related to stem density (p = 0.03 and p< 0.001, respectively), multicollinearity was avoided by dropping stem density from the models describing relationships between stand structure,h_sd,h_gsand productivity.h_sd showed a negative correlation withANPPwood (b = -0.78, p = 0.048), whereas the relationship between h_gsandANPP_wood was positive (0.40) but not significant (p = 0.16).

3.4 Discussion