Figure 5. Terrestrial laser scanning point clouds from example trees
with varying structural complexity (i.e. box dimension).
When assessing drivers of structural complexity, all stem, crown, and
growth attributes were found significant (p<0.05) in the
nested two-level linear mixed-effects models (Table 3). Benefit-to-cost
ratio and light availability, on the other hand, were not. Intensive
thinning increased tree height, benefit-to-cost ratio, height growth,
and light availability significantly (p<0.05), whereas
benefit-to-costa ratio, height growth, light availability was
significantly (p<0.05) smaller in the linear mixed-effects
models (Table 3). However, coefficient of determination was
<0.2 for all other architectural attributes (Figures S1-S4)
except for crown dimensions where it was 0.5 between box dimension and
crown projection area and crown volume (Figure S5).
Thinning treatment was statistically significant (p<0.05) in
mixed-effects models where height and volume growth, benefit-to-cost
ratio, light availability, and each stem attribute was included as
predictor variable at a time. Tukey’s honest significance test revealed
that there was a statistical difference (p<0.05) between
moderate and intensive thinning in all stem attributes, benefit-to-cost
ratio, height and volume growth, and light availability. Thinning,
either moderate or intensive, resulted in significant difference
(p<0.05) in all stem attributes, crown width, benefit-to-cost
ratio, all growth attributes, and light availability when compared to
trees without a thinning treatment.
Table 3. Results of the nested two-level linear mixed-effects models
with box dimension as dependent variable and thinning treatment together
with stem and crown attributes, benefit-to-cost ratio, growth
attributes, and light availability as independent variables. * denotes
statistical significance of p-value<0.05.