Correlation of biotic and abiotic factors to insect community
composition
The ordination of FIT plots in the beetle species community applying the
NMDS method yielded a significant two convergent solution (stress value
= 0.04) after 20 iterations through a Bray-Curtis distance measure and
clearly separated the groups according to their respective elevation
(Fig. 3). The plots within the tropical Bubeng category of low elevation
were slightly scattered. However, the plots in the middle and higher
elevation categories showed an obviously clustered pattern. Plots from
different elevation classes could be very dissimilar and the ordination
ellipses for each elevation class did not overlap much. Therefore, the
beetle species composition of the different elevations does not converge
to the same scores (Fig. 3).
The results of a PERMANOVA model showed that major variations were
distributed across the three most important environmental explanatory
variables, including ELE, MTCM, and AMT, which together explained 45%
of the variation in beetle species composition space.
The Mantel tests showed that β-diversity of beetles was significantly
associated with tree species composition at all sampling plots at the
macro scale (Table S1.2). Variation partitioning of RDA revealed that
selected tree species and tree phylogenetic β-diversity separately
explained 64% and 62% of the variation, respectively, with the joint
effect of geographical distance and environment in beetle species
composition at the macro scale. The pure effect of tree species and tree
phylogenetic β-diversity was 10% and 6%, respectively, while the pure
effect of the environment was 5% (Fig. 4-II) and 3% (Fig. 4-I)
separately. However, the pure effect of geographic distance was very
small at 1% (Fig. 4-II) and 0 (Fig. 4-I). The remaining unexplained
variation of the main matrix was 28% (Fig. 4-II) and 31% (Fig. 4-I),
respectively.
Through analysis of linear-mixed effect model, we found that the best
model (i.e., delta AIC is equal to 0) retained both plant diversity and
environment variables with significant correlation. All residuals of the
models showed no significant spatial patterns (p = 0.8351), indicating
that our mixed model explicitly incorporated the spatial dependence
between plots, transects and regions .The best model showed that Fixed
effects (i.e., lakes nested in regions) explained considerable
variations of the models, with 65.08% (Tables 1). And random effect
explained 32.28% (Tables 2). For the environment metrics, beetles
standardized Shanoon diversity is positively correlated with AHR_stdz,
AMH_stdz, MTCM_stdz and MTWM_stdz. But negatively correlated with
ATR_stdz. And for the plant diversity metrics, beetles standardized
Shanoon diversity is negatively correlated with PlaSimpson_stdz and
PlaPD_stdz, but positively correlated with PlaChao1_stdz and
PlaShannon_stdz.