2.4 Statistical analyses
The abundance of the plant community was determined by the total number
of individuals in each 10 × 10 m grid cell. The species diversity of
each
10
× 10 m grid cell was calculated using the
Shannon-Weiner
index (, where is the number of individuals of the th species, is the
total number of individuals of all species) (Magurran 2004). The
availability and heterogeneity of each variable for the 10 × 10 m grid
cell were calculated using
four
measurements from the 2 × 2 m quadrat of
each
grid cell, the mean of four measurements was used to express resource
availability, and the coefficient of variation (CV) was employed as the
measure of resource heterogeneity (Marchand and Houle 2006; Reynolds et
al. 2007; Shirima 2016; Ulrich et al. 2018).
Prior to further statistical
analysis, all data were checked for normality using the Shapiro-Wilk
test and
homogeneity
of variance using Levene’s test. Any data that did not meet normality
and homogeneity of variance were transformed using an appropriate
method. All above-mentioned analyses were performed using SPSS 13.0
(SPSS Inc. IL, USA). The influence of elevation on the plant community
can be neglected in this study because the elevation of different plant
communities have a small change with 17m in shrubland and 177m in
woodland (Table 2). Therefore, in the following data analyses, elevation
was not included among the variables.
To test the effects of resource availability and heterogeneity on
species diversity and abundance in different vegetation types, we first
quantified the main axes of availability and heterogeneity of overall
variables using principal component analysis (PCA). The first axis
scores of the PCA were used as
a
multivariate proxy for availability and heterogeneity of overall
variables.
Then we tested the relationship between species diversity and abundance
with
the
first PCA axis scores of resource availability and heterogeneity by
linear regression. The PCA was performed using the “princomp” function
in the R stats package (R Core Team, 2014). Linear regression and
Pearson’s correlation analysis were performed using Origin 8.5 (Origin,
Northampton, MA, USA).
The variation partitioning approach was used to analysis the relative
influence of resource availability and heterogeneity on community
species composition. The variation of community species composition was
partitioned into
the
components explained by resource availability and heterogeneity
together, as well as the components explained by each of these
independently. Before
variation
partitioning, forward selection was applied within each of the two sets
of explanatory variables to identify the significant environmental
factors (Blanchet et al. 2008). The variation in species composition was
decomposed in R 3.5.3 using the “varpart” function of the vegan
package (Oksanen et al. 2012), and
forward
selection was performed in R 3.5.3 using the “forward.sel” function of
the adespatial package (Dray et al. 2017).
Finally, we used a
structural
equation model (SEM) to explore the direct and indirect relationships
between the resource availability and
species
diversity and abundance for different vegetation types. SEM was used
because it enables the testing of direct and indirect hypothesized
relationships among the variables and provides more insights into
complex systems than univariate analyses (Kubota et al. 2004).
Initially, all plausible interaction paths among all variables were
considered in a full model. Then, some modified models were developed by
removing
direct
and indirect pathways with low and nonsignificant path coefficients
until an adequate fit was obtained (Grace et al. 2010). All direct and
indirect path coefficients (λ) were standardized regression
coefficients, and the pathways of SEMs were significant if P< 0.05 of the standardized regression coefficient (Désilets
and Houle 2005; Van der Sande et al. 2017; Kumar et al. 2018). The
relative importance of causal factors for species diversity and
abundance was compared using the total effect from direct and indirect
effects. The goodness of fit for the working model was determined by the
maximum likelihood Chi-squared statistic
(χ2), the
comparative fit index (CFI) and the standardized root mean square
residual (SRMR). The model was judged as a reasonable fit ifP >0.05, which indicates that fitting covariance
matrices are not significantly different from
observed
covariance matrices (Grace et al. 2010). CFI> 0.95 suggests
a very good fit, which is little affected by sample size compared to the
Chi-square test (Rosseel et al. 2012). SRMR ≤ 0.05 indicates a very
close fit between a model and the observed data (Browne and Cudeck
1993). All models were implemented in R 3.5.3 using the “sem” function
of the lavaan package (Rosseel et al. 2019).
Results
3.1
Effects of resource availability and heterogeneity onspecies
diversity and abundance
The first PCA axis (PC1) accounted for 40% of the resource availability
variation for
shrubland,
55% of that for woodland and 30% of the resource heterogeneity
variation for both shrubland and woodland; moreover, the PC1
eigenvectors of both resource availability and heterogeneity
were
greater than 2.00; therefore PC1 explained the majority of the
variability in the resource availability and heterogeneity in this study
(Fig. A1, Table A1, Supplementary
material).
With increasing resource availability, species diversity and abundance
significantly increased in both
shrubland and
woodland
except species diversity in the shrub layer of woodland (Fig. 2a, b, c
d).
Species
diversity and abundance were both significantly negatively related to
resource heterogeneity in shrubland (Fig. 3a, b). However, community
abundance significantly increased with increasing resource heterogeneity
in
woodland
(Fig. 3c). There was no significant relationship between species
diversity and
resource
heterogeneity in woodland (Fig. 3d).