Statistical analyses
Morphological attributes were all log-transformed and centralized before
calculating trait-based community structure. Principal component
analysis (PCA) was applied for size and shape related morphological
attributes, and the first two components were used to calculate
morphological dispersion. Standardized phylogenetic and trait-based
dispersion (SES.MPD and SES.PW) were determined with two-tailed t-test:
if community structure is significantly higher than those in null
communities (SES >1.97), community will be defined
phylogenetically or functionally dispersed; if community structure is
significantly lower than expected (SES < -1.97), community is
characterized as phylogenetically or functionally clustered. If
community structure is non-significant different from that in null
communities (-1.975< SES < 1.975), community is
characterized as random (Kembel et al. 2010; Webb et al.2008; Webb et al. 2002; Webb 2000). Underlying process
(environmental filtering, interspecific exclusion and stochastic
process) and the functional role of traits were estimated according to
our revised empirical framework. The elevational patterns of
phylogenetic and trait dispersion were determined using polynomial
regressions. Best predictive model were selected according to Akaike
information criterion (AIC). Besides, we also quantified the elevational
pattern of species richness, which is reasonable in estimating
environmental fitness and habitat heterogeneity (Brown 2001).
As ecological process and the functional role displayed by trait are
habitat-specified, hence, the approach of linear regression model or
correlation, to some extent, is more convenient but probably
underestimate the dependence between phylogenetic and trait-based
community structure. In contrast, pairwise comparison of this kind
approach would be more accurate in predicting assembly process and the
functional role of a certain trait. In order to obtain comparable
results, we applied these two approaches (i.e., correlation analysis and
pairwise comparison) in inferring the relationship between phylogenetic
and morphological structure. In comparison analysis, if both
phylogenetic and trait dispersion showed same status (clustered, random
or dispersed), we called it phylogenetic-trait consistence or
congruence; or else, we named it phylogenetic-trait inconsistence or
incongruence. Pairwise comparison and correlation analysis have been
repeatedly conducted between phylogenetic dispersion and morphological
dispersion of size and shape related traits.
With the aim to estimate the environmental dependence for phylogenetic
and trait dispersion, we conducted forward selection procedure to choose
the best climatic predictor(s). Structure equation models (SEM) were
applied to estimate the direct and indirect effects of climatic factors
on phylogenetic and trait dispersion. Environmental variables were all
log-transformed and centralized in best predictor selection and SEMs.
All of these calculations were accomplished under R environment (ver.
3.5.1) (Team 2013). phylogenetic signal detection was carried out with
the package ‘phytools’ (Revell 2012); PCA analysis, correlation analysis
and polynomial regressions were all accomplished with default packages
in R (Team 2013); Following the approach in Swenson (2014), phylogenetic
and trait-based structure was calculated with package ‘picante’ (Kembel
2009) and package ‘vegan’ (Oksanen et al. 2007; Dixon 2003);
Forward selection in climatic factors was finished with ‘leaps’ (Miller
2002) and ‘vegan’ (Oksanen et al. 2007; Dixon 2003) packages;
SEMs were performed with package ‘lavaan’ (Rosseel 2012).