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\begin{document}

\title{``Realistic strategies'' and neutral processes drive the community
assembly based on leaf functional traits in a subtropical evergreen
broad-leaved forest}



\author[1]{Lijuan Zhao}%
\author[1]{Wenhua Xiang}%
\author[1]{Jiaxiang Li}%
\author[1]{Wenqian Liu}%
\author[1]{Yanting Hu}%
\author[1]{Huili Wu}%
\author[1]{Yiling Zhang}%
\author[1]{Xing Cheng}%
\author[1]{Weijia Wang}%
\author[1]{Wentao Wang}%
\author[1]{Shuai Ouyang}%
\affil[1]{Central South University of Forestry and Technology}%


\vspace{-1em}



  
  \date{March 13, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
We sought to assess effect of plant environmental adaptation strategies
and evolutionary history and quantify the contribution of ecological
processes to community assembly by measuring functional traits and
phylogenetic composition in local forest community. We selected 18
dominant tree species in a Lithocarpus glaber--Cyclobalanopsis glauca
evergreen broad-leaved forest and measured nine leaf functional traits
and phylogenetic data of each species. We analyzed the variation in
traits and trade-off relationships, tested phylogenetic effects on leaf
functional traits, explored the influence of phylogeny and environment
on leaf functional traits, and distinguished the relative effects of
spatial and environmental variables on functional traits and
phylogenetic compositions. The results showed the following: (i) Leaf
traits had moderate intraspecific variation, and significant
interspecific variation existed especially among life forms. (ii)
Significant phylogenetic signals were detected only in leaf thickness
and leaf area. The correlations among traits both supported ``the leaf
economics spectrum'' at the species and community levels, and the
relationships significantly increased or only a little change after
removing the influence of phylogeny, which showed a lack of consistency
between the leaf functional trait patterns and phylogenetic patterns. We
infer the coexistent species tended to adopt ``realism'' to adapt to
their habitats. (iii) Soil total potassium and phosphorus content,
altitude, aspect, and convexity were the most critical environmental
factors affecting functional traits and phylogenetic composition. Total
environmental and spatial variables explained 63.38\% of the variation
in functional trait composition and 47.96\% of the variation in
phylogenetic structures. Meanwhile, the contribution of pure spatial
factors was significantly higher than that of the pure environment.
Neutral- theory-based stochastic processes played dominant roles in
driving community functional trait assembly, but niche-theory-based
determinative processes such as environmental filtering had a stronger
effect on shaping community phylogenetic structure at a fine scale.%
\end{abstract}%



\sloppy


\textbf{``Realistic strategies'' and neutral processes drive the
community assembly based on leaf functional traits in a subtropical
evergreen broad-leaved forest}

Lijuan Zhao\textsuperscript{1}, Wenhua Xiang\textsuperscript{1},
Jiaxiang Li\textsuperscript{2}, Wenqian Liu\textsuperscript{2}, Yanting
Hu\textsuperscript{1}, Huili Wu\textsuperscript{1}, Yiling
Zhang\textsuperscript{1}, Xing Chen\textsuperscript{1}, Weijia
Wang\textsuperscript{1}, Wentao Wang\textsuperscript{1}, Shuai
Ouyang\textsuperscript{1}

\emph{\textsuperscript{1}Faculty of Life Science and Technology, Central
South University of Forestry and Technology, Changsha 410004, Hunan,
China}

\emph{\textsuperscript{2}College of Forestry, Central South University
of Forestry \& Technology, Changsha 410004, Hunan, China}

\textbf{Running title:} Realistic strategies and neutral processes drive
community assembly

\textbf{Correspondence}

Jiaxiang Li

E-mail: csfuljx@163.com

\textbf{Funding information}

National Natural Science Foundation of China, Grant/Award Number:
31901136

\textbf{Abstract:} We sought to assess effect of plant environmental
adaptation strategies and evolutionary history and quantify the
contribution of ecological processes to community assembly by measuring
functional traits and phylogenetic composition in local forest
community. We selected 18 dominant tree species in a \emph{Lithocarpus
glaber--Cyclobalanopsis glauca} evergreen broad-leaved forest and
measured nine leaf functional traits and phylogenetic data of each
species. We analyzed the variation in traits and trade-off
relationships, tested phylogenetic effects on leaf functional traits,
explored the influence of phylogeny and environment on leaf functional
traits, and distinguished the relative effects of spatial and
environmental variables on functional traits and phylogenetic
compositions. The results showed the following: (i) Leaf traits had
moderate intraspecific variation, and significant interspecific
variation existed especially among life forms. (ii) Significant
phylogenetic signals were detected only in leaf thickness and leaf area.
The correlations among traits both supported ``the leaf economics
spectrum'' at the species and community levels, and the relationships
significantly increased or only a little change after removing the
influence of phylogeny, which showed a lack of consistency between the
leaf functional trait patterns and phylogenetic patterns. We infer the
coexistent species tended to adopt ``realism'' to adapt to their
habitats. (iii) Soil total potassium and phosphorus content, altitude,
aspect, and convexity were the most critical environmental factors
affecting functional traits and phylogenetic composition. Total
environmental and spatial variables explained 63.38\% of the variation
in functional trait composition and 47.96\% of the variation in
phylogenetic structures. Meanwhile, the contribution of pure spatial
factors was significantly higher than that of the pure
environment.\emph{Neutral- theory-based stochastic processes} played
dominant roles in driving community functional trait assembly,
but\emph{niche-theory-based determinative processes} such as
environmental filtering had a stronger effect on shaping community
phylogenetic structure at a fine scale.

\textbf{Key words:} community assembly, environmental and spatial
variables, leaf functional traits, phylogeny, subtropical evergreen
broad-leaved forest

\textbf{1. Introduction}

Community assembly mechanisms have always been a topic of ecological
research, and natural communities are generally believed to be
structured by a set of processes (Chase et al., 2014; Levine et al.,
2017; Wang et al, 2021). \emph{Niche theory-based determinative
processes} , including the influence of the abiotic environment on
fitness (Wang et al, 2021; e.g. habitat filtering) and biotic
interactions, in particular interspecific competition (Leibold, 1998),
and \emph{neutral theory-based stochastic processes} , including spatial
dispersal limitation, demographic stochasticity, and ecological drift
(Hubbell, 2005; Zhou \& Zhang, 2008; Chase \& Myers, 2011) have been
regarded as two primary ecological mechanisms driving community assembly
(Li et al., 2019). The relative importance of these processes tends to
vary among ecosystems (Liu et al., 2013; Jiang et al., 2018), spatial
scales (Zhang et al., 2021), community succession (Csecserits et al.,
2021) and even in different environments, especially extreme
environments (Wang et al., 2021). For example, deterministic processes
may play a greater role than stochastic processes in adverse
environments (Chase \& Myers, 2011). Interspecific interactions and
density-dependent mechanisms should be strongest at the neighborhood
scale where individual organisms interact, and environmental filtering
should be stronger than interspecific interactions at the habitat scale
(Cavender-Bares et al., 2009; Purschke et al., 2017).

Plant functional traits are usually used as proxies to determine whether
different tree species have different ecological strategies for resource
capture and reproduction (McGill et al., 2006; Baraloto et al., 2012;
Adler et al., 2013; Liu et al., 2020). Analyses of the distribution of
trait values within communities yield insights of the ecological
processes constraining their assembly (Kraft et al., 2008; Paine et al.,
2011). If the niches of two species overlap, it is generally expected
that the two species are similar in a range of functional traits, and
vice versa (Westoby \& Wright, 2006). Based on competition theory,
higher similarity in functional traits for a community could lead to an
increased intensity of interactions among neighboring individuals
(Uriarte et al., 2004; Paine et al., 2011; Funk et al., 2016).
Consequently, communities with scattered trait values are primarily
shaped by niche differentiation, whereas environmental filtering is the
dominant process shaping ecological communities when trait value range
is narrower than predicted (Paine et al., 2011). Thus, based on the fact
that functional traits could represent the key aspects of physiology,
investigating the variation in functional traits at the species level
(i.e., intraspecific and interspecific variation) and at the community
level could be beneficial for a deeper understanding of how
physiological processes shape the assembly of ecological communities.
Plant leaves are critical organs for the exchange of matter and energy
with the photosynthetic organs of plants, and several biological
processes such as plant growth, survival, reproduction, and ecosystem
function are fully influenced by leaf parameters (e.g., leaf area,
length, and dry mass) (Surya et al., 2020). Leaf functional traits are
sensitive to changes in environmental factors. They can adjust resource
utilization strategies to adapt to different habitats through trade-offs
of various traits, which can reflect the driving mechanisms of the
environment on community assembly (Wright et al., 2004; Tian et al.,
2016; Zhang et al., 2020).

Although functional traits could provide a way to infer prevailing
ecological process information based on morphological, physiological,
and ecological characteristics, plant functional traits are not only
affected by environmental factors but also by species evolution history
(Swenson, 2013). Species coexisting in the same habitat might be
relatives sharing common functional traits influenced by evolutionarily
conserved or perhaps distant relatives adopting convergent traits to
adapt to the habitat (Cavender-Bares et al., 2009). Thus, phylogeny and
functional traits do not necessarily present similar information and
patterns (Cadotte et al., 2019), and testing the phylogenetic signals of
functional traits is a necessary key step to more accurately infer the
mechanism of community assembly (Baraloto et al., 2012; Cheng et al.,
2019). Phylogeny is an indirect estimation of ecological similarity
based on species affinity and an estimation of the impact of historical
factors on the existing community (Swenson et al., 2013; Jiang et al.,
2018). Therefore, the combined analysis of phylogeny and functional
traits can not only reveal the impact of community evolutionary history
and functional traits simultaneously on the current community ecological
process (Webb et al., 2002; Zhou et al., 2021), but also contribute to
revealing the ecological processes responsible for evolution and
functional assembly (Zhou et al., 2021). In other words, combined
trait-based and phylogenetic-based approaches is a powerful way to
detect community assembly processes (Kraft \& Ackerly, 2010; Gianuca et
al., 2017; Li et al., 2019).

Subtropical region of China holds the largest evergreen broadleaved
forest in the world and harbors abundant seed plants and endemic species
(Xu et al., 2017), which play an important role in biodiversity
protection and carbon balance. However, due to the prolonged and
frequent anthropogenic interferences, vegetation degradation is severe,
and ecological problems are prominent in this area. Research based on
the typical community structure and ecological processes in zonal
vegetation has become an important means of vegetation restoration and
reconstruction (Zhao et al., 2015; Ouyang et al., 2016; Zhang et al.,
2020). As one of the typical types of evergreen broad-leaved forests in
the subtropical China, \emph{Lithocarpus glaber--Cyclobalanopsis
glauca}forests exhibit high species diversity, stable community
structure, and high ecosystem function and service values (Zhao et al.,
2015). To study the assembly processes and mechanisms, we carried out a
series of studies focusing on species composition, spatial patterns, and
effects of topographic and soil factors on woody species assembly in
this long-term monitoring community. We found that aggregation was the
major spatial pattern and environment (topographic and soil factors)
explained 28.10\% of the species assembly (Zhao et al., 2015), and it is
apparent that species-based approaches to understand community assembly
are limited. Here, we re-examined this issue by integrating functional
and phylogeny-based approaches to explore the community assembly
processes. The degree to which patterns of functional traits and
phylogenetic dispersion may be easily explained by the abiotic
environment and spatial relationship has been also discussed. We propose
that a variance partitioning approach can be applied to simultaneously
address these challenges (Durate et al., 2013; Zhang et al., 2021). The
degree to which the abiotic environment, dispersal limitation, and/or
their joint effects affect trait dispersion should be determined by
partitioning variance in trait dispersion into pure environmental,
spatial and joint effects. We also measured 9 leaf functional traits and
phylogenetic data of 18 dominant woody species with important value
\textgreater{}1.00\% and accurate topographic and edaphic data sets to
address the following: (i) To clarify the variation and trade-off
relationship of leaf functional traits; (ii) To explore the effects of
phylogeny and environment on trait variation; (iii) To disentangle the
relative importance of niche and neutral processes shaping community
assembly at a fine scale.

\textbf{2 Materials and Methods}

\textbf{2.1 Study site and species selection}

This study was conducted in the Dashanchong National Forest Park
(28\selectlanguage{ngerman}°23\selectlanguage{english}'58\selectlanguage{english}"--28deg24\selectlanguage{english}'58\selectlanguage{english}" N, 113deg17\selectlanguage{english}'46\selectlanguage{english}"--113deg19\selectlanguage{english}'8\selectlanguage{english}" E), Changsha County,
Hunan Province, China. This region is typical of a low hilly landscape
55\selectlanguage{english}-217.40 m above sea level. The climate is humid mid-subtropical
monsoon climate with an annual mean temperature of 17.30 degC and mean
monthly temperatures of \selectlanguage{english}-10.30 \selectlanguage{ngerman}°C in the coolest month (January) and
39.80 °C in the warmest month (July). The mean annual precipitation is
1416 mm (Ouyang et al., 2016; Wu et al., 2019). The soil type was
well-drained clay loam red soil developed on slate and shale rocks. The
regional climax vegetation was subtropical evergreen broadleaved
forest.\emph{L. glaber--C. glauca} forest, a well-preserved evergreen
broadleaved forest is a multi- dominant, unevenly aged forest with
structural stability (Zhao et al., 2015) that represents the succession
direction of vegetation in the subtropical hilly region.

One-hectare permanent plot (100 × 100 m horizontal distance) of \emph{L.
glaber--C. glauca} evergreen broadleaved forest was established in 2009.
The plot was divided into 100 10 ×10 m subplots to conduct a census. The
second survey and leaf sampling were conducted in 2019. In this study,
18 dominant species with importance value \selectlanguage{english}[?]1.00\% were examined
accounting for 89.38\% (Table 1).

\subsection*{2.2 Leaf sample collection and functional traits
measurement}

{\label{leaf-sample-collection-and-functional-traits-measurement}}

For each leaf trait, ten replicates were obtained from individuals of
three standard trees of each dominant species (Perez-Harguindeguy et
al., 2013) growing on a 10 m x 10 m subplot. Fully expanded sun leaves
above the crown were collected and packed in sealed moist plastic bags
with damp paper that were transported to the laboratory in a cooler with
ice to preserve the water saturation of the leaves until the time of the
measurements. We measured the following structural functional traits:
leaf thickness (LT, mm), leaf area (LA, mm2), specific leaf area (SLA,
cm2*g\textsuperscript{-1}), leaf dry matter content (LDMC,
mg*g\textsuperscript{-1}), and chemical functional traits: leaf carbon
(LC, mg*g\textsuperscript{-1}), leaf nitrogen (LN,
mg*g\textsuperscript{-1}), and leaf phosphorus (LP,
mg*g\textsuperscript{-1}) contents; leaf nitrogen:phosphorus ratio LN:LP
and leaf carbon:nitrogen ratio (LC:LN) (Fig. 1).

We used different methods to measure the leaf thickness of the
broad-leaved and coniferous trees. For broad-leaf species, thickness
values were measured at five points per leaf from the front, middle, and
end with digital calipe\selectlanguage{english}(Dasqua 150 mm Special Glass Grating Big Screen
Digital Calipe\selectlanguage{english},Sichuan, China, accurate to 0.02/0.001 mm), avoiding the
mid-vein and secondary veins to reduce sampling variation, and then the
average value was taken as LT. For conifer species, LT was obtained from
the middle of the needles (He et al., 2020). The blades were then laid
on A4 papers, flattened with a transparent plastic sheet to ensure that
each blade was fully unfolded, and scanned to obtain a plane image.
Adobe Photoshop CS 6 software (Xiao et al., 2005) was used to calculate
the pixel points occupied by each leaf, and the average value was taken
as the LA of broad-leaf species. For conifer species, leaf length (LL)
and middle leaf width (LW) were determined to calculate as LA = .

Ten leaves per species were randomly selected and soaked in deionized
water for 12 h in the dark. After removing the leaves, the excess water
was immediately absorbed, and the saturated fresh weight (g) was weighed
using an electronic balance (AR2140, OHAUS, Guangzhou Jingbo Electronics
Co., Ltd\selectlanguage{english},accurate to 0.0001). The leaves were then oven-dried to a
constant weight at 65 degC for at least 72 h and then weighed using an
electronic balance. Leaf area and dry mass (g) were then used to
calculate the leaf dry matter content (LDMC = leaf dry weight/saturated
fresh weight) and specific leaf area (SLA = leaf area/leaf dry weight)
(Yang et al., 2021).

After measuring structural traits of leaf, we oven-dried the leaves at
105 degC for 5 min to eliminate the green and then dried them again at 85
degC to a constant weight. The leaves were milled and sieved through a
0.15 mm mesh. LC, LN, and LP were measured using the potassium
dichromate heating, Kjeldahl, and molybdenum- antimony colorimetric
methods, respectively (Wu et al., 2020). Two sets of parallel
experiments and two blank tests were performed simultaneously for all
parameter measurement to reduce the experimental errors. The average
value was taken as the value of the parameter of the subplot and species
and was considered to calculate the LN:LP and LC:LN.

\subsection*{2.3 Variables used in redundancy analysis (RDA) and
variation
partitioning}

{\label{variables-used-in-redundancy-analysis-rda-and-variation-partitioning}}

Environmental and spatial variables were generated as descriptors, and
functional and phylogenetic compositions were generated as response
variables before RDA and variation partitioning. Four topographic
variables (mean altitude, slope, convexity, and aspect) and seven soil
variables (mean soil moisture; pH; and concentrations of total carbon
(TC), total nitrogen (TN), total phosphorus (TP), total potassium (TK),
and available phosphorus (AP)) were measured as environmental variables.
All topographic variables were calculated based on the elevations at the
four corners of each subplot. Edaphic variables were measured using
systematic and random sampling approaches to collect soil samples near
the central point of each subplot. Detailed descriptions and formulas of
the topographic and edaphic sampling methods can be found in the study
by Zhao et al. (2015).

Principal coordinates of neighbor matrix (PCNM) eigenvectors were
generated to describe the spatial variables and were then used as
indirect proxies of dispersal-based processes (Borcard \& Legendre,
2002). First, we used the central coordinates (X and Y) of each subplot
to derive the Euclidean distance matrix. Second, a truncated matrix was
generated by retaining and replacing the values in the distance matrix
using a threshold (Jiang et al., 2018). Finally, the PCNM variables were
generated by performing principal coordinate analysis on the truncated
distance matrix (Dray et al., 2012). A total of 57 PCNM variables with
eigenvalues higher than zero were generated as spatial variables at a
fine spatial scale (10 m x 10 m) using the PCNM function in the vegan
package.

To compare the effects of different response variables on the detection
of ecological processes, we used 11 response variables, including
multivariable, single functional, and phylogenetic variables. First, we
calculated the community-weighted mean (CWM) (Bruelheide et al., 2018)
taking the relative importance value of tree species within each subplot
as the weight. The formula used is as follows:
Trait\emph{\textsubscript{c}} = \selectlanguage{greek}Σ\selectlanguage{english}\emph{P\textsubscript{i}} \selectlanguage{ngerman}×
trait\emph{\textsubscript{i}} , where Trait\textsubscript{\emph{c}}
represents the CWM of \emph{i} functional trait of leaf.
Trait\emph{\textsubscript{i}} and \emph{P\textsubscript{i}} are the
values of functional traits of each species and the important value for
species \emph{i} in a subplot. Second, we used phylogenetic dendrograms
and employed the phylogenetic fuzzy weighting (PFW) method to generate
phylogenetic compositions (Jiang et al., 2018). Detailed descriptions of
the calculations and background of the PFW approach can be found in the
study by Duarte et al. (2016). This analysis was conducted using
principal coordinates of phylogenetic structure (PCPS) (Debastiani \&
Duarte, 2014). Before analysis, all environmental variables (except
convexity) and CWM of traits were log-transformed, and phylogenetic
compositions were Hellinger-transformed.

\subsection*{2.4 Statistical analysis}

{\label{statistical-analysis}}

To test the intraspecific and interspecific variations in leaf
functional traits, we performed descriptive statistics on the leaf trait
data of each species, calculated the mean, range, standard deviation
(SD), standard error (SE), coefficient of variation (CV), and skewness
of each trait and then tested the difference of each trait using the
multiple comparison method of least significant difference (LSD).

To detect the phylogenetic signals of traits, phylogenetic independence
analysis (phylogenetic independent contrasts, PICs) was performed and a
phylogenetic dendrogram combined with functional traits was constructed.
The species details (family/genus/species) were input into Phylomatic
(\emph{http://www.phylodiversity.net/ phylomatic}) to construct a
phylogenetic tree. For this, the Angiosperm Phylogeny Group's APG III
consensus tree was taken as the pedigree skeleton and evolutionary
branch length using Figtree was obtained (Webb et al., 2008). Next, we
used Blomberg's \emph{K} test, which compares the variance of PICs with
that expected in a Brownian motion (random) model (Blomberg et al.,
2003). This analysis implemented the ``phylosignal'' function in the
``picante'' package (Kembel et al., 2010) and the phylogenetic tree of
the species with 999 randomizations. A significant \emph{p} value
(\textless{} 0.05) in this analysis indicates that a phylogenetic signal
exists and that phylogenetically close species are more similar than
random pairs of species.

To explore the relationships among the nine traits, we determined
correlations existing between traits and trait CWMs. The Pearson's
correlation coefficients among leaf traits at the species and community
levels were determined. Then we examined the species-level trait
relationships using PICs and community-level trait relationships using
partial regression analysis by controlling the phylogenetic variables.

Further, RDA analysis was used to explore the influence of topography
and soil on leaf functional trait and phylogenetic compositions at
community level using the vegan package (Oksanen et al., 2013). To
distinguish the effect of stochastic and niche processes on community
assembly, variation partitioning was used to assess the relative
importance of environmental and spatial variables as explanatory
variables for the variations of functional traits (CWMs) and
phylogenetic compositions using the rdacca.hp function in the rdacca.hp
package (Lai et al., 2022).

All analyses were performed using R version 3.6.0 (R Development Core
Team, 2016).

\textbf{3 Results}

\subsection*{3.1 Variations in leaf functional
traits}

{\label{variations-in-leaf-functional-traits}}

The intraspecific variation in majority of the leaf functional traits
was moderate, with a coefficient of variation (CV) ranging from
0.10--0.30 (Fig. 1). The variation in the structural traits was
relatively less, CV ranging from 0.02--0.55. Among the chemical traits,
LC exhibited the least variation, while LP (CV = 0.07--0.83) and LN:LP
(CV \emph{=} 0.09--0.62) showed the greatest variations (Fig. 1).

In contrast, leaf functional traits varied significantly among the
species (\emph{p} \textless{} 0.05).\emph{Pinus massoniana} had the
highest LT and the lowest LA and SLA. \emph{Alangium kurzii}
and\emph{Quercus fabri} had higher LA, SLA and LN but lower LDMC and
LC:LN. The LN of the evergreen species was significantly lower than that
of the deciduous and coniferous species. Deciduous species had higher
LA, SLA, LN, and LP values than those of both evergreen and coniferous
species. Coniferous species had the lowest LA and SLA (\emph{p}
\textless{} 0.05) but the highest LT (Fig. 1).

At the community level, the variations in most of the traits was
moderate (CV = 0.15--0.36), except LC\textsubscript{C},
LN\textsubscript{C}, and LC:LN\textsubscript{C}, where the CV
\textless{} 0.10 (Table 2). Among the structural characters, the
greatest variation was seen in LA\textsubscript{C} (CV = 0.36) ranging
from 7.57--57.37 cm\textsuperscript{2}. Among the chemical traits, LP
varied greatly, especially LN:LP\textsubscript{C} was 20.49 and the
values ranged from 10.39--46.35 (CV = 0.24) (Table 2).

\subsection*{3.2 Phylogenetic signals and correlations of leaf functional
traits}

{\label{phylogenetic-signals-and-correlations-of-leaf-functional-traits}}

The functional dendrogram (Fig. 2) showed that the leaf functional
traits of the 18 coexisting woody species were phylogenetically
structured, and species of the same genus clustered and had similar
functional traits. Significant phylogenetic signals were detected only
for LT and LA (\emph{K} \textgreater{} 1, \emph{p} \textless{} 0.01)
among different species. The SLA showed a moderate phylogenetic signal
(\emph{K} = 0.53, \emph{p} \textless{} 0.05). However, the LDMC and
chemical traits (\emph{K} = 0.05--0.40) were randomly distributed in the
phylogeny because there were no significant phylogenetic signals
(\emph{p} \textless{} 0.05, Table 3).

At the species level, irrespective of phylogenetic influence, there were
significant negative correlations between SLA with LT, LDMC, and LC:LN;
and LN with LT. There was a clear positive relationship between SLA with
LA, LN; and LN, LP with LA respectively (Table 4). Interestingly, after
removing the influence of phylogeny, the correlations between SLA with
LT, LN; and LN with LA, LC; and LC with LT, LDMC; and LP with LC, LN
were significantly enhanced (Table 4). At the community level,
non-phylogenetic correlations based on CWMs of the nine leaf traits were
largely consistent with these results (Tables 5). However, after
controlling for phylogenetic variables, most of the relationships showed
a little weakening trend, and only the correlations between
SLA\textsubscript{C} with LC\textsubscript{C}; and
LP\textsubscript{C}with LA\textsubscript{C}, LT\textsubscript{C}
increased slightly (Table 5).

\subsection*{3.3 Primary environmental factors affecting leaf functional
traits}

{\label{primary-environmental-factors-affecting-leaf-functional-traits}}

At the community level, 23.19\% of the variation in leaf functional
traits was explained by environmental factors. TK was a unique
significant edaphic factor (\emph{R} \textsuperscript{2} = 17.83\%), and
altitude and aspect were the two significant topographical factors
explaining 9.58\% and 8.94\% of the variation, respectively (Fig. 3a).
For phylogenetic compositions, all factors showed significant effects
except slope, which together explained 38.56\% of the total variation.
Among them, the most important environmental factors were TP, altitude,
and convexity, each with an interpretation rate of over 20\% (Fig. 3b).

\textbf{3.4 Environmental and spatial effects on functional traits and
phylogenetic compositions}

The results of variation partitioning showed that the pure spatial
factors (C) explained a higher variation in each functional trait at
community-level than pure environmental factors (A), except for
LP\textsubscript{c} and LN:LP\textsubscript{c} (Fig. 4). Environmental
and spatial factors comprehensively accounted for 63.38\% of the total
variation in all community-level functional traits and 47.96\% of the
phylogenetic structures. Meanwhile, the contribution of the pure
environment and the combined effect of environment and space (B) to the
phylogenetic structure were both significantly higher than those of the
community-level functional traits. However, for each community-level
trait, pure spatial variables explained 52.06\%, 41.56\%, and 37.07\%
variations of the LDMC\textsubscript{c}, SLA\textsubscript{c}, and
LC\textsubscript{c}, respectively. The pure environment could explain
18.48\% of the variation in LN\textsubscript{c} at most, whereas it
showed no influence on LC\textsubscript{c}, LN:LP\textsubscript{c}, and
LC:LN\textsubscript{c}. The combined effect of environment and space on
LT\textsubscript{c} was the highest (13.75\%), but it had no effect on
LC\textsubscript{c} and LN\textsubscript{c} (Fig. 4).

\textbf{4 Discussion}

\textbf{4.1 Variation in leaf functional traits and trade-off
relationships}

We found that the mean values of nine leaf functional traits of 18
dominant tree species in this community were within the ranges of global
leaf trait variation (Chen et al., 2016; Zhao et al., 2020), and it was
moderate in intraspecific and community-level variations, while
significant in interspecific variation, indicating that accurate
measurements of multi-source variation of functional traits were
significant for deep understanding of the processes of community
assembly (Westerband et al., 2021). Compared to that in other vegetation
types (Li et al., 2017; Liu et al., 2020; Wang et al., 2021), the
intraspecific variation in this community was at a lower level and
supported the ``a spatial trait variance partitioning hypothesis,'' that
is, due to the limitation of environmental heterogeneity and individual
number, the importance of intraspecific variation was less at the fine
scale.

The functional traits of plant leaves are determined by both genetic and
environmental factors. They usually show niche differentiation and
divergence of ecological strategies through intraspecific variation so
as to reduce the intensity of competition (Kang et al., 2017) and to
adapt to a broad environmental gradient. In this community, the
intraspecific variation in leaf chemical characteristics (LC, LN, LP,
and LC:LN) of dominant tree species was higher than that of structural
characteristics (LA, SLA, and LDMC). However, the interspecific
variations showed the opposite trend except for the related-LP traits,
indicating that the variation in leaf chemical traits had higher
plasticity in coping with local environmental changes. Zhao (2015) found
that the soil and topographic variables in the community had moderate
spatial variation, which had significant effects on the species
composition and spatial distribution of the community. And as we all
know, the availability of nitrogen and phosphorus in soil is the main
influencing factor influencing LN and LP contents, respectively (Li et
al., 2017). Therefore, to adapt to more complex environmental
conditions, dominant species increase their distribution space along the
environmental gradient by adjusting the plasticity of leaf chemical
traits. Compared to chemical traits, leaf structural traits are mainly
limited by genetic factors and are relatively stable and closely related
to life forms and species type; therefore, interspecific variation is
larger (Ackerly \& Reich, 1999). In addition, it greatly differs for
species at different phylogenetic stages in leaf size, leaf life span,
LN content and photosynthetic ability. Eighteen dominant species in this
community belonging to 10 families, including Fagaceae, Theaceae,
Anacardiaceae, Pinaceae, and Taxodiaceae (Table 1), exhibited
significantly different phylogeny background and thus may help them
coexist through the coordination of functional traits. In addition,
needle-leaf trees are generally thought to be better adapted to cold or
nutrient-poor environments than broadleaf trees (Liu et al., 2020; Liu
et al., 2021). With an increase in water stress, plants tend to exhibit
xerophytic leaves with a thicker leaf structure (Guerful et al., 2009;
Werden et al., 2017). Similarly, our results showed that coniferous
species, especially \emph{P. massoniana} had the highest LT and the
lowest LA and SLA in this community, which may be because it was the
first pioneer tree species to enter this community; and in order to
resist the arid and barren environment, they possessed leaf functional
traits such as thicker leaves suitable for storing water and lower LA
and SLA to reduce water loss through transpiration. This result was
consistent with those of previous studies that showed larger SLA and
thinner leaves of broad-leaved trees than those of coniferous trees
(Tian et al., 2016). Furthermore, the LN content of evergreen species
was significantly lower than that of deciduous species because the
generation cost of leaves was related to seasonal variation; leading to
different adaptive strategies of evergreen and deciduous trees based on
the variation of traits to adverse environments (Liu et al., 2021). In
summary, the variations in leaf functional traits in \emph{L. glaber--C.
glauca} evergreen broad-leaved forest community were mainly attributed
to the life form and interspecific variation, which was significantly
affected by genetic background and taxon, and provided an important
prerequisite for community assembly and species coexistence.

Meanwhile, there was a correlation between leaf structure and chemical
traits in the community. SLA, LDMC, and LN all reflect adaptation
strategies to the environment (Wright et al., 2004; Xun et al., 2020),
and there was a significant positive correlation between SLA and LN,
reflecting photosynthetic capacity and nutrient turnover at the species
and community levels. SLA significantly correlated with LT, LDMC, and LC
negatively, while no correlation was observed between LDMC and LT (Table
4 and 5), indicating that LT and LDMC affected SLA in different ways in
this community. Simultaneously, the construction of a leaf defense
structure requires a large amount of photosynthate, and LC is usually
used to compensate for consumption during development (Schulze et al.,
1994). Therefore, LC increased with an increase in LDMC and LN. In
addition, light is an important factor affecting SLA (Wyka et al.,
2012), and LDMC is closely related to water (Saura-Mas et al., 2009).
The results showed that LDMC had lower interspecific variation and did
not correlate with the chemical traits (except with LC), indicating that
subtropical evergreen broad-leaved forests had sufficient hydrothermal
conditions and that the major factor affecting community assembly should
be light rather than water.

\subsection*{4.2 Phylogenetic effects on leaf functional
traits}

{\label{phylogenetic-effects-on-leaf-functional-traits}}

The evolution is close to Brownian motion; that is, species with similar
phylogenetic positions have similar characteristics and have certain
evolutionary conservation. Species with similar functional traits are
often phylogenetically similar (Losos, 2008). When a strong phylogenetic
signal is detected in functional traits, environmental filters are
probably selected for phylogenetically close species, causing
phylogenetic clustering (Amaral et al., 2021). Here, only a number of
leaf structural traits (LA and LT) showed strong and significant
phylogenetic signals, indicating that LA and LT were closely related to
phylogenetic history and showed strong phylogenetic conservation; that
is, the more phylogenetically close species were more similar to LA and
LT. All the dominant tree species had considerably high values of LA,
except the deciduous tree species (\emph{Q. fabri} and \emph{A.
kurzii}var. \emph{Kurzii} ). All evergreen tree species belonging to the
same family and genus had more similar traits, especially for the two
most dominant species (\emph{L. glaber} and \emph{C. glauca} ) in the
community, both belonging to Fagaceae (Table 2 and Fig. 1).

The distributions of chemical traits (\emph{K} \textless{} 1,
\emph{p}\textgreater{} 0.05) (Fig. 1) were not consistent with the
phylogenetic relationships, indicating that the phylogenetic signals of
these traits were random or divergent. Therefore, compared to the leaf
chemical traits, the formation and development of structural traits were
more affected by genetic differences, which is consistent with the
results of Cao et al. (2013). In other words, the phylogenetic signal
test based on functional traits showed a lack of consistency between the
leaf functional trait patterns and phylogenetic patterns in this
community, and no specific trend or relationship between them was
observed. The phylogenetic relationships of \emph{L. glaber--C. glauca}
evergreen broad-leaved forest community were inconsistent with the
changes in functional traits with the historical processes. This
observation was supported by the work of Cheng et al. (2019) on the
construction mechanism of tropical cloud forest communities.

Numerous studies have indicated that phylogeny has a significant effect
on the functional trait composition and that the relationships among
traits are generally weakened after removing phylogeny (Cadotte et al.,
2019; Wang et al., 2020; Liu et al., 2021). This study also found that
the traits of coexisting species in the community had a phylogenetic
structure; however, only a few leaf traits (LA and LT) showed strongly
phylogenetic signals. But the relationship among traits after removing
the influence of the phylogeny remained a little changed at community
level or even significantly enhanced at species level. This indicated
that in the local community, environment had a greater impact on the
spatial distribution of individuals, and plants follow the ``realism''
strategy to adapt to the environment, meaning, plants would adjust these
trait-off relationships according to their habitats to archive the best
survival state, which was less limited by the evolutionary history. It
was clear that the functional community structure was not consistent
with the phylogenetic structure. Combined analysis of phylogenetic and
functional trait structures will more accurately infer the main
ecological processes driving species coexistence.

\subsection*{4.3 Community assembly mechanisms of integrated phylogenetic
leaf functional
traits}

{\label{community-assembly-mechanisms-of-integrated-phylogenetic-leaf-functional-traits}}

Many studies have attempted to distinguish between determinative and
stochastic processes by partitioning the variation of species
composition into environmental and spatial components (Legendre et al.,
2009; Chang et al., 2013; Qiao et al., 2015). However, species niches
are determined by their functional traits, which further influence their
distribution along environmental gradients (McGill et al., 2006). Thus,
the effects of determinative processes on community assembly are
expected to be underestimated based on species identity, which does not
consider the functional properties of species. However, we found that
functional traits (12.35\%) did not improve the interpretation rate of
niche-based processes by considering only species identity (28.10\%,
Zhao et al., 2015) and phylogenetic structure (29.80\%) (Fig. 4). This
observation was similar to that by Jiang et al. (2018) for temperate
deciduous broad-leaved Korean pine forests, in which functional traits
could not better reveal ecological processes compared with that by
species.

However, integrating functional traits with phylogeny can greatly
improve the ability to infer determinative and stochastic processes, and
trait- and phylogenetic-based approaches are powerful ways to detect
community assembly processes (Li et al., 2019; Amaral et al., 2021). We
found that the interpretation rate of community assembly based on
functional traits (63.38\%) was higher than that based on phylogeny
(47.96 \%). However, the pure space variable could significantly explain
higher functional traits than that by environmental variables (Fig. 4),
indicating that the neutral stochastic process played a leading role in
the construction of community functional traits. Compared to the
community functional trait composition, the environment had a greater
contribution to the spatial variation in the phylogenetic structure
(Fig. 4). This indicates that the phylogenetic structure of the
community was aggregated (mainly affected by habitat filtration);
however, leaf functional trait composition showed a dispersion pattern
(mainly affected by stochastic processes). It also verified the opinion
that species identity is a more holistic concept and could better depict
multiple traits of a plant, while leaf functional traits could depict
one or certain facets of a plant (Jiang et al., 2018). At the same time,
among all edaphic and environmental variables, only altitude, aspect,
and TK reflecting light and water utilization of plants had a
significant effect on community functional composition (Fig. 3), which
was related to the fact that leaves were key organs of plant
photosynthesis and mainly exercise the functions of photosynthesis and
nutrient turnover. Therefore, we need to consider more functional traits
(such as height and wood density) to provide more detailed information
to improve the interpretation rate of community functional trait
composition.

\textbf{5 Conclusions}

The phylogenetic and trait-based analyses conducted in this study showed
that functional traits and phylogeny provide a meaningful way to detect
community assembly processes. The results show that there were
significant interspecific variations in leaf traits at \emph{L.
glaber--C. glauca} evergreen broad-leaved forest community. Leaf
functional traits composition of coexisting species showed dispersion
pattern and tended to adopt ``realism'' to adapt to their habitats.
Strong phylogenetic signals were detected only in LA and LT. In
addition, TK, TP, altitude, aspect, and convexity were the primary
influencing environmental factors. Neutral theory-based stochastic
processes were the main drivers in the development of community leaf
functional traits, but niche-based habitat filtration was more important
influencing mechanism of community phylogenetic structure. In future
research, more functional traits as well as a larger scale should be
taken into account.

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\textbf{Table 1 Nomenclature and important values of 18 dominant woody
species in \emph{L. glaber}--\emph{C. glauca} evergreen broad-leaved
forest community.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Species & Abbreviation & Family & Life form & Important value
(\%)\tabularnewline
\midrule
\endhead
\emph{Lithocarpus glaber} & LG & Fagaceae & Evergreen &
21.78\tabularnewline
\emph{Cyclobalanopsis glauca} & CG & Fagaceae & Evergreen &
12.02\tabularnewline
\emph{Pinus massoniana} & PM & Pinaceae & Evergreen &
10.60\tabularnewline
\emph{Cleyera japonica} & CJ & Pentaphylacaceae & Evergreen &
9.33\tabularnewline
\emph{Choerospondias axillaris} & CA & Anacardiaceae & Deciduous &
6.29\tabularnewline
\emph{Cunninghamia lanceolata} & CL & Taxodiaceae & Evergreen &
4.45\tabularnewline
\emph{Ilex formosana} & IF & Aquifoliaceae & Evergreen &
3.95\tabularnewline
\emph{Ilex viridis} & IV & Aquifoliaceae & Evergreen &
3.88\tabularnewline
\emph{Elaeocarpus chinensis} & EC & Elaeocarpaceae & Evergreen &
2.86\tabularnewline
\emph{Eurya muricata} & EM & Pentaphylacaceae & Evergreen &
2.34\tabularnewline
\emph{Loropetalum chinense} & LC & Hamamelidaceae & Evergreen &
2.15\tabularnewline
\emph{Symplocos setchuensis} & SS & Symplocaceae & Evergreen &
2.06\tabularnewline
\emph{Alangium kurzii} & AK & Cornaceae & Deciduous &
1.85\tabularnewline
\emph{Quercus fabri} & QF & Fagaceae & Deciduous & 1.51\tabularnewline
\emph{Symplocos pendula} var\emph{. hirtistylis} & SP & Symplocaceae &
Evergreen & 1.48\tabularnewline
\emph{Ilex chinensis} & IC & Aquifoliaceae & Evergreen &
1.12\tabularnewline
\emph{Symplocos stellaris} & ST & Symplocaceae & Evergreen &
1.01\tabularnewline
\emph{Symplocos sumuntia} & SU & Symplocaceae & Evergreen &
1.00\tabularnewline
Total & & & & 89.38\%\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2 Descriptive statistics of the community weighted leaf
functional traits based on important values of each species} .\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Traits & Mean & SE & Minimum & Maximum & CV & Skewness\tabularnewline
\midrule
\endhead
LT\textsubscript{C} (mm) & 0.27 & 0.00 & 0.19 & 0.40 & 0.15 &
1.14\tabularnewline
LA\textsubscript{C} (cm\selectlanguage{ngerman}²) & 22.83 & 0.86 & 7.57 & 57.37 & 0.36 &
1.07\tabularnewline
SLA\textsubscript{C} (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & 116.40 & 3.27 & 61.27
& 213.03 & 0.27 & 1.17\tabularnewline
LDMC\textsubscript{C} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 608.55 & 13.16 &
379.06 & 1088.17 & 0.21 & 0.66\tabularnewline
LC\textsubscript{C} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 470.46 & 1.25 & 433.63
& 498.51 & 0.03 & -0.45\tabularnewline
LN\textsubscript{C} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 15.88 & 0.15 & 11.53 &
19.47 & 0.09 & 0.07\tabularnewline
LP\textsubscript{C} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.94 & 0.02 & 0.47 &
1.67 & 0.21 & 0.50\tabularnewline
LN:LP\textsubscript{C} & 20.49 & 0.53 & 10.39 & 46.35 & 0.24 &
2.04\tabularnewline
LC:LN\textsubscript{C} & 30.97 & 0.30 & 25.22 & 40.99 & 0.09 &
0.43\tabularnewline
\bottomrule
\end{longtable}

LT\textsubscript{C}, LA\textsubscript{C}, SLA\textsubscript{C},
LDMC\textsubscript{C}, LN\textsubscript{C}, LC\textsubscript{C},
LP\textsubscript{C}, LN:LP\textsubscript{C}, and
LC:LN\textsubscript{C}represent leaf thickness, leaf area, specific leaf
area, leaf dry matter content, leaf nitrogen content, leaf carbon
content, leaf phosphorus content, leaf nitrogen-phosphorus ratio, and
leaf carbon-nitrogen ratio at the community level, respectively. SE:
Standard error, CV: Coefficient of variation

\textbf{Table 3 Phylogenetic signals of leaf functional traits for all
species.}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Traits & \emph{K} & \emph{p} value\tabularnewline
\midrule
\endhead
LT (mm) & 1.5 & \textbf{0.002}\tabularnewline
LA (cm\selectlanguage{ngerman}²) & 1.05 & \textbf{0.002}\tabularnewline
SLA (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & 0.53 & \textbf{0.033}\tabularnewline
LDMC (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.4 & 0.107\tabularnewline
LC (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.25 & 0.298\tabularnewline
LN (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.2 & 0.374\tabularnewline
LP (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.05 & 0.949\tabularnewline
LN:LP & 0.06 & 0.922\tabularnewline
LC:LN & 0.19 & 0.398\tabularnewline
\bottomrule
\end{longtable}

LT, LA, SLA, LDMC, LC, LN, LP, LN:LP and LC:LN represent leaf thickness,
leaf area, specific leaf area, leaf dry matter content, leaf carbon
content, leaf nitrogen content, leaf phosphorus content, leaf
nitrogen-phosphorus ratio, and leaf carbon-nitrogen ratio,
respectively.\emph{K} : Blomberg's \emph{K} values, and significant
values at \emph{p}\textless{} 0.005 are shown in bold. All the trait
data were log-transformed. \emph{p} -values for traits with significant
phylogenetic signals are highlighted in bold.

\textbf{Table 4 Pearson's correlation coefficients (lower diagonal) and
phylogenetically independent contrasts (PIC, upper diagonal) among nine
leaf functional traits for all species.}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Traits & LT (mm) & LA (cm\selectlanguage{ngerman}²) & SLA (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & LDMC
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LC (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LN
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LP (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LN:LP &
LC:LN\tabularnewline
\midrule
\endhead
LT (mm) & 1 & -0.02 & -0.65\textsuperscript{**} & -0.40 &
-0.74\textsuperscript{***} & -0.65\textsuperscript{**} &
-0.63\textsuperscript{**} & -0.11 & 0.16\tabularnewline
LA (cm\selectlanguage{ngerman}²) & -0.08 & 1 & 0.50\textsuperscript{*} & -0.14 & -0.01 &
0.48\textsuperscript{*} & 0.35 & -0.14 & -0.43\tabularnewline
SLA (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & -0.49\textsuperscript{***} &
0.53\textsuperscript{***} & 1 & -0.02 & 0.33 & 0.71\textsuperscript{**}
& 0.40 & 0.13 & -0.28\tabularnewline
LDMC (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.06 & -0.09 &
-0.32\textsuperscript{***} & 1 & 0.72\textsuperscript{***} & 0.12 &
0.52\textsuperscript{*} & -0.14 & 0.27\tabularnewline
LC (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.10 & -0.05 & -0.09 & 0.17 & 1 & 0.49*
& 0.77\textsuperscript{***} & -0.34 & 0.09\tabularnewline
LN (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.21\textsuperscript{***} &
0.53\textsuperscript{***} & 0.52\textsuperscript{***} & -0.14 & 0.02 & 1
& 0.66\textsuperscript{***} & 0.08 &
-0.75\textsuperscript{***}\tabularnewline
LP (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.10 & 0.27\textsuperscript{***} & 0.17
& -0.10 & -0.02 & 0.23\textsuperscript{***} & 1 & -0.47 &
-0.26\tabularnewline
LN:LP & 0.01 & 0.01 & 0.04 & -0.02 & -0.07 & 0.20\textsuperscript{***} &
-0.70\textsuperscript{***} & 1 & -0.29\tabularnewline
LC:LN & 0.08 & -0.39\textsuperscript{***} & -0.27\textsuperscript{***} &
0.10 & 0.29\textsuperscript{***} & -0.85\textsuperscript{***} & -0.17 &
-0.26\textsuperscript{***} & 1\tabularnewline
\bottomrule
\end{longtable}

LT, LA, SLA, LDMC, LC, LN, LP, LN:LP and LC:LN represent leaf thickness,
leaf area, specific leaf area, leaf dry matter content, leaf carbon
content, leaf nitrogen content, leaf phosphorus content, leaf
nitrogen-phosphorus ratio, and leaf carbon-nitrogen ratio, respectively.
*** \emph{p} \textless{} 0.001; ** \emph{p} \textless{} 0.01; *
\emph{p}\textless{} 0.05\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Traits & LT\textsubscript{c} (mm) & LA\textsubscript{c} (cm\selectlanguage{ngerman}²) &
SLA\textsubscript{c} (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & LDMC\textsubscript{c}
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LC\textsubscript{c}
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LN\textsubscript{c}
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LP\textsubscript{c}
(mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & LN:LP\textsubscript{c} &
LC:LN\textsubscript{c}\tabularnewline
\midrule
\endhead
LT\textsubscript{c} (mm) & 1 & -0.21 & -0.62*** & -0.03 & 0.13 & -0.37**
& -0.39*** & 0.30* & 0.30*\tabularnewline
LA\textsubscript{c} (cm\selectlanguage{ngerman}²) & -0.22* & 1 & 0.25 & 0.14 & -0.10 & 0.38** &
0.31* & -0.13 & -0.41**\tabularnewline
SLA\textsubscript{c} (cm\selectlanguage{ngerman}²·g\textsuperscript{-1}) & -0.62*** & 0.26** & 1
& -0.25 & -0.37** & 0.43** & 0.22 & -0.15 & -0.35*\tabularnewline
LDMC\textsubscript{c} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.02 & 0.13 &
-0.26** & 1 & 0.30* & -0.07 & 0.03 & -0.10 & 0.11\tabularnewline
LC\textsubscript{c} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & 0.11 & -0.08 & -0.35***
& 0.29** & 1 & 0.18 & -0.20 & 0.28* & 0.08\tabularnewline
LN\textsubscript{c} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.38*** & 0.39*** &
0.44*** & -0.08 & 0.19 & 1 & 0.08 & 0.22 & -0.91***\tabularnewline
LP\textsubscript{c} (mg\selectlanguage{ngerman}·g\textsuperscript{-1}) & -0.37*** & 0.28** &
0.20 & 0.04 & -0.21* & 0.05 & 1 & -0.72*** & -0.06\tabularnewline
LN:LP\textsubscript{c} & 0.28** & -0.11 & -0.14 & -0.11 & 0.29*** &
0.23* & -0.72*** & 1 & -0.21\tabularnewline
LC:LN\textsubscript{c} & 0.30*** & -0.41*** & -0.35*** & 0.12 & 0.07 &
-0.91*** & -0.05 & -0.22* & 1\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 5 Pearson's correlation coefficients (lower diagonal) and
phylogenetically independent contrasts (PIC, upper diagonal) among the
nine leaf functional traits at the community level.}

LT\textsubscript{C}, LA\textsubscript{C}, SLA\textsubscript{C},
LDMC\textsubscript{C}, LN\textsubscript{C}, LC\textsubscript{C},
LP\textsubscript{C}, LN:LP\textsubscript{C}, and
LC:LN\textsubscript{C}represent leaf thickness, leaf area, specific leaf
area, leaf dry matter content, leaf nitrogen content, leaf carbon
content, leaf phosphorus content, leaf nitrogen-phosphorus ratio, and
leaf carbon-nitrogen ratio at the community level, respectively. ***
\emph{p} \textless{} 0.001; **\emph{p} \textless{} 0.01; * \emph{p}
\textless{} 0.05.

\textbf{Figure captions}

\textbf{Fig. 1 Intraspecific and interspecific variations in leaf
functional traits of the 18 dominant species.} Different letters and
their combinations represent the significance levels of interspecific
differences (\emph{p} \textless{} 0.05). The numbers in brackets
indicate the intraspecific variation coefficients of each species. See
Table 1 for species abbreviation codes.

\textbf{Fig. 2 Functional dendrogram of the 18 dominant species
in\emph{L. glaber--C. glauca} evergreen broad-leaved forest community.}

\textbf{Fig. 3 Effects of topographic and} \textbf{edaphic variables on
leaf functional traits and phylogenetic} \textbf{compositions at the
community level.} TP, TC, TN, TK: total soil phosphorus, nitrogen,
carbon and potassium, respectively; AP, available phosphorus. Alt, Asp,
Con, Slo: altitude, aspect, convexity, slope, respectively. (a) The
effect of each variable (topography or soil) on community-weighted mean
(CWMs) of leaf functional traits. (b) The effect of each variable
(topography or soil) on phylogenetic compositions.

\textbf{Fig.4} \selectlanguage{ngerman}\textbf{Variation partitioning of phylogenetic and
functional traits structures (adjusted \emph{R}\textsuperscript{2} ×
100\%) in the effects of different response variables at the community
level} . The interpretation rates of purely environmental (A), shared
environmental and spatial (B) and purely spatial (C) fractions on
different response variable are indicated by red, blue, and green,
respectively. Response variables are the all community-level functional
traits (Funt\textsubscript{c}), phylogenetic (Phy\textsubscript{c})
structures and each community-level traits composition.

\textbf{Fig. 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 4}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

