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\begin{document}

\title{Trait-based adaptability of Phragmites australis to the effects of soil
water and salinity in the Yellow River Delta}



\author[1]{Dayou Zhou}%
\author[1]{Yuehan Ni}%
\author[1]{Xiaona Yu}%
\author[2]{Kuixuan Lin}%
\author[1]{Ning Du}%
\author[1]{Lele Liu}%
\author[3]{Xiao Guo}%
\author[1]{Weihua Guo}%
\affil[1]{Shandong University}%
\affil[2]{Chinese Research Academy of Environment Sciences}%
\affil[3]{Qingdao Agricultural University}%


\vspace{-1em}



  
  \date{March 14, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Phragmites australis is the dominant species in the Yellow River Delta
and plays an important role in wetland ecosystems. Ecological responses
of the P. australis community to soil properties were investigated in 96
areas along the coastal-inland regions in the Yellow River Delta of
China. The aim was to evaluate the relationship between phenotypic
variation and environmental factors, reveal which functional traits
could well respond to changes in electrical conductivity and soil water
content, and the ecological strategies of P. australis. Within the range
of soil water content (9.39--36.92\%) and electrical conductivity
(0.14--13.29 ms/cm), the results showed that the effects of soil water
content and salinity were not equally important for the characterization
of the morphological and physiological variability, and that plant
functional traits including leaf traits and stem traits responded more
strongly to soil salinity than soil water content. Our results suggested
that salinity leads to reduced average height, specific leaf area, leaf
area, and base stem diameter, but increased leaf water content and leaf
thickness. The relationships between functional traits and electrical
conductivity were generally linear and logarithmic. The coefficients of
variation of morphological traits showed more phenotypic plasticity than
the physiological traits. Salinity also led to the stress
tolerator/competitor-stress tolerator (S/CS) strategies of P. australis;
with the decrease of environmental stress, the main strategy gradually
moved to the competitor (C) strategy, making P. australis the dominant
species in the Yellow River Delta. KEYWORDS: Soil water content,
Electrical conductivity, Functional traits, Plasticity, Life strategies.%
\end{abstract}%



\sloppy


Trait-based adaptability of \emph{Phragmites australis} to the effects
of soil water and salinity in the Yellow River Delta

\textbf{(Dayou Zhou\textsuperscript{1}, Yuehan Ni\textsuperscript{1},
Xiaona Yu*\textsuperscript{1}, Kuixuan Lin\textsuperscript{2}, Ning
Du\textsuperscript{1}, Lele Liu\textsuperscript{1}, Xiao
Guo\textsuperscript{3}, Weihua Guo*\textsuperscript{1})}

1. Institute of Ecology and Biodiversity, Shandong Provincial
Engineering and Technology Research Center for Vegetation Ecology,
School of Life Sciences, Shandong University, Qingdao 266237, China

2. State Environmental Protection Key Laboratory of Estuarine and
Coastal Environment, Chinese Research Academy of Environment Sciences,
Beijing 100012, China

3. College of Landscape Architecture and Forestry, Qingdao Agricultural
University, Qingdao 266109, China

*Corresponding author:

Xiaona Yu, PhD

Institute of Ecology and Biodiversity, Shandong Provincial Engineering
and Technology Research Center for Vegetation Ecology, School of Life
Sciences, Shandong University, Qingdao 266237, China

TEL: +86-18963098076

Email: yuxiaona\_sdu@163.com

*Corresponding author:

Weihua Guo, PhD, Professor

Institute of Ecology and Biodiversity, Shandong Provincial Engineering
and Technology Research Center for Vegetation Ecology, School of Life
Sciences, Shandong University, Qingdao 266237, China

TEL: +86-13589056676

Email: whguo@sdu.edu.cn

\textbf{Abstract}

\emph{Phragmites australis} is the dominant species in the Yellow River
Delta and plays an important role in wetland ecosystems. Ecological
responses of the \emph{P. australis} community to soil properties were
investigated in 96 areas along the coastal-inland regions in the Yellow
River Delta of China. The aim was to evaluate the relationship between
phenotypic variation and environmental factors, reveal which functional
traits could well respond to changes in electrical conductivity and soil
water content, and the ecological strategies of \emph{P. australis} .
Within the range of soil water content (9.39--36.92\%) and electrical
conductivity (0.14--13.29 ms/cm), the results showed that the effects of
soil water content and salinity were not equally important for the
characterization of the morphological and physiological variability, and
that plant functional traits including leaf traits and stem traits
responded more strongly to soil salinity than soil water content. Our
results suggested that salinity leads to reduced average height,
specific leaf area, leaf area, and base stem diameter, but increased
leaf water content and leaf thickness. The relationships between
functional traits and electrical conductivity were generally linear and
logarithmic. The coefficients of variation of morphological traits
showed more phenotypic plasticity than the physiological traits.
Salinity also led to the stress tolerator/competitor-stress tolerator
(S/CS) strategies of \emph{P. australis} ; with the decrease of
environmental stress, the main strategy gradually moved to the
competitor (C) strategy, making \emph{P. australis} the dominant species
in the Yellow River Delta.

\textbf{KEYWORDS:} Soil water content, Electrical conductivity,
Functional traits, Plasticity, Life strategies.

\textbf{INTRODUCTION}

Plant functional traits could be divided as different types as traits of
groups of plants that reflect similar responses to environmental factors
or exert similar effects on community dynamic processes based on plant
morphology and physiology (Duckworth et al., 2000; Guo et al., 2017).
Morphological and physiological features that could reflect plant
ecological strategies (Spasojevic et al., 2014; Chai et al., 2016).
Owing to the adaptability and plasticity to environmental gradients,
individual plants have highly variable traits. The variation in plant
functional traits determines the feedback of plants in response to
various environmental factors (Li et al., 2014; Guan et al., 2017).
Functional traits can be used to quantify a wide range of natural and
anthropogenic disturbances (Wang et al., 2015; Chai et al., 2016).

Numerous studies have reported the relationships among plant communities
and other environmental factors such as salinity in various habitats
(Wang et al., 2012; Gonz\selectlanguage{ngerman}ález-Alcaraz et al., 2014, McCoy-Sulentic et
al., 2017). These studies have greatly contributed to our understanding
of soil--plant interactions that benefit wetland ecosystem restoration.
Plant growth is highly dependent on soil water content. Many researchers
have reported that community structure (Pérez-Ramos et al., 2012),
species composition (Li et al., 2008), and vegetation growth (Yu et al.,
2014; Gong et al., 2014) could be affected by soil water content.
Salinity is one of the major environmental factors limiting plant growth
and productivity (Sdouga et al., 2019). Salinity also affects multiple
trait strategy-dimensions, causes consequences for ecosystem functions
(De Battisti et al., 2020), and leads to environmental filtering that
drives plant community assembly processes (Yi et al., 2020). Plants
adapt to heterogeneous habitats through plasticity in growth strategies
and functional traits, as well as the optimal allocation and trade-offs
of various traits (Mason et al., 2013; Donovan et al., 2014; Guan et
al., 2017). Plant functional traits have been useful in answering many
important ecological questions at a range of scales (Mason et al., 2013;
Pérez-Harguindeguy et al., 2013), providing a tool for determining the
feedback of plants under stressful conditions.

Competitor, stress tolerator, ruderal (CSR) theory is a prominent
strategy scheme advanced by Grime (1977) and reviewed by Grime \& Pierce
(2012), in which the three principal strategies represent viable trait
combinations. They have also been used to investigate and interpret
community processes, such as succession and the relationship between
species richness and productivity (Caccianiga et al., 2006; Cerabolini
et al., 2016). Three core leaf functional traits, specific leaf area
(SLA), leaf dry matter content (LDMC), and leaf area (LA), were used as
criteria to determine the ecological strategies of the individual or
population of the plant being studied. The plant CSR strategy taxonomy
goes beyond the previous research that focused on analyzing plant
ecological strategies solely by using CSR strategies or functional trait
pedigree (Pierce et al., 2017; Xu et al., 2019).

\emph{Phragmites australis} , a widespread species occurring in both
freshwater and brackish habits, has strong environmental adaptability
and phenotypic plasticity (Yang et al., 2014). The broad range of
adaptions to soil water content and salinity has led to the successful
growth of \emph{P. australis} in high-salinity areas (Burdick et al.,
2001; Achenbach et al., 2013; Guo et al., 2018). A field investigation
was carried out in the Yellow River Delta in order to reveal the
responses of community characteristics and functional traits of \emph{P.
australis} to soil water content and salinity. Here, we hypothesized
that (1) the effects of soil water content and salinity were not equally
important in characterizing the morphological and physiological
variability; and (2) the differences in morphological traits,
physiological traits, adaptive mechanisms, and ecological strategies
of\emph{P. australis} in response to environmental stress were dominated
by functional traits.

\textbf{MATERIALS AND METHODS}

\textbf{Study region and sampling sites}

The study site was located in the Yellow River Delta (36°55\selectlanguage{english}'--38deg16\selectlanguage{english}'N,
117deg31\selectlanguage{english}'--119deg18\selectlanguage{english}'E) in Dongying, Shandong Province, northern China. The
Yellow River Delta is one of the fastest-growing estuarine wetlands in
the world (Cui et al., 2011). The mean annual precipitation in the study
area is approximately 628.6 mm and the mean annual temperature is about
11.9degC (Zhou et al., 2020). The region is simultaneously influenced by
river water, ground water, and seawater, and is characterized by strong
temporal and spatial heterogeneity of soil water and salinity gradients
(Zhou et al., 2020). To describe and characterize the patterns and
processes influencing the \emph{P. australis} communities, 96 quadrats
(1 x1 m) selected at random were investigated along the coastal-inland
regions (37deg43\selectlanguage{english}'--38deg05\selectlanguage{english}'N, 118deg41\selectlanguage{english}'--119deg13\selectlanguage{english}'E) under different gradients
of soil water content and electrical conductivity in the Yellow River
Delta in 2013. Samples were chosen in terms of typical \emph{P.
australis} communities with little human disturbance, but with
significant differences in soil water content and salinity gradient
(Figure 1).

\textbf{Figure 1.} Distribution patterns of soil water content and
electrical conductivity for 96 different samples.

\textbf{Measurements of soil} \textbf{properties and functional traits}

Soil layer samples were obtained at a depth of 0--20 cm to measure soil
water content (SWC) and electrical conductivity (EC). SWC was measured
by drying soil samples at 105degC to constant weight.

Soil water content (SWC) = (wet soil weight - dry soil weight) x100\% /
dry weight of soil

EC was measured by taking 10 g grated dried soil sample and placing it
in an Erlenmeyer flask. Water and soil were mixed in a volume ratio of
1: 2.5 and a magnetic stirrer was used to dissolve the salt for about 40
min. A conductivity meter (DDS12A, LIDA, China) was used to measure the
EC in the upper suspension.

For each sample, 10 functional traits were measured including average
height, number of plants, soil plant analysis development (SPAD) values,
leaf water content, specific leaf area, leaf area, leaf thickness, pitch
number, internode height, and shoot basal diameter. Leaf thickness was
measured by a micrometer caliper at the widest point while avoiding the
midrib. The base stem diameter was determined using a vernier caliper.
SPAD values were determined using a chlorophyll meter (SPAD-502PLUS,
Konica Minolta, Japan). The fresh mass of leaves (LFM) was weighed and
then oven-dried at 80degC for 48 h, after which the dry mass of leaves
(LDM) was weighed. Leaf area (LA) was analyzed using a broadleaf
analysis system (WinFolia pro LM2400P, Regent) through scanning with a
Canon scanner (Canon 2000). Specific leaf area was calculated using the
following equation:

SLA=LA/LDM, (1)

where SLA (cm\textsuperscript{2}/g) was the specific leaf area, LA was
the leaf area (cm\textsuperscript{2}), and LDM (g) was the dry mass of
the leaf. Leaf water content was calculated using the following
equation:

LWC= (LFM-LDM)/LFM=1-(LDM/LFM), (2)

where LWC (g/g) was leaf water content, LFM (g) was the fresh mass of
the leaf, and LDM (g) was the dry mass of the leaf.

We used LA, LDMC, and SLA to assign combinations of leaf traits to CSR
strategies under different salt conditions (Pierce et al., 2017).

\textbf{Data analysis}

One-way analysis of variance was conducted to identify differences in
environmental traits and functional traits. The normality was tested
prior to one-way analysis. A logarithmic transformation was necessary to
meet the statistical requirement because the data were not normally
distributed. Redundancy analysis (RDA), a multivariate direct gradient
analysis method, was performed using the Canoco 5.0 software to
elucidate the relationship between functional traits and soil
environmental factors based on standardized data. The primary
statistical analyses, functional traits relationship, partial
correlation and curve estimation were performed using SPSS 20.0 (SPSS
Inc., Chicago, IL, USA). The CSR model was calculated by the globally
calibrated CSR analysis tool `StrateFy' and the picture was output by R
(R Development Core Team 2013). Figures were plotted using Origin Pro
8.0 (Originlab Co., Northampton, MA, USA).

\textbf{RESULTS}

\textbf{3.1 Trade-off among \emph{P. australis} traits}

The \emph{P. australis} height traits had obvious positive correlations
with the growth traits of leaf and stem, such as SLA (P\textless{}0.01),
leaf area (P\textless{}0.01), pitch number (P\textless{}0.01), internode
height (P\textless{}0.01), and shoot basal diameter (P\textless{}0.01).
The SPAD and leaf thickness had significant negative correlations with
other traits (Table 1).

\textbf{Table 1.} Trade-off among \emph{P. australis} traits.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
& H (cm) & Nu & SPAD & LWC (\%) & SLA & LA (cm\textsuperscript{2}) & LT
(cm) & Lpnu & Lpih (cm) & BSD (cm)\tabularnewline
\midrule
\endhead
H (cm) & & & & & & & & & &\tabularnewline
Nu & -0.05 & & & & & & & & &\tabularnewline
SPAD & \textbf{-0.286\textsuperscript{**}} & -0.128 & & & & & & &
&\tabularnewline
LWC (\%) & 0.069 & 0.165 & 0.168 & & & & & & &\tabularnewline
SLA & \textbf{0.261\textsuperscript{*}} & -0.002 &
\textbf{-0.225\textsuperscript{*}} & \textbf{-0.375\textsuperscript{**}}
& & & & & &\tabularnewline
LA (cm\textsuperscript{2}) & \textbf{0.498\textsuperscript{**}} & -0.089
& 0.006 & 0.000 & 0.037 & & & & &\tabularnewline
LT (cm) & \textbf{-0.366\textsuperscript{**}} & -0.055 &
\textbf{0.253\textsuperscript{*}} & -0.085 &
\textbf{-0.475\textsuperscript{**}} & -0.148 & & & &\tabularnewline
Lpnu & \textbf{0.680\textsuperscript{**}} & -0.105 & -0.177 & 0.162 &
0.116 & \textbf{0.247\textsuperscript{*}} &
\textbf{-0.318\textsuperscript{**}} & & &\tabularnewline
Lpih (cm) & \textbf{0.852\textsuperscript{**}} & -0.001 &
\textbf{-0.253\textsuperscript{*}} & -0.09 &
\textbf{0.261\textsuperscript{*}} & \textbf{0.528\textsuperscript{**}} &
-0.196 & \textbf{0.277\textsuperscript{**}} & &\tabularnewline
BSD (cm) & \textbf{0.555\textsuperscript{**}} & -0.18 &
\textbf{-0.252\textsuperscript{*}} & \textbf{-0.246\textsuperscript{*}}
& \textbf{0.225\textsuperscript{*}} & \textbf{0.385\textsuperscript{**}}
& -0.143 & \textbf{0.446\textsuperscript{**}} &
\textbf{0.456\textsuperscript{**}} &\tabularnewline
\bottomrule
\end{longtable}

Pearson correlation coefficients are shown in the
table.\textsuperscript{*}, P \textless{} 0.05; \textsuperscript{**}, P
\textless{} 0.01. Abbreviations: H, average height; Nu, number of
plants; SPAD, SPAD values; LWC, leaf water content; SLA, specific leaf
area; LA, leaf area; LT, leaf thickness; Lpnu, pitch number; Lpih;
internode height; and BSD, shoot basal diameter.

\textbf{3.2 Response of functional traits to environmental variables}

Partial correlations were conducted to determine the relationships
between environmental variables and functional traits (Table 2). The
results indicated that functional traits could better respond to changes
in electrical conductivity than soil water content. The average height,
specific leaf area, leaf area, pitch number, internode height, and base
stem diameter were significantly negatively correlated with electrical
conductivity (P\textless{}0.01), while significantly positive
relationships were only discovered between soil water content and leaf
water content, and between electrical conductivity and leaf water
content and leaf thickness (P\textless{}0.05). When compared with plant
traits and leaf traits, the relationship between stem traits and
environmental variables was much closer.

\textbf{Table2.}Partial correlation coefficient matrix between
environmental variables and selected functional traits (plant traits,
leaf traits, and stem traits) of \emph{P. australis} .\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Index\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Index\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
SWC\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
SWC\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
EC\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
EC\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
r\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
P\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
r\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
P\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plant traits\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Average height\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.204\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.060\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{-0.563\textsuperscript{***}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Number of plants\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.019\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.862\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.901\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leaf traits\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
SPAD values\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-0.123\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.259\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.063\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.564\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leaf water content\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.238\textsuperscript{*}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.027\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.228\textsuperscript{*}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Specific leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.088\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.423\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{-0.442\textsuperscript{***}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Leaf area\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.068\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.532 9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{-0.619\textsuperscript{***}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leaf thickness\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-0.047\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.668\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.232\textsuperscript{*}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.031\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Stem traits\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Pitch number\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.142\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.193\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{-0.349\textsuperscript{**}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Internode height\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.075\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.494\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{-0.524\textsuperscript{***}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Base stem diameter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.072\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.510\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{-0.290\textsuperscript{**}}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Asterisks indicate a significant correlation between environmental
variables and functional traits (2-tailed)
(\textsuperscript{*}P\textless{}0.05;\textsuperscript{**}P\textless{}0.01;\textsuperscript{***}P\textless{}0.001).
Abbreviations: SWC, soil water content; EC, electrical conductivity.

Significant regression relationships were only observed between
functional traits and electrical conductivity, and these relationships
were generally linear and logarithmic (Figure 2). For whole-plant traits
variables, significantly logarithmic relationships were only found
between the electrical conductivity and average height
(P\textless{}0.001) of \emph{P. australis} . For leaf traits,
significant correlations were discovered in the relationship between
electrical conductivity and leaf water content (P\textless{}0.05),
specific leaf area (P\textless{}0.01), leaf area (P\textless{}0.001),
and leaf thickness (P\textless{}0.01). For stem traits, significant
correlations were observed between electrical conductivity and all
selected stem traits, including pitch number (P\textless{}0.001),
internode height (P\textless{}0.001), and base stem diameter
(P\textless{}0.01). Moreover, positive relationships between electrical
conductivity with leaf water content and leaf thickness were discovered.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Regression relationship between functional traits
of\emph{P. australis} and soil water content.

(A--J), electrical conductivity (K--T). A, K, average shoot height; B,
L, number of shoots; C, M, SPAD values; D, N, leaf water content; E, O,
specific leaf area; F, P, leaf area; G, Q, leaf thickness; H, R, pitch
number; I, S, internode height; and J, T, shoot basal diameter.

\textbf{3.3 Plant-environment relationships}

Redundancy analysis (RDA) was applied to further investigate the
contribution of soil water content and electrical conductivity to
functional traits of \emph{P. australis} . As shown in Figure 3,
selected environmental variables completely explained 15.43\% of the
variance in functional trait variables (F=7.8, P\textless{}0.01). The
first axis explained 14.34\% in the ordination with electric
conductivity. The second axis explained 1.08\%, with soil water content.
The relationships with electrical conductivity were negative, including
SPAD, height, and stem traits, but the functional trait variables had
almost no correlation with soil water content. The response of leaf
traits to environmental variables were inconsistent. Leaf water content
had positive relationships with soil water content and electrical
conductivity. Leaf thickness had a strong positive relationship with
electrical conductivity, while leaf area and specific leaf area had
strong negative correlations with electrical conductivity.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/395954/articles/509074-trait-based-adaptability-of-phragmites-australis-to-the-effects-of-soil-water-and-salinity-in-the-yellow-river-delta}

\textbf{Figure3.} Biplot of environmental factors (soil water content
and electrical conductivity) with community characteristics and
functional trait variables by redundancy analysis (RDA). Hollow black
arrows are soil water content and electrical conductivity; solid blue
arrows are functional traits of \emph{P. australis} . Abbreviations: EC,
electrical conductivity; SWC, soil water content; H, average height; Nu,
number of plants; SPAD, SPAD values; LWC, leaf water content; SLA,
specific leaf area; LA, leaf area; LT, leaf thickness; Lpnu, pitch
number; Lpih; internode height; and BSD, base stem diameter. Green
indicates plant traits including H and Nu; red indicates leaf traits
including SPAD, LT, LA, SLA, and LWC; black indicates stem traits
including Lpih, Lph, Lpnu, and BSD.

\textbf{3.4 \emph{P. australis}} adaptability and plasticity

Environmental factors and selected functional traits of \emph{P.
australis} varied greatly (Table 3), indicating the strong phenotypic
plasticity and environmental tolerance of \emph{P. australis} . The
coefficient of variation of soil water content was larger than 25\%,
while the electrical conductivity was up to 87\%. The amplitude of soil
water content (9.39--36.92\%) and electrical conductivity (0.14--13.29
ms/cm) indicated the high tolerance of \emph{P. australis} to
environmental variables in the Yellow River Delta. The coefficients of
variation of leaf traits were lower than 20\%, except for the leaf area,
while the stem traits were higher than 20\%.

\textbf{Table} 3. Statistical characteristics of environmental variables
and functional traits for \emph{P. australis.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Index & Index & Minimum & Maximum & Mean & SD & CV (\%)\tabularnewline
\midrule
\endhead
Environmental variables & Soil water content (\%) & 9.39 & 36.92 & 24.65
& 6.4 & 25.95\tabularnewline
& Electrical conductivity (ms/cm) & 0.14 & 13.29 & 4.55 & 3.9 &
86.51\tabularnewline
Plant traits & Average height (cm) & 20 & 170 & 82.73 & 37.67 &
45.54\tabularnewline
& Number of plants & 1 & 352 & 42.36 & 44.91 & 106.01\tabularnewline
Leaf traits & SPAD values & 22.2 & 55.53 & 43.45 & 4.46 &
10.25\tabularnewline
& Leaf water content (\%) (g/g) & 32.71 & 48.82 & 43.31 & 0.03 &
5.96\tabularnewline
& Specific leaf area (cm\textsuperscript{2}/g) & 83.46 & 164.41 & 121.48
& 16.88 & 13.89\tabularnewline
& Leaf area (cm\textsuperscript{2}) & 4.46 & 44.19 & 26.96 & 9.68 &
35.91\tabularnewline
& Leaf thickness (cm) & 0.16 & 0.3 & 0.21 & 0.03 & 15.93\tabularnewline
Stem traits & Pitch number & 6 & 16 & 11.14 & 2.37 &
21.25\tabularnewline
& Internode height (cm) & 1.77 & 11.5 & 6.34 & 2.26 &
35.59\tabularnewline
& Base stem diameter (mm) & 1.83 & 7.68 & 3.33 & 0.96 &
28.87\tabularnewline
\bottomrule
\end{longtable}

Abbreviations: SD, Standard Deviation; CV, Coefficient of Variation.

\textbf{3.5 Ecological strategy of \emph{P. australis} under different
environmental factors}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} . Ecological strategies of \emph{P. australis} under
different environmental conditions based on the 96 areas. Abbreviations:
SWC, soil water content; EC, electrical conductivity; C: competitor; S:
stress tolerator, R: ruderal.

The ecological strategies of \emph{P. australis} in the Yellow River
Delta under different salt and water conditions mainly consisted of
stress tolerator/competitor-stress tolerator (S/CS) (57.3\%, C: S:
R=28.5: 66.7: 4.7), competitor-stress tolerator (CS) (27.1\%, C: S:
R=36.0: 57.3: 6.7), stress tolerator/ competitor-stress
tolerator-ruderal (S/CSR) (11.5\%, C: S: R=29.9: 57.8: 12.3), and rarely
competitor-stress tolerator/ competitor-stress tolerator-ruderal
(CS/CSR) (3.1\%) and stress tolerator (S) (1\%) (Figure 4). The major
strategies consisted of S/CS and CS (84.4\%). The three strategies of
S/CS-CS-S/CSR formed a S-C tradeoff strategy axis with weak R,
indicating the competition and stress tolerance in high-salinity areas
of the Yellow River Delta. The R strategy was the minor one in the
Yellow River Delta, mainly distributed in the low-salinity areas. There
were no obvious different strategies of \emph{P. australis} under
different soil water contents, while the S strategy was dominant in the
Yellow River Delta. However, with the decrease of soil salinity and the
increase of soil water content, \emph{P. australis} gradually shifted to
the C strategy.

\textbf{DISCUSSION}

\textbf{4.1 Effects of environmental factors on functional traits
of\emph{P. australis}}

\emph{Phragmites australis} is a type of salt-tolerant plant (Mauchamp
et al., 2001; Santos et al., 2016); higher salinity inhibits their
growth. \emph{Phragmites australis} is more likely to be found in sites
of surface ground water discharge and appears to access deeper, less
saline water (Burdick et al., 2001; Guo et al., 2018; Veldhuis et al.,
2019). Salinity stress results in a clear stunting of plants (Wang et
al., 2016) and reduced leaf area expansion (Parida et al., 2005; Gong et
al., 2020). As previously reported, the height reduction of \emph{P.
australis} growth was a marked feature under salinity stress (Lissner et
al., 1997; Guo et al., 2018; Sdouga et al., 2019). Leaf traits are
closely related to the growth, ability to utilize resources, and
survival strategies of plants under environmental changes (Mason et al.,
2013). Specific leaf area associated with allocation strategy tends to
scale positively with leaf nitrogen concentration, and negatively with
leaf longevity (P\selectlanguage{ngerman}érez-harguindeguy et al., 2013; Spasojevic et al.,
2014). Leaf thickness plays a key role in determining the physical
strength of leaves, being higher in sunnier, drier, and less fertile
habitat, as well as in longer-lived leaves (Wu et al., 2008). Our
results clearly verified these conclusions, as indicated by the average
height of \emph{P. australis} being negatively correlated with
electrical conductivity (Figure 3). Based on our results, salinity
affects plant leaves through reduced specific leaf area and leaf area,
but increased leaf water content and leaf thickness enhance the ability
to resist environmental stress, altering the living strategy from growth
to survival.

Stem traits play a vital role in the entire plant life cycle, supporting
ground organization, holding water and nutrients, and conducting water
and elements (Liu et al., 2015). Based on our results, the stem traits
had positive correlations among themselves (Table 1), which were
generally linear and logarithmic relationships with electrical
conductivity (Figure 3). Under low electrical conductivity, the plant
had a relatively high growth rate, with stem elongation, an increased
number of sections, and thickened base stem diameter to support more
leaves and better compete for resources. Under high-salinity
environments, plants changed their growth strategy to thicken and
strengthen their structures to preserve internal resources to survive.
The results of the present study verified that the electrical
conductivity of environments was the dominating factor controlling the
functional properties of \emph{P. australis} in the Yellow River Delta.

Many studies have reported that community structure (Pérez-Ramos et al.,
2012), species composition (Li et al., 2008), and vegetation growth
(Gong et al., 2014a) were affected by soil water content. Generally, a
non-linear relationship with an obvious soil moisture threshold value
appeared for most plants (Yu et al., 2012), and plant growth could be
limited by both a water deficit and excess soil water. The relationship
between soil water content and growth traits was approximately linear in
dry habits (Gong et al., 2014b). However, based on our laboratory
experiments, \emph{P. australis} has no obvious specific correlation
between soil water content and functional traits, except for the leaf
water content (Table 1, Figure 3). \emph{P. australis} had a wide range
of niche adaptations to water and had high tolerance to both drought and
waterlogged conditions; the soil water conditions used in our research
were not extreme enough to cause stress. The results indicated a high
adaptability to soil water content for the growth of \emph{P. australis}
, however, soil water content was not the limiting factor based on the
96 samples in our research.

\textbf{4.2 The plasticity of morphological and physiological traits}

Functional traits can be divided into morphological traits and
physiological traits (Liu \& Ma, 2015). Strong plasticity of variability
was observed in whole plant traits, especially in the morphological
traits, and coefficient of variation was the accepted method used to
standardize variability among traits with fundamentally different units
(Acasuso-Rivero et al., 2019). Morphological traits involve the shape
and structure of each organ, responses of individuals to environmental
changes based on internal genes, and adaptations of plants to
environmental heterogeneity (Hu et al., 2008). In our research, the leaf
area, stem pitch number, height, and shoot basal diameter were
attributed to morphological traits. All these traits had high
coefficients of variation, which were higher than 20\%, indicating that
morphological traits are sensitive under the stress of environmental
factors. Higher phenotypic plasticity contributed to \emph{P.
australis}being able to adapt to a wide range of niches under salinity
stress in the Yellow River Delta.

Physiological traits are related to biochemical processes and
reproduction (Zhang \& Luo, 2004). Some researchers showed that
physiological traits were more susceptible to soil properties with
regard to morphological traits (Schneider et al., 2017). Compared with
morphological traits, the coefficient of variation values of
physiological traits contained the SPAD, leaf thickness, SLA, and leaf
water content in our research and were lower than 20\%. The lower
coefficients of variation indicated relatively lower plasticity and
stable intrinsic properties of \emph{P. australis} under various
environmental stressors. SLA and leaf thickness, although shown as
morphological traits (Smith \& Knapp, 2001), were regarded as
physiological traits in some research (Zhang \& Luo, 2004; Wright et
al., 2004). They remain relatively stable under specific environments,
and were not only influenced by soil factors, but also influenced by
factors including leaf tissue characteristics (such as the epidermis
cells and mesophyll cells) and chemical composition.

In the Yellow River Delta, the mode of \emph{P. australis} morphological
traits is an integrated system in growth, but physiological traits have
different mode to reflect the environmental stress. Grouping plants by
functional trait types rather than by organs could help us more
accurately understand the process of plant growth and the response to
environmental stress. It's worth noting that while plasticity is not
equal to fitness (Acasuso-Rivero et al., 2019), the relationship between
them is still worth studying in the Yellow River Delta under
environmental stress.

\textbf{4.3 The model of \emph{P. australis} life strategies}

The trade-off between growth and survival is the heart of the economic
spectrum theory of leaves (Reich, 2014; Díaz et al., 2016). Three core
leaf traits, SLA, LDMC, and LA, were used as criteria to determine the
ecological strategies of individuals (Pierce et al., 2017). The
application of the CSR strategy in community construction has been
controversial. Many studies have verified the explanation of CSR
strategy classification for community species composition in habitats
where species strategy distribution was concentrated (Negreiros et al.,
2014, Paula et al., 2015). However, CSR strategies are usually used to
research the different species in communities, and there are few studies
on the strategies of the same species under different environmental
stresses.

According to the top-level succession theory, the plant community in the
Yellow River Delta should be in the early stage of succession and salt
is a limiting factor for most herbaceous species (Tan et al., 2012).
Therefore, the plants with strong salinity stress tolerance under the
S/CS strategy have more advantages, and the plants near the S strategy
have greater survival opportunities. S is the dominant strategy
for\emph{P. australis} in the Yellow River Delta. Plants near the S
strategy end have lower LDMC, while plants near the C strategy end have
higher SLA traits, representing revenue capacity, than those near the S
strategy end. With the decrease of soil salinity and the increase of
soil water content, the strategy of \emph{P. australis} in the Yellow
River Delta gradually shifted to C. For the C strategy, plant selection
and growth are mainly aimed at rapidly occupying resources, and a
relatively large SLA is needed to grow rapidly (Xu et al., 2019). Plants
under the R strategy are adapted to environments with strong
disturbance, tend to invest many resources in reproduction to offset the
effects of disturbances on the population, achieve the purpose of
survival and continuation of the population (Pierce et al., 2017). In
the Yellow River Delta, \emph{P. australis} have a minor ruderal (R)
strategy.

As a perennial herb, \emph{P. australis} tends to grow rapidly and
spread by underground stems, forming a single community, which is common
in the Yellow River Delta. Due to the habits of \emph{P. australis} ,
the R strategy rarely appears in the Yellow River Delta. There are
similar modes of strategies of \emph{P. australis} related to the soil
salinity and the soil water content. We believe that environmental
factors partly determined the C or S strategy of \emph{P. australis} ,
while with the decrease of environmental stress, the mainly strategy
gradually shifts from S to C. This indicated that \emph{P. australis}
has stronger tolerance and competitive ability in the communities from
the coastal wetland to inland areas, therefore becoming the dominant
species in the Yellow River Delta.

\textbf{4.4 Summarizes and Perspectives}

Gradients of environmental stress and disturbance are important drivers
of morphological and physiological variability for \emph{P. australis}in
the Yellow River Delta. The response of functional traits to the
environment could be summarized as follows: (1) The relationships
between functional traits and electrical conductivity were generally
linear and logarithmic, indicating that soil salinity was the main
factor that influenced the functional traits of \emph{P. australis} in
the Yellow River Delta. (2) The plant morphological traits had higher
plasticity based on the coefficients of variation, which were much
higher than 20\%. However, compared with the morphological traits, the
coefficients of variation for physiological leaf traits were lower than
20\%. This indicated that physiological traits are more stable than
morphological traits in response to environmental variables. (3) The
variability of strategies for \emph{P. australis} indicated that in the
high-salinity area, competition (C) and stress tolerator (CS) were the
main strategies that affected the growth and distribution for \emph{P.
australis.} With the decrease of environmental stress, the mainly
strategy gradually moved from S to C. Further studies, including studies
focused on underground traits and genetics, are needed to clearly
understand different responses to related ecotypes.

\textbf{ACKNOWLEDGEMENTS}

This work was financially supported by the National Key R\&D Program of
China (No. 2017YFC0505905) and the National Natural Science Foundation
of China (Nos. 31970347; 31770361). Wang Y, Li MY, Kong XL, Liu YF, and
Xu ZW from Shandong University contributed to the data collection and
lab experiments. We thank LetPub (www.letpub.com) for its linguistic
assistance during the preparation of this manuscript.

\textbf{CONFLICT OF INTEREST}

The authors declare no conflict of interest in the publication of this
paper.

\textbf{AUTHOR CONTRIBUTION}

\textbf{Dayou Zhou} : Conceptualization (equal); data curation (equal);
formal analysis (equal); investigation (equal); methodology (equal);
writing-original draft (lead); writing-review and editing
(equal).\textbf{Yuehan Ni} : Conceptualization (equal); data curation
(equal); formal analysis (equal); methodology (equal); writing-original
draft (supporting). \textbf{Xiaona Yu} : Conceptualization (equal);
formal analysis (equal); data curation (equal); methodology (equal);
validation (equal); writing-review and editing (lead). \textbf{Kuixuan
Lin} : Funding acquisition (equal); investigation (equal); project
administration (equal); writing-review and editing (equal). \textbf{Ning
Du} : Formal analysis (equal); investigation (equal); methodology
(equal); writing-review and editing (equal). \textbf{Lele Liu} : Formal
analysis (equal); methodology (equal); visualization (equal);
writing-review and editing (equal). \textbf{Xiao Guo} : Formal analysis
(equal); methodology (equal); writing-review and editing
(equal).\textbf{Weihua Guo} : Conceptualization (equal); data curation
(equal); funding acquisition (lead); methodology (equal); project
administration (lead); writing-original draft (supporting);
writing-review and editing (equal). All authors contributed critically
to the drafts and gave final approval for publication.

\textbf{DATA AVAILABILITY STATEMENT}

Plant functional traits and environmental factors data input files are
available at: https://doi.org/10.5061/dryad.x3ffbg7j4.

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