A Threshold-like Effect on the
Interaction Between Hydrological Connectivity and Dominant Plant
Population in Tidal Marsh Wetlands
Jiakai Liu1, Ying Liu1, Lumeng Xie1, Shiqiang Zhao1, Yili Dai, Zhenming
Zhang1*, Mingxiang Zhang1*
1 School of Nature Conservation, Beijing Forestry University, Beijing,
CN
Jiakai Liu:timberfield1991@163.com
*Corresponding authors:
Mingxiang Zhang:zhangmingxiang@bjfu.edu.cn
Zhenming Zhang:zhenmingzhang@bjfu.edu.cn
Highlights
- Population density and structure size increase along with hydrological
connectivity.
- Population with biomass higher than 2.2kg/m2 has
been determined to have lower hydrological connectivity.
- Soil salinity and soil chemistry have narrow ranges in the tidal
marsh.
- Large area restoration of P. australis may cut freshwater
connection off.
Abstract
Tidal marsh wetlands in the Yellow River Delta provide valuable
eco-services to the local population and global ecology. However, this
area is suffering from serious degradation under the stresses of social
development and climate change. Hydrological connectivity, a new
framework in hydrology and ecology, has been proposed as the main factor
affecting the ecological processes in coastal wetlands; however, its
role in hydrology–soil–vegetation interactions remains unclear. In
this study, we parametrically
quantified the hydrological connectivity in the tidal marsh wetlands and
analyzed its relationship with Phragmites australis , one of the
dominant species in this area. Our results showed threshold-like effects
on the interaction between hydrological connectivity and P.
australis on the plot scale. When biomass is lower than 2.2
kg/m2, the population density and structure size were
found to increase with hydrological connectivity. When the biomass is
higher than the threshold, the plots disconnected hydrologically because
of high water consumption. Compared with soil chemistry, salinity, and
water soil content, hydrological connectivity in the surface soil layer
is more strongly linked to the plant traits and spatial structure in the
tidal marsh wetlands due to the narrow ranges of other variables. Based
on the authors’ analysis, the researchers do not recommend dense
plantation of P. australis , especially near the freshwater
sources in the tidal marsh, because of its high reproduction ability and
competitive nature, which may cut the freshwater connectivity off,
lowering the richness of plant species and habitat diversity.
Keywords: Tidal marsh wetlands; Hydrological connectivity;Phragmites australis ; Threshold-like effect; Wetland restoration
1 Introduction
Tidal marsh wetlands have been providing valuable ecological services
(Barbier, 2013; Zhao et al., 2016), such as coastal protection (Costanza
et al., 2008), habitat support for multiple species (Kelly and Condeso,
2017), and carbon stoke (Van de Broek et al., 2019). Moreover, it is
also considered as one of the most vulnerable ecosystems, suffering from
severe degradation brought about by the stresses from both climate
change and human activities in the last few decades (Morris et al.,
2002; Wiegand and Moloney, 2014). In China, rapid urbanization and
economic growth have turned about 51 % of their coastal wetland areas
into other land types (An et al., 2007). Apart from that, agriculture
and industry development have also led to seawater pollution and
petroleum hydrocarbon contamination (Huang et al., 2017; Meng et al.,
2014). Restoring and protecting the coastal wetlands became one of the
priorities in the comprehensive environmental improvement plans not only
of the Chinese government
(http://www.cnwm.org.cn/wildren/index.asp)
but also of other countries worldwide (Newton et al., 2012; Thorne et
al., 2018).
Ecological restoration and protection are based on key processes and
should follow the basic ecological laws, which require guidance from
theoretical research. Hydrological connectivity is considered the
fundamental factor (Hiatt and Passalacqua, 2015; Keesstra et al., 2018)
that connects all the ecological elements, which in turn affects other
ecological processes (Perkin et al., 2015) occurring in the tidal marsh
wetlands. Previous studies in Saint Lawrence River estuary in North
America showed that water depth can affect the native species
distribution, and enhancing hydrological connectivity by digging
channels can increase both density and cover rates of Carex ,Juncus , Eleocharis , and Cyperus plants (Farrell et
al., 2010). In the west coast of the Pacific Ocean, studies also
showed that changes in (ground) water depth can affect the traits of
dominant species, such as Phragmites communis , Suaeda
salsa , and Tamarix chinensis (Cui et al., 2010; Guan et al.,
2017; Hua et al., 2012; Li et al., 2019). However, as shown in
the previous text, several studies lacked a parameterization method for
this new framework, and consequently, the relation remains unclear.
Although there are several parametrization methods already developed,
the concepts and ecological meanings of hydrological connectivity vary
depending on the scales, environment backgrounds, and study aims
(Bracken et al., 2013; Bracken and Croke, 2007). Most of the existing
parametrization methods are developed based on forest basins, which aim
to quantify the correlation between hydrological processes, such as
water yield, and hydrological connectivity. Consequently, those indices
can hardly be used for ecology studies in coastal areas.
Recently, several studies have attempted to find the proper methods to
parametrize hydrological connectivity in coastal wetlands for further
eco-hydrology analysis. Hydrological connectivity in the coastal
wetlands is mainly determined by the tidal creek morphology, soil water
content, and topography. A directional connectivity index based on
topography (the presence of surface water) applied in Everglades, South
Florida, USA, revealed that the loss of hydrologic connectivity occurs
more rapidly and is a more sensitive indicator of declining ecosystem
function than other parameter (e.g., habitat area) used previously
(Larsen et al., 2012). Another study in the Yellow River Delta, China,
quantified the hydrological connectivity based on the micro-topography
and soil water content in the surface soil layer where distinct
differences among different wetland classes were found (Liu et al.,
2019). A follow-up research also reported that the distribution of
species and biomass in high salinity areas are affected by hydrological
connectivity (Liu et al., 2020; Liu et al., 2020). As several other
studies found that the distribution and cover rate of plant species are
also influenced by the distribution and morphology of tidal creeks
(Fleri et al., 2019; Wu et al., 2020), studies examining the
relationship between dominant species population structure and
hydrological connectivity remain to be limited.
The Yellow River Delta is one of the important international wetlands
located in China. It is located at the east economic center of China,
and it is also a pivot airport of bird migration routes, East
Asia–Australia Flyway, specifically. Thus, this area holds the balance
between economic development and ecological safety (Wang et al., 2016),
both regionally and globally. The conflict between wetland conservation
and soil development is also looming. From 1986 to 2005, around 65.09
km2 tidal marsh wetlands were converted to farmland
(Huang et al., 2012), and the condition of the remaining wetlands has
been seen to be deteriorating (Qing et al., 2010; Zang et al., 2017),
with several ecological issues, such as patch isolation, soil pollution,
and decreasing biodiversity (Li et al., 2019; Ma et al., 2019; Wang et
al., 2012). In order to resolve the conflict, the Chinese government has
been increasing its investment and launched several projects in the last
5 years to help in the conservation of the tidal marsh wetlands. The
local governments are also seeking cooperation from institutions to
manage the Yellow River Delta more efficiently from a hydrological
perspective.
Phragmites australis is one of the most dominant species in the
Yellow River Delta, which provides important ecological functions in the
ecosystem, such as constructing habitats for migration bird populations
on the East Asia–Australasia Flyway (Li et al., 2019) and regulating
biochemical cycle (Huang et al., 2012). Multiple projects carried out at
several sites in the tidal marsh wetlands since 2001 also seek to
restore wetlands using this dominant species (Wang et al., 2012). In
this study, the researchers aim to reveal the interaction between theP. australis population and hydrological connectivity, through
the following steps: (1) parameterizing the hydrological connectivity of
each plot, (2) grouping the plots based on plant traits and spatial
structures, respectively, (3) comparing the difference of environmental
factors and plant traits/spatial structures among different groups, and
(4) analyzing the interactions among them.
2 Material and methods
2.1 Experiment design
Yellow River Delta, considered as the world’s youngest neoteric delta in
a temperate zone, is located in the southwestern part of Bohai Bay
(Zhang et al., 2017). New coastal wetlands were seen to form quickly
since 1976 (Wang et al., 2016); this could be attributed to channel
changes and large amounts of sediments in the estuary area coming from
the Yellow River. As shown in Fig. 1, this experimental tidal marsh
wetland is located at the northern part of the modern Yellow River
channel, and the shoreline is on the northeast edge of this wetland. The
dominant plant species are Phragmites australis , Suaeda
salsa , and Tamarix chinensis (Liu et al., 2020; Liu et al.,
2020). This experiment was conducted from July to August of 2018, and
the sampling period was from 4 am to 8 am to avoid the influence of
tides. This is a period of low tide according to the Chinese National
Sea Service (www.cnss.com.cn), and
this is the only time the researchers can access the whole experimental
area.
The researchers set five east–west transects every 700 m, and each of
them is approximately measuring 1,500 m long. Then, six experimental
plots were randomly set in each transect. The plot size is 1 m × 1 m,
and the soil water content in the rhizosphere layer (0–20 cm) of the
experimental plots and eight adjacent 1 m × 1 m patches (Mckee and
Mcmorris, 1999) was measured by time domain reflectometry (TDR)-based
potential soil moisture measuring instrument (TRIME-PICO TDR, IMKO Co.
Ltd, Germany). Moreover, in each plot, the researchers also took one
ring-cut sample and a soil sample. The soil samples were air-dried and
crushed to pass through a 2-mm steel mesh before subsamples of 50 g were
ground in a mortar to pass through a 0.25-mm sieve. Then, the soil total
nitrogen (TN), total phosphorus (TP), and soil salinity of the sieved
samples were then determined. TN concentrations of soil samples were
measured using the Kjeltec method (Zhang et al, 2019), TP was determined
colorimetrically after wet digestion with
H2SO4 plus HClO4 (Xiao
et al, 2012), and salinity was measured using soil electrical
conductivity (Feng et al., 2017).
Plant data were also recorded. First, the researchers set the
north–east corner of each plot as the coordinate origin; then, the
coordination, height, and basal stem diameter of each P.
australis were noted; and finally, all were harvested and weighed
immediately in the field using digital portable scales (SPX622ZH,
NAVIGATORTM, OHAUS Co. Ltd, USA).They were later kept
in sealed labeled bags. The fresh materials were dried at 60 °C, and the
samples were weighed every 24 h until the weights remained the same.
2.2 Hydrological connectivity parameterization
In this current study, the researchers used the over-field capacity
method (OFCI) developed in the Yellow River Delta based on the surface
soil water condition and graph theory (Liu et al., 2019), and then the
Point Scale Hydrological Index (PHCI) and Degree Centrality Index
(Degree) were calculated by parametrizing the hydrological connectivity.
First, the field capacity was calculated as:
\(z=[\left(w_{s}-w_{n}\right)-(w_{s}-w_{d})]/w_{n}\)(1)
where z is the field capacity (g/g); ws is
the saturated soil mass (g); wd is the sample
mass (g) after 2-day drainage under non-evaporative conditions (Cassel
et al., 1986); and wn is the net soil mass (g) of
the ring-cut sample. The water soil content and the field capacity of
all the cells were calculated based on the ring-cut samples and inverse
distance weighted interpolation.
The OFCI method was improved from ICSL (Liu et al., 2019; Liu et al.,
2019) based on water soil content and graph theory. The (dis)connection
of each cell was determined by field capacity. For plots with water soil
contents higher than the field capacity, the values will be replaced by
1; otherwise, 0; calculated as:
\(I_{\text{ij}}=\left\{\par
\begin{matrix}\ w_{\text{ij}}-\ z_{\text{ij}}\text{\ \ \ \ \ if\ }w_{\text{ij}}>\ z_{\text{ij}}\\
\ 0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ else\\
\end{matrix}\right.\ \) (2)
where wij is the water soil content of the plotVij and zij is the field
capacity of plot Vij . All plots with value higher
than 0 are connected nodes. The edge of these pairs is the hydrological
distance between Vij andVrc, which can be calculated as:
\({HD\ (V}_{\text{ij}},\ V_{\text{rc}})=I_{\text{ij}}-I_{\text{rc}}\)(3)
And the Degree Centrality Index is the number of the connected patches
around the target plots, whereas PHCI can be calculated as:
\(PHCI=\sum\text{HD}\) (4)
where if PHCI < 0, then it means that the soil water potential
of the target plot is relatively low, and it can get water supply from
the connected patches and vice versa. Higher PHCI absolute values and
degree indices indicate a stronger hydrological connectivity. All the
indices were calculated using the self-writing code in MATLAB software
version 2019b.
2.3 Plant traits and spatial structure
The researchers used two metrics to demonstrate the traits and spatial
structure of P. australis . The plant density
(stems/m2), mean height (cm), mean stem diameter (cm),
and biomass (kg/m2) of each plot were used to quantify
the plant traits. Furthermore, the researchers constructed a Delaunay
triangulation network and relevant parameters to estimate the spatial
structures (Zenner, 2005; Liu et al., 2018; Zenner and Hibbs, 2000).
Firstly, the network of each plots was established based on the
coordination and heights of the individuals, and then the triangle
numbers, mean triangle perimeters (cm), mean triangle area
(cm2), and Structure Complexity Index (SCI) of each
network were calculated. The four parameters indicate the size,
connection, distribution model, and complexity of the spatial structure.
The SCI is the ratio of the three-dimensional total side lengths and
projected total side lengths, calculated as:
\(SCI=\text{SCI}^{*}/\sum_{i=1}^{N-1}\sqrt{{(x_{i}-x_{i+1})}^{2}+{(y_{i}-y_{i+1})}^{2}}\)(5)
where SCI* is the sum of the three-dimensional side lengths,
defined as:
\(\text{SCI}^{*}=\sum_{i=1}^{N-1}\sqrt{{(x_{i}-x_{i+1})}^{2}+{(y_{i}-y_{i+1})}^{2}+{(z_{i}-z_{i+1})}^{2}}\)(6)
where N is the number of individuals, xiand yi are the spatial coordinates of individualsi , and zi is the height of individuali. The lower bound for the SCI is 1, which means all the plants
have the same height. The spatial networks and parameters are calculated
based on a field study data in R software version 3.6.3
(https://www.r-project.org/).
2.4 Statistic analysis
In the Results section, the researchers used PCA (Jolliffe, 1986; Ding
and He, 2004) and K-means clustering (Yadav and Sharma, 2013) to analyze
the plant traits and the spatial structure metrics, respectively, and
all the plots were then later divided into multiple groups based on the
analysis. Then, the plant characteristics and environmental factors were
compared to find the differences of both independent factors and
response variables. Regression analysis was performed to highlight the
interaction between hydrological connectivity and plant traits/spatial
structures. In order to compare the impacts of hydrological connectivity
with other environmental variables, such as soil chemistry, CCA (ter
Braak, 1986) analysis was conducted as explained in the Discussion
section, and the researchers also tested the deduction of the
threshold-like effect by further grouping the data from the regression
analysis. All statistical analyses were conducted using the R software
version 3.6.3.
3 Results
3.1 Parameterization of hydrological connectivity
Two indices, PHCI and Degree Centrality Index, were utilized to quantify
hydrological connectivity in the inter-tidal marsh on the plot scale.
The results, presented in Figure 2, indicate that about 38.09 % of the
plots were disconnected, and their Degree Centrality Index and PHCI
values were recorded to be at 0. The PHCI values of all the connected
plots were negative, and the average value was at −5.63, which means
that those plots, with relatively lower soil water content and soil
water potential, have the ability to get water supply from neighboring
areas. Half of the PHCI values were distributed from −1 to 0, whereas
the range is approximately 17.21. The average Degree Centrality Index
value of the connected plots was 4.07, and they were equally distributed
based on their change interval ([1,8], for the connected plots).
Higher absolute values of PHCI indicate more water supply that the plots
can get from the connected patches, and similarly, higher degree values
demonstrate that the target plots can get water supply from larger
adjacent areas. Moreover, the absolute values of PHCI and Degree
Centrality Index are positively correlated (P < 0.001,
R2 = 0.51 ). Thus, in this study, higher absolute
values of both indices indicate better hydrological connectivity, which
means more water supply.
3.2 Plant traits and their interaction with hydrological connectivity
The mean population density, plant height, stem diameter, and biomass ofP. australis were 11.47 ± 7.62 stems/m2, 56.62
± 8.77 cm, 0.35 ± 0.13 cm, and 2.73 ± 2.23 kg/m2,
respectively. Figure 3A presents the PCA analysis of plant traits. The
first two dimensions account for the 76.08 % of the explainable
variance, and the densities, heights, diameters, and biomasses
contribute 23.14 %, 6.14 %, 22.86 %, and 23.93 %, respectively.
These four factors can strongly explain why plant traits and plant
height were relatively less important. By combining PCA and cluster
analysis, the plots were divided into two groups (Figure 3B), and the
clustering coefficient was determined at 0.34. The density, average
height, average diameter, and biomass of each group are given in Table 1
(mean ± standard error). The average heights of groups A and B did not
show significant differences (P = 0.320 ). However, the density,
mean stem diameters, and biomass of group A were found to be
significantly higher than those of the group B (P ≤ 0.001).Meanwhile, individuals in group A were determined to be stronger with a
denser distribution than that in group B, and they consequently led with
a higher biomass.
Different environmental variables could be the most possible cause why
plants present differences. Thus, the researchers compared hydrological
connectivity, soil salinity, TP, TN, and SWC between the two groups. As
shown in Table 2, for plant traits, all the plots in group A were
disconnected, and the PHCI and degree indices were significantly
different. The soil salinity, TP, TN, and SWC of the two groups did not
show any significant difference as well (P ≥ 0.05 ). P.
australis in disconnected plots were also found to have higher biomass,
which can led to higher evapotranspiration and water consumption,
lowering the SWC below the field capacity and subsequently disconnecting
the plots.
We did regression analysis in order to specifically reveal the
interactions between hydrological connectivity and plant traits, and the
results, as presented in Figure 4, demonstrate that for the connected
plots (group B), population density (P = 0.00;
R2 = 0.51 ) was found to be positively correlated with
degree indices, whereas stem diameter (P = 0.001,
R2 = 0.66 ) was negatively related to degree indices.
Population density was positively related to PHCI absolute values at a
95 % confidence level (P = 0.011 , R2 =
0.46 ). Plant height and biomass did not show any statistical
correlation with neither indices. Population densities have been seen to
increase along with hydrological connectivity; this is because
sufficient water supply can ensure more stems sprouting from the
rhizome; however, because of the limitation brought about by other
nutrient and environmental stress, the biomass remains low, and the stem
diameters were found to be lower in those plots with more stems.
Moreover, combined with the comparison between groups, the interaction
presented a threshold-like effect. When the biomass was higher than 3.5
kg/m2, P. australis disconnected the plot by
increasing the water consumption and lowering the SWC below the field
capacity, whereas when the biomass was relatively low, better
hydrological connectivity increased the number of stems.
3.3 Spatial structures and their interaction with hydrological
connectivity
The researchers constructed the Delaunay triangulation networks based on
the spatial three-dimensional coordinates of individuals in each plot
and estimated triangle numbers, mean perimeter, mean area, and SCI to
quantify the spatial structure. These four parameters measure the size,
closeness, betweenness, and complexity of the structure.
The PCA analysis based on the spatial structure ofP. australis has been
presented in Figure 5A. The first two dimensions account for 72.89 % of
the explainable variance, and the contribution of triangle numbers, mean
perimeter, mean area, and SCI was at 16.16 %, 22.49 %, 12.24 %, and
22.01 %, respectively. These four factors can strongly explain why the
spatial structure and mean perimeter, as well as SCI, were relatively
more important. By combining PCA and cluster analysis, the plots were
then divided into two groups (Figure 5B), and the clustering coefficient
was at 0.37. Spatial structure factors were given in Table 1 (mean ±
standard error). The average triangle numbers of group A were
significantly more than those in group B (P < 0.001 ),
and the SCI value of group A was also significantly higher than that of
group B (P < 0.001 ). However, the triangles’ mean
perimeter (P = 0.578 ) and mean area (P = 0.173) between
the two groups did not show any statistical difference. Triangle
numbers and SCI have illustrated the density and heterogeneity (Liu et
al., 2018) of the spatial structure, and the plots in group A have been
found to be relatively denser and more heterogeneous spatial structures,
which probably led to higher population stability (Zenner and Hibbs,
2000; Zenner et al., 2015).
The environmental variables between the two groups are also shown in
Table 2. All the plots in group A were also disconnected, and the
absolute values of PHCI and degree indices in group B were significantly
higher than those in group A. The soil salinity, TP, and TN in the two
groups did not show any significant difference (P ≥ 0.05 ), except
for SWC in group A, which was found to be significantly lower than that
in group B (P = 0.019 ).
A regression analysis was also conducted to specify the interactions
between spatial structure parameters and hydrological connectivity
indices. As shown in Figure 6, for the connected plots, triangle numbers
were positively correlated to Degree Centrality Index (P = 0.002;
R2 = 0.58 ) and absolute values of PHCI (P =
0.005; R2 = 0.52 ) at a 95 % confidence level;
however, other parameters did not show any statistical correlation with
hydrological connectivity indices. Meanwhile, in the disconnected plots,
the triangle numbers were more than the connected plots, which means
that the interaction between structure size and hydrological
connectivity may also have a threshold-like effect. Thus, the
researchers have compared the biomass, where it was determined that the
biomass of disconnected plots was significantly higher than the
connected plots and the positive correlation can be found in the plots
with biomass less than about 2.2 kg/m2.
4 Discussion
4.1 Influence of environment factors on plants
Hydrology–soil–vegetation interaction is the most fundamental process
in coastal wetland ecosystems (Alkarkhi and Alqaraghuli, 2019; Cronk and
Fennessy, 2016; Liu et al., 2020), and most of the previous studies
highlighted the impacts of soil chemistry on plants. In salt marsh
wetlands, soil salinity is one of the main stresses for plant traits,
especially for glycophytic species. Increasing soil salinity leads to
reduction of species number and biomass (Cui et al., 2009; Gough and
Grace, 1998); this is often attributed to the changing soil
microorganism community (Paul and Nair, 2008) and declining the
photosynthetic carbon assimilation and electron transfer capacities
(Hanganu et al., 1999; Lu et al., 2017; Munns, 1993). Combined with the
anaerobic environment caused by tidal inundation, plants allocate more
energy to resist environmental stresses instead of using it for
productive processes (Luo et al., 2016; Spalding and Hester, 2007), and
older individuals are observed to be more vulnerable for several species
(Touchette et al., 2012). Studies involving P. australis also
found that higher salinity will increase the content of superoxide
dismutase, peroxidase, and catalase in plants, which can cause damage to
plants’ organisms (Burdick and Konisky, 2003; Xu et al., 2012); this
will consequently lower the biomass and plant height. However, it also
has a high tolerance to salinity (approximately 40 ppt or 3 %) by
accumulating Na+ and Cl− in old
organs and reducing water uptake and vacuole compartmentalization of
toxic ions (Achenbach et al., 2014; Achenbach et al., 2013). Moreover,
the root colonization potential of the strain of plant growth-promoting
rhizobacteria could not be hampered with higher salinity in soil (Paul
and Nair, 2008).
Hydrology is another key factor for plant growth, and most researches
only use (ground) water depth to link hydrology and vegetation,
especially for field studies in coastal wetlands (de Szalay, 2004; Hua
et al., 2012; Li et al., 2019; Yu et al., 2012). In recent years,
researchers realized that hydrological connectivity can also affect
plant traits, species distributions, and several other characteristics
of the vegetation (Kang and King, 2013; Liu et al., 2020; Paillex et
al., 2009; Pringle, 2003). Case studies in the Yellow River Delta showed
that the depth of a tidal creek had a strong correlation with the
density (P = 0.04; R2 = 0.25 ) and height
(P = 0.01; R2 = 0.40 ) of P. australis(Wu et al., 2020). The parameters of tidal creeks were also linked to
the terrain-based structural and dynamic hydrological connectivity
(Dawidek and Ferencz, 2016; Volk et al., 2018) on the horizontal
direction. Besides, the distribution of plant species and communities
are mainly dependent on soil salinity (Cui et al., 2008; Cui et al.,
2010; Liu et al., 2020; Meng et al., 2016). Several field studies also
reported that for the communities in high-salinity habitat, such asSuaeda salsa–Phragmites australis and Suaeda
salsa–Tamarix chinensis communities in salt marshes, the biomass and
plant cover rates are found to increase along with hydrological
connectivity in surface soil layers (Liu et al., 2020; Liu et al.,
2020).
In this current study, researchers have highlighted the effects of the
interactions between hydrological connectivity and plant traits, as well
as its spatial structures. We conducted an RDA analysis to estimate the
effects of other environmental variables, and the results are shown in
Figure 7. For plant traits shown in Figure 7A, the first two and three
dimensions accounted for 42.60 % and 43.11 % of the explainable
variance, respectively, and only the Degree Centrality Index was
correlated to the plant traits’ matrix at a 95 % confidence level
(P = 0.017 ). For the spatial structures shown in Figure 7B, the
first two and three dimensions accounted for 36.87 % and 43.76 % of
the explainable variance, respectively, and only SWC was correlated to
the plant traits’ matrix at a 95 % confidence level (P = 0.032 ).
In addition, TN and TP were also reported as determinants for the growth
and community succession of P. australis (González-Alcaraz et
al., 2012; Soana et al., 2020) but failed to show significant impacts
here. Further analysis showed that TN ranged from 0.11 g/kg to 0.26
g/kg, and TP ranged from 0.41 g/kg to 0.70 g/kg in the experimental
area. A long-term field monitoring study across the whole Yellow River
Delta showed that the average TN in surface soil layer (0–30 cm) was
0.38 ± 0.21 g/kg, and the TP was 0.59 ± 0.91 g/kg (Yu et al., 2016).
Higher TN content (0.51 g/kg) was distributed in the cropland, whereas
in the coastal wetlands, TN content was found to be lower than the other
sites, but the distribution of TP did not obviously change among
different places. Similarly, the soil salinity ranged from 5.22 Ms/cm to
7.21 Ms/cm across all the plots. Thus, in the salt marsh wetland in the
Yellow River Delta, the narrow ranges and insignificant soil chemistry
heterogeneity led to their insignificant effect on P. australis.
Furthermore, based on the authors’ analysis, researchers also propose a
threshold-like effect, that is, when the biomass is relatively low, the
increasing hydrological connectivity also increases the stem numbers and
spatial structure sizes. In order to verify this conclusion, researchers
have conducted a group regression analysis between population
density/triangle numbers and hydrological connectivity indices based on
biomass. As shown in Figure 8, in the plots with biomass lower than 2.2
kg/m2, the population density (P = 0.007;
R2=0.57 ) and triangle numbers (P = 0.004;
R2 = 0.63 ) were positively correlated to the degree
indices. Similarly, the PHCI indices also showed a negative correlation
with both population density (P = 0.002; R2 =
0.67 ) and triangle numbers (P = 0.002; R2 =
0.68 ). However, the plots with biomass higher than 2.2
kg/m2 did not show such statistical relation. The
results from the control experiments in greenhouses achieved a
supportive conclusion for the authors’ deduction that the germination ofP. australis is influenced by the (ground) water depth (Yu et
al., 2012); however, the optimum value of the (ground) water depth
remains controversial from different references (Shao et al., 2009;
Whigham, 2009). The hydrological connectivity indices in this study were
estimated based on the soil moisture, which directly correlates to
(ground) water depth, but the specific threshold in different
environmental backgrounds and its influencing factors remain unclear in
either previous studies or this study.
The researchers also found that almost all the plots with biomass higher
than 2.2 kg/m2 were disconnected hydrologically. In
these plots, P. australis started to affect the hydrological
connectivity by decreasing the SWC below the filed capacity, and there
may be no free water connected to the target plot from the adjacent area
theoretically.
4.2.2 Ecological functions of population spatial structures
The spatial structures of plant populations or meta-populations are
essential factors that control species dynamics (Liu et al., 2020;
Monzeglio, 2007). The results of this study can provide information
about inter-species relations and plant responses to environmental
stresses (Omelko et al., 2018; Xu et al., 2020). Different spatial
structures were also linked to different ecological functions, such as
biodiversity (Ahmad et al., 2020; Chi et al., 2019), water yield (Liu et
al., 2018), productivity (Wang et al., 2019; Wang et al., 2020), and
micro-scale atmosphere conditions (Abdi et al., 2020; Liu et al., 2016).
Traditional quantitative ecology has utilized the two-dimensional
(usually on the horizontal direction) coordination of individuals to
parameterize the structure (Omelko et al., 2018); however, in this
current study, the researchers opted to use a three-dimensional
parameter, Structure Complexity Index (SCI), based on the coordination
and heights of individuals. This method was developed in forests (Zenner
and Hibbs, 2000) to compare the structure heterogeneity with tree
species dynamics (Zenner, 2005; Zenner et al., 2015), and previous
studies also found that SCI was related to several ecological processes,
such as shallow sub-surface runoff (Liu et al., 2018). However, there
are few studies that have used such three-dimensional methods in
herbaceous species studies.
In the Yellow River Delta, based on two-dimensional spatial
quantification, P. australis had an aggregated distribution in a
1 m × 1 m scale in the tidal marsh (Liu et al., 2020). Similar results
were also found in this study. The average projected area of the
structural networks was about 2175.59 cm2 in each
plot, which only accounted for about 21.76 % of the sample area. Our
regression analysis also showed a significant relation between
population structure sizes and hydrological connectivity, but no
evidence indicated that the three-dimensional structure was correlated
with hydrological connectivity. The plant population or community
three-dimensional structures link to the ecological functions although
it has not attracted much attention yet. The spatial heterogeneity came
from different species, age structure, and genetic diversity, which are
all essential for ecosystem stability (Pérez-España and
Arreguı́n-Sánchez, 1999; Zhang et al., 2015). On another note, spatial
structure with high complexity may also increase habitat diversity and
consequently increase species numbers and habitat quality (Hillard et
al., 2017; Nicol et al., 2016; Somerville et al., 2017).
4.2.3 Applications and uncertainty
Different researchers hold controversial views toward P.
australis. Several studies consider this species as the footstone or at
least a key species for coastal wetland restoration (Huang et al.,
2017). The wide range of salinity (Hurry et al., 2013) and inundation
(Wang et al., 2017; Yu et al., 2012) tolerance help the population
expand and connect habitat patches quickly. However, wide ranges of
niche and clonal reproduction also make P. australis highly
competitive; thus, it lowers plant diversity with its encroachment. As a
non-native species, it has displaced native plant species and has widely
degraded wetland habitats (Pengra et al., 2007; Tulbure et al., 2007) in
North America. In origin, this species is capable of competing with
invasive species, such as Spartina alterniflora (Cui et al.,
2020), and overplantation or restoration in large areas may also cause
ecological issues. Studies in the Yellow River Delta reported that inP. australis- restored areas, the species’ richness and
Shannon–Wiener index increased at the first few years and then
decreased with restored time (Wang et al., 2012). P. australiscan also alter the allocation of the total phosphorus between the plants
and the soils in coastal wetlands, affect the above-ground and
below-ground mechanisms, as well as carbon cycling (Cui et al., 2019;
Martin and Moseman-Valtierra, 2017), and finally change the abiotic
environment.
As per the authors’ opinion, coastal wetland management and restoration
should focus on the ecological processes instead of the components.P. australis is just one component of the ecosystem, and it can
be an indicator of either health or degradation of wetlands, but neither
a restoration method nor a restoration goal. Ecological processes, such
as hydrological connectivity, are drivers of the ecosystem and key for
restoration, especially for maintaining the self-sustainability of
coastal wetlands. Thus, the current study links process (hydrological
connectivity) to the component (P. australis ), which provides a
theoretical guidance for managers that the researchers can enhance the
surface soil hydrological connectivity to increase the population
density of P. australis , whereas its large-scale colonization may
also block hydrological connectivity. In the coastal wetlands,
freshwater availability is the foundation for the vulnerability of plant
species (Osland et al., 2014). Hence, researchers do not recommendP. australis plantation or restoration around Yellow River and
other freshwater channels.
However, this study also keeps several uncertainties that should be
highlighted here. First, the authors’ hydrological connectivity
parameterization method is based on the soil moisture on a horizontal
direction, whereas hydrological connectivity has three-dimensional
spatial characteristics, and how the vertical connectivity influenceP. australis remains uncertain. Moreover, hydrological
connectivity is highly scale-affected, which is the reason why the
interactions in larger scales require further experiments. Finally, how
static and dynamic factors, such as soil texture (Hu et al., 2020) and
tidal movements, influence hydrological connectivity is also crucially
important to reveal the mechanism of hydrology–soil–plant
interactions, where research gaps still remain.
5 Conclusions
In this current study, the researchers found a threshold-like effect on
the interaction between hydrological connectivity and P.
australis on the plot scale. When the biomass was lower than 2.2
kg/m2, the population density and structure size
increased with the hydrological connectivity. However, the hydrological
connectivity was observed to decrease dramatically when the biomass was
higher than the threshold value, because increased water consumption and
transpiration due to plant growth lowered the surface water content all
the way below the field capacity. Based on the plant traits, all the
plots with higher and stronger individuals were disconnected
hydrologically, and similarly, based on the spatial structure, all the
plots with larger network size were also disconnected. Compared with
soil chemistry, salinity, and water soil content, hydrological
connectivity in the surface soil layer was more strongly linked to plant
traits and spatial structure in the tidal marsh wetlands due to the
narrow ranges of the other variables. Finally, in restoration projects,
overplantation or large-scale area restoration of P. australis is
not recommended, especially near freshwater sources in the tidal marsh;
this is because of its high reproduction ability and competitiveness,
which cuts the freshwater connectivity off and lowers the richness of
plant species and habitat diversity.
Acknowledgement
This work was supported by the National Natural Science Foundation of
China (41771547) and the National Key R&D Program of China
(2017YFC0505903).
Conflict of Interest Statement
All the authors have no conflict of interests.
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Tables
Table 1 Plant traits and spatial structure parameters of different group
(mean ± standard error)