Conflict of interest
The authors have no competing interests.
Abstract: Nitrogen enrichment affects and changes the community
structure of
terrestrial
ecosystems. Recently, intransitivity competition networks have been
widely considered to in maintaining coexistence and biodiversity.
However, whether the structure and function of the species competition
network changes under nitrogen enrichment remains unclear. In this
paper, we applied the Apriori algorithm to find dominant species
assemblies to simulate competition matrices.
We found that (1) intransitive
competition networks derived by a species-dominant assembly accounted
for a relatively large
proportion;
(2)Under N enrichment, the complexity of the networks varied from an
intransitive network to a sub-competition structure, and transitive
networks was a transition structure between these two structures; (3)
the negative effect of N-enrichment on species network was counteracted
by mowing and rainfall; and (4)
increasing the number of loops
enhances the diversity in a community, while high competition among
species is a representation of an intransitive competition network.
1 Introduction
The proportion of nitrogen (N) in nature is relatively stable, but the
process of sequestering N from the atmosphere into the biosphere is
accelerated by excessive emission (Vitousek etal .
1997; Erisman et al . 2013). Significant changes in N cycles
(Canfield et al . 2010) have a negative impact on richness at both
local and spatial scales (Chalcraft et al . 2008; Lan et
al . 2015). Competition is an inherent driver of species richness and
symbiosis (Stevens et al . 2004; Bobbink et al . 2010), so
as understanding competition among species is important for
comprehending community structure and symbiosis. However, the dynamic
effects of environmental factors are still poorly understood.
Competition can be an interaction of pairwise species or indirect
effects among species in a group (Levine 1976; Wootton 1994), and these
species and their effects constitute a species-competition network
(SCN). Competition is generally divided into two categories:
transitive
(hierarchical) competition and intransitive competition (Solivereset al . 2018). Intransitivity is figuratively similar to the
rock-paper-scissors game (such as
A>B>C>A), and there is no best
competitor and worst loser (Gilpin 1975; May & Leonard 1975). The
theoretical hypothesis of intransitivity (Gilpin 1975) has received
increasing attention from ecologists and greatly promoted “modern
coexistence theory” (Soliveres et al . 2011; Ulrich et al .
2014). Perfect transitivity has distinctly hierarchical competition.
Plant competition has always been considered to be a clear hierarchy,
among which superior species will eventually win over inferior species
(Tilman 1988; Paul et al . 1989). However, the fact that inferior
species are always found in sample plots indicates that this theory is
still open to discussion (Allesina et al . 2011).
Competition is not strictly hierarchical (Aarssen et al . 1992);
in particular, outcomes of coexistence after environmental disturbances
are unpredictable (Carsten 2007). Intransitivity can also occur
frequently in plant communities (Mayfield & Stouffer 2017; Solivereset al . 2018) and promote the coexistence of species (Huismanet al . 2001; Reichenbach et al . 2007; Laird & Schamp
2009). The intransitivity level might be influenced by environmental
heterogeneity (Allesina & Levine 2011) and successional stages (Worm &
Karez 2002).
Intransitivity has important effects on the stability of species
coexistence on an ecological time scale (Landi et al . 2018). Many
architectural patterns found in real food webs tend to enhance community
stability and species coexistence (Thebault et al . 2010).
Theoretically, if a loop contains an odd number of species, it can
induce population growth and promote species coexistence (Allesina &
Levine 2011; Huisman & Weissing 1999). If a loop includes an even
number of species, minor disturbances will be amplified by the cycle and
lead to coexistence instability and even local extinction of one or more
species (Gallien et al . 2017).
In addition, climate variation may change the temporal heterogeneity of
resources and further affect intransitivity (Soliveres et al .
2015), especially in arid areas, where water availability tends to be
more volatile than in humid areas (Whitford 2002). Under N enrichment,
the effect of climate on richness cannot be generalized but may depend
on the indirect effect of intransitive competition. However, few studies
have examined the effects of N enrichment and climate factors on SCN
structures and their ecological functions.
Relative biomass is a direct
manifestation
of differences in interspecific competitivity (Meserve et al .
1996; Levine & Rees 2002). The assembly rule and biomass are the final
manifestations of interactive mechanisms, but the assembly mode and
species number in assemblies are still under discussion. Based on the
hypothesis that dominant species were more likely to produce
intransitivity, the strategy of a fixed number of species for assembly
(Soliveres et al . 2015; Ulrich et al . 2018) is limited to
superior species, which led to a relatively small proportion of the
inferred intransitivity and ignored the fact that inferior species can
survive in long-term fierce competition (Fox 2013).
Here, we tested the following
hypotheses: (1) species with small total biomass (inferior species) but
high-frequency occurrence in an assembly play a crucial role; (2) under
different N addition rates, SCNs may vary from intransitive competition
networks (ICNs) to transitive competition networks (TCNs) and finally
degenerate to a sub-competitive structure (SCS) dominated by
environmental factors; (3) precipitation and mowing can alleviate the
negative effect of heavy N-enrichment and increase the probability of
formation of intransitive structures
embedded
in SCNs; and (4) an ICN is
favourable to maintain richness, while a TCN is maintained for a longer
period during grassland degradation.
2 Materials and methods
2.1 Experimental sites and
sampling
Field experiments were carried out on a temperate steppe (43°32’51”N,
116°40’23”E) in Xilin, Inner Mongolia Autonomous Region, China. From
2008 to 2020, average annual temperature was 1.45°. The average annual
precipitation was 329.9 mm. This simulated N-deposition experiment began
in September 2008 following a randomized complete block design. Each
plot was 8 m× 8 m, and sampling was carried out in the same 0.5×2
m2 area. Purified
NH4NO3 (>99%) was added to plots at nine different rates
(0, 1, 2, 3, 5, 10, 15, 20, and 50 g
N m-2 year-1, of which
concentrations of 0 to 10 g N m-2year-1 were considered low nitrogen addition and
concentrations of 15 to 50 g N m-2year-1 were considered high nitrogen addition) at two
frequencies (monthly and twice a year). In the mowing groups, no-mowing
groups, and two control groups, there were 38 experimental treatments,
each of which was replicated 10 times. A total of 4,930 plots of field
samples were collected over 13 years. These data were used to mine the
SDA of above-ground species and construct SCNs.
2.2 Methods
Interactions, such as competition or mutualism, are often considered to
be essential drivers of ecosystem services, but directly measuring them
is challenging (Olff et al . 2009). Interaction is usually
measured by the co-occurrence number (Wang et al. 2020) or
correlation coefficient (Feng et al . 2017) based on repeated
experimental data. Repeated experiments play an important role in
obtaining reliable indirect metrics of competitivity determined from
field experimental data. The Apriori algorithm was used to mine SDAs
from species presence-absence data, and then the RECC model (Ulrichet al . 2014) was used to simulate SCNs of the SDAs with abundance
data. The complexity and structure of the SCNs, centrality of important
nodes, their ecological functions, their variation with biotic or
abiotic factor changes, and their significance were explored, as shown
in the schematic experimental diagram of this study (Fig. 1).
2.2.1 Excavation of species-dominant
assembly
Although spatial heterogeneity in adjacent areas may increase
opportunities for niche differentiation and enhance intransitive
competition, it also makes it more complex and difficult to estimate
levels of competition based on the observed abundance of species (Ulrichet al . 2014). Therefore, sample plots with matching species
assembly should be as environmentally homogeneous as possible. N
enrichment can reduce environmental heterogeneity in small communities
(Western 2001; Fraterrigo et al . 2005), and a similar environment
is likely to promote similar community dynamics (Western 2001; Wescheet al . 2012), reducing spatial asynchrony. Therefore, we believe
that under N enrichment, SDA can occur with high probability in multiple
sample plots. For this reason, we used the Apriori algorithm to find
SDAs (Wang et al . 2020).
The Apriori algorithm is an unsupervised learning algorithm that can
mine species assemblies frequently occurring in 10 repeated experimental
plots. When the support degree exceeds the threshold, a species assembly
with a high occurrence frequency is considered a SDA (Agrawal 1994; Wanget al . 2020). Species
absent
from a plot will be excluded from the SDA, so it will naturally be out
of the numerical simulation. We set 0.5 as the threshold for SDA
selection, ensuring that the estimation of species competition was based
on at least 5 plots. Species numbers in some SDAs can be up to 9.
Considering the efficiency of calculation and Apriori algorithm theory,
an SDA comprising the largest number of species was selected as the
numerical base.
2.2.2 Measurement of
intransitivity
The RECC model proposed by Ulrich et al. (2014) was used to simulate the
competition matrix of SDAs. Many competition matrices C (100,000,
m×m matrices, where m is a species number) were generated then
transformed into transfer matrix P by the colonization
competition model. Since the abundance orders ofPU and U are completely
positively correlated (r2=1, P<0.05), the
Spearman test was performed between PU and U . The average
rank correlation coefficient (rs) was used as an index
to select the optimal matrices C and P .
The rs is mainly used to measure the relative
essentiality between interspecific competition mechanisms and other
factors acting on communities. In the field experimental data, the value
is usually low, indicating that interspecific competition mechanisms
play a relatively small role, possibly due to environmental
heterogeneity (Dufour et al . 2006) or other more important
mechanisms (Ulrich et al . 2018). Therefore, if rs≥ 0.6, competition is thought to occur, and C is a competition
matrix (or optimal matrix).
If competitions among a group are completely transitive in an SCN, there
will be a species whose competitivity is significantly higher than that
of others or that will rank first in C , suggesting that the
species will eventually outcompete the others. An intransitive loop
marked by matrix C indicates that there is an intransitive
competition structure derived from an SDA. According to the level of
complexity, three types of SDNs are defined as follows (Fig. S1).
- An ICN (or loop network) is an SCN containing at least one
intransitive structure.
- A TCN (or chain network) is an SCN with a perfectly hierarchical
competition structure rather than comprising any loop structure.
- The SCS is a community (in a plot) in which rs is less
than 0.6 and might be dominated by environmental factors such as N
enrichment. How to vividly describe its structure needs further
research.
2.2.3 Complexity measurement of network
structure
Luo and Magee (2011) proposed flow hierarchy (simplified as h) ,
which was defined as the percentage of edge number in chain paths to the
total number of all edges in links maintaining their overall direction,
that is, the percentage of edges not in any loop paths to total edges
(Luo & Magee 2011). If the network is a pure structure, such as a tree
structure, then h =1. Therefore, the complexity of an SCN was
redefined as follows,
\begin{equation}
C\text{om}plexity=\left\{\begin{matrix}0,if\frac{h*c}{N_{\text{node}}}\geq 1\\
1-\frac{h*c}{N_{\text{node}}},if\ 0\leq\frac{h*c}{N_{\text{node}}}<1\\
\end{matrix}\right.\ \nonumber \\
\end{equation}where h is the measure of the flow hierarchy,\(N_{\text{node}}\)is the species number of the network, and c is
a constant. The numerical experiment proves that c = 10 is
optimal for complexity to the
interval [0,1].
2.2.4 Betweenness
centrality
The betweenness centrality(BC) of a node refers to the number of
shortest paths through the node in a network (Linton 1977) and is used
to estimate its importance. A node with high BC can effectively raise
the complexity boundary of a network. If the node was removed from the
network, the structure would be segmented into several disconnected
subnetworks.
In this paper, The Apriori Algorithm in the mlxtend package in Python
was used to mine SDAs. The NetworkX package in Python was applied to
construct SCN diagrams and calculate the complexity of networks and BC
of each species.
3 Results
3.1 Statistical description of species competition
networks
In this paper, plants in the experimental plots were structuralized and
divided into three structure types: ICNs, TCNs and SCSs. Environmental
changes were the main inducer of transformation in competitive networks.
At N = 0 g N m-2 year-1, 95.84% of
the plots were determined by interspecific competition. With the
increase in N addition rates from 0 to 50
g
N m-2 year-1, the proportion of the
plots dominated by competition decreased by approximately half to
44.23%. When N = 50 g N m-2 year-1,
the proportion of ICN decreased to 9.61% from 51.92%, while the SCS
eventually increased to 50%. The variation in the TCN proportion was
relatively stable because the
proportion of TCNs degenerating to the SCS might be supplemented by the
proportion of ICNs degenerating to TCNs. With increasing N addition
rate, some ICNs transformed into TCNs, while some TCNs transformed into
SCSs, which basically maintained the distribution of TCNs in a dynamic
balance (Fig. S2). The proportion of ICNs under mowing was significantly
higher than that without mowing. The high and low frequencies of N
addition had no significant effect on the distribution of the three
structures.
Species(18 kinds) in SCNs were divided into three kinds species based on
total biomass: inferior species(< 500
g)、sub-superior
species(500≤total biomass ≤10000 g)、superior species(>
10000 g)(Tab.1). Frequent two-species and three-species assemblies were
also mainly composed of superior and sub-superior species (Tab. S1&S2).
Inferior species usually more easily survive in ICNs and rarely in TCNs,
although they are infrequently in SCNs (Tab. 1). A three-species
intransitive structure is considered the minimal loop. There were 168
different minimal loops in all SCNs. Among all the minimal loops, there
were different 94 pairs and 17 single species.
BC emphasizes the important role of a species in controlling the
structural complexity of an SCN. Once the species with high BC were
eliminated from an SCN, the SCN was broken into several disconnected
small networks. There was a significantly positive correlation between
the complexities of SCNs and the maximal BC of nodes in themselves
(r2=0.772,
P<0.001 in Fig. S3). The inferior species had fewer
opportunities to appear in an SCN, and once they appeared in an SCN, the
opportunity to play an intermediary role increased. Therefore, these
species played a significant bridging role in their SCNs (Tab. 1).
3.2 Effects of species competition network on community
attributes
Species richness were similar in the control environment. Here were
significant differences in inter-annual variation trends of species
diversity, especially at high nitrogen (Tab. S4). The lower the N
addition rate was, the greater the species richness (Fig. 2). Their
polygonal lines ran in a compact way. However, for TCNs, the increasing
richness under each N addition rate was not obvious, and their polygonal
lines ran in a dispersed way. There were more plots labelled by TCNs
than by ICNs under high N concentrations (N ≥ 15 g N
m-2 year-1), while ICNs at low
nitrogen had better maintaining biodiversity. The N-cumulative effect
accelerated the decomposition of TCNs and increased the randomness of
species assembly (Steiner & Leibold 2004; Chase & J.M. 2010).
Eventually, this destroyed species co-occurrence and competitive
structure, resulting in the interspecific ecological function of the
community being entirely lost. Especially in 2020, the biodiversity was
not only maintained but also promoted, but that of TCNs had the opposite
trend.
Biomass and the number of RP are two essential attributes portraying the
plant community and also directly embody interspecific competition. We
found that network structure affected the attributes of communities and
their associations in a sample plot. In the plots identified as an ICN,
there was no significant correlation between the average biomass and
number of RPs (r2=0.116). While for the plots
identified as a TCN, there was a significant positive correlation
(r2= 0.482***). Interspecific competition in TCNs is
not only for biomass but also for the number of RPs, indicating that the
network was strong overall.
With regard to ICNs, 49.36% of samples in the 95% confidence region
were the plots under low N addition rates and mowing (Fig. 3). A low N
addition rate is beneficial to the number of RPs under intransitive
competition, and mowing is beneficial to biomass. The 95% confidence
region for TCNs was dominated by a low N addition rate and no mowing,
accounting for 40.10% of the 95% confidence region. The sample plots
outside the confidence region were mostly those treated with high N
addition rates and mowing (Fig. 3). In conclusion, there was more ICNs
than TCNs at low N addition rates, while more TCNs at high nitrogen had
a strong correlation between biomass and the number of RPs. The area
outside of the 95% confidence region cannot provide any information
about the effect of network structure on the attributes of communities
under high or low N addition frequencies.
3.3 Responses of species competition network to N
addition
A TCN is a kind of simple competitive structure, also known as a
hierarchical network, the complexity of which is labelled as 0. As the
number of loops in an ICN increases, complexity will increase from 0 to
1. As shown in Fig. 4, the complexities gradually decreased from the
upper left to the lower right with the nitrogen accumulation effect,
suggesting that the network structure evolved from a complex ICN to a
simple TCN and finally to NaN. The trend of complexity was similar to
that of precipitation over time (Fig. S4), which was also verified by
logistic regression (Tab. 2). With the N cumulative effect, complexities
decreased several steps and then reached zero, even those for SCSs. The
peak complexities were mainly stabilized at 2 g N m-2year-1, indicating that it may be the most beneficial
to form ICNs or maintain richness under the accumulative effect of N
addition.
Fig. 4 shows that more intransitive competition existed under N addition
rates less than 15 g N m-2 year-1.
With increasing duration and N addition rate, the number of loops in the
SCNs gradually decreased from the upper left to the lower right and
gradually changed to a simpler transitive mode. In particular, when the
N addition rate was 50 g N m-2year-1, TCNs lasted 8 years. Species competition in
2019 and 2020, after long-term environmental filtering, became sparse,
and species assemblies were more random than in other cases. Therefore,
an intransitive network was beneficial to maintain richness but
susceptible to nitrogen with a short periodicity, while a TCN was
maintained for a longer period during grassland degradation under high
nitrogen addition rates.
3.4 Logistic regression analysis of species competition
network
Through univariate logistic regression analysis, we found that the
community attribute variable, number of RPs, and three environmental
variables (N addition rate, mowing, and precipitation) were significant
predictors of TCN and ICN formation, and the accuracy for TCNs or ICNs
is shown in Tab. 2. When the community attributes and environmental
variables were combined to perform multivariate logistic regression, the
four above-mentioned variables were still significant. The number of RPs
was an important community attribute variable to identify network
structure (Tab. 2). As shown in Fig. 3, under the same average biomass,
the number of RPs in ICNs was greater than that in TCNs.
With increasing N addition rates, the probability of ICN formation
gradually decreased, while the opposite was true for TCN formation (Tab.
S5). The regression coefficient gradually changed from positive to
negative, and the probability of ICN formation decreased from 0.595 to
0.217, while that of TCN formation increased from 0.405 to 0.783. When N
≥15 g N m-2 year-1, the significance
of the regression coefficient became increasingly stronger, which was
why we grouped the sample plots at 15 g N m-2year-1 in Fig. 4. The difference in probabilities
between the two network structures intensified under high N- addition
rates (Tab. S5).
4 Discussion
4.1 Selection of species
assembly
Species assembly is a reliable
prerequisite
for detecting species interactions and a necessary step for constructing
SCNs in this study. Currently, there are two main rules for the
selection of species assembly: (1) based on the assumption that
intransitivity is more common among superior species (Soliveres et
al . 2015; Ulrich et al . 2018), and (2) all species were brought
into the network to represent complex relationships in the natural state
to the greatest extent (Hugo Saiz et al . 2018). The two
strategies of selecting species assemblies had shortcomings for
acquiring network structures and their corresponding ecological
functions and information.
It is well known that the null model is a powerful tool for empirical
and theoretical studies for community isolation or aggregation
(Dullinger et al . 2007; De Bello et al . 2009), but
nonrandom species assembly cannot be determined (Gotzenberger et
al . 2012). Based on species co-occurrence in repeated plots, frequent
assemblies can be screened out by the Apriori algorithm (Wang et
al . 2020), which breaks the limitation of only using biomass to
investigate species assembly and emphasizes co-occurrence frequency.
Moreover, the Apriori algorithm maintained nonrandomness in species
assembly, which motivated species with a small biomass but frequent
occurrence in the SCN with a certain probability.
Therefore, SDAs with a high occurrence probability were taken as the
starting point of this study and overcame the shortcomings in the above
selection rules. We concluded that 78.95% of the test plots were
dominated by interspecific competition (rs ≥ 0.6), 49%
of which contained intransitive structures. Compared with prior studies
(Soliveres et al . 2015). Two main reasons for this observation
are that the selection rule does not limit superior species, and other
species with high occurrence frequencies are also considered;
environmental filtering causes plants on the grassland to evolve and
substitute, resulting in species assemblies that also change constantly.
4.2 Inferior species are more likely in an intransitive
competition
network
In general, it is difficult for inferior species to adapt widely to
grasslands. Their persistence depends on the interactions between
species (Chler, Michalet & Callaway 2001). There may be differences in
the importance of intransitivity between superior and inferior species
(Hugo Saiz et al . 2018). Intransitivity can inhibit the superior
utilization efficiency of resources and urge interspecific-competition
hierarchical differences to be small, providing more occurrence
opportunities for the inferior group (Maynard et al . 2017).
Inferior species in a plot always play a role as a defeater that only
occasionally appears; therefore, inferior species are more likely to
survive in ICN. If an inferior species was in an SCN, it commonly
promoted network complexity as an intermediary, transforming the TCN
into a more complex ICN. For example, an ICN including Potentilla
acaulis had 6 loops, 5 of which covered Potentilla acaulis (Fig.
S7), which had the largest BC (Tab. 1). Under suitable environments or
sufficient resources, inferior species can play a crucial role and can
even directly determine the complexity of an SCN.
4.3 Effect of species competition network on community
attributes
Intransitivity focuses more on symbiosis than on resource constraints
(Huisman et al . 2001; Allesina & Levine 2011), and Soliveres
(2015) proposed that intransitive structures embedded in species
networks can better explain coexistence, which is also the reason why
networks with intransitive structures are defined as ICNs. We found that
ICNs could better maintain species richness under low N addition.
Intransitive structures are a sign of complex competition, and changes
in competition intensity are key drivers of interspecific interactions
(Tilman 1988; Carsten 2007).. An increase in independent loops in a
community will increase the number of species falling into loops, which
has a stabilizing effect on the community (Gallien et al . 2017).
TCN is also a common interaction pattern among plant species (Freckletonet al . 2000), and with great differences in interspecific
competition, inferior species will be eliminated (Keddy & Shipley
1989). As species disappear, complex ICNs gradually degenerate into
simpler TCNs. TCNs are a transition pattern of the species network from
ICNs to SCSs, from a competition-dominated to environment-dominated.
Our results also confirmed that the species number in the plots labelled
by TCNs was greater than that labelled by ICNs. This difference became
greater with an increase in the N addition rate. TCNs can better
maintain interspecific dominance of a species, while the main ecological
function of ICNs was to maintain species symbiosis. In a plot labelled
by ICNs, the individual biomass was relatively small, but the number of
RPs was large, which was very different from that plots labelled by
TCNs. This can explain why the number of RPs and not biomass can be used
as a predictor to distinguish the type of SCN.
4.4 Environmental effects on network
structure
Prior studies have shown that adding nitrogen to grassland ecosystems
through fertilization or atmospheric N enrichment is beneficial to
primary productivity (Lebauer & Treseder 2008; Zhang et al .
2015). However, a continuous increase in nitrogen to plant communities
may have unforeseen consequences, including loss of richness (Stevenset al . 2004; Payne et al . 2017), a decrease in the degree
of intransitivity (Soliveres et al . 2018), and threats to
community stability (Hautiler et al . 2014; Zhang et al .
2016).
Environmental disturbance can affect competitive relationships (Goldberget al . 1999). N-enrichment events significantly reduce richness
(Zhang et al . 2019). An increase in N enrichment reduces network
complexity and causes species in a community to gradually compete for a
single nutrient resource, enhancing the competition hierarchy among
species and decreasing competition intransitivity at the same time. This
is mainly because excessive perturbations in environmental factors
increase adaptivity differences, leading to more asymmetrical
competition (such as the transition from nutrient to light competition
(De Malach et al . 2017)) or more transitive competition
(Soliveres et al . 2018).
We found that much precipitation was beneficial to enhance the
probability of ICN formation in the Inner Mongolia steppe, which seems
to contradict the prior results that drought aggravates intransitivity
(Soliveres et al . 2015; Gallien et al 2018). The reasons
for this may be as follows: (1) the interannual differences in the
drought gradient in the experimental area are smaller than those in
global vision. Moreover, high temperature accelerates evaporation of
surface water (Berget et al . 2014; Diro & Sushama 2017);(2) high
N enrichment causes excessive nutrition in the experimental area, but a
large amount of precipitation will dilute the N- concentration, thus
enhancing the probability of ICN formation; and (3) a lack of water
resources in the area makes rainwater a priority resource for species to
compete for, tending to promote intransitive competition in a community.
Mowing reduces the temporal heterogeneity of biomass (Osem et al .
2002; Grman et al . 2010), variation in the heterogeneity of
community richness and species asynchrony under N- enrichment (Hautileret al .,2014; Zhang et al . 2017). Nutrients and water are
important resources for plant growth in temperate steppe, but mowing
will aggravate the loss of soil nutrients (such as N, P, K, etc.),
resulting in nutrient imbalance (Giese et al . 2013; Liu et
al . 2015). Redistribution of nutrients intensifies competition for
resources after mowing. Our numerical results indicated that under N
enrichment, mowing contributed to enhance species complexity from an
environment-dominated SCS to a TCN or from a TCN to an ICN. Because the
negative effect of N enrichment on plant diversity is partially
counteracted by infrequent annual mowing after the reproductive phase of
plants (Collins et al . 1998; Storkey et al . 2015; Zhanget al . 2017). In addition, mowing may impair the potential
balance among species, such as resource uptake and pollinator attraction
(Aarssen 1992), but it also reclassifies competitive hierarchy and
increases intransitive competition, such as the co-existence of inferior
species.
Low frequency will lead to low colonization (acquisition of new species)
(Zhang et al . 2015). The low colonization rate may be due to soil
ammonium poisoning under low fertilization frequencies (Britto &
Kronzucker 2002; Berg et al . 2005). Although N addition frequency
is not the main factor affecting network structure and community
attributes, ICN at low frequencies is slightly higher than that at high
frequencies (top right panel in Fig. 3). The cause of this is that low
colonization could not break down the adaptive relationship of the
original species as the result of stabilization of ICN. High frequency
destroyed the competitive balance of the original community, resulting
in decreasing intransitivity competition.
5.Conclussion
In this paper, we first used the Apriori algorithm to mine SDAs from
presence-absence data of plants. Inferior plants were included in the
dominant assembly due to their high co-occurrence (Tab. 1). ICNs
included not only superior but also inferior both existing in SDAs,
which is one of the successes of this paper. Superior species are
usually the main component of TCNs, while inferior species are more
likely to enter ICNs. TCNs can be regarded as transition modes from more
complex ICNs to SCSs mainly dominated by environmental factors.
Environmental changes can affect variations in network structure. An
increase in the N addition rate caused the network structure to change
from an ICN to a TCN (Fig. 4). However, low N addition rates, mowing and
rainfall were external drivers of ICN formation, which could maintain
biodiversity better than TCNs.
Acknowledgements
This work was supported by the National Key Research and Development
Project of China (2016YFC0500705) and the Fundamental Research Funds for
the Central Universities
(2015ZCQ-LY-01).
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Figure Legends:
Figure 1 Schematic diagram of this experimental study.