Title
page
Title :Structures of intransitive competition network affect
functional attributes of plant community under nitrogen enrichment
Sun Qingqinga,1, Yang Junjieb,1,
Yang Fengyana, Zhao Yuyinga, Zhang
Guangmingc, Wei Cunzhengb, Han
Xingguob,*, Li Jinshana,1,*
a College of Science, Beijing Forestry University,
Beijing 100083, China
b State Key Laboratory of Vegetation and Environmental
Change, Institute of Botany, Chinese Academy of Sciences, Beijing
100093, China
c Department of Pharmaceutical Science, Changzhi
Medical College, Changzhi 046000, China
* Corresponding author : Han Xingguo, e-mail:xghan@ibcas.ac.cn; Li Jinshan,
e-mail:lijinshan@bjfu.edu.cn
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).
Conflict of interest : The authors declare they have no
competing interests.
Authors contributions:Sun
Q.Q. and Li J.S.
conceived
the ideas and designed methodology; Yang J.J. collected the data; Sun
Q.Q., Yang J.J. and Li J. S. analyzed the data and wrote the manuscript
with feedback from Zhang G.M., Yang F. Y. and Zhao Y. Y. All authors
contributed critically to the drafts and gave final approval for
publication.
Abstract
Atmospheric nitrogen (N) deposition is a potential danger factor for
grassland ecology, and will cause unpredictable consequences to plant
communities. However, how plant species interactions response to N
enrichment and then affect ecological functions are not fully known. We
investigated how intransitive competition network was related to the
functional attributes of plant community under a 13-years N-deposition
experiment. Results showed that intransitive competition network was not
a single structure, but a complexly interwoven structure of various
simple structures. Nested work was more common, accounting for 76.96%,
and gained new species at a higher colonization rate than short network
did. The network had a long-term mechanism to maintain the small-scale
Alpha diversity, and a significant lag effect on the large-scale Gamma
diversity. Under the conditions of N ≥ 2 g
N·m-2·year-1, without mowing and
under high fertilization frequency, the increase of network complexity
significantly decreased plot biomass gradually.
The
relationship between biomass and network complexity is quadratic curves,
also between abundancy and the complexity, but with the opposite bending
directions, which indicated that biomass and abundance were
complementary to each other, which may be a mechanism of maintaining the
relative balance of species competition. In addition, the decrease of
species asynchronism changing with the increase of N-enrichment
gradually destroyed ecosystem stability. However, at medium N
enrichment, intransitive network counteracted the negative effects of N
enrichment and maintained or even improved the biomass ecosystem
stability. Our results suggested that intransitive competition network
is an internal mechanism of self-restoration of a
grassland
ecosystem. Under nitrogen enrichment conditions, competitive networks
complexity is reduced, leading to a reduction in species diversity.
These analyses emphasize the important role of intransitive network
structure to stabilize grassland ecosystem. In order to achieve
sustainable development of grassland, it is indispensable to control
nitrogen addition rate.
Key words: biodiversity,
complexity,
ecosystem stability, nested network,
species
asynchrony
1. Introduction
Nitrogen (N) fertilization management on natural grasslands and pastures
can significantly increase yield of plants and animals, which has
brought great convenience and abundant benefits to modern agriculture
and animal husbandry (Tilman 1987; Zhang et al. 2015). Nutrient
enrichment generally increases primary production but reduces
biodiversity. Theoretically, nutrient enrichment can destabilize
ecosystems (Rosenzweig 1971). In grassland ecosystem, the species
extinction and community composition are caused by overfertilization or
atmospheric Nitrogen deposition
(Clark
and Tilman 2008; Socher et al. 2013; Zhang et al. 2019).
Some
studies
(Freckleton
and Watkinson 2001; Wang et al. 2020)) clearly indicate that changes in
competitive intensity are also key drivers of community composition. The
intensity is a kind of quantification of competition structure, while
competitive structure changes with the variation of environment
(Allesina and Levine 2011) and is susceptible to the influence of human
factors (Soliveres et al. 2015). Competition network is an important
determinant for community functions, with a gradient from weak
competition community to strong competition community (Maynard et al.
2017b). Structure of networks is a driver of biodiversity and
coexistence in natural communities (Sun et al. 2021). In addition, the
nature and intensity of competitive interactions also affect growth,
productivity, and biomass of surviving individuals. Therefore, structure
of competition networks, i.e., the result of pairwise species
competition (Allesina and Levine 2011), should play an important role in
community
attributes.
The
immediate cause of existence for competition is that the species
composition in the focus community, since species have different
functional and life-history characteristics, the particular combination
of which maintains intransitivity or hierarchical competition (Aschehoug
et al. 2016). Intransitive competition is more common in vascular
plants, and there is a significant positive relationship between species
richness and intransitivity (Soliveres et al. 2015; Gallien 2017a),
which reveals complex competitive interactions among species (Allesina
and Levine 2011;
Engel
and Wetzin 2008) and greatly promotes the theory of species symbiosise.
Empirical analysis and simulation study (Ulrich et al. 2017) both showed
that intransitive competition, as mechanism for maintaining high alpha
diversity
(Laird
and Schamp 2006), can promote symbiosis of competitors. Therefore,
understanding the good competition is important to understand the way
that community is structured and how coexistence of species occurs.
In
addition, ecosystems are always exposed to environmental perturbations
that tend to alter temporal stability of plant communities (Ma et al.
2017; Douda et al. 2018). Species asynchrony occurs in natural
communities due to interspecific competition and environmental stresses.
Empirical observations have shown that there are positive covariances
among species biomass in many communities, which do not indicate absence
of competitive interactions. Besides the effects of diversity itself on
community function, the interaction of diversity and competition, as two
key attributes, determines biomass production, respiration, and
carbon-use efficiency. On other hand, availability of abiotic resources
varying over time, potentially breaks down inherent species competition.
However, it is unclear to understand how species competitive
interactions respond to N enrichment and affect plant community function
(Gross et al. 2017; Mouillot et al. 2011).
We tested the following hypotheses: (1) the intransitive network with
nested structures is more common in vascular plants; (2) at the small
scale, the function of nested network to maintain species richness was
more significant; (3) the intransitive network complexity could maintain
the relative balance between biomass and plant number; (4) the negative
effect of nitrogen can be mitigated by intransitive network.
2. Materials and methods
2.1 Experimental sites and
sampling
The field experiments were carried out on the temperate steppe (43 ° 32
’51 ”N, 116 ° 40’ 23” E) in Xilinhot, Inner Mongolia Autonomous Region,
China, where belongs to the typical temperate semi-arid climate and has
relatively flat terrain. Since 1999, 50 hectares of the experimental
area had been fenced off to eliminate the impact of large-scale grazing,
and no fertilizer had been applied before the beginning of the
experiment. The mean annual temperature in 2008-2020 was 1.45°C, the
mean temperature in the growing season was 17.04°C, the annual
precipitation was 329.9mm, and the precipitation in the growing season
was 227.6mm. Two C3 perennial herbaceous plants, Stipa grandis and
Leymus chinensis, are the dominant species in the vegetation community.
The nitrogen used in the experiment was ammonium nitrate
(NH4NO3,>99%), which was
added to the experimental plots during the growing season and the
non-growing season by wet deposition (ammonium nitrate dissolved in
distilled water) and dry deposition (ammonium nitrate mixed in fine
sand), respectively. Species traits such as biomass, plant abundance et,
al., as the sample data, were measured on a 0.5m×2m strip plot in every
experimental block of 8 𝑚2. The experiments designing
with completely randomized block, and setting up with 9 nitrogen annual
adding rates (0, 1, 2, 3, 5, 10, 15, 20, 50 g
N·𝑚-2·𝑦𝑒𝑎𝑟-1), 2 kinds of
fertilization frequency (2 times a year, 1 time a month), and 2
processings of mowing (yes or not), in addition to two control groups, a
total of 38 different treatments were performed, each of which was
repeated 10 times.
2.2 Methods
2.2.1 Species richness
Based on theoretical work (Wang and Loreau 2014), as well as previous
empirical studies (e.g., Wilcox et al. 2017; Zhang et al. 2019), the
Alpha diversity was defined as the species richness of
1m2 plot (small scale), and the Gamma-diversity as the
total richness of 10 repeated plots of 1m2 (large
scale) under a same treatment. In addition, soil ammonium toxicity is
greater under N enrichment, leading to low colonization rate and even
high species extinction rate (Zhang 2015). In order to explore the
effect of intransitive competition network on the community attributes,
species gained or lost have also become the key point of the discussion.
2.2.2 Spatial variability
Lloyd’s central theory (1967) demonstrated that average density is
difficult to explain ecological causality between competition and
density regulation. At individual level, Lloyd designed average clumping
as a measurement of spatial patches to capture spatial aggregation
associated with intraspecific competition for resources. In order to
explore the influence of N enrichments on spatial differences in species
richness, we used Lloyd’s variance-to-mean ratio,\(I=\frac{\sigma^{2}}{\mu^{2}}-\frac{1}{\mu+1}\). Where 𝜇 and
𝜎2 are the mean and variance of species richness under
different treatments or variables. When I<1, the spatial
difference of species richness was small and the distribution was more
uniform; when I>1, the spatial difference of species
richness was great; and when I = 1, it is the expectation of the
Poisson’s random distribution.
2.2.3 Measurement of
intransitivity
Nitrogen
enrichment can reduce environmental heterogeneity in small communities
(Western
2001;
Fraterrigo
et al. 2005), therefore, similar environment is likely to promote
similar community dynamics (Wesche et al. 2012). We introduced the
Apriori algorithm to mine the species dominant assembly in spatial
clumping and ensured that the inference of a species competition network
was based on at least 5 plots in 10-repeated ones (Wang et al. 2020; Sun
et al. 2021).
Starting from the dominant assembly, the ‘reverse engineering’ and
colonial competition model proposed by Ulrich et al. (2014) was used to
simulate the best competition matrix, entries of which are competition
coefficients between species. Simulations were done to produce a large
number of competitive matrix C , as well as transfer probability
matrix P , obtained from C by colonial competition model.
Then Spearman tests between PU and the relative abundance vectorU of experimental plots under same treatment were done. The
average of Spearman coefficients
(𝑟𝑠) from the tests was used as a selection index for
best C and P . Spearman coefficients acquired from the
experimental data were generally low, showing that the role of
interspecific competition mechanism was relatively small, while
environmental heterogeneity (Dufour et al. 2006) or other more important
mechanisms (Ulrich et al. 2018) maybe play important role. Therefore,
only when \(r_{s}\geq 0.6\), interspecific competition is the dominant
factor for a assembly, and then
the matrix C was going to be the optimal competition matrix.
Species-competition network was built up by the matrix C, in which
species were vertices and edge weights were the entries of C.
We used deformation of the Flow Hierarchy (Luo 2011; Sun et al. 2021) to
measure the complexity of intransitive network.
\begin{equation}
\text{Complexity}=\left\{\begin{matrix}0,\text{\ \ \ \ \ \ \ \ }\text{if}\ \frac{h*c}{N_{\text{node}}}>1\\
1-\frac{h*c}{N_{\text{node}}},\text{if}\ \ 0\leq\frac{h*c}{N_{\text{node}}}<1\\
\end{matrix}\right.\ \nonumber \\
\end{equation}where h is the measure of flow hierarchy, \(N_{\text{node}}\)is
the species number of the focal network, and c is a constant.
When complexity is 0, there is only chain structure (e.g.,
A>B>C). When complexity is 1, there are a
large loop structures that all member of network in it.
2.2.4 Biomass stability and species
asynchrony
Following previous work (Tilman 1999), biomass stability was calculated
as \(S_{\text{community}}=\frac{\mu_{T}}{\sigma_{T}}\),where\(\mu_{T}\) and \(\sigma_{T}\) are the interannual mean and standard
deviation of community biomass over the 13 years respectively.
For each 1-m2 strip, the degree of asynchrony in the
population dynamics of constituent species of its local community was
quantified as
1-\(\frac{\sigma_{T}^{2}}{{(\sum_{i=1}^{M}{\sigma_{i})}}^{2}}\), where\(\sigma_{T}^{2}\) is the temporal variance of community biomass, and\(\sigma_{i}\) is the standard deviation of biomass of species i in theM -species community (Loreau and de Mazancourt 2008).
2.2.5 Statistical analysis
Linear or quadratic polynomial least squares regression was used to
study the relationships between variables, such as plot biomass and
N-addition rates, plant abundance of plot and network complexity etc.
The independent sample T-test was used to test the significance of
differences among diversity of different kinds of network. The Python
packages used in this paper were ”StatsModels”, ”NextWorkX” and ”SciPy”.
3. Results
3.1 Network structure affects
richness