An ecological-network-analysis based perspective on the biological control of algal blooms in Ulansuhai Lake, China

An ecological-network-analysis based perspective on the biological control of algal blooms in Ulansuhai Lake, China

Ecological Modelling 386 (2018) 11–19 Contents lists available at ScienceDirect Ecological Modelling journal homepage: www.elsevier.com/locate/ecolm...

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Ecological Modelling 386 (2018) 11–19

Contents lists available at ScienceDirect

Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

An ecological-network-analysis based perspective on the biological control of algal blooms in Ulansuhai Lake, China

T



Xufeng Maoa, Xiaoyan Weib, Donghai Yuanc, , Yanxiang Jina, Xin Jina a

Key Laboratory of Physical Geography and Environmental Processes, College Geography Science, Qinghai Normal University, Qinghai, Xining, 810000, China Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing Climate Change Response Research and Education Center, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China c School of Economics and Management, Qinghai Normal University, Qinghai, Xining, 810000, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Algal bloom Control analysis Utility analysis Ecological network analysis Ulansuhai Lake

Biological control is considered as an environmentally and effective means of reducing or mitigating harmful algal blooms. By biological remediation approach (addition of predators or competitors), it may alter the ecological relationships between the dominant algae and their associated populations in ecosystems. However, it cannot quantify the indirect effects of this approach and the long-term and system-level ecological consequences are hard to assess. Here we focus on the "bloom" areas of the Ulansuhai Lake in Inner Mongolia. Based on historic data, field monitoring and experimental data, we constructed a quantitative phosphorus cycle network model. Using Ecological Network Analysis (ENA), we evaluated how indirect flow influences dominant algal species and alters network control and utility relationships. Results indicate that: (1) Indirect flows have strong influence on the mutual and control relationships between blooming algae and its related functional groups in the Ulansuhai Lake; (2) Some opposite interspecific relationships between the blooming algae and other functional components have been found by ENA; (3) Key populations controlling the blooming algae are observed from the intuitive Zooplankton and Detritus to network-based Microorganisms, Zoobenthos, Zooplankton and Detritus(water). The research results indicate the importance of including indirect relationship into the control of lake algae bloom. The application of ENA was good at revealing indirect effects for ecological restoration of eutrophic lakes.

1. Introduction Algal blooms severely reduce marine and fresh water quality, seriously harm aquatic organisms, threaten human health, and weaken aquatic ecosystem services, with serious ecological consequences (Carpenter et al., 1996; Postel and Carpenter, 1997; Smith et al., 2006; Brooks et al., 2016). Biological control method is considered an environmentally-friendly, economical, and efficient way of reducing or mitigating algal blooms through both bottom-up (competition for nutrients) and top-down (increased predation on algae) (Carpenter et al., 1995). Taking advantage of biological and biochemical functions and food-chain control, biological methods degrade and transform excessive nutrient salts in lakes, inhibit algae growth, and purify the water (Sharipo et al., 1975). At present, biological control methods mainly include planting vascular plants, microbial purification, food chain control technology, etc., from or through predation, competition for nutrients, mutual benefit, and other ecological relationships that directly or indirectly control algae growth (Quiros, 1998; Waajen et al.,



2016; Sun et al., 2018). The relationship between bloom-forming algae and other aquatic components has received great attention because they are reported to closely interact (Olrik et al., 1984; Elser et al., 2000). Different aquatic organisms interact through the transfer of matter and energy, contributing to ecological networks with complex relationships, such as predation, competition, symbiosis and so on (Schindler, 2006). The current biological control technologies tend to inhibit algae growth by targeting directly-related organisms, leading to the function of other related communities and the entire aquatic biological network being overlooked (D’Alelio et al., 2016). In fact, any organism at any trophic level may be affected by other organisms through predation, competition or mutually-beneficial effects (Lima-Mendez et al., 2015). During the biological control of lake bloom, all algae-related biological communities should be taken into account in application (Carpenter et al., 1996). First, system-level ecological network relations should be examined so one can take appropriate measures to inhibit algae growth with respect to the network integrity and sustainability (Šulčius et al.,

Corresponding author. E-mail address: [email protected] (D. Yuan).

https://doi.org/10.1016/j.ecolmodel.2018.07.020 Received 27 January 2018; Received in revised form 30 July 2018; Accepted 30 July 2018 0304-3800/ © 2018 Elsevier B.V. All rights reserved.

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It is a typical shallow lake, covering an area 293 km2, with an average depth of 0.8–1.5 m. It is in a typical temperate continental monsoon climate zone, with 200 mm of annual rainfall and 2300 mm of annual evaporation. Freshwater lakes in dry grasslands and deserts, such as Ulansuhai Lake, are rare. The lake provides important ecosystem services (e.g., fish supply, water and soil conservation, habitats) to the local region (Zhang et al., 2017). As it is downstream of the Hetao Irrigation District, the lake receives a large amount of nitrogen and phosphorus from farmland irrigation return water discharged from nearly 10 upstream channels each year. The ecosystem structure and function are continuously being degraded by nutrient inputs that exceed the water environmental capacity of the lake, resulting in algal bloom under appropriate conditions. Due to the relatively closed topology and low flow velocity of water bodies, a bloom-prone area is forming in the Xidayang region in Ulansuhai that covers about 10km2. The surface of the region is covered by aquatic plants, the two dominant species being Phragmites communis and Potamogeton pectinatus L. The average depth of the region is 1.2 m and water flow velocity is 0-0.7 m/ s.

2017). One of the current difficulties is in identifying the ecological relationship between dominant algae and related species. Networks are one tool that we can use to examine these ecological relationships. Ecological network analysis (ENA) was derived from economic input–output analysis (Leontief, 1966). It is assumed that the system is a network composed of a series of nodes (also called subdivisions or units) and connecting arcs. A specific analytical method, such as control or utility analysis, is used to study the direct and indirect effects of matter and energy flow in ecosystems, and to analyze the ecological relationships among different organisms and depict all of the characteristics of these ecosystems (Ulanowicz, 1997; Fath and Patten, 1999). One of the characteristics of this method is that it can include direct and indirect ecological processes in the system and provide a quantitative analysis and judgment on ecological relations among the system nodes (Fath and Borrett, 2006). ENA has been applied in many fields, such as ecology, society and economy, and has led to abundant research achievements, such as distributed control in ecosystems, global virtual water trade system, urban water metabolism and so on (Schramski et al., 2006; Zhang et al., 2010; Mao and Yang, 2011; Yang et al., 2012a; Zhang et al., 2016; Haak et al., 2017; Borrett et al., 2018). Based on the findings of ENA, we can comprehensively consider the direct and indirect ecological processes among populations, interpret and judge the ecological relations in ecosystems, and provide more complete and accurate information for ecosystem restoration and management. This study focused on the bloom-prone area in the Ulansuhai Lake, Inner Mongolia. Based on dynamic field monitoring and laboratory experiments in the field, a network model based on phosphorous nutrient cycling was constructed and quantified. The ENA was used to determine which population(s) directly or indirectly affected algal growth and to analyze the quantitative ecological relationships between the algae and related populations. Network-based biological control measures were proposed for the lake based on these results.

3. Network model construction and analysis 3.1. Network model construction Long-term monitoring results indicate that phosphorus is a limiting factor for algal bloom in this region (Cui, 2013). A network model containing 11 components was constructed based on the phosphorus cycle, see Fig. 2. The dominant algal genus in the lake is Chaetoceros, and it is accompanied by other filamentous green algae including Spirogyra, Zygnema and Mougeotiarotunda. Filamentous algae attach to submerged macrophytes and the sediment surface during the primary growth stage. With increasing biomass, it aggregates and floats up to the water surface with the aid of bubbles produced by photosynthesis. Based on the whole growth process of algae, we included microbes, zooplankton, zoobenthos, fish, water birds, detritus, sediments, submerged and emergent plants in the ecological network model. The ecological relationships resulting from the physical, chemical and biological processes are depicted in Supplementary material.

2. The research area Ulansuhai, China’s eighth largest lake, is situated in the Urad Qianqi, Bayannao’er City, Inner Mongolia (N40°36′∼41°03′, E108°43′∼108°57′), see Fig. 1.

Fig. 1. Location of the Ulansuhai Lake. 12

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Fig. 2. Network model of Ulansuhai Lake1-Phytoplankton; 2-Microorganism; 3-Zooplankton; 4- Zoobenthos; 5-Fish; 6-Water birds;7-Detritus(water); 8-Detritus (sediments); 9-Bottom sediments; 10-Submerged plant; 11-Emergent plants; z- boundary input; y-boundary output; f-interflows.

achieve steady state within a certain tolerance of 0.05% of throughflow at each node. Steady-state model data are shown in Table 1, where z is the boundary input of phosphorus (mgP/m2day−1); y is the boundary input of phosphorus (mgP/m2day−1); f represents phosphorus exchanges between the subsystems (mgP/m2day−1); and S is phosphorus storage (mgP/m2). The main data, parameters and their sources can be found in Supplementary material.

3.2. Data sources Model data and key parameters came from on-site monitoring and laboratory analysis (May–October from 2015 to 2017), published literature and historical data from local fisheries, and the Ulansuhai Bird Management Station. The Ecopath model is used for network construction and verification of the mass-balance model on the lake ecosystem (Christensen et al., 2005). The model requires balancing to Table 1 Quantified data matrix for the Ulansuhai network model. Component

1

2

3

4

5

6

7

8

9

10

11

z

1

0

0

0

0

0

0

0

0

0

0

2

0

0

0

0

0

0

0

4.59 (1.25) 3.71 (1.34) 0

0

0

0

0

2.60 (1.283) 0

0

12.94 (3.82) 9.50 (4.86) 5.06 (2.36) 7.34 (4.36) 22.5 (11.02) 0

5.58 (3.26) 0

3.72 (2.16) 0

3

0

0

0

0

0

0

0

0

0

0

0

12.49 (7.64) 5.3 (2.36) 11.35 (5.36) 0

0

0

0

0

0

18.61 (10.36) 10.83 (5.29) 0

0

0

0 10.2 (4.55) 0

10.62 (2.36) 9.55 (4.61) 41.87 (16.34) 0

0

8.49 (5.13) 0

1.03 (0.83) 9.51 (4.32) 7.07 (2.36) 14.26 (8.27) 0

59.04 (22.54) 8.61 (3.36) 4.34 (2.23) 14.9 (5.64) 14.36 (6.33) 0

2.89 (1.88) 19.21 (5.15) 0

6.03 (2.36) 15.4 (8.34) 0

0

0

0

0

0

0

10

5.42 (2.36) 0

0

0

0

0

11

0

0

0

0

0

y

0

0

0

S

198.50 (42.36)

62.04 (25.51) 22.10 (14.11) 52.9 (20.96) 1242.53 (521.61)

101.33 (45.66) 0

900.24 (211.36)

1710.74 (455.21)

4 5 6 7 8 9

0

0

0

48.65 (19.31) 0

0

0

17.79 (8.22) 7.02 (4.15) 0

0

0

0

0

43.09 (26.47) 0

0

0

0

0

0

0

0

0

0

57.49 (34.36)

86.06 (51.36)

31.98 (12.36) 111.40 (56.84)

11.8 (5.14) 254.51 (117.82)

0

28.81 (7.05)

22.60 (10.4) 75.44 (19.32)

88.45 (42.35)

Note: We present the mean value and standard deviation for each flow(mgP/m2day−1) and storage(mgP / m2). 13

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functional groups of the lake ecosystem. Each component in the ecosystem consists of two environs, one receiving flows (e.g., phosphorus in the current case) and one generating flows. The receiving flows are the terminus of all the interactions leading up to the component, and the generating flows from the component are the source of new interactions (Fath and Patten, 1998). The flow of matter and energy between different components can be understood as "utility" in economics. This transfer of utility forms various ecological relations among components. In the NUR, direct utility considers the direct interactions between compartments, while integral utility assesses the integral relationships that encompass both direct and indirect effects. Direct intercompartmental flow utilities (DU) are given by duij= (fij -fji)/Ti., where fij is a flow (e.g., P flow) from compartment j to compartment i; Ti is the sum off lows into or out of the i-th compartment when the system is at steady state. When the network indirect influence caused by indirect flows with longer pathways, the dimensionless integral utility intensity (IU) matrix is given by matrix U, which is computed as Eq. (9) (Fath and Borrett, 2006).

3.3. Ecological network analysis Derived from economic input–output analysis, ENA is used to analyze the direct and indirect relationships, the interdependence of different organisms, and the holistic attributes of ecosystems (Fath and Patten, 1999; Schramski et al., 2011; Borrett and Matthew, 2014). A number of important branches of analysis, such as embodied energy analysis, ascendency theory, and network environ analysis (NEA), have developed since ENA was introduced (Patten, 1978; Ulanowicz, 1980; Herendeen, 1981). NEA is widely used to determine the relations among system nodes and calculate the basic network properties of ecosystems. Here, we introduce NEA to investigate how different ecosystem components influence the algal populations in direct and indirect ways and provide novel insights into the control of algal blooms in Lake Ulansuhai. The network analyses were performed using the MATLAB function developed by Fath and Borrett (2006). 3.3.1. The network flow intensity The lake ecosystem can be represented as a network of nodes (components) and connections between them. The network can be further quantified by matrix boundary input Z=(zj), Boundary output Y=(yi), interflow F=(fij)(an observed flow from compartment j to compartment i) and Storage X=(xi). The sum of flows into or out of the n Ti(in) = ∑ j = 1 fij + z i and i-th compartment is throughflow

IU = (uij) = I + D1 + D2 + D3 + ···+Dm = (I-G)−1

where I is the identity matrix and U accounts for interflows overall pathways in the system of lengths 1,2,…,m. ΔU = ΣIU – ΣDU is a matrix that describes net utility value of each component in the network. ΔUi is sum of ith (i = 1……11) column elements of matrix ΔU, which reflects net output utility of component i, ΔU’j is sum of jth (j = 1……11) row elements of matrix ΔU, which reflects net input utility of component j. If we designate the sign of any element in DU and IU as SD and SI, respectively, for example, the subscripts SI12 and SI21 represent the flow of utility from Microorganism to Phytoplankton and from Phytoplankton to Microorganism, respectively, and this lets us understand network utility relationship between the two compartments. For example, (SI12, SI21) = (+, +) stands for mutual benefit, (SI12, SI21) = (-, -) stands for competition, (+, −) for predation and (0, 0) for neutrality between component 1 and component 2.

n

Ti(out ) = ∑ j = 1 f ji + yi , Ti(in) = Ti(out ) at steady sate. Nondimensional, output-oriented and input-oriented intercompartmental flows G =(gij) and G’=(g’ij) are given by fij/ Ti(in) and fij/ Ti(out ) ,respectively. G and G’ could be called direct flow intensity matrices. They are used to calculate the dimensionless integral output and input flow matrices N=(nij) and N’=(n’ij), which are can be computed as the convergent power series formulated by Eqs. (1) and (2), respectively. N and N’ reflects the integral flow intensity of the network (Fath and Borrett, 2006). N

=

integral

N′ = integral

I + G + G2 + initial

2

I + G′ + G′ + initial

direct

G3 + ...G m = (I − G )−1 indirect

direct

3

(1)

m

G′ + ...G′ = (I − G′)−1 indirect

(9)

(2) 3.3.3. Network control relationship (NCR) Each component of the system will input and output of matter-energy at the same time (Patten, 1978). This simultaneous input and output process will exert influence on other components and form the control relationship between components in an ecosystem. One way to consider internal control relationship in an ecosystem is measuring the control or dominance of one compartment over another via their input/ output environ. The NCR can be expressed as a control matrix CN= (cnij). The CN is calculated by the ratio of integral flow from compartment j to i to the integral flow from i to j. Compartment j is said to dominate i if its output effect on i is larger than is input effect on j (cnij=nij/nji > 1). This control relationship was further modified such that when cnij =nij/nji < 1, cnij = 1-nij/nji otherwise cnij = 0 (Fath, 2004). Based the matrix CN, some important network control indices have been utilized for interpretation and understanding of system flows (Schramski et al., 2006; Yang et al., 2012a; Chen et al., 2011, 2015; Chen and Chen, 2012). Inspired by their control measures, a holistic control intensity indicator (CI) was developed to explore how much a functional group influences by or on algae in the lake. The input control intensity CIiin and output control intensity CIiout are formulated by Eqs. (10) and (11), respectively.

Where I is the identity matrix, and m = 1, 2, …… account for interflows over all pathways in the system of lengths. A series of network properties, such as control and utility relationships, can be conducted to the basic model to develop insight into the interconnected functional nodes with ecological network. Input-oriented and output-oriented boundary flow intensity (BFI and BFI’), direct network flow intensity (DFI and DFI’) formulated by Eqs. (3), (4) and (5), (6). Input-oriented and output-oriented integral network flow intensity (IFI and IFI’) can be formulated by Eqs. (7) and (8).

′ BFIi = Gi0 ∑n (G )i BFI ′j = Gj0 ∑n (G′)j i=1 j=1

(3),(4)

′ DFIi = Gi ∑n (G )i DFI ′j = Gj ∑n (G′)j j=1 i=1

(5),(6)

′ IFIi = Ni ∑n (G)i IFI ′j = N j ∑n (G′)j j=1 i=1

(7),(8)

′ is boundary output of comGi0 is boundary input of component i, Gj0 ponent j. BFI (BFI’), DFI (DFI’) and IFI (IFI’) demonstrate how these metrics are just partitioning the flow of phosphorous through the ecosystem. BFI could address the importance of external inputs/ outputs for eutrophication in the lake. When compared with direct flow intensity matrices (DFI and DFI’), with IFI and IFI’, the ΔFI = IFI – DFI and ΔFI’= IFI’–DFI’ reveal the strength of indirect flows (i.e., flows for which m > 1) contributing to algal blooms.

CIiin = cni 1 ∑n (cn)i1 i=1

(10)

CI jout = cn1j ∑n (cn)1j j=1

(11)

n ∑i = 1

(cn)i1 (i = 1…n) is the integration of 1 st row elements in where n matrix CN and ∑ j = 1 (cn)1j (j = 1…n) is the integration of 1st column

3.3.2. Network utility relationship (NUR) NUR is utilized to reveal the mutual relationships between different 14

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elements in matrix CN. The above two formulations reflect the total import-oriented control and the total export-oriented control of component one (algae), respectively. Index CIiin reflects how much an individual component influences the lake algae through importing phosphorus and index CIiout reflects how much an individual component influences the lake algae through exporting phosphorus.

Table 2 BFI, DFI and IFI and ΔFI the lake network model.

BFI DFI IFI ΔFI BFI’ DFI’ IFI’ ΔFI’

3.3.4. Unit environ analysis (UEA) To distinguish the system boundaries from the boundaries of components, Patten (1978) considered each component is composed of both input and output Environs. Generating by system boundary input or output, each component establishes the internal connection through the input-oriented Environ flows and the output-oriented Environ flows. Unit Environ Analysis in NEA can depict the internal phosphorus flows and the boundary output (input) flow caused by one unit, dimensionless boundary input (output) (Fath and Borrett, 2006). NEA gives birth to several (equal to the number of system component) matrices formulated by Eqs. (12) and (13).

Ek = eji, k = (G − I ) × diag (N (:,i)) (output-oriented)

(12)

E’k = e‘ji, k = (G’ − I ) × diag (N‘ (:,i)) (input-oriented))

(13)

1

2

3

4

5

6

7

8

9

10

11

0.06 0.11 0.92 0.81 0 0.08 0.74 0.66

0 0.10 1.03 0.93 0 0.06 0.76 0.70

0 0.11 0.88 0.77 0 0.04 0.52 0.48

0 0.07 0.86 0.79 0 0.04 0.50 0.46

0 0.06 0.56 0.5 0.36 0.04 0.45 0.41

0.09 0.05 0.45 0.4 0.52 0.02 0.42 0.40

0.05 0.10 0.96 0.86 0.05 0.36 1.45 1.09

0 0.11 0.86 0.75 0 0.24 0.65 0.41

0.68 0.06 0.67 0.61 0.40 0.01 1.03 1.02

0 0.11 0.93 0.82 0 0.05 0.71 0.66

0 0.11 1.01 0.9 0 0.01 0.33 0.32

0.09 and the average IFI is 0.83; Component 1, 2, 3, 7, 8, 10 and 11 appeared same level of flow intensity in DFI, while the IFI revealed that component 2, 7 and 11 have the strongest flow intensity in the system. In output direction, the average DFI’ is 0.09 while the average IFI’ is 0.78. Component 7 and 8 appeared the highest flow intensity in DFI’, while the IFI’ revealed that component 7 and 9 ranged the highest flow intensity in the system. Besides, BFI and BIF’ indicate external inputs and outputs exert important influences on eutrophication in the lake, especially the boundary phosphorus output.

K-the kth component; diag (N (:,i)) is a functional function, a new matrix with the primary diagonal element is the i-th column element in the matrix N and the non-primary diagonal element is zero. Ek =eji,k refers to the flow of node j → i caused by a unit of phosphorus input of component k; E’k= e’ji,k refers to the phosphorus flow of component j → i caused by maintaining a unit of phosphorus output of component k. Here, we used SEk (k = 1……11) to summarize integral interflows intensity of component i (i = 1……11) that generated by a unit phosphorus input of component k (k = 1, 6, 7, 9) and SE’k (k = 1……11) was used to summarize integral interflows intensity of component i (i = 1……11) generated by a unit phosphorus output of component k (k = 5, 6, 7, 9).

4.2. Switching roles for network control The network control matrix is shown in Table 4. The integral control results show that no component has absolute control in algae in either direction. In relation to input, algae are controlled by components 3, 4, 5, 6 and 8, indicating that algae are major sources of phosphorus for zooplankton, zoobenthos, fish, and even birds. The input control intensity (CI) on component one (algae) is ranged as: CI5 (28.1%) > CI6 (25.2%) > CI3 (17%) > CI4 (16.5%) > CI8 (13.2%). In relation to output, components 2, 7, 9, 10 and 11 is controlled by algae, indicating that microorganisms, debris (water bodies), sediments, emergent and sub-emergent plants are important phosphorus sources of algae. The output control intensity (CI’) on component one (algae) is ranged as: CI’10 (31.4%) > CI’11 (28.7%) > CI’9 (20.6%) > CI’7 (10.2%) > CI’2 (9.1%). The input or output control of other function groups is also multitargeted. For example, fish not only exert output-oriented control on algae, but also exert varying degrees of control over all components except birds. It is noteworthy that emergent plants, sub-emergent plants, detritus and sediments are the most important output-oriented controllers, and they are important providers of phosphorus to algae. Benthic animals, fish and birds are the most important input-directed controllers of algae as they store large quantities of phosphorus through predation.

3.3.5. Uncertainty analysis There is unavoidable uncertainty in model flows and storages. Uncertainty analysis is important since it can show how variability inherent in model parameters affects model outcomes. In recent years, a number of valuable examples have conducted to ecological network models, such as comparative coefficient of variation (CV) (Borrett and Osidele,2007; Kaufman and Borrett, 2010), linear inverse modelling (LIM) (Guesnet et al., 2015; Hines et al., 2015, 2016, 2018), which continues to mature for ENA use in informing decisions. Here, we conducted uncertainty analysis through two comparative network models. These two comparative models (M1 and M2) were constructed by the mean flow value ± SD (M1:mean flow value-SD; M2: mean flow value + SD). Indirect flows are key focus in the current case study, we focused on CI, IFI and ΔU indicators that capture the indirect flows and indirect influences. By comparing these indicators of three models (one base model M0 and two comparative models M1 and M2), we try to detect much uncertainty brings to model results.

4.3. Changed flow utility and ecological relationships A comparison of direct utility and overall utility intensity is shown in Table 3. From the change in net utility (ΔU = ΣIU – ΣDU), the overall utility of the system increased from ΣDU = 0.152 (direct utility) to ΣIU = 0.343 (integral utility), indicating that the lake system presented a mutually beneficial universal relationship, i.e. the system components were mutually supportive. Certainly, this mutual benefit may also promote algae growth and bloom. In view of ΔU and ΔU’, component 7 and component 2 are the largest utility net output component (ΔU7 = 0.161) and net input component (ΔU’2 = 0.052), respectively. With respect to net utility variation the algae, components 3, 4, 5, 6, 7, 9, 10 and 11 showed increased import utility from algae, while components 2 and 8 showed decreased import utility from algae. The algae decreased their integral output utility to components 2, 3, 4, and 8, and increased their integral output utility to components 5, 6, 7, 9, 10, and 11. There are 62 components with increased integral utility and 48

4. Research results 4.1. Changed system flow intensity The direct and integral flow intensity based on network analysis is shown in Table 2. Considering direct and indirect relations in the system, the algae is a strongly connected component in the network, and all nodes are reachable from all other nodes in the integral matrix. All components were involved in the growth of component one (algae) and had a direct or indirect impact on lake bloom. T-test was utilized to test significant difference between IFI and DFI. Results indicate the IFI (IFI’) of system component was significantly higher than the DIF (P < 0.01). In input direction, the average DFI is 15

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Table 3 Comparison of DU and IU(×10−2).

Note: 0.0 indicate a value smaller than 0.001. The gray square stands for a changed element sign in IU comparing with its corresponding sign in DU.

between components 4 (zoobenthos) and 10 (Submerged plants) changed from neutral (SD1,11, SD11,1) = (0, 0) to (SI1,11, SI11,1) = (0, −) to amensalism.

Table 4 Network control matrix. CN

1

2

3

4

5

6

7

8

9

10

11

1 2 3 4 5 6 7 8 9 10 11

0 0.19 0 0 0 0 0.21 0 0.42 0.64 0.58

0 0 0 0 0 0 0.01 0 0.47 0.63 0.68

0.33 0.45 0 0 0 0 0.24 0 0.54 0.70 0.74

0.32 0.41 0.10 0 0 0 0.28 0 0.55 0.72 0.76

0.55 0.57 0.59 0.61 0 0 0.51 0.24 0.73 0.84 0.82

0.50 0.42 0.48 0.47 0.48 0.00 0.37 0 0.64 0.79 0.78

0 0 0 0 0 0 0 0 0.60 0.63 0.68

0.26 0.03 0.11 0.12 0.00 0.05 0.38 0 0.00 0.33 0.40

0 0 0 0 0 0 0 0.09 0 0 0

0 0 0 0 0 0 0 0 0.60 0 0.11

0 0 0 0 0 0 0 0 0.54 0 0

4.4. Integral Interflows Intensity generated by unit boundary flows The addition or removal of one unit of phosphorus at the boundary caused a much greater change in integral interflow intensity, as shown in Fig. 3. On the whole, one unit of boundary phosphorus input drove an average of 7.8 ± 1.37 units of phosphorus interflow intensity, while one unit boundary phosphorus output drove an average of 8.03 ± 1.86 units of phosphorus interflow intensity. In generally, components 7 presents higher sensitive to both unit boundary input and unit boundary output. One unit of boundary input into component 1, 5, 6 and 7 will result in 2.26 ± 0.45, 1.28 ± 0.22, 1.32 ± 0.25 and 3.04 ± 0.66, respectively, units of integral interflow intensity in component 7, respectively. One unit of boundary output from component 5, 6, 7 and 9 will result in 2.6 ± 0.55, 2.10 ± 0.18, 3.04 ± 0.58 and 0.91 ± 0.15, respectively, unit of integral interflow intensity in component 7. The integral interflow intensity of other component depends on specified boundary input or output components. For example, component 5 is more sensitive to boundary input and component 9 is robust to boundary output. Focusing on the algae, it was found that one unit of boundary input on algae will generate 8.68 ± 2.25 units of phosphorus interflows in the system. The largest interflows, as mentioned above, are 2.26 ± 0.45 units of component 7. One unit of boundary output from component 5, 6, 7 and 9 will result in 0.77 ± 0.15, 0.70 ± 0.14, 0.64 ± 0.11 and 0.24 ± 0.05, respectively, unit of integral interflow intensity in component 1. These results indicate that input or output of phosphorus at the boundary is not the only factor controlling algal bloom. Endogenous phosphorus is also a key factor.

components with reduced integral utility. Depending on the extent to which utility varies, the pair-wise ecological relations between algae and it related groups can be divided into three categories: (1) Changed from (0, 0) to = (−, −), indicating that the relationship changed from a neutral ecological relationship to a network competition relationship. For example, the relation between component 1 (algae) and component 2 (microbes) changed from neutral (SD12, SD21) = (0, 0) to (SI12, SI21) = (−,−). (2) Changed from (0, 0) to (+. +), indicating that there is no direct relationship between the two components, but in fact the two components are mutually beneficial. For example, the relationship between components 1 and 10 changed from neutral (SD1,10, SD10,1) = (0, 0) to (SI1,10, SI10,1) = (+, +) mutual benefit. Due to the decay of emergent plants, a large amount of phosphorus was rereleased into the water body, which provided a source of phosphorus to the algae (Cui, 2013). (3) Changed from (0, 0) to (0, −), for example, the relationship 16

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5. Discussion Network analytics has become an increasingly important support for ecosystem management (Yang et al., 2017). In this study, we applied throughflow analysis, utility analysis, control analysis and unit environ analysis to study the process of phosphorus flux and reveal the relationships between algae and their related populations in the Ulansuhai Lake. Network analysis results show that there are obvious differences between the results of direct observation and network analysis. This difference reminds us that it is very important to implement ENA to ecological management at a system-wide and holistic level. In the specific case analyzed, the overall flow intensity matrix showed that all components were involved in algal metabolism and exerted direct or indirect effects on algal blooms. Thus, not only directly-related species but also indirectly-related components should be considered in the processes of biological control of algal bloom (Schindler, 2006). For example, algae fish, zooplankton is generally added to control algal growth, but microbes are in fact a significant competitor of algae. Microbes population has a significant positive correlation with phosphorus in water, which acts as a nutrient competitor of algae. Similarly, there is no direct relationship between algae and wetland plants, and they are mutual competitors for nutrition. However, network utility analysis results showed that they are mutual benefactors. It is likely that the decay of wetland plants releases a significant amount of phosphorus into the water, providing a large degree of endogenous phosphorus sources to algae (Zhao et al., 2017). Therefore, improper biological control techniques, such as planting wetland plants without harvesting them, may increase the risk of algal bloom. In addition, merely controlling the input of exogenous nutrients may not be decisive for algal bloom management (David et al., 2008). The algae exerted output-oriented control of zooplankton, zoobenthos, fish, and even birds; sediments, emergent and sleeping plants were important sources input-oriented algal control. The results of unit environ analysis demonstrated that boundary input and output effect the flow and circulation of phosphorus in the system. However, system storage itself will also participate in the phosphorus cycle and have an important impact on the algal bloom. Therefore, to control algal bloom in the lake, algae control and salt reduction should be carried out based on ecological relationships and phosphorus storage in the system (for example, submerged plants may have accumulated over many years, forming an important sediment phosphorus pool for aquatic organisms (Wei et al., 2016). A large amount of phosphorus is released from sentiments disturbed by strong winds. The phosphorus and other nutrients released readily diffuse to the surface in shallow water and stimulate the rapid growth of algae (Fu et al.,2013). In summary, the key species controlling harmful algae are not only fish and zooplankton, but also phosphorus-containing bacteria and benthic animals. Zoobenthos plays an especially important role in controlling the early biomass of harmful algae (Lindegaard, 1994). When nutrient control is being planned, sediment phosphorus, bird excretion, and wetland plant decay processes should be considered, as they are important sources of nutrients for algae. The ENA incorporate both input-dominated and out-dominated influence that reveals both top-down and bottom-up ecology. Of course, there is a certain degree of uncertainty in the results of the current model. The complexity of the ecological network means that the interactions between the components evolve over time. For example, an algal bloom may produce negative effects on the diversity and community composition of water microbes (Yang et al., 2012b; Berry et al., 2017). The structure and function of the system with and without algal bloom also vary dynamically (Turner and Chislock, 2010; D’Alelio et al., 2016). In addition, algae blooms are affected water temperature, flow rate, wind speed, pH value, light intensity and even atmospheric deposition (Paerl, 1997; Xie and Xie, 2002). A combination of the physical and chemical characteristics of water and uncertainty analysis-based ENA results should be carried out in future research

Fig. 3. Integral interflow intensity generated by boundary flow. (a) boundary input (b) boundary output. 1-Phytoplankton; 2-Microorganism; 3-Zooplankton; 4- Zoobenthos; 5-Fish; 6-Water birds;7-Detritus(water); 8-Detritus(sediments); 9-Bottom sediments; 10-Submerged plant; 11-Emergent plants. SEk (k = 1…… 11) summarizes integral interflows intensity of component i (i = 1……11) that generated by a unit phosphorus input of component k (k = 1, 6, 7, 9);SE’k (k = 1……11) summarizes integral interflows intensity of component i (i = 1……11) generated by a unit phosphorus output of component k (k = 5, 6, 7, 9).

4.5. Uncertainty analysis In general, a certain degree of uncertainty appeared in analysis results due to uncertainty of model data or parameters. The average values (ΔFI, CI and ΔU) with their standard deviation of three models are shown in Fig. 4. The IFI is obviously larger than DFI in both input direction and output direction (Fig. 4(a) and (b). Variation intensity was changed due to model uncertainty, but the variation trend remains unchanged. The average values of input-oriented and output-oriented flow intensity variation of three models are 0.59 ± 0.15和0.50 ± 0.15, respectively. Dominant control components remain unchanged in uncertainty analysis Fig. 4(c) and (d). Component 5, 6, 4, 3, 8 and 11, 10, 9, 7, 2 are, respectively, input-oriented and output-oriented controllers of blooming algae. However, there are different orders in CI values of three models. For example, component 5 and component 10 are, respectively, the strongest input-oriented and output-oriented controllers of algae in W1. However, component 6 and 11 are, respectively, the biggest input-oriented and output-oriented controllers in W2. A similar trend can be found in ΔU and ΔU’ depicted in Fig. 4(e) and (f). The integral utility is greater than direct utility in three models. The net utility variations of three models are +0.19, +0.44 and +0.99, respectively. When variation intensity of each component is considered, we found the maximum net utility output is changed from component 7 in WS1 to component 6 in WS2 and WS3. A system component (e.g., component 2, 4, 10 and 11) with small throughflow is susceptible to model uncertainty. For example, the ecological relationship between component 2 and 11 is changed from (-, -) in WS1 to (-, +) in WS3. Component 11 has the lowest throughflow low in three systems.

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Fig. 4. Results of Uncertainty analysis (a) input-oriented flow intensity variation (b) output-oriented flow intensity variation (c) input-oriented control intensity (d) output-oriented control intensity (e) input-oriented net utility variation (f) output-oriented net utility variation.

be done to highlight the application of ENA to the biological control of the algal blooming in eutrophic lake, such as dynamic assessment and unbalance condition assessment. More case studies should address for system-oriented simulation of real ecosystems.

aiming to better control algal blooms.

6. Conclusions To inform management of the algal blooming Ulansuhai Lake, China, we constructed a phosphorous food-web model for algal communities based on field monitoring data and literature information. ENA was applied to detect how much indirect flows influence alters network control and utility relationships between dominant algal species and its related species. Main conclusions are as follows:

Conflicts of interest The authors declare no conflict of interest.

Acknowledgement

(1) The integral flow intensity is obviously larger than direct flow intensity (P < 0.01), indicating indirect flows dominated the model flow configuration of the Ulansuhai Lake. (2) No component has absolute control on algae in system. Fish and Submerged plants are found to be, respectively, the largest inputand output-oriented controllers on blooming algae species. (3) Competition, mutually beneficial and amensalism appeared in the network relationships between algae and its related components, which are different from direct perceived observation that only direct flows are considered.

This work was supported by the National Natural Science Foundation of China (51669028 & 51409144) and the State Key Research and Development Program (2016YFC0501906-01) and Natural Science Basic Research Project in Qinghai Province (2018-ZJ712). Thank support and assistance from Ulansuhai Bird Management Station and Fisheries during the course of data collection.

Appendix A. Supplementary data

Integral flow configuration in ecosystems may differ from empirical observation when important and even dominant indirect flows are involved. ENA can capture the indirect flows and it is important to incorporate ENA into management decisions. There is still much work to

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ecolmodel.2018.07. 020. 18

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