Simulation of a forest-grass ecological network in a typical desert oasis based on multiple scenes

Ecological Modelling 413 (2019) 108834

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Simulation of a forest-grass ecological network in a typical desert oasis based on multiple scenes

T

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Kai Su, Qiang Yu , Depeng Yue, Qibin Zhang, Lan Yang, Zhili Liu, Teng Niu, Xiaoting Sun Beijing Key Laboratory for Precision Forestry, Beijing Forestry University, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Multi-scene simulation Ecological resistance FG eco-network Desert oasis area Ecological gravity

Many areas on the earth are facing the threat of ecological degradation, and unreasonable economic development has further exacerbated environmental problems. Constructing ecological security patterns based on an ecological network is such an integrated approach to protecting regional ecological sustainability. This paper selected Denko County of Bayannaoer City, Inner Mongolia, which is located in the desert oasis area, as the study area. Ecological sources were identified through ecosystem importance assessment, and interaction force theory was used to model ecosystem processes in heterogeneous landscapes via comparing the minimum cumulative ecological resistance and the maximum ecological gravity between ecological sources, and thus to identify ecological corridors and to constructing forest-grass ecological network (FG eco-network) of the study area. This paper also set 11 development scenarios to study the impact of different development strategies on the FG ecological network. Moreover, the complex network theory was used to analyze the topology and statistical characteristics of the FG eco-network in 11 development scenarios. The results show that the ecological network in the study area has been gradually destroyed with the increase of the proportion of economic development in the development scenario; the connection between some ecological patches has changed and the connectivity and coreness, PageRank, etc. have gradually decreased. The study found that the network density increases slightly in the (0.9, 0.1) mode, while it gradually decreases in other scenarios with the increase of the proportion of economic development. Moreover, in the (0.9, 0.1) mode, although the destruction occurred at the edge of the desert, the FG eco-network within the sample circle expanded. This represents that economic development does not necessarily lead to the deterioration of the ecological environment in this area. Under the existing natural conditions, the study area still has room for economic development, but space is limited. Based on the interaction force theory, this study provides a new approach to identifying the ecological network. Also, the development scenarios set up in this study can provide a reference for sustainable development.

1. Introduction The ecosystem is the material basis for human survival and development. It is also the life support system of human beings (Hooper et al., 2005). The survival and development of human beings depend on the stability and health of the ecosystem. However, over the past century, the scale and intensity of land use and exploitation have increased dramatically, particularly in arid regions. This increased exploitation eventually led to serious damage to the structure and function of ecosystems, resulting in a series of ecological problems, such as a sharp decline in biodiversity (Myers et al., 2000), soil degradation (Zika et al., 2017) and desertification. In arid regions, as the transition zones between water-deficient deserts and water-rich farmland systems, desert-

oasis ecotones play a key role in ensuring regional ecological security and maintaining regional internal stabilization. However, they are also fragile systems and can be modified easily by development strategy changes (Sawut et al., 2013). For a long time, the ecological security and sustainable development of desert oasis ecotone have been the focus of scientific research. The ecological security pattern in arid areas is of great significance for maintaining regional ecological security and sustainable development. As a key component of regional ecological security pattern, the ecological network connects isolated landscape patches through ecological corridors. From the perspective of the ecological network, the process of land desertification is the process of destroying ecological nodes and ecological corridors in ecological networks (Zhang et al.,

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Corresponding author. E-mail addresses: [email protected] (K. Su), [email protected] (Q. Yu), [email protected] (D. Yue), [email protected] (Q. Zhang), [email protected] (L. Yang), [email protected] (Z. Liu), [email protected] (T. Niu), [email protected] (X. Sun). https://doi.org/10.1016/j.ecolmodel.2019.108834 Received 5 May 2019; Received in revised form 28 September 2019; Accepted 2 October 2019 0304-3800/ © 2019 Elsevier B.V. All rights reserved.

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is very important for the evolution simulation of the ecological network. The ecological network is complex. Analysis of the ecological network using the complex theory is one of the frontier directions in this field (Hoctor et al., 2000). Therefore, this paper took the typical desert-oasis ecotone in Northwestern China as the study area, using the gravity model and minimum cumulative resistance (MCR) model to extract the forest-grass ecological network (FG ecological network) in the study area. Ecological sources which were identified through ecosystem importance assessment. The minimum cumulative ecological resistance between ecological sources was calculated by using the improved ecological resistance model, and the improved ecological gravitation model was used to calculate the ecological gravitation between ecological sources. Based on the interaction force theory, the ecological corridor is identified by comparing the resultant forces among different ecological sources one by one. Additionally, a variety of scenarios of development strategies have been set up, by adjusting the proportion of economic and ecological protection in the development strategy the impact of different development strategies on the ecological network and the possibility of economic development in the study area have been explored. The topology analysis and spatial structure analysis of the forest and grass ecological networks in the study area were analyzed using complex network analysis methods. This paper hopes to provide a new method for the simulation of ecological network and a new perspective for the study of economic development and ecological environment protection in arid areas, to provide a reference and guide for arid areas ecological protection policy-making and planning, and also to provide theoretical support for sustainable development in arid areas.

2009). In this process, the integrity and ecological functions of the network structure are continuously declining, and the channels of material and energy transmission in the network are gradually blocked (Ishiyama et al., 2018). The ecological network connects the isolated ecological patches in the desert-oasis ecotone through linear corridors, which constitute a spatial ecological security pattern that combines “point, line, and surface,” enhancing the ability of landscape self-regulation, maintaining the stability of the regional ecological environment and resisting the risk of desertification (Niu, 2009). At present, the construction of an ecological network has formed one research paradigm, including the identification of the ecological source, and ecological corridor. It mainly identifies the ecological source by assessing ecological suitability, ecological importance, or ecological connectivity (Su et al., 2016; Teng et al., 2011; Zhang et al., 2016). Among these methods, ecological importance evaluation is the most common (Li et al., 2010; Liang et al., 2018; Lin et al., 2016). The method of identifying ecological corridor through constructing the resistance surface (Mcrae, 2006), which is commonly based on the value assignment of land cover (Keeley et al., 2016). Additionally, the leastcost path analysis is often used to extract ecological corridors (Adriaensen et al., 2003; Doetzer Rosot et al., 2018). Although these studies considered the hindrance of landscape heterogeneity to ecological flow, they neglected the attraction between ecological sources and did not consider the interaction between ecological sources. The application of gravitational model in spatial interaction provides a way of thinking for studying the interaction between ecological sources in this paper. The gravitational model was first applied to the study of spatial structure between cities (Cao et al., 2018), and was later widely used to study regional economy (Meng and Lu, 2011), urban group interaction (Chen, 2009), and inter-city trade studies(Gordillo et al., 2010). At present, gravity models have been widely used in the study of regional economic linkages or spatial interactions. Based on the interaction force theory, the improved MCR model and gravity model are used to extract the FG ecological network of the study area by comparing the minimum cumulative ecological resistance and the maximum ecological gravity between ecological sources. However, the ecological network is dynamic. It is a fragile system, which is affected by both natural conditions and economic activities (Fath et al., 2017). Different development strategies will have different impacts on the ecological environment, whether economic priority or ecological priority. Policymakers need to use scientific and evidencebased scenario models to study regional characteristics and fully assess the impact of different development strategies, whether they focus on economic growth or environmental protection. Sustainable development can be achieved by formulating development strategies adapted to local conditions. In arid areas, the contradiction between economic development and ecological development is dualistic and difficult to coordinate (Christina et al., 2012). Economic activities are the primary obstacles to ecological development. Therefore, based on the current state of the natural environment, by adjusting the proportion of economic development and ecological protection in the development strategy, namely: increase the proportion of the economy, reduce the proportion of ecological protection. To explore the impact of different development strategies on ecological networks and the possibility of economic development in the study area. Therefore, it is of great significance to simulate the ecological network of the study area in multiple scenarios. This paper set up a variety of development scenarios to simulate and analyze the evolution characteristics of the current ecological network. It is not only helpful to explore the development strategies suitable for arid areas but also helpful for policy makers to adjust the relevant planning and social development strategies in the region to promote regional sustainable development. However, the evolution of the ecological network is different from the evolution of land use. It is not only affected by the ecological matrix, but also by the interaction between ecological patches. Therefore, a quantitative description of the interaction between ecological patches

2. Materials and methods 2.1. Study area The study area (106 7′–107 23′ E, 39 56′–40 56′ N) in this paper is located on the northeastern margin of the UlanBuh Desert, with an area of 5923.9 km2. The majority of the study area was within a range of 968–1338 m above the average sea level. The eastern part of the study area is China's mother river, the Yellow River; the northeastern plain is the famous Hetao Plain, which is an important grain-producing area in China; the west side of the study area is the Wolf Mountain, and its southwest is the hinterland of the UlanBuh Desert. According to the meteorological data for 1983–2016 from the Denko Desert Ecological Station, which belongs to the national forestry and grassland Bureau of China, the annual average precipitation in the region is 144.5 mm, while the average annual evaporation is 2398 mm. The location of the study area is shown in Fig. 1. 2.2. Data and processing The remote sensing image of Sentinel 2 was used in this paper, with a resolution of 20 m. The dataset was provided by the European Space Agency (http://www.esa.int/ESA). Image preprocessing was performed by radiometric calibration, atmospheric correction, image enhancement, and geometric calibration. The maximum likelihood supervised classification method was used to classify the remote sensing image and extract the land use in the study area. The water network and road network were extracted from land use data, and distance factor data were obtained by multiple buffer analysis. The residential area, road network, and water network were extracted from the land use data, and density factor data were obtained by density analysis. The Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Temperature Vegetation Drought Index (TVDI) were extracted from the processed images. The DEM was provided by the Geospatial Data Cloud, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn). Soil 2

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Fig. 1. Location of the study area.

The weights of the patch area, NDVI average, MNDWI average, and shape index were determined by entropy method, and the ecosystem importance of each landscape patch was evaluated comprehensively. Then, according to the statistical distribution characteristics of the degree of importance of patch ecosystem, the top 60% of the patches were selected as the ecological sources.

data set was provided by Cold and Arid Regions Sciences Data Center at Lanzhou (http://westdc.westgis.ac.cn). The ecological-life-production land spatial planning data was obtained from the Denko County Ecological Environment Bureau (http://www.nmgdk.gov.cn/). The spatial distribution data of groundwater depth were derived from the research results of Ma Huan, which are the preliminary research results of this study (Ma et al., 2017).

2.3.1.2. Interaction between ecological sources 2.3. Method

a ecological resistance The minimum cumulative resistance model (MCR) was proposed by the Dutch ecologist Knappen (Knaapen et al., 1992; Li et al., 2015). This model was first applied to the diffusion process of species, and it can describe the difficulty of species crossing different habitat patches, and study the horizontal resistance of species to ecological processes. It has since been widely used in landscape pattern analysis to study the effects of landscape heterogeneity on ecological flow (Albanese and Haukos, 2017; Beier et al., 2008; Spear et al., 2010). The model can be used to measure the magnitude of ecological resistance quantitatively (Marrotte et al., 2014). The formula is:

2.3.1. Identifying ecological network The essence of identifying ecological network is to construct a pattern that contains an integrated network of ecological sources, corridors, which can well guarantee regional ecosystem services and ecological processes. Three steps are included in identifying the ecological network in this study. The first was to identify ecological sources based on the assessment of the importance of the ecosystem. The second was to extract ecological corridors based on interaction force theory. The third was to simulate the ecological network. The specific framework was shown in Fig. 2.

i = m, j = n

2.3.1.1. Extracting ecological sources. The ecological source is high quality and quantity of key ecological patch that provides a variety of ecosystem services, maintains the stability of the ecosystem, and plays an important role in preventing ecosystem degradation (Martin et al., 2005). Therefore, ecological sources can be identified by evaluating the importance of the ecosystem. This study selected three kinds of landscape types of forest land, grassland, and water, which have important ecological significance to the study area. The ecological source was identified by the size of the patch, the NDVI means, the MNDWI means, and the shape index. Patch area was calculated by using ArcGIS software (ESRI, Inc., Redlands, CA, USA), NDVI average and NDWI average were calculated by using Zonal Statics tools, and patch shape index was calculated by using FRAGSTATS (Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http:// www.umass.edu/landeco/research/fragstats/fragstats.html).

VMCR = fmin

∑ i = 1, j = 1

(Dij RI ) (1)

Where VMCR represents the value of the minimum cumulative resistance surface; fmin represents the function of the minimum resistance value; Dij represents the spatial distance from the source to the landscape unit, and RI represents the coefficient of resistance in the process of source movement. The MCR model only considers the distance between the ecological source and the matrix characteristics when calculating the resistance. This paper improved the MCR model based on the characteristics of the study area. We believe that different sources have different ecological energies, and their ability to radiate and transmit energy is not only related to their area, but also the type and shape of the source. At present, some scholars have conducted 3

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Fig. 2. Framework for identifying Ecological Network.

the ecological source expansion; and mi represents the ecological quality of the source i. b ecological gravity The Gravity Model is a model that studies the interaction of two objects or spaces, which is derived from Newton's law of universal gravitation (Katsikadelis, 2018). In the 19th century, scholars introduced the gravity model into the study of geospatial space when studying the interaction of urban systems (Liu et al., 2014). Later, geographers conducted extensive research on the gravity model (Pekkanen et al., 1997) and used it to analyze the interactions between cities or countries (Bergstrand, 1985; Yang and Wong, 2012; Zhang, 1989). In 2010, Keum demonstrated the usability of gravity models in international trade and tourism (Keum, 2010). In this paper, we try to introduce the gravity model into the landscape ecology field and calculate the ecological gravity between the ecological sources. Because of the differences between the research fields and objects, we modified the model so that it can be applied to landscape ecology. Ecological gravitation is inversely proportional to the square of the minimum cumulative cost distance between them and is proportional to the quality of the source. The ecological gravitational model is as follows:

research and discussion on this (Yu et al., 2017). The size of the ecological source will have a strong influence on the material exchange or energy flow (Urban et al., 1987), and its edge can also affect the material, energy, and future species distribution (Ewers et al., 2007; Ribeiro et al., 2016). Therefore, this paper uses a shape index (Li ), NDVI (Defries and Townshend, 1994) and MNDWI (Liu et al., 2016) to describe the characteristics of the ecological source. Because the study area is located in an arid area, the importance level of the blue source (river, lake) should be higher than that of the green source (forest, grassland). Moreover, we proposed the concept of ecological source quality to quantitatively evaluate the ecosource. The definition of the quality of the ecological source is that any ecological source has the property of radiating outward, transmitting energy or receiving energy, and its quality of ecological resources is related to the area, shape, and attributes of the source.

Li =

Ei 2 πAi

(2)

Where Li represents the shape index of the ecological source i; Ei represents the perimeter of the ecological source i, and Ai represents the area of the ecological source i.

mi = Ai Li I¯i

Fij = G

(3)

Where mi represents the quality of the ecological source i; Ai represents the area of the ecological source i; Li represents the shape index of the ecological source i, and I¯i represents the average of the normalized index of the ecological source i. The improved MCR model considers three factors: ecological source quality, distance, and matrix characteristics. The improved formula is:

∑ i = 1, j = 1

Dij2

(5)

Where Fij represents the ecological gravitation between the source i and the source j; Dij2 represents the spatial distance between the source i and the source j; αi, αj represents the type of the ecological source i, j; mi , mj represents the ecological quality of the source i, j; and G represents a constant and is generally expressed as 1(Grosche et al., 2007; Jiang et al., 2014). 2.3.1.3. FG eco-network simulation. Through eco-networks, a more stable ecological pattern has been established in arid regions and maintained regional ecological security (Xue et al., 2013). In the ecological network, an ecological source is a spatial unit or ecosystem that provides various substances and energy, and the ecological corridor is an important channel for energy and material flow. The

i = m, j = n

VMCR = fmin

αi mi ∙αj mj

(Dij Rj mi ) (4)

Where VMCR represents the minimum cumulative resistance value of 4

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Fig. 3. Schematic diagram of the interaction forces between ecological sources.

model of the study area can be regarded as a development model based on ecological protection. The development strategy of each development scenario is defined as 1. The proportion of ecological protection is expressed in m, while n represents the proportion of economic development, so that m + n = 1. When m = 1.0 and n = 0 (1.0, 0) represents the Current development mode, that is, the development strategy of the research area with the priority of ecological protection. When m = 0 and n = 1.0, (0, 1.0) represents a purely economic development mode, that is, the study area prioritizes the development of the economy. By changing the weights of N and m, 11 developmental scenarios were obtained: (1.0, 0), (0.9, 0.1), (0.8, 0.2), (0.7, 0.3), (0.6, 0.4), (0.5, 0.5), (0.4, 0.6), (0.3, 0.7), (0.2, 0.8), (0.1, 0.9), (0, 1.0).

interaction between ecological sources will engender ecological gravity, but at the same time, it will also be subject to resistance from the matrix, as shown in Fig. 3. Detailed algorithm steps of the eco-network multi-scenario simulation model are as follows: (1) Calculate the maximum ecological gravity and minimum cumulative resistance between ecological sources. When the ecological gravity is greater than the ecological resistance, an ecological corridor will be formed between the sources, and the ecological flow will be transmitted between the sources through the corridor. When the ecological gravity is less than the ecological resistance, there will be no ecological corridor between the sources. When the ecological gravity equals the ecological resistance, the ecological corridor between the sources will be very fragile, and slight changes in the matrix may lead to ecological corridor fracture. (2) The calculation will not stop until the entire FG eco-network source traverse, as well as the FG eco-network spatial distribution grid simulation results, are finally obtained. Then, the spatial distribution of the FG ecological network in the next development scenario is simulated, until all scenarios are simulated.

2.3.3. Eco-network performance analysis In this paper, six graph theory indicators for measuring network topology and node importance in complex network analysis were used to reveal the connection characteristics of FG ecological networks of various development strategies, which could provide an important reference for the optimization of FG ecological networks. The performance of an ecological network is analyzed from network integrity and importance of nodes. The characteristics of the network are evaluated by four indicators: connectivity (c(G ) ) (Shanahan and Wildie, 2012), coreness (k(G ) ) (Li, 2013), density (d(G ) ) (Lancichinetti and Fortunato, 2012), average number of nodes connected a(G ) . Betweenness (Bi ) and PageRank (Pi )evaluate the importance of nodes (Liu et al., 2017). (1) c(G ) c(G ) represents the connectivity of a complex network, including point-connectivity and edge-connectivity. Generally speaking, the higher the connectivity of a network, the better its stability. This paper focuses on the point-connectivity of eco-network, and its definition is given below.

2.3.2. Multi-scenario development program Most scholars focus on the study of future development scenarios (Kraxner et al., 2013). However, few studies have focused on current natural conditions. Due to the impact of different development strategies on the ecological environment, whether to develop economy first or to protect ecology first, especially at the turning point of current urbanization, population growth, economic prosperity and environmental protection (Hartmann, 1998). It is necessary for policymakers to use scientific and evidence-based scenario models to guide future social development and to fully assess the impact of different development strategies, whether they focus on economic growth or environmental protection (Koricheva and Kulinskaya, 2019). The ecological environment of the study area is extremely fragile and is an important area for desertification control. Moreover, the Chinese government has promulgated the strictest ecological protection policy, imposing strict restrictions on the economic development of the region, which mainly focuses on improving and protecting the ecology. Since the implementation of the policy is uniform, it is often not tailored to local conditions (Cheng et al., 2000). Based on the current natural environment situation, this study explored the impact of different development strategies on the ecological network and the possibility of economic development in the study area by adjusting the proportion of economic and ecological protection in the development strategy (i.e., increasing the proportion of the economy and reducing the proportion of ecological protection). These scenarios not only help to explore development strategies suitable for arid areas but also help policy makers adjust relevant planning and social development strategies in arid areas. Two basic scenarios were set up: 1. Current development mode; 2. Pure economic development mode. The development of the research area has both ecological protection and economic development. However, the ecological environment in this area is extremely fragile, and the government restricts economic development by implementing strict ecological protection policies. Therefore, the current development

c(G) = min{|S|, ω (G − S ) ≥ 2 or G − S is trivial graph} S∈V

(6)

Where V represents the node-set of the network G, S represents the proper subset of V, ω (G-S) represents the number of connected branches in the sub-network G-S, after deleting the point set S from the network G, and G-S represents the dataset, after deleting all nodes in S and all edges associated with it in network G. It can be seen that the node connectivity in the network will be the minimum number of nodes deleted, if G is not connected or becomes a trivial graph, namely, a graph with only one node and no edges. If c(G ) = 0, G can be considered as a disconnected network or a trivial graph. When G indicates a complete graph with N nodes, c(G ) =N-1. (2) k(G ) k(G ) represents the maximum coreness of a complex network. The coreness of a complex network represents a subset of the remaining network, after the nodes with a degree less than k have been removed. Coreness is a parameter to measure the characteristics of complex networks, for example, the connectivity. The larger the coreness of a network, the stronger its connectivity and the robustness of its random faults become. The maximum coreness of all nodes in the network is the coreness of the network. (3) d(G ) d(G ) represents the tightness of connections between nodes in 5

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weight, and the importance degree evaluation result of each patch was obtained, as shown in Fig. 4a. According to the statistical distribution characteristics of the importance degree of each plaque, the first 60% of the plaques in the importance ranking were selected as the ecological source, and finally, 481 ecological sources were obtained. The finely divided and isolated plaques were largely removed during the screening process, and the core plaques in the landscape unit were retained. The extraction results are shown in Fig. 4b.

complex networks. The density of the complex network G is defined as:

d(G ) = 2M /[N (N − 1)]

(7)

Where M represents the actual number of connections in the network, and N represents the number of nodes in the network. (4) a(G ) a(G ) represents the average number of nodes connected, the number of nodes connected with other nodes in aggregation; also known as the average path length, the larger the average number of nodes connected, the more paths through the network, the more complex the network connection. (4) Bi Betweenness can reflect the role and influence of nodes in the whole network. This paper focuses on the point-betweenness of eco-network; the betweenness of the node i is defined as:

Bi =

∑ 1≤j
3.1.2. Ecological resistance and ecological gravity simulation a Construction of the Resistance Model The resistance value is not only related to the distance of ecological flow but also is associated with land cover and human disturbance. Consequently, most researches were based on expert experiences to assign resistance values corresponding to land use types (Keeley et al., 2016). In this study, an evaluation system of ecological resistance was established from 16 factors including topographic slope, vegetation cover, hydrological distribution, soil, land cover, government planning, distance factor, and density factor. The factor affecting the resistance value of the matrix consists of two parts. On the one hand, they are influenced by current natural conditions. For example, the smaller the NDVI value of the ecological flow, the smaller the resistance, and otherwise, the greater the resistance. The closer the distance to the water, the easier it is for the ecological source to replenish water, and the more favorable it is for the development of the ecological source. On the contrary, the more unfavorable the development of the ecological source. On the other hand, economic development as the main cause of interference affecting ecological development will affect the resistance value of some factors, and ultimately affect the distribution of the resistance of the matrix in the study area. For example, the spatial planning of ecological-living-production land is a mandatory measure implemented by the Chinese government to regulate the scope of production and living activities. Currently, this plan has been launched nationwide. In the development strategy of priority ecological protection, ecological sources and other ecological spaces will be well protected and developed, and ecological development is limited to natural conditions. While, in the development strategy of priority economic development, the ecological protection policy that constrains economic development will not play a role, and areas with better water conditions and vegetation conditions such as ecological conservation redline (Eco-redline) will be used immediately to develop the economy. At this time, the ecology will be destroyed, the resistance of ecological development will suddenly

⎡ njl (i) n ⎤ jl ⎣ ⎦ (8)

Where njl represents the shortest path between nodes vj and vl, and njl(i) represents the number of the shortest path between nodes vj and vi passing through node vi, and n represents the total number of nodes in the network. (5)Pi PageRank can describe the importance of nodes in the network. The PageRank of the node i is defined as:

Pi =

wij PR (Pj ) 1−d + d∑ Pj ∈ M (Pj ) kj n

(9)

Where wij represents the edge weight between node i and node j, kj represents the degree of node j, d represents the damping coefficient, n represents the total number of nodes, PR (Pj represents the PageRank value of node Pj , Pj represents the ecological node, and M (Pj ) represents the set of all entries of node Pj . 3. Results 3.1. FG ecological network identification 3.1.1. Ecological source extraction The patch area, shape index, and NDVI, MNDWI of each patch were normalized. The weights of each index were calculated by the entropy method in the MATLAB. The weights of each index were 0.5878, 0.2539, and 0.1583, respectively. The weighted summation calculation was performed by using the calculation results of each index and its

Fig. 4. The importance degree evaluation result (a) and distribution of ecological sources in the study area (b). In Figure b, lapislazuli represents a water-type ecological source, big sky blue represents a canal-type ecological source, leaf green represents a forest-type ecological source, and tarragon green represents a grasstype ecological source. And the blank areas in the study area represent non-ecological sources. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Table 1 (continued)

Table 1 Ecological resistance evaluation index system. First-Grade Factor

Second-Grade Factor

Level

Natural resistance

Economic resistance

Landform and Physiognomy

DEM

761–1025 m 1025–1335 m 1335–1635 m 1635–1935 m 1935–2620 m 0–3° 3–9° 9–18° 18–27° > 27°

1 3 5 7 9 1 3 5 7 9

1 3 5 7 9 1 3 5 7 9

Slope

Vegetation Cover

NDVI

<0 0–0.2 0.2–0.33 0.33–0.6 > 0.6

9 7 5 3 1

1 3 5 7 9

Hydrology

Groundwater Depth

0–2 m 2–4 m 4–6 m 6–8 m >8m <0 0–0.24 0.24–0.51 0.51-0.72 > 0.72 <0 0–0.22 0.22–0.6 0.6–0.8 > 0.8

1 3 5 7 9 9 7 5 3 1 9 7 5 3 1

9 7 5 3 1 1 3 5 7 9 1 3 5 7 9

Eco-redline Ordinary ecological space Cultivated Land industrial zone urban built-up area, rural living land, irrigationsilted soil Saline soil Aeolian sandy soil Brown soil Grey desert soil Construction Land Desert Cultivated Land Forest Land Water

1 3

9 7

5

5

7 9

3 1

1

9

3 5

7 5

7 9 9

3 1 1

7 5

3 5

3 1

7 9

0 100 m 100–200 m 200–300 m 300–400 m > 400 m 0–100 m 100–200 m 200 300 m 300–400 m > 400 m

1 3 5 7 9 1 3 5 7 9

9 7 5 3 1 9 7 5 3 1

TVDI

MNDWI

Ecological-LivingProductive land

ecological land

productive land living land

Soil

Landscape

Distance

Soil type

Land Use Type

Distances to Roads

Distances to Water

First-Grade Factor

Second-Grade Factor

Level

Natural resistance

Economic resistance

Density

Residential Density

0–0.10 0.10–0.35 0.35–0.73 0.73–1.2 > 1.2 0–0.20 0.2–0.50 0.50–0.85 0.85–1.25 > 1.25 0–0.10 0.10–0.36 0.36–0.60 0.60–0.90 > 0.90

1 3 5 7 9 9 7 5 3 1 9 7 5 3 1

9 7 5 3 1 1 3 5 7 9 1 3 5 7 9

Road Network Density

Water Network Density

increase, and the resistance value of the original town is relatively small. The resistance values of each factor are set according to the evaluation system, shown in Table 1. The impact factors were divided into five levels, and their resistance levels were expressed by 1, 3, 5, 7, and 9, respectively. The results of each single factor evaluation were made by ArcGIS software, and the comprehensive evaluation results of the ecological resistance surface of the current development model and the pure economic development model were obtained by overlapped grid calculation, as shown in Fig. 5. The ecological resistance surfaces of the current development model (1.0, 0) and pure economic development model (0, 1.0) are shown in Fig. 5. The value at each raster location represents the cost-perunit distance for moving through the raster. In (1.0, 0) mode, the ecological resistance of the matrix is 17–34, which gradually increases from northeast to southwest. The areas with a lower resistance value are mainly located in the northeast, because of the abundant water resources near the Yellow River, while the areas with a higher resistance value are located in the UlanBuh Desert, near the Wolf Mountains and on the East Bank of the Yellow River, which is the western margin of the Kubuqi Desert. In this development model, the economic development is strictly restricted, and the development strategy of ecological protection is given priority, so that water resources and forestry and grassland are better protected. The ecological sources of the study area and the surrounding areas have lower ecological resistance values. In the (0, 1.0) model, the ecological resistance of the matrix is 22.2–34, water resources are mainly used for agricultural production and industrial manufacturing and are rarely used for ecological construction and protection, which causes higher matrix resistance values in the northeast region. In areas like the UlanBuh Desert, in the central part and the frontal alluvial fan of the wolf mountain, the matrix resistance is relatively lower. The above situation shows that a vigorous development of the economy, given the present situation, will seriously hinder the flow of ecological flow between sources and affect the development of ecology (Tables 2 and 3). The minimum cumulative ecological resistance is the minimum cumulative cost of the ecological flow, from one ecological source to another, to overcome the ecological resistance surface. The ecological resistance surface, as the resistance coefficient of each landscape unit of the ecological flow in the study area, can only represent the resistance value of the ecological flow through a single grid. Hence, based on the improved minimum cumulative resistance surface model. The cost distance model was used to calculate the ecological cumulative resistance between different ecological sources. The cost distance raster identifies, for each cell, the minimum cumulative ecological resistance over a cost surface to the 7

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Fig. 5. Ecological Resistance Surface in (0, 1.0) mode (a) and (1.0, 0) mode (b).

northern part of the study area, there is also a region with a large cumulative resistance value. While the ecological conditions are good at this location, the area has cultivated the land, which is an ecological barrier with a large resistance value. Moreover, water resources are widely used in agricultural production, including groundwater and surface water, resulting in lower water supply from ecological sources, which also leads to a larger ecological cumulative resistance value of the matrix in the (0, 1.0) development model. As can be seen from Fig. 6(b), in the (0, 1.0) development scenario, the maximum cumulative ecological resistance value is 349,835, and the minimum value is 17,467. The area with a higher cumulative resistance value is also mainly located in the Southern UlanBuh Desert due to poor ecological conditions. Unlike the (0, 1.0) model, the area with a smaller ecological resistance value increased significantly in this model. b Ecological Gravity Model By inputting the quality of the ecological sources and minimum cumulative resistance distance between them into the ecological gravity model, the ecological gravitational force between any ecological sources could be calculated. While ecological sources are hindered by the matrix, they may still form links through ecological corridors. The greater the ecological gravitation, the closer the relationship between ecological sources, and the more frequent the ecological flow between them, transferring energy and matter, is. Moreover, when ecological gravity is large enough, the ecological source will expand its scale by continuously absorbing and merging external resources. However, if the ecological gravity is very small, it is unlikely that the likelihood of forming an ecological corridor between ecological sources will be high, and there will be no connection between them unless the matrix changes. Taking ecological source 1 as an example, the table shows the ecological gravitation between source 1 and other sources. Because the ecological gravitation ranks first between source 1 and source 342 and source 233, the possibility of forming an ecological corridor between them is the highest. In comparison, it is very difficult to form an ecological corridor between source 1 and source 35 or source 66, because they have the least ecological gravitation between them.

Table 2 Ecological gravitation between ecological sources (source 1). Starting source

Target source

distance

M1

M2

Ecological gravity

1 1 1 1 1 1 1 1 1 1

233 342 52 35 122 113 124 66 57 9

1,563 1,423 2,168 8,447 6,169 3,008 3,684 7,215 5,613 4,994

197,376 197,376 197,376 197,376 197,376 197,376 197,376 197,376 197,376 197,76

528,156 697,911.6 242,301.6 1,104,874.8 687,538.8 260,030.4 262,326 839,635.2 597,915.6 1,292,268

42,671.56 68,027.57 10,174.94 3,056.34 3,565.84 5,672.34 3,815.01 3,183.55 3,745.79 10,227.03

Table 3 The structure characteristics of the FG eco-network in the 11 simulated scenarios. mode

c (G )

k (G )

d (G )

a (G )

Bi

Pi

(1,0) (0.9,0.1) (0.8,0.2) (0.7,0.3) (0.6,0.4) (0.5,0.5) (0.4,0.6) (0.3,0.7) (0.2,0.8) (0.1,0.9) (0,1.0)

329 334 311 303 294 293 286 281 279 271 265

6 6 5 5 5 4 4 4 3 3 3

0.167 0.170 0.151 0.127 0.115 0.090 0.071 0.053 0.046 0.035 0.029

2.9012 2.9203 2.7306 2.5814 2.4533 2.309 2.2148 2.0926 1.9704 1.9345 1.9143

240238.7 255480.4 222602.9 205861.2 196847.3 183970.4 171092.9 163452.5 158215.4 145337.8 136976.1

0.021158 0.021097 0.019684 0.018171 0.017587 0.016705 0.015697 0.014509 0.013546 0.012493 0.011445

identified ecological source locations. The minimum cumulative ecological resistance surfaces of the current development model (1.0, 0) and pure economic development model (0, 1.0) are shown in Fig. 6. As can be seen from Fig. 6(a), in the (0, 1.0) development scenario, the maximum cumulative resistance value is 382,673, and the minimum value is 28,459. The areas with large cumulative resistance values are mainly located in the Southern UlanBuh Desert. On the one hand, because the ecological and environmental conditions in the region are relatively harsh, the resistance values of groundwater depth, water density, NDVI, and other factors are higher. On the other hand, the ecological landscape of the region is less plaque and farther away from other ecological sources, which makes the cumulative value of ecological resistance larger. In the

3.1.3. FG eco-network simulation By simulating the minimum cumulative resistance surface of the matrix in different development scenarios, comparing the ecological resistance and gravity between ecological sources to determine whether they can form an ecological corridor, the FG ecological network of the 8

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Fig. 6. Minimum cumulative ecological resistance in (0, 1.0) mode (a) and (1.0, 0) mode (b). Figures (a) and (b) show the distribution of the minimum cumulative ecological resistance of the matrix in the study area of the current development scenario and the pure economic development scenario, respectively. The colors represent the value of minimum cumulative ecological resistance. Gray represents the minimum value. Magenta represents the maximum value.

research area is constructed. The Python scripting language was used to simulate the interaction between the ecological gravitation and resistance in ecological sources and construct the FG eco-network. The simulation results are as follows: In Fig. 7, the FG ecological network is unevenly distributed. For example, the number of ecological corridors between ecological resources in the north is greater, and the ecological network is more complex, while the FG eco-network in the south shows the opposite characteristics. It is obvious that the FG eco-network of the (1.0, 0) mode is more complex than that of the (0, 1.0) mode, and the connections between the ecological sources in the (1.0, 0) mode are more closely connected. In the transition from the (1.0, 0) mode to the (0, 1.0) mode, the proportion of economic development in the development strategy increases, and the ecological resistance in some regions gradually increases. The minimum cumulative ecological resistance between the ecological sources is greater than the ecological gravitation between them, which causes the ecological corridor to break and leads to the destruction of the FG eco-network. For example, the FG econetwork, in the sample circle of the study area, has changed significantly.

ecological resistance between the sources is closely related to the matrix. This study set up 11 development scenarios: (1.0, 0), (0.9, 0.1), (0.8, 0.2), (0.7, 0.3), (0.6, 0.4), (0.5, 0.5), (0.4, 0.6), (0.3, 0.7), (0.2, 0.8), (0.1, 0.9), (0, 1.0). Among them, the current development model (1.0, 0) and pure economic development model (0, 1.0) had been obtained. Based on the (1.0,0) status development model, (0,1.0) pure economic development model, using the Raster Calculator module of ArcGIS software, the minimum cumulative ecological resistance surface of the other nine modes was obtained by changing the weight values of n, m. As shown in Fig. 8, the ranges of cumulative resistance in the 9 modes is 27,360–379,389, 26,261–376,105, 25,161–372,821, 24,062–369,538, 22,963–366,254, 21,864–362,970, 20,765–359,686, 19,665–356,403, and 18,566–353,119. It can be seen that the areas with large cumulative resistance values are located in the UlanBuh Desert in the south and the agricultural development areas near the Hetao Plain in the north. As the weight of economic development increases, the weight of ecological protection gradually decreases, and the gray areas that represent less ecological accumulation resistance are gradually decreasing, which will make the possibility of energy and material exchange between ecological resources smaller.

3.2. Multi-scenario simulation

3.3. Eco-network performance analysis

The ecological gravitation is determined by the nature of ecological sources and gravitational radii between them and does not fluctuate with changes in the matrix. In comparison, the minimum cumulative

Matlab was used to calculate the connectivity, number of cores, and density of the FG eco-networks of different scenarios. The (1.0, 0) mode is the current development model of the research area, which has the Fig. 7. FG eco-network in the (0, 1.0) mode (a) and (1.0, 0) mode (b). Figures (a) and (b) show the extraction results of FG ecological network in the current development scenario and pure economic development scenario, respectively. In Figure a and b, fir green represents an ecological corridor, leaf green represents a forestgrass type ecological source, blue and crestan blue represents a canal-water type ecological source. Moreover, the blank areas in the study area represent non-ecological sources. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 8. Minimum cumulative ecological resistance from the (0.1, 0.9) mode to the (0.9, 0.1) mode.

mode.

largest network connectivity, 329, accounting for 68.24% of the total number of ecological sources in the network. The results show that the removal of 329 ecological sources can transform the FG eco-network of the (1.0, 0) model into a trivial graph, and then the ecological function of the network will be completely lost. As the weight of economic development increases and the weight of ecological protection decreases in the development strategy, the connectivity of the FG eco-networks will gradually decrease, and the stability of the FG eco-networks will deteriorate with the decrease of connectivity. In the pure economic development (0, 1.0) model, the FG eco-network has the lowest connectivity. Only 265 ecological resources need to be removed to transform the FG eco-network of the (0, 1.0) mode into a trivial graph. The changing coreness trend is the same as that of the connectivity. As the proportion of economic development gradually increases, the number of cores in the FG eco-network decreases, and the connectivity and robustness of the network are gradually weakened. In terms of network density, the density is the highest in the (0.9, 0.1) mode, and it decreases rapidly during the evolution from the (1.0, 0) to the (0, 1.0)

3.4. Eco-network sample circle analysis According to the results, when the proportion of economic development is 0.1, and the proportion of ecological protection is 0.9, although the connectivity of the (0.1, 0.9) mode is slightly less than that of the (0, 1.0) mode, the (0.1, 0.9) mode and (1.0, 0) mode have the same coreness, and the network density of the (0.1, 0.9) mode is greater than that of the (0, 1.0) mode, which indicates that economic development does not necessarily lead to the deterioration of the FG econetwork. To more accurately analyze the changes in the FG ecological network, from the evolution of (1.0, 0) to (0, 1.0), the periphery area of the Jinshataohai (sample circle), which was seriously damaged in the northern part of the study area, was selected for detailed study (Fig. 9). It can be seen, from the above figures, that in the (0.9, 0.1) mode, the resistance value in the middle of the sample circle increased, the corridors between ecological sources appeared to break, and some 10

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Fig. 9. The ecological network within the sample circle in different development modes.

both sides of the sample circle was weakened, and the degree of damage to the network also increased. In the (0.7, 0.3) mode, the resistance value of the sample circle in the west increased significantly, the corridor disappeared more seriously, and the complexity of the network

corridors disappeared, but new connections were created between the eastern ecological sources. In the (0.8,0.2) mode, the resistance value in the middle of the sample circle continued to increase, the corridor continued to break, the connection between the ecological sources on

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area; evaluate the habitat status and landscape suitability; extract the key nodes in the landscape and evaluate the landscape connectivity; and finally, extract the ecological network. Hu et al. (2018) identified the ecological source and ecological nodes using the cumulative cost resistance model and established an ecological network. These methods point out the basic steps involved in establishing an ecological network: identify the source/node, extract a corridor, and establish a network. These studies only consider the hindrance of the matrix but ignore the ecological gravity within the network, which is crucial in landscape expansion. In this paper, an improved gravitational model is used to quantify the ecological forces. Based on the land use data, the ecological source is screened according to the ecological importance of the ecological land. Considering a variety of factors, including topographic factors, vegetation cover factors, hydrological factors, government planning, and density factors, ecological resistance surfaces in the study area can be constructed. The minimum cumulative resistance model was used to calculate the cumulative ecological resistance of the matrix, and the improved gravity model was used to calculate the ecological gravity between the sources. By comparing the minimum ecological cumulative resistance and the ecological gravity between the ecological sources to judge whether the conditions of the corridor are satisfied, the FG ecological network of the study area can be established. This paper also used the theory of complex networks to analyze the topology of ecological networks. There has been controversy concerning economic development and ecological protection (Warford, 2010). For example, Murphy and Gouldson (2010) established an economic industrial structure optimization model based on the water environment. In the recommended industrial structure, the economy can be guaranteed to develop at the speed of planning, and it can also achieve the goal of controlling water pollution, protecting the water environment, and achieving the coordinated development of the water environment and economy. However, other researchers believe that economic development leads to ecological changes, and different policy systems will cause different economic states, leading to the ecological state having different characteristics (Gowdy, 1994; Li, 2003). These scholars all believe that ecological problems appear in the process of economic development, but they do not describe how the proportion of economic development and ecological protection in development strategies should be coordinated and only put forward development proposals. Additionally, existing research usually only focuses on the future development trend and predicts the future development scenario (Kareiva, 2001). Few scholars have studied the impact of different development strategies on ecology, based on current natural conditions. However, it is necessary for policy makers to use scientific, evidencebased simulation models to assess the impact of different development strategies (Behague et al., 2009). Despite this, the Chinese government has strictly restricted economic development in ecologically fragile areas and implemented the strictest ecological protection policy ever, with a view to protecting and improving the ecological environment. However, the policy is standardized and lacks pertinence. It may make some areas limited by environmental protection policies and unable to properly develop the economy in order to improve people's living conditions and reduce the number of poor people. This study, based on the current natural environment situation, this study explored the impact of different development strategies on the ecological network and the possibility of economic development in the study area by adjusting the proportion of economic and ecological protection in the development strategy (i.e. increasing the proportion of economy and reducing the proportion of ecological protection). To explore how economic development affects ecology about development strategies, we use the scenario simulation method and set up 11 development strategies for research. The results show that, with the increase in the proportion of economic development, the complexity, robustness, and stability of the FG ecological network decrease. However, the study found that, in the study area, when the proportion

structure decreased. In the (0.6, 0.4) mode, the value of the cumulative ecological resistance increased in the middle of the sample circle, which further weakened the connection between the eastern and western ecological sources. In the (0.5, 0.5) mode, the eastern FG eco-network differentiation was further enhanced with the increase of the resistance value in the eastern part of the sample circle. Some ecological corridors near the Nalintaohai farm disappeared, and corridor reorganization between ecological sources, which led to changes in the network structure, took place. In the (0.4, 0.6) mode, the cumulative resistance value of the sample center is higher than the ecological gravity, resulting in an inability to form an ecological corridor connecting the FG eco-networks on both sides. In the (0.3, 0.7) mode, the FG eco-network grid near the Sun Temple farm in the north of the sample circle gradually disappeared, and the ecological corridors in the east and south were broken. In the (0.2, 0.8) mode, the eastern and southern FG econetworks of the sample circle became more fragmented, and the corridor disappeared. In the (0.1, 0.9) mode, the eastern ecological corridor of the sample circle disappeared, and the ecological source became an isolated island, which could not form any connection with other ecological resources. In the (0, 1.0) mode, the ecological corridor disappeared in large quantities, the ecological source was isolated, and an ecological corridor only formed between large ecological sources through the ditch. With the decrease of the proportion of ecological protection in the development strategy, the minimum ecological cumulative resistance value of the matrix in the study area gradually increases, which leads to the collapse of the ecological corridor and damage to the FG eco-network. According to research, a significant increase in the proportion of economic development will cause irreparable damage to the FG econetwork in the study area. While in the (0.9, 0.1) mode, there is a new connection between the ecological sources within the sample circle, which makes the FG eco-network expand slightly. However, the FG ecological network, in the subsequent development model, showed a state of continuous destruction. This indicates that the study area may have room for further economic development, but the space is not large. 4. Discussion In the past 40 years, while developing the economy and realizing modernization, China's ecological environment has been under tremendous pressure, resulting in different levels of ecological imbalance (Gao, 2004). Northwest China is located in the hinterland of the mainland, with water shortages and fragile ecology. Unreasonable economic development activities in the western region have led to land degradation and rapid desert expansion (Wei et al., 2018). The desertoasis ecotone in arid areas is the priority area of desertification, which responds quickly to ecological changes: if the ecology improves, the desert will gradually become an oasis, and if the ecology degenerates, the oasis will gradually become a desert, which is the process of desertification (Wang et al., 2007). Therefore, achieving the ecological stability and healthy development of the desert-oasis ecotone is of great significance for maintaining regional ecological security, controlling desert expansion, and improving the ecological environment. Identifying, and protecting ecological networks that are important to regional ecological security can provide a win-win solution for economic development and ecological protection. An ecological network, formed by the ecological corridor connecting the isolated ecological patches in the desert oasis-interlaced area, can enhance the self-regulation ability of ecological patches, control desert expansion, and maintain ecological stability. However, there are many ways to establish an ecological network. Noss proposed three main steps, including the identification of special areas, feature analysis, and network extraction (Noss, 1983). Ashley et al. (2004) believe that the construction of an ecological network should generally include the following steps: first, assess the land-use status of the study 12

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economic development in arid areas, under the present natural conditions, will cause tremendous damage to the FG eco-network. 2 In the northern part of the study area, a sample circle was selected to study the changes in the evolution of the FG eco-network from (1.0, 0) to (0, 1.0). The study found that the FG eco-network of the (0.9, 0.1) mode is more complex, and the stability and connectivity indicators of the network are larger. However, with the increase of economic development, the structure of the FG ecological network has been destroyed more and more seriously. This indicates that the study area may have room for economic development, but the space is not large.

of ecological protection is 0.9, and the economic development is 0.1, new ecological corridors were created between the ecological sources inside the sample circle. The FG ecological network of the (0.9, 0.1) mode is more complex, and the stability and connectivity indicators of the network are larger. This shows that moderate economic development may not cause the ecological environment to deteriorate, and there is a specific space for economic development in arid regions. The coverage of the FG ecological network can be expanded in reasonably to properly develop the economy while protecting the ecology. However, with the increase of economic development, the structure of the FG ecological network has been destroyed more and more seriously. This indicates that the study area may have room for economic development, but the space is not large. In the arid regions, the economy should be developed according to local conditions, and desertification caused by ecological deterioration should be avoided. It should be noted that this conclusion only applies to the study area selected in this paper, and further research in other fields needs to be conducted. However, some key issues related to ecological security patterns are still difficult to answer. For example, how many percents of the ecosystem's important grade should be used to determine the ecological source? How large should it be in proportion to the total research area? Most of the ecological corridors extracted by this method are potential ecological corridors. How to evaluate the rest except a few that can be verified? How to verify the reliability of this method? And whether the normalized index, area index, and shape index can accurately measure the state of ecological sources, and what other indicators should be considered? There are some doubts about the gravitational constant, whether its size is as mentioned in the references: the gravitational constant is 1, how to evaluate its reliability, and what method should be used to determine the gravitational constant? In addition, the distribution of resistance values and the assessment of the ecological source status are still worthy of further research to obtain more scientific and accurate results. The distribution of resistance values depends on expert experience, but there is some subjectivity. It is difficult to assess what will happen if these values are incorrectly assigned. Bendoricchio and Palmeri (2005) argued that benefit/cost and supplydemand balance could be used to measure ecosystem status. This provides a good idea for improving the assessment of ecological source status in this study. Hence, much more researches, considering multiple case studies across different scales, are needed to resolve all of these scientific questions.

Acknowledgments This work is supported by “the Fundamental Research Funds for the Central Universities (NO. BLX201806): Study on Ecological Network Structure and Its Crash Threshold at the northeastern edge of the UlanBuh Desert and Certificate of China Postdoctoral Science Foundation Grant (2018M641218). Special thanks to Hongqiong Guo, Qianqian Long, Xueqing Mao for their hard work in the text review and references format modification of this article. References Adriaensen, Chardon, J.P., Blust, D., Swinnen, E., Villalba, 2003. The application of’ leastcost’ modelling as a functional landscape model. Landsc. Urban Plann. 64 (4), 233–247. Albanese, G., Haukos, D.A., 2017. A network model framework for prioritizing wetland conservation in the Great Plains. Landsc. Ecol. 32 (1), 115–130. https://doi.org/10. 1007/s10980-016-0436-0. Ashley, C., Xiang, W.N., Jeff, Y., David, W., 2004. Planning for multi-purpose greenways in Concord, North Carolina. Landsc. Urban Plann. 68 (2–3), 271–287. Behague, D., Tawiah, C., Rosato, M., Some, T., Morrison, J., 2009. Evidence-based policymaking: the implications of globally-applicable research for context-specific problemsolving in developing countries. Soc. Sci. Med. 69 (10), 1539–1546. https://doi.org/ 10.1016/j.socscimed.2009.08.006. Beier, P., Majka, D.R., Spencer, W.D., 2008. Forks in the road: choices in procedures for designing wildland linkages. Conserv. Biol. 22 (4), 836–851. https://doi.org/10. 1111/j.1523-1739.2008.00942.x. Bendoricchio, G., Palmeri, L., 2005. Quo vadis ecosystem? Ecol. Model. 184 (1), 5–17. https://doi.org/10.1016/j.ecolmodel.2004.11.005. Bergstrand, J.H., 1985. The gravity equation in international trade: some microeconomic foundations and empirical evidence. Rev. Econ. Stat. 67 (3), 474–481. Cao, S., Hu, D., Hu, Z., Zhao, W., Chen, S., Yu, C., 2018. Comparison of spatial structures of urban agglomerations between the Beijing-Tianjin-Hebei and Boswash based on the subpixel-level impervious surface coverage product. J. Geogr. Sci. 28 (3), 306–322. https://doi.org/10.1007/s11442-018-1474-0. Chen, Y., 2009. Urban gravity model based on cross-correlation function and Fourier analyses of spatio-temporal process. Chaos Solitons Fractals 41 (2), 603–614. https:// doi.org/10.1016/j.chaos.2008.02.030. Cheng, G.D., Zhang, Z., Li, R., 2000. On some issues of the ecological construction of West China and proposals for policy. Sci. Geogr. Sin. 6, 503–510. Christina, E., Claudia, K., Stefan, D., 2012. Derivation of biomass information for semiarid areas using remote-sensing data. Int. J. Remote Sens. 33 (9), 2937–2984. Defries, R.S., Townshend, J.R.G., 1994. NDVI-derived land cover classifications at a global scale. Int. J. Remote Sens. 15 (17), 3567–3586. Doetzer Rosot, M.A., Maran, J.C., da Luz, N.B., Garrastazu, M.C., Malheiros de Oliveira, Y.M., Franciscon, L., Clerici, N., Vogt, P., de Freitas, J.V., 2018. Riparian forest corridors: a prioritization analysis to the landscape sample units of the Brazilian national forest inventory. Ecol. Indic. 93, 501–511. https://doi.org/10.1016/j. ecolind.2018.03.071. Ewers, R.M., Thorpe, S., Didham, R.K., 2007. Synergistic interactions between edge and area effects in a heavily fragmented landscape. Ecology 88 (1), 96–106. https://doi. org/10.1890/0012-9658(2007)88[96:sibeaa]2.0.co;2. Fath, Brian, Scharler, D., Ursula, M., Robert, E., Hannon, Bruce, 2017. Ecological network analysis: network construction. Ecol. Model 208 (1), 49–55. Gao, J., 2004. Problems of ecological environment in Western China. Chin. Educ. Soc. 37 (3), 15–20. Gordillo, D.M., Stokenberga, A., Schwartz, J., 2010. Understanding the benefits of regional integration to trade: the application of a gravity model to the case of Central America. Policy Res. Work. Pap. 136 (2), 289–297. Gowdy, 1994. Coevolutionary economics: the economy, society and the environment. Nat. Resour. Manag. Policy 5 (3), 375–384. Grosche, T., Rothlauf, F., Heinzl, A., 2007. Gravity models for airline passenger volume estimation. J. Air Transp. Manag. 13 (4), 175–183. Hartmann, B., 1998. Population, environment and security: a new trinity. Environ. Urban. 10 (2), 113–127. https://doi.org/10.1177/095624789801000202. Hoctor, T.S., Carr, M.H., Zwick, P.D., 2000. Identifying a linked reserve system using a

5. Summary and conclusion This paper selected Denko County of Bayannaoer City, Inner Mongolia, which is located in the desert oasis area, as the study area. This study provided a new approach to the construction of an ecological network, based on interaction force theory. Additionally, a variety of development strategies have been set up to explore the impact of the proportion of ecological protection and economic development on the evolution of ecological networks on development strategies. Moreover, the topology and statistical characteristics of the FG ecological network are analyzed based on the complex network theory. The specific conclusions are as follows: 1 The results show that the FG ecological network is unevenly distributed. The number of ecological corridors between ecological resources in the northern part of the study area is greater, and the ecological network is more complex, while the eco-network in the south shows the opposite characteristics. From the (1.0, 0) mode to the (0, 1) mode, with the increasing proportion of economic development and the decreasing proportion of ecological protection, the FG eco-network is gradually destroyed. In the (0, 1.0) model, the structure of the FG eco-network is greatly destroyed, and the connectivity, stability, coreness, and PageRank, etc. of FG ecological network reach the lowest level. This indicates that large-scale 13

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